Cytoskeletal Alterations in Age-Related Diseases: A Transcriptomics Perspective for Biomarker and Therapeutic Discovery

Abstract

This article synthesizes current research on transcriptomic alterations in cytoskeletal genes across major age-related diseases, including neurodegenerative and cardiovascular conditions. It explores the foundational role of the cytoskeleton in aging, details cutting-edge computational methodologies like machine learning for identifying key gene signatures, and addresses critical challenges such as sex-dimorphic aging and technical validation. Aimed at researchers and drug development professionals, the content provides a framework for leveraging transcriptomic data to validate potential cytoskeletal biomarkers and therapeutic targets, ultimately bridging computational findings with clinical application for age-related diseases.

The Cytoskeleton in Aging: Exploring Transcriptomic Foundations of Age-Related Disease

The cytoskeleton is a dynamic, adaptive network of filamentous polymers and regulatory proteins that spatially organizes cellular contents, connects the cell to its external environment, and generates coordinated forces essential for cell movement and shape change [1]. Far from being a static scaffold, this integrated system of microfilaments, intermediate filaments, and microtubules forms a mechanically robust yet adaptable framework that maintains cellular integrity while responding to physical and biochemical cues [1]. In recent years, research has increasingly demonstrated that long-lived cytoskeletal structures may function as epigenetic determinants of cell shape, function, and fate, with cytoskeletal alterations now recognized as significant contributors to age-related diseases [1] [2]. Transcriptomic analyses have further revealed that the expression of cytoskeletal genes becomes dysregulated during aging and in pathological conditions, providing new insights into disease mechanisms and potential therapeutic targets [2] [3] [4]. This review comprehensively compares the structural and functional attributes of the three major cytoskeletal components and examines their collective role in maintaining cellular homeostasis, with particular emphasis on transcriptomic alterations associated with aging and disease.

Comparative Analysis of Cytoskeletal Components

The eukaryotic cytoskeleton comprises three structurally and functionally distinct polymer systems that collectively maintain cellular homeostasis through integrated mechanical and signaling functions.

Table 1: Structural and Functional Properties of Cytoskeletal Components

Characteristic	Microfilaments	Intermediate Filaments	Microtubules
Protein Subunits	Actin (globular) [5]	Various fibrous proteins (e.g., keratin, vimentin, lamins, neurofilament subunits) [5] [3]	α-tubulin and β-tubulin heterodimers [5]
Diameter	~7 nm [5]	~8-10 nm [5]	~25 nm [5]
Structural Organization	Two intertwined strands of polymerized actin [5]	Several strands of fibrous proteins wound together [5]	Hollow tubes composed of tubulin protofilaments [5]
Polarity	Polar (barbed and pointed ends) [1]	Non-polar [1]	Polar (plus and minus ends) [1]
Primary Functions	Cell movement, shape maintenance, cytokinesis, muscle contraction [5] [6]	Mechanical stability, tension bearing, organelle anchoring [5]	Intracellular transport, mitotic spindle formation, cell shape maintenance, chromosome segregation [5]
Nucleotide Requirement	ATP [5]	None	GTP [1]
Dynamic Behavior	Treadmilling, rapid assembly/disassembly [5]	Stable, less dynamic [5]	Dynamic instability [1]
Associated Motor Proteins	Myosin [5] [1]	None	Kinesin and dynein [5] [1]
Response to Mechanical Stress	Network reinforcement, stress fiber formation [1]	Network extension, providing mechanical resilience [1]	Reorganization, orientation along stress directions [1]

Methodologies for Cytoskeletal Research in Aging and Disease

Transcriptomic Analysis of Cytoskeletal Genes

Advanced computational approaches have enabled comprehensive profiling of cytoskeletal gene expression patterns associated with aging and disease. Recent research has employed machine learning algorithms to identify cytoskeletal gene signatures that accurately discriminate between patients and healthy controls across multiple age-related conditions [2]. The experimental workflow typically involves:

• Data Acquisition: Transcriptome data is obtained from public databases such as The Cancer Genome Atlas (TCGA) or Genotype-Tissue Expression (GTEx) project, focusing on normal-appearing tissues adjacent to pathological specimens or postmortem samples [4] [2].
• Gene Selection: Cytoskeletal genes are identified using the Gene Ontology browser (GO:0005856), which includes approximately 2,300 genes associated with microfilaments, intermediate filaments, microtubules, and related structures [2].
• Computational Analysis: Support Vector Machine (SVM) classifiers with Recursive Feature Elimination (RFE) have demonstrated the highest accuracy in identifying the most discriminative cytoskeletal genes for specific diseases [2]. Differential expression analysis is performed using tools like DESeq2 or Limma package to identify statistically significant changes in cytoskeletal gene expression between patient and control groups [2].
• Validation: Candidate genes are validated using Receiver Operating Characteristic (ROC) analysis on external datasets, with area under the curve (AUC) values quantifying predictive performance [2].

Quantitative Immunofluorescence in Human Tissues

Age-related cytoskeletal alterations in specific tissues can be quantified through immunofluorescence staining of human biopsies:

• Sample Collection: Tissue samples (e.g., human skin biopsies containing sensory axons) are collected from healthy donors across different age groups, with careful attention to exclusion criteria (e.g., metabolic diseases, neuromuscular disorders) [3].
• Processing and Sectioning: Samples are fixed in Zamboni's solution, incubated in sucrose solutions for cryoprotection, and sectioned at specific thicknesses (e.g., 50 μm) using a cryostat [3].
• Immunostaining: Sections are incubated with primary antibodies against cytoskeletal components (e.g., anti-βIII-tubulin for microtubules, anti-NF200 for neurofilaments, phalloidin for F-actin), followed by appropriate fluorescent secondary antibodies [3].
• Image Acquisition and Analysis: High-resolution confocal microscopy is used to capture images, followed by quantitative analysis of fluorescence intensity (measuring cytoskeletal mass) and morphometric measurements (axon caliber) using image analysis software [3].
• Statistical Correlation: Cytoskeletal parameters are correlated with donor age using appropriate statistical tests to identify age-dependent changes [3].

Table 2: Key Research Reagents for Cytoskeletal Analysis

Reagent/Category	Specific Examples	Research Application	Functional Role
Antibodies for Immunostaining	Anti-βIII-tubulin (TUBB3), Anti-neurofilament subunits (NfL, NfM, NfH), Anti-vimentin, Anti-keratin [3]	Identification and quantification of specific cytoskeletal polymers in tissues and cells	Target-specific binding allows visualization and measurement of cytoskeletal structures
Fluorescent Probes	Phalloidin (F-actin staining), DAPI (nuclear counterstain), fluorescent secondary antibodies [3]	Visualization of cellular structures and specific antigens	High-affinity binding to target structures enables precise localization
Computational Tools	SVM classifiers with RFE, DESeq2, Limma package, WGCNA [2] [4]	Analysis of transcriptomic data to identify cytoskeletal gene signatures	Identifies statistically significant expression patterns and classifies samples
Databases	Gene Ontology Browser (GO:0005856), TCGA, GTEx [2] [4]	Source of curated gene sets and transcriptome data	Provides standardized annotations and large-scale molecular data for analysis

Cytoskeletal Alterations in Age-Related Diseases: Transcriptomic Evidence

Transcriptomic analyses have revealed consistent patterns of cytoskeletal dysregulation across multiple age-related conditions, providing molecular insights into disease mechanisms.

Neurodegenerative Diseases

In Alzheimer's disease (AD), computational frameworks have identified several cytoskeletal genes with discriminatory power, including ENC1, NEFM, ITPKB, PCP4, and CALB1 [2]. These genes participate in neuronal structure maintenance, signaling pathways, and calcium homeostasis. Additionally, age-dependent increases in all three cytoskeletal components—neurofilaments, microtubules, and actin—have been observed in peripheral sensory axons, with corresponding increases in axon caliber particularly prominent in males [3]. This suggests that cytoskeletal accumulation may represent a fundamental aging phenotype in neurons that potentially increases susceptibility to neurodegeneration.

Cardiovascular Disorders

Transcriptomic studies of hypertrophic cardiomyopathy (HCM) and coronary artery disease (CAD) have revealed distinct cytoskeletal gene expression signatures. For HCM, ARPC3, CDC42EP4, LRRC49, and MYH6 have been identified as key discriminators, while CSNK1A1, AKAP5, TOPORS, ACTBL2, and FNTA show diagnostic potential for CAD [2]. These genes encode proteins involved in actin regulation (ARPC3, CDC42EP4), contractile function (MYH6), and signaling pathways that interface with the cytoskeleton. Furthermore, pathway analyses of aging thyroid tissue have revealed enrichment of cytoskeletal proteins associated with cardiac muscle contraction and hypertrophic cardiomyopathy, suggesting shared cytoskeletal alterations across endocrine and cardiovascular aging [4].

Metabolic Diseases

In Type 2 Diabetes Mellitus (T2DM), the ALDOB gene has been identified as a significant cytoskeletal-related discriminator [2]. This finding connects glycolytic metabolism with cytoskeletal organization, highlighting the interplay between metabolic pathways and structural components in disease pathogenesis. Additionally, studies of smooth muscle cell migration—a process relevant to vascular complications in diabetes—have demonstrated how dynamic remodeling of all three cytoskeletal systems coordinates cell movement in response to metabolic and mechanical cues [6].

Integrated View of Cytoskeletal Dynamics in Homeostasis and Disease

The cytoskeleton functions as an integrated mechanical system where all three components cooperate to maintain cellular homeostasis. Actin filaments generate protrusive forces and contractility, intermediate filaments provide mechanical resilience, and microtubules organize intracellular space and direct traffic [1]. This cooperation is particularly evident in cell migration, where spatially and temporally coordinated interactions between all three systems enable polarized movement with actin-driven protrusion, microtubule-guided delivery of membrane and signaling components, and intermediate filament-mediated stabilization of cell-ECM attachments [6].

With advancing age, transcriptomic analyses consistently reveal alterations in cytoskeletal gene expression patterns across tissues. These changes include upregulated expression of neurofilament subunits in peripheral neurons [3], altered expression of actin-binding proteins in cardiovascular tissues [2], and modified tubulin isotypes in multiple organs [4]. The molecular mechanisms driving these changes likely involve age-related shifts in transcriptional regulation, post-translational modifications of cytoskeletal proteins, and responses to accumulating cellular stress.

The clinical implications of cytoskeletal alterations in aging are substantial. As the cytoskeleton influences essentially all aspects of cell behavior—from division and motility to signaling and mechanical integrity—age-related dysregulation of cytoskeletal components contributes to functional decline across multiple organ systems. The identification of specific cytoskeletal genes associated with age-related diseases offers potential for novel diagnostic biomarkers and therapeutic targets [2]. For instance, cytoskeletal proteins detected in serum or cerebrospinal fluid (e.g., neurofilament light chain) are already emerging as valuable biomarkers for neuronal damage [3].

The cytoskeleton serves as a central determinant of cellular homeostasis through the integrated functions of microfilaments, intermediate filaments, and microtubules. Transcriptomic research has revealed that age-related dysregulation of cytoskeletal components represents a common pathway in the pathogenesis of diverse conditions including neurodegenerative, cardiovascular, and metabolic diseases. The identification of specific cytoskeletal gene signatures associated with these conditions provides not only insights into disease mechanisms but also potential diagnostic biomarkers and therapeutic targets. Future research directions should focus on elucidating the precise mechanisms linking cytoskeletal gene expression changes to functional deficits in aging, developing interventions to maintain cytoskeletal integrity throughout lifespan, and exploring tissue-specific cytoskeletal alterations that may drive organ-specific aging phenotypes. As computational methods and single-cell technologies advance, our understanding of cytoskeletal biology in aging and disease will continue to grow, potentially enabling novel approaches to promote healthy aging and combat age-related pathologies.

The cytoskeleton, a dynamic network of intracellular filaments, serves as a fundamental determinant of cellular shape, integrity, and function. Comprising actin microfilaments, microtubules, and intermediate filaments, this structural framework facilitates essential processes including intracellular transport, cell division, and mechanotransduction. Recent transcriptomic and genomic analyses have revealed that the cytoskeleton undergoes profound alterations during the aging process, contributing to the functional decline of tissues and organs. Single-cell transcriptomic studies of the human prefrontal cortex across the lifespan have identified a widespread downregulation of cytoskeletal genes, including TUBB3, TUBA1A, and TUBA4A, as a hallmark of brain aging [7]. These molecular changes correlate with structural deficits and functional impairments observed in age-related diseases.

The investigation of cytoskeletal aging intersects with multiple hallmarks of aging, including genomic instability, loss of proteostasis, and mitochondrial dysfunction. As a critical integrator of mechanical and biochemical signals, the cytoskeleton serves as both a sensor and effector of age-related cellular decline. This review synthesizes recent advances in understanding transcriptional and structural changes in the aging cytoskeleton, with particular emphasis on single-cell omics approaches, computational modeling, and experimental methodologies that illuminate the pathophysiological mechanisms underlying age-related functional decline.

Transcriptomic Hallmarks of Cytoskeletal Aging

Global Transcriptional Changes in the Aging Brain

Comprehensive single-nucleus RNA sequencing (snRNA-seq) studies of the human prefrontal cortex across the lifespan, from infancy to centenarian years, have revealed cell-type-specific patterns of cytoskeletal gene expression during aging. Analysis of 367,317 nuclei identified a consistent downregulation of essential cytoskeletal components across multiple neural cell types [7]. Excitable neurons, particularly L2/3 excitatory neurons, demonstrated the most pronounced alterations, with 1,273 genes significantly downregulated in the elderly compared to adults [7].

A key finding from these transcriptomic analyses is the preferential vulnerability of specific cytoskeletal elements. Microtubule components show particularly marked age-dependent reductions, with TUBB3 (βIII-tubulin) downregulated in 12 out of 13 brain cell types, TUBA4A in 10 out of 13 cell types, and TUBB in 9 out of 13 cell types [7]. These transcriptional changes likely underlie structural impairments in neuronal processes and intracellular transport mechanisms that are essential for maintaining neuronal function throughout life.

Table 1: Age-Associated Downregulation of Cytoskeletal Genes Across Brain Cell Types

Gene Symbol	Protein Name	Cytoskeletal Component	Cell Types Affected	Functional Consequences
TUBB3	βIII-tubulin	Microtubules	12/13	Impaired axonal transport, reduced structural plasticity
TUBA1A	α-tubulin 1A	Microtubules	13/13	Decreased microtubule stability, disrupted intracellular organization
TUBA4A	α-tubulin 4A	Microtubules	10/13	Compromised neuronal integrity
TUBB	β-tubulin	Microtubules	9/13	General microtubule dysfunction
VAMP2	Vesicle-associated membrane protein 2	Synaptic vesicles	13/13	Impaired vesicle trafficking, synaptic dysfunction

Cell-Type-Specific Vulnerability Patterns

The transcriptional landscape of cytoskeletal aging exhibits notable cell-type-specific patterns, with distinct vulnerability profiles among different neural populations. While excitatory neurons show the most extensive transcriptomic alterations, inhibitory neurons display increased transcriptional variability with advancing age [7]. Specifically, IN-SST neurons (somatostatin-expressing inhibitory neurons) show a significant increase in the coefficient of variation of their transcriptome in elderly brains, suggesting a loss of transcriptional fidelity [7].

Notably, the expression of cell-type-defining cytoskeletal genes decreases significantly with age. The expression of somatostatin (SST) in IN-SST neurons and vasoactive intestinal peptide (VIP) in IN-VIP neurons shows fold changes of -2.63 and -1.46, respectively, in elderly individuals [7]. This loss of functionally important markers, combined with increased transcriptional variability, suggests that inhibitory neurons undergo fundamental changes in their cytoskeletal organization during aging, potentially contributing to circuit-level dysfunction in the elderly brain.

Computational Frameworks for Cytoskeletal Biomarker Discovery

Machine Learning Approaches for Cytoskeletal Gene Analysis

Advanced computational frameworks have emerged as powerful tools for identifying cytoskeletal genes associated with age-related pathologies. Integrative approaches combining machine learning models with differential expression analysis have enabled the systematic identification of cytoskeletal biomarkers across multiple age-related diseases [2]. Studies utilizing Support Vector Machines (SVM) classifiers have achieved high accuracy in discriminating between patients and controls based on cytoskeletal gene expression profiles [2].

Recursive Feature Elimination (SVM-RFE) has identified a compact set of 17 cytoskeletal genes that effectively classify age-related diseases, including Hypertrophic Cardiomyopathy (HCM), Coronary Artery Disease (CAD), Alzheimer's Disease (AD), Idiopathic Dilated Cardiomyopathy (IDCM), and Type 2 Diabetes Mellitus (T2DM) [2]. This computational approach highlights the transcriptional dysregulation of cytoskeletal genes as a common molecular substrate across seemingly distinct age-related conditions.

Table 2: Machine Learning-Identified Cytoskeletal Genes in Age-Related Diseases

Disease	Identified Cytoskeletal Genes	Classifier Performance	Potential Clinical Utility
Alzheimer's Disease (AD)	ENC1, NEFM, ITPKB, PCP4, CALB1	SVM Accuracy: >90%	Diagnostic biomarkers, therapeutic targets
Hypertrophic Cardiomyopathy (HCM)	ARPC3, CDC42EP4, LRRC49, MYH6	SVM Accuracy: >90%	Disease stratification, prognostic markers
Coronary Artery Disease (CAD)	CSNK1A1, AKAP5, TOPORS, ACTBL2, FNTA	SVM Accuracy: >90%	Risk assessment, therapeutic development
Idiopathic Dilated Cardiomyopathy (IDCM)	MNS1, MYOT	SVM Accuracy: >90%	Diagnostic precision, treatment monitoring
Type 2 Diabetes Mellitus (T2DM)	ALDOB	SVM Accuracy: >90%	Complication prediction, targeted interventions

Cross-Disease Transcriptional Signatures

Comparative analyses of cytoskeletal gene expression across multiple age-related diseases have revealed shared molecular pathways, suggesting common mechanisms of cytoskeletal decline. While no single cytoskeletal gene was dysregulated across all conditions examined, several genes demonstrated alterations in multiple disease contexts [2]. For instance, ANXA2 (Annexin A2) showed convergent dysregulation in Alzheimer's disease, idiopathic dilated cardiomyopathy, and type 2 diabetes mellitus, suggesting its involvement in generalized mechanisms of age-related cellular compromise [2].

The cytoskeletal cross-talk between different cellular compartments emerges as a critical factor in age-related functional decline. Computational models have identified TPM3 (Tropomyosin 3) as a shared transcriptomic feature in Alzheimer's disease, coronary artery disease, and type 2 diabetes mellitus, while SPTBN1 (Spectrin Beta, Non-Erythrocytic 1) showed common alterations in Alzheimer's disease, coronary artery disease, and hypertrophic cardiomyopathy [2]. These findings point to an interconnected network of cytoskeletal dysregulation that transcends traditional disease boundaries.

Diagram 1: Integrated Framework of Cytoskeletal Aging Mechanisms. This diagram illustrates the hierarchical relationship between transcriptomic changes, structural consequences, and functional outcomes in cytoskeletal aging.

Experimental Models and Methodological Approaches

Single-Cell Omics Technologies

The investigation of cytoskeletal aging has been revolutionized by single-cell omics technologies that enable high-resolution analysis of transcriptional and genomic changes in specific cell populations. Integrated approaches combining single-nucleus RNA sequencing (snRNA-seq), single-cell whole-genome sequencing (scWGS), and spatial transcriptomics have provided unprecedented insights into cell-type-specific aging trajectories [7]. These methodologies have revealed that somatic mutations accumulate in human neurons during aging, with specific mutational signatures that correlate with both gene transcription and repression [7].

The experimental workflow for comprehensive cytoskeletal aging analysis typically involves: (1) Fresh-frozen tissue collection from donors across the lifespan; (2) Nuclei isolation and purification for single-cell sequencing; (3) Droplet-based snRNA-seq library preparation; (4) Single-cell whole-genome sequencing to identify somatic mutations; (5) Spatial transcriptomic validation using techniques such as MERFISH (Multiplexed Error-Robust Fluorescence In Situ Hybridization); and (6) Computational integration of multi-omics datasets [7]. This integrated approach has demonstrated that gene length- and expression-level-dependent rates of somatic mutation in neurons correlate with the transcriptomic landscape of the aged human brain.

Cytoskeletal-Focused Animal Models

Targeted animal models have been instrumental in establishing causal relationships between specific cytoskeletal alterations and age-related functional decline. Inducible, cell-type-specific knockout systems have enabled researchers to isolate the effects of acute cytoskeletal disruption in adult animals, avoiding confounding factors from developmental processes [8]. For example, microglia-specific Profilin 1 (Pfn1) knockout in adult mice has demonstrated that loss of this cytoskeletal regulator is sufficient to trigger senescence-associated secretory phenotype (SASP) and drive circuit-specific synaptic decline [8].

The experimental protocol for investigating cytoskeletal aging in animal models typically includes: (1) Design of inducible knockout systems (e.g., Cx3cr1CreERT2 mice crossed with floxed Pfn1 alleles); (2) Tamoxifen induction in adult animals to achieve cell-type-specific gene deletion; (3) Multimodal phenotypic characterization using intravital two-photon imaging, electrophysiology, and behavioral assessments; (4) Molecular analysis including RNA-seq, proteomics, and phosphoproteomics; and (5) Structural validation through super-resolution microscopy and 3D reconstruction [8]. This comprehensive approach has established Pfn1 as a critical checkpoint against microglial senescence and identified the Pfn1-cytoskeleton axis as a potential therapeutic target for enhancing brain resilience in aging.

The Research Toolkit: Essential Reagents and Methodologies

Table 3: Essential Research Reagents and Platforms for Cytoskeletal Aging Studies

Reagent/Platform	Specific Example	Research Application	Functional Role
Single-cell RNA-seq	10x Genomics Chromium	Transcriptomic profiling	Cell-type-specific expression analysis of cytoskeletal genes
Spatial Transcriptomics	MERFISH (Multiplexed Error-Robust FISH)	Spatial validation	Mapping cytoskeletal gene expression in tissue context
Inducible Knockout Systems	Cx3cr1CreERT2; Pfn1fl/fl	Causal validation	Cell-type-specific cytoskeletal disruption in adult animals
Intravital Imaging	Two-photon microscopy	Dynamic structural analysis	Real-time visualization of cytoskeletal dynamics in living tissue
Computational Frameworks	SVM-RFE (Support Vector Machine-Recursive Feature Elimination)	Biomarker discovery	Identification of cytoskeletal genes associated with aging phenotypes
Super-Resolution Microscopy	Structured-illumination microscopy (SIM)	Ultrastructural analysis	High-resolution visualization of cytoskeletal organization
SCD1 inhibitor-4	SCD1 inhibitor-4|Potent SCD1 Inhibitor|For Research		Bench Chemicals
PF-9363	PF-9363, MF:C20H20N4O6S, MW:444.5 g/mol	Chemical Reagent	Bench Chemicals

Integrated Signaling Pathways in Cytoskeletal Aging

The molecular mechanisms underlying cytoskeletal aging involve interconnected signaling networks that translate transcriptomic alterations into structural and functional deficits. Recent research has identified several key pathways that mediate cytoskeletal decline in aging cells. The ERK/NF-κB signaling axis has been implicated in driving the senescence-associated secretory phenotype (SASP) following cytoskeletal disruption in microglia [8]. Additionally, actin-microtubule coupling mechanisms emerge as critical determinants of cellular resilience, with their dysfunction leading to collapsed morphodynamics and impaired responsive capacity [8].

Diagram 2: Signaling Pathways in Cytoskeletal Aging. This diagram illustrates the molecular cascade linking Profilin 1 loss to functional decline through defined signaling mechanisms.

The mechanotransduction pathways that translate physical forces into biochemical signals have emerged as central players in cytoskeletal aging. These pathways form a feedback loop between antagonistic and integrative hallmarks of aging, potentially driving accelerated aging phenotypes in conditions such as microgravity, which shares cellular similarities with biological aging [9]. Understanding these integrated signaling networks provides opportunities for therapeutic interventions aimed at maintaining cytoskeletal integrity throughout the lifespan.

The comprehensive analysis of transcriptomic and structural changes in the aging cytoskeleton reveals a complex landscape of cell-type-specific vulnerabilities and shared molecular pathways. The concerted downregulation of essential cytoskeletal genes across neural cell types, combined with increased transcriptional variability in specific neuronal populations, points to a progressive loss of cytoskeletal integrity as a fundamental hallmark of brain aging. Computational frameworks have identified conserved cytoskeletal biomarkers across multiple age-related diseases, suggesting common mechanisms of cellular decline that transcend traditional diagnostic boundaries.

Future research directions should focus on therapeutic strategies that target cytoskeletal resilience, including small molecules that stabilize microtubule networks, interventions that enhance actin dynamics, and approaches that maintain cytoskeletal cross-talk. The integration of single-cell multi-omics, advanced imaging technologies, and computational modeling will continue to illuminate the spatial and temporal dynamics of cytoskeletal aging. Furthermore, the investigation of cytoskeletal-immune interactions and their role in neuroinflammatory processes represents a promising frontier for understanding the interface between cellular architecture and systemic aging. As our molecular understanding of cytoskeletal aging deepens, so too will our capacity to develop targeted interventions that preserve cellular integrity and function throughout the lifespan.

The cytoskeleton, a dynamic network of filamentous proteins, is fundamental to cellular integrity, shape, motility, and intracellular transport. Recent transcriptomic and mechanistic studies increasingly identify cytoskeletal dysregulation as a critical pathophysiological mechanism shared across major age-related diseases, including Alzheimer's disease (AD), cardiovascular diseases (CVDs), and diabetes mellitus. This commonality suggests the existence of a conserved pathological axis where age-induced alterations in cytoskeletal gene expression and protein function disrupt tissue homeostasis in neural, vascular, and metabolic systems. Understanding these shared pathways provides not only insights into disease mechanisms but also reveals potential therapeutic targets for intervention across multiple conditions.

This guide synthesizes cutting-edge research from transcriptomic profiling, single-cell RNA sequencing, and functional studies to compare how cytoskeletal disruption manifests in and contributes to the progression of Alzheimer's, cardiovascular diseases, and diabetes. By presenting structured experimental data, detailed methodologies, and integrative pathway analysis, we aim to provide researchers and drug development professionals with a comprehensive resource for developing targeted diagnostic and therapeutic strategies.

Comparative Analysis of Cytoskeletal Alterations Across Diseases

Key Cytoskeletal Genes and Proteins Dysregulated in Age-Related Diseases

Table 1: Summary of Key Dysregulated Cytoskeletal Genes and Their Functional Consequences

Disease Area	Dysregulated Gene/Protein	Direction of Change	Primary Function	Experimental Evidence	Citation
Alzheimer's Disease (AD)	WIPF3	Downregulated	Actin cytoskeleton regulation, immune cell infiltration	WGCNA, LASSO Cox, Random Forest on bulk sequencing	[10]
	Profilin 1 (PFN1)	Downregulated	Actin polymerization, microglial morphodynamics	Inducible microglial knockout, 2-photon imaging, RNA-seq	[8]
	SPTBN1	Dysregulated	Spectrin cytoskeleton, mechanotransduction	Machine learning (SVM) on transcriptomic data	[2]
Cardiovascular Disease	SPTBN1	Dysregulated	Spectrin network, endothelial barrier integrity	Identified in atherosclerotic microvessels	[11]
	Profilin 1	Dysregulated	Actin cytoskeleton, endothelial function	Endothelial-specific disruption causes severe pathology	[11]
	βIV-Spectrin	Dysregulated	Angiogenesis, mechanotransduction	Mediates endothelial tip/stalk cell selection	[11]
Diabetes	Spectrin Network	Modified/Cross-linked	RBC membrane skeleton, deformability	AFM shows RBC stiffening; glycosylation of cytoskeleton	[12]
	Actin	Modified/Cross-linked	RBC shape and flexibility	AFM shows RBC stiffening; glycosylation of cytoskeleton	[12]
	ALDOB	Dysregulated	Cytoskeletal association (indirect)	Machine learning (SVM) on transcriptomic data	[2]

Functional Consequences of Cytoskeletal Disruption

Table 2: Functional and Phenotypic Outcomes of Cytoskeletal Dysregulation

Disease Area	Cellular Phenotype	Tissue/Systemic Outcome	Key Signaling Pathways Implicated
Alzheimer's Disease	Microglial process retraction, failed injury response	Chronic neuroinflammation, synaptic loss, cognitive decline	ERK/NF-κB (SASP), RAGE	[10] [13] [8]
	Neuronal cytoskeletal collapse, axonal transport defects	Neurofibrillary tangle formation, neurodegeneration	GSK-3β, CDK5 (Tau phosphorylation)	[13]
Cardiovascular Disease	Endothelial barrier disruption, increased permeability	Atherosclerosis, vascular inflammation, impaired hemodynamics	RAGE, RhoA/ROCK, PKCα	[14] [11]
	Impaired mechanotransduction, stiffening	Hypertension, vascular remodeling, poor angiogenesis	Shear stress pathways, LINC complex	[11]
Diabetes	RBC stiffening, reduced deformability	Impaired microcirculation, tissue hypoxia, ischemia	AGE-RAGE, Oxidative Stress	[14] [12]
	Skeletal muscle insulin resistance, fiber-type shift	Hyperglycemia, metabolic dysfunction, muscle wasting	Insulin signaling, inflammatory pathways	[15]

Detailed Experimental Protocols for Cytoskeletal Research

Transcriptomic Identification of Cytoskeletal Biomarkers

Objective: To identify cytoskeletal genes associated with age-related diseases using an integrative machine learning and differential expression approach [2].

Workflow:

• Data Collection & Preprocessing:

  • Retrieve transcriptome data from public repositories (e.g., GEO). The study used GSE36980 and GSE173955 for AD, and GSE43292 and GSE100927 for atherosclerosis [10] [2].
  • Normalize data and remove batch effects using packages like Limma in R [10] [2].
  • Obtain a curated list of cytoskeletal genes from the Gene Ontology Browser (GO:0005856) [2].

• Machine Learning-Based Feature Selection:

  • Train multiple classifiers (Support Vector Machines (SVM), Random Forest, k-NN) on expression data.
  • Utilize Recursive Feature Elimination (RFE) with SVM, which has demonstrated high accuracy for gene expression data, to select the most discriminative cytoskeletal genes between patient and normal samples [2].
  • Validate model performance using five-fold cross-validation and metrics like accuracy, F1-score, and AUC [2].

• Differential Expression Analysis (DEA):

  • Perform DEA using DESeq2 or Limma to find significantly dysregulated genes.
  • Overlap the results of DEA and RFE-SVM to identify high-confidence cytoskeletal gene signatures [2].

• Validation:

  • Confirm the diagnostic power of identified genes using Receiver Operating Characteristic (ROC) analysis on external validation datasets [2].

Transcriptomic Biomarker Discovery Workflow: This diagram outlines the computational pipeline for identifying cytoskeletal disease biomarkers from transcriptomic data.

Functional Validation via Single-Cell RNA Sequencing and Intravital Imaging

Objective: To validate the role of cytoskeletal genes in specific cell types and assess dynamic functional deficits in vivo.

Workflow (as applied to microglial PFN1 in Alzheimer's):

• Inducible Cell-Type-Specific Knockout:

  • Generate conditional knockout mice (e.g., Cx3cr1CreERT2; Pfn1fl/fl).
  • Induce gene deletion in adult animals (e.g., with tamoxifen) to avoid developmental confounds [8].

• Single-Cell RNA Sequencing Analysis:

  • Process single-cell RNA-seq data (e.g., from GEO like GSE175814) using Seurat.
  • Perform quality control, normalization, PCA, and cluster cells using UMAP.
  • Annotate cell types (e.g., microglia, neurons, astrocytes) using canonical markers (e.g., microglia: TYROBP, CX3CR1) [10].
  • Analyze cell portion and gene expression changes across conditions.

• Intravital Two-Photon (2P) Imaging:

  • Use Cx3cr1CreER-dependent reporters to label microglia in vivo.
  • Image the cortical parenchyma in anesthetized, live mice under baseline conditions and after laser-induced injury (LI).
  • Quantify microglial surveillance capacity, protrusion motility, and injury response dynamics using computational tracking of Euclidean distances and velocity parameters [8].

• Multi-Omics Integration:

  • Combine transcriptomic (RNA-seq) and proteomic data from isolated microglia to identify dysregulated pathways (e.g., ERK/NF-κB) and SASP factors [8].

Mechanistic Pathways: From Cytoskeletal Disruption to Disease

The Cytoskeletal Dysregulation Pathway in Alzheimer's and Diabetes

The diagram below illustrates a shared pathway where cytoskeletal dysfunction, induced by age or metabolic stress, contributes to pathology in Alzheimer's disease and diabetes, often via the AGE-RAGE axis and inflammatory signaling.

Cytoskeletal Dysregulation in Age-Related Diseases: This pathway shows how AGE-RAGE signaling and downregulation of cytoskeletal regulators like Profilin 1 converge on actin dysregulation, driving microglial dysfunction, neuroinflammation, and vascular pathology.

Cytoskeletal Mechanotransduction in Cardiovascular Disease

In the cardiovascular system, the endothelial cytoskeleton is the primary mediator of mechanotransduction. Its disruption is a key event in the pathogenesis of atherosclerosis and other vascular diseases.

Endothelial Cytoskeleton in Cardiovascular Disease: This diagram illustrates how disturbed blood flow leads to cardiovascular disease via defective mechanotransduction and cytoskeletal remodeling, resulting in endothelial barrier disruption and inflammation.

The Scientist's Toolkit: Key Research Reagent Solutions

Table 3: Essential Reagents and Tools for Cytoskeletal Research in Age-Related Diseases

Category	Reagent/Tool	Specific Example	Function in Research	Citation
Computational Biology	R Limma Package	Bioconductor Package	Normalization, batch effect correction, differential expression	[10] [2]
	R WGCNA Package	Weighted Gene Co-expression Network Analysis	Identify co-expressed gene modules associated with disease traits	[10]
	Seurat	Single-Cell Analysis Toolkit (R)	Preprocessing, integration, clustering, and annotation of scRNA-seq data	[10]
Molecular Biology	siRNA/Oligonucleotides	Tβ4 siRNA, Scrambled siRNA (Dharmacon)	Knockdown of target genes (e.g., Tβ4) to study function in vitro	[14]
Protein & Biochemical Assays	G-Actin/F-Actin In Vivo Assay Kit	Cytoskeleton, Inc. (BK037)	Quantify the ratio of globular (G) to filamentous (F) actin	[14]
Cell Culture & Treatment	Advanced Glycation End Products (AGEs)	AGEs modified with BSA (BioVision)	Induce diabetic-like cytoskeletal stress and RAGE activation in ECs	[14]
Imaging & Microscopy	Intravital Two-Photon Microscopy	N/A	Real-time imaging of cellular dynamics (e.g., microglial motility) in live animals	[8]
	Structured-Illumination Microscopy (SIM)	deepSIM	Super-resolution imaging of cytoskeletal structures (e.g., microglial morphology)	[8]
Biomechanical Analysis	Atomic Force Microscopy (AFM)	N/A	Nanoscale measurement of cell mechanical properties (e.g., RBC stiffness)	[12]
PQR620	PQR620, MF:C21H25F2N7O2, MW:445.5 g/mol	Chemical Reagent	Bench Chemicals
JH-X-119-01	JH-X-119-01, MF:C25H20N6O3, MW:452.5 g/mol	Chemical Reagent	Bench Chemicals

The evidence compiled in this guide underscores that the cytoskeleton is not merely a structural scaffold but a dynamic, integrative signaling platform whose dysregulation is a common denominator in the pathogenesis of major age-related diseases. In Alzheimer's disease, deficiencies in regulators like WIPF3 and Profilin 1 cripple microglial function and promote neuroinflammation [10] [8]. In cardiovascular disease, spectrin and actin dynamics are perturbed in endothelial cells, disrupting barrier function and mechanotransduction [11]. In diabetes, hyperglycemia-induced cross-linking of spectrin and actin compromises red blood cell deformability, impairing microcirculation [12].

The convergence of pathology on cytoskeletal networks presents a compelling opportunity for novel therapeutic strategies. Future research should focus on:

• Developing small-molecule stabilizers of actin dynamics or microtubule networks.
• Exploring gene therapy approaches to restore the expression of downregulated cytoskeletal regulators like PFN1 in specific cell types.
• Utilizing the identified cytoskeletal gene signatures, such as those from machine-learning models [2], as companion diagnostics to stratify patients and identify those most likely to benefit from cytoskeleton-targeted therapies.

Concerted efforts to target the cytoskeletal core of these pathologies hold significant promise for developing treatments that could simultaneously address the debilitating progression of multiple age-related conditions.

The neuronal cytoskeleton, a complex network of intracellular filaments, is fundamental to maintaining cellular structure, facilitating axonal transport, and ensuring synaptic integrity. Composed primarily of neurofilaments, microtubules, and actin microfilaments, this dynamic structure is particularly crucial in neurons, which possess extensive and morphologically complex processes. During the process of aging, this carefully regulated system undergoes significant alterations, which are increasingly recognized as central to the decline of neuronal function and the pathogenesis of age-related neurodegenerative diseases. Transcriptomic research has begun to illuminate the precise genetic and molecular changes underpinning these cytoskeletal modifications, revealing a complex program of gene expression changes that evolve with age.

A core aspect of this aging program involves a shift in the expression of key cytoskeletal genes. Studies comparing genome-wide expression in the cortex of humans, rhesus macaques, and mice have identified a small subset of conserved age-related changes, but also a dramatic, recently evolved repression of neuronal genes in primates, many of which are associated with synaptic function and the cytoskeleton [16]. This review will objectively compare the performance of key cytoskeletal components—specifically neurofilaments and tubulins—as biomarkers and functional elements in the aging nervous system, synthesizing data from cross-sectional studies, cohort analyses, and mechanistic investigations to provide a resource for researchers and drug development professionals.

Comparative Analysis of Key Cytoskeletal Genes in Aging

The following sections provide a detailed, data-driven comparison of the major cytoskeletal genes and their encoded proteins, focusing on their alteration during aging and their consequent utility in both basic research and clinical applications.

Neurofilament Light Chain (NfL)

Neurofilament Light Chain (NfL) is a neuron-specific intermediate filament subunit responsible for determining axonal diameter and facilitating electrical conduction velocity. As a biomarker, it has garnered significant attention due to its release into cerebrospinal fluid and blood upon axonal damage.

Table 1: Key Characteristics of Neurofilament Light Chain (NfL) in Aging

Aspect	Experimental Data & Findings
Age-Related Change	Serum NfL (sNfL) levels exhibit a strong positive correlation with age (r=0.553, p<0.001), with a more pronounced increase in men (r=0.824, p<0.001) than in women (r=0.281, p=0.230) [17].
Association with Cognition	In US elderly (≥60 years), higher ln(sNfL) is negatively correlated with cognitive test scores after adjustment for confounders: Immediate Recall Test (β=-0.763), Delayed Recall Test (β=-0.308), Animal Fluency Test (β=-1.616), Digit Symbol Substitution Test (β=-2.790) [18].
Association with Physical Function	In men, higher NFL levels correlate with worse physical performance: negative correlations with normal walking speed (r=-0.460, p=0.041) and grip strength (r=-0.535, p=0.015) [17].
Association with Body Composition	In men, NFL levels positively correlate with fat mass (r=0.482, p=0.031) and visceral fat area (r=0.516, p=0.02), and negatively correlate with muscle quality (r=-0.603, p=0.005) [17].
Influencing Factors	sNfL concentration is tightly associated with renal function (cystatin C: r=0.501, p<0.0001; eGFR: r=-0.492, p<0.0001). It is also dependent on age and BMI, but not sex in some cohorts [19].

Experimental Protocols for NfL Measurement

The quantification of NfL in biological fluids relies on highly sensitive immunoassays. A common methodology used in large-scale studies, such as the NHANES analysis, involves the following steps [18]:

• Sample Collection: Serum or plasma samples are collected and stored under standardized conditions.
• Automated Immunoassay: Samples are analyzed using a high-throughput immunoassay platform (e.g., the Atellica platform from Siemens Healthineers). The assay is based on direct acridine ester (AE) chemiluminescence detection.
• Antigen-Antibody Complex Formation: Serum sNfL antigen binds to AE-labeled antibodies. The mixture is then combined with paramagnetic particles (PMP) coated with a capture antibody to form an antigenic complex.
• Washing and Detection: Unbound AE-labeled antibodies are isolated and removed. The subsequent addition of acids and bases initiates chemiluminescence, and the light emission is quantified.
• Data Analysis: A fully automated system performs all phases. Results are normalized, and statistical analyses (e.g., weighted multiple linear regression for population studies) are applied, often using natural log-transformed sNfL values (ln[sNfL]) to normalize the distribution.

This method has been validated against the single-molecule array (Simoa) assay, another widely used and highly sensitive technology cited in multiple studies [18] [19].

Diagram 1: Neurofilament Light Chain (NfL) as a Biomarker of Neuronal Health. This pathway illustrates the release of NfL following axonal damage and its subsequent measurement, which correlates with key age-related clinical declines.

Tubulins and Microtubule-Associated Genes

Microtubules, polymers of α- and β-tubulin heterodimers, are critical for neuronal migration, axon and dendrite growth, and intracellular transport. The specific expression of different tubulin isotypes is a key regulatory mechanism for microtubule function.

Table 2: Key Characteristics of Tubulin Genes in Aging and Neurodevelopment

Gene / Protein	Experimental Data & Findings
TUBA1A (α-tubulin)	The most commonly mutated tubulin gene in human brain malformations (tubulinopathies) [20]. Mutations cause a spectrum of disorders including lissencephaly and polymicrogyria, highlighting its critical role in brain development [20].
TUBB3 (β-III-tubulin)	A neuron-specific isotype of β-tubulin exclusively expressed in neurons [3]. Its regulation is crucial for neuronal-specific microtubule functions.
Microtubule Dynamics	In peripheral sensory axons from human skin biopsies, the content of cytoskeletal components, including tubulin, shows an age-dependent increase in both sexes [3].
Evolutionary Conservation	Genome-wide analysis shows only a small subset of age-related expression changes (154 genes) are conserved in mouse, rhesus macaque, and human cortex. This indicates that many aspects of neuronal aging, potentially involving cytoskeletal genes, are not well-modeled in lower organisms [16].

Experimental Protocols for Analyzing Tubulin Function

Research into tubulin genes often combines genetic, molecular, and cell biological approaches. A typical workflow for investigating a tubulinopathy-associated gene like TUBA1A is outlined below [20]:

• Genetic Sequencing: Identification of de novo missense mutations in patients with specific brain malformations via whole-exome or genome sequencing.
• In Silico Modeling: Computational analysis of how mutations affect tubulin structure, particularly at critical sites like the GTP-binding E-site, which influences heterodimer interactions and microtubule dynamic instability.
• Cell-Based Assays: Expression of mutant TUBA1A in cultured cells (e.g., neuronal precursors or human induced pluripotent stem cell (iPSC)-derived neurons) to assess:
  • Microtubule Polymerization: Using live-cell imaging with fluorescently tagged tubulin to monitor polymerization and depolymerization rates (dynamic instability).
  • Protein-Protein Interactions: Co-immunoprecipitation to determine if mutations disrupt binding to microtubule-associated proteins (MAPs) or motor proteins.
• Animal Models: Generation of transgenic mice carrying patient-specific mutations to study the impact on brain development in vivo, including neuronal migration and axon pathfinding.

Diagram 2: Mechanistic Pathway of TUBA1A Mutation in Brain Malformations. This diagram logicalizes how mutations in the key neuronal α-tubulin gene TUBA1A disrupt microtubule function and lead to neurodevelopmental disorders.

The Scientist's Toolkit: Research Reagent Solutions

This table details key reagents and tools essential for conducting research on cytoskeletal alterations in aging.

Table 3: Essential Research Reagents for Cytoskeletal Aging Studies

Research Reagent / Tool	Function & Application in Cytoskeletal Research
Anti-NfL Antibodies	Used in immunoassays (e.g., Simoa, ELISA) to quantify NfL levels in CSF, serum, or plasma as a biomarker of axonal damage [18] [19].
Siemens Healthineers Atellica IM sNfL Assay	A fully automated, high-throughput immunoassay for measuring serum NfL in large cohort studies, validated against Simoa technology [18].
Quanterix Simoa/SR-X Platform	A single-molecule array technology offering extreme sensitivity for measuring low-abundance neuronal proteins like NfL in blood-based samples [19].
Anti-TUBB3 Antibodies	Used in immunostaining (e.g., of human skin biopsies) to specifically label neuronal microtubules and quantify changes in cytoskeletal mass during aging [3].
Human Skin Biopsies	A minimally invasive tissue source for studying age-dependent changes in cytoskeletal protein content (neurofilaments, tubulin, actin) and sensory axon caliber via quantitative immunofluorescence [3].
C2C12 Mouse Myoblast Cell Line	A common in vitro model for studying myotube atrophy and the effects of cytoskeletal-related therapeutics (e.g., DLK1) in muscle aging [21].
Transgenic Killifish Models	Vertebrate models (e.g., Nothobranchius furzeri) for studying age-related transcriptional programs like the fasting-like transcriptional program (FLTP) in adipose tissue and its connection to metabolism and cytoskeleton [22].
FR-229934	FR-229934, CAS:405551-89-5, MF:C21H23Cl2N3O3S, MW:468.4 g/mol
Resiquimod-D5	Resiquimod-D5, MF:C17H22N4O2, MW:319.41 g/mol

The objective comparison of cytoskeletal genes in aging reveals a clear dichotomy: Neurofilament Light Chain (NfL) has emerged as a robust, quantifiable biomarker for tracking age-related neuronal compromise and its functional consequences, particularly in men. In contrast, tubulin genes like TUBA1A play a more foundational, structural role, where dysregulation primarily manifests in severe neurodevelopmental disorders, with more subtle alterations likely contributing to age-related functional decline. The data underscores that interpretation of these biomarkers, especially sNfL, requires careful consideration of confounding factors like renal function. Furthermore, transcriptomic evidence suggests that the aging trajectory of the neuronal cytoskeleton has evolved significantly in primates, highlighting the need for prudent model system selection in preclinical drug development. Future research integrating these cytoskeletal markers with advanced transcriptomic and metabolomic data will be crucial for developing a unified understanding of neuronal aging and for identifying novel therapeutic targets.

The cytoskeleton, comprising microtubules, actin filaments, and intermediate filaments, forms a dynamic structural framework essential for cellular homeostasis, polarity, intracellular transport, and mechanical integrity. Beyond these structural roles, the cytoskeleton functions as a sophisticated signaling platform through a complex language of post-translational modifications (PTMs) that create specific biochemical signatures—termed the "tubulin code" and "actin code." These PTM patterns extensively enrich the functional diversity of cytoskeletal proteins without requiring gene expression changes, allowing cells to rapidly respond to environmental cues, mechanical stresses, and signaling events [23] [24] [25].

Emerging research places these cytoskeletal codes at the center of aging biology and age-related disease pathogenesis. During aging, cumulative molecular damage and progressive decline in cellular homeostasis are reflected in altered PTM patterns on both tubulin and actin. These alterations contribute significantly to neuronal decline, cardiac dysfunction, and other age-related pathologies by disrupting intracellular transport, impairing organelle function, and compromising cellular architecture [26] [24] [27]. This review synthesizes current understanding of how tubulin and actin PTMs influence aging processes and disease states, with particular emphasis on experimental approaches for investigating these relationships and therapeutic strategies targeting cytoskeletal PTMs.

The Tubulin Code: Molecular Mechanisms and Functional Consequences

Key Tubulin Post-Translational Modifications

Microtubules, composed of α/β-tubulin heterodimers, undergo an array of PTMs that precisely tune their properties and functions. These modifications create a biochemical "code" read by microtubule-associated proteins (MAPs), motor proteins, and other effectors to regulate cellular processes [23] [28]. The major tubulin PTMs include:

• Acetylation: Occurs at lysine 40 (K40) of α-tubulin within the microtubule lumen, mediated by α-tubulin acetyltransferase 1 (αTAT1) and reversed by histone deacetylase 6 (HDAC6) and sirtuin 2 (SIRT2). This modification enhances microtubule flexibility and protects against mechanical stress [23] [29].
• Detyrosination/Tyrosination: Involves the cyclic removal and re-addition of the C-terminal tyrosine of α-tubulin, regulating interactions with motor proteins and MAPs [28] [27].
• Glutamylation: Addition of glutamate chains to the C-terminal tails of both α- and β-tubulin, influencing microtubule severing, motor protein traffic, and cilia function [28] [27].
• Glycylation: Addition of glycine chains primarily to tubulin C-terminal tails, particularly important in ciliated organisms [28].

Table 1: Major Tubulin Post-Translational Modifications and Their Functional Roles

PTM Type	Modification Site	Writer Enzymes	Eraser Enzymes	Primary Functional Consequences
Acetylation	α-tubulin K40	αTAT1	HDAC6, SIRT2	Enhances flexibility, prevents mechanical damage, regulates motor protein binding
Detyrosination	α-tubulin C-terminal tyrosine	Vasohibins (VASH1/2) with SVBP	Tubulin tyrosine ligase (TTL)	Stabilizes microtubules, regulates kinesin and dynein binding
Tyrosination	α-tubulin C-terminal glutamate	Tubulin tyrosine ligase (TTL)	Vasohibins (VASH1/2) with SVBP	Marks dynamic microtubules, promotes kinesin-1 binding
Polyglutamylation	α- and β-tubulin C-terminal tails	TTLL family enzymes	CCPs (cytosolic carboxypeptidases)	Regulates microtubule severing, motor protein traffic, cilia function
Polyglycylation	α- and β-tubulin C-terminal tails	TTLL family enzymes	-	Stabilizes microtubule structures, particularly in cilia

Experimental Evidence: Tubulin PTMs in Aging and Neurodegeneration

The role of tubulin PTMs in aging and disease has been extensively investigated through multiple experimental approaches. In age-related neurodegenerative conditions including Alzheimer's disease (AD), Parkinson's disease, and Huntington's disease, defects in microtubule acetylation have been consistently observed [29] [27]. These alterations directly impact axonal transport through impaired motor protein function, leading to abnormal organelle and vesicle accumulation [29].

Research in mammalian models demonstrates that microtubule acetylation regulates intracellular vesicular transport by modulating kinesin-1 recruitment and function [29]. Mouse models of Alzheimer's disease exhibit axonal defects characterized by accumulated microtubule-associated organelles and vesicles, paralleling early human AD pathology. Importantly, reducing kinesin-1 dosage exacerbates these axonal defects and increases amyloid-β peptide levels and deposition in these models [29]. In Parkinson's models, pathogenic leucine-rich repeat kinase 2 (LRRK2) mutations preferentially associate with deacetylated microtubules and inhibit axonal transport in primary neurons and Drosophila models [29].

Beyond neurological contexts, tubulin PTMs contribute significantly to cardiac aging and dysfunction. In cardiomyocytes, the microtubule network aligns longitudinally along the myofibrillar matrix, functioning as both a transport system and a mechanical element that bears compressive loads during contraction [28]. Age-related changes in tubulin detyrosination and acetylation alter microtubule mechanical properties, contributing to impaired cardiac contractility in heart failure [28].

Figure 1: Tubulin PTM Dysregulation in Aging and Disease. Age-related changes in PTM-regulating enzymes alter microtubule properties and functions, contributing to neurodegeneration and cardiac dysfunction.

The Actin Code: Cytoskeletal Dynamics in Aging

Actin Polymerization Imbalance in Brain Aging

While tubulin PTMs have been extensively studied, recent research has revealed equally critical roles for actin dynamics in aging, particularly in the nervous system. Contrary to earlier assumptions that actin primarily serves structural functions, evidence now demonstrates that actin polymerization states play active regulatory roles in age-related cellular decline [30].

A landmark 2024 study in Drosophila models revealed a striking age-related increase in filamentous actin (F-actin) in brain tissue, with F-actin-rich rod-like structures accumulating in aged brains that were absent in young animals [30]. These actin aggregates parallel the Hirano bodies observed in human aging and Alzheimer's disease brains. Crucially, this F-actin accumulation correlated strongly with health status—flies undergoing dietary restriction or rapamycin treatment, two evolutionarily conserved longevity interventions, showed significantly reduced F-actin accumulation in aged brains [30].

Experimental reduction of F-actin levels in aging neurons through genetic or pharmacological approaches prevented age-onset cognitive decline and extended organismal healthspan [30]. Mechanistically, researchers demonstrated that actin dysregulation directly impairs autophagic activity in the aged brain. Excess F-actin polymerization disrupts autophagosome-lysosome fusion and cargo degradation, leading to accumulation of damaged proteins and dysfunctional mitochondria [30]. Remarkably, disrupting actin polymerization in aged animals with cytoskeletal drugs restored brain autophagy to youthful levels and reversed cellular hallmarks of brain aging [30].

Table 2: Experimental Evidence Linking Cytoskeletal PTMs/Dynamics to Aging Phenotypes

Experimental System	Cytoskeletal Target	Intervention	Key Findings	Citation
Drosophila brain aging	F-actin	Neuron-specific inhibition of Formin (Fhos)	Improved cognitive function in aged flies; enhanced multiple healthspan markers	[30]
Drosophila brain aging	F-actin	Cytochalasin D, Latrunculin A (actin depolymerizers)	Restored brain autophagy to youthful levels; reversed cellular aging markers	[30]
Mouse Alzheimer's model	Microtubule acetylation	HDAC6 inhibition	Reduced tau levels, improved memory, ameliorated cognitive defects	[29]
Rat Parkinson's model	Microtubule acetylation	Paclitaxel (taxol)	Increased tubulin acetylation, reversed dopaminergic neuron death	[29]
Striatal precursor cells (Huntington's)	Microtubule acetylation	HDAC6 inhibition	Compensated for intracellular protein transport deficit	[29]

Methodological Approaches: Studying Cytoskeletal Codes in Aging

Experimental Workflows for Cytoskeletal PTM Analysis

Investigating tubulin and actin codes in aging requires specialized methodological approaches. For tubulin PTM analysis, researchers employ a combination of immunofluorescence microscopy with modification-specific antibodies (e.g., anti-acetylated tubulin for K40 acetylation), enzyme-linked immunosorbent assays (ELISAs) for quantitative assessment, and high-resolution microscopy techniques to detect PTM-specific subcellular localization [23] [29] [30].

Advanced transcriptomic technologies have revolutionized aging research, with RNA sequencing (RNA-seq), single-cell RNA sequencing (scRNA-seq), and spatial transcriptomics enabling comprehensive analysis of gene expression patterns associated with cytoskeletal aging [31]. These approaches have facilitated the development of transcriptomic aging clocks like Transcriptomic Mortality-risk Age (TraMA), which predicts aging rates based on RNA-seq data from human cohorts [32]. Transcriptomic analyses reveal that aging is associated with heightened transcriptional variability, decreased relative abundance of long transcripts, and increased transcriptional elongation speed, all impacting cytoskeletal regulation [31].

For actin dynamics assessment, researchers utilize phalloidin staining for F-actin visualization, genetic reporters (e.g., actin-GFP fusion proteins), and pharmacological interventions with cytoskeletal drugs (e.g., cytochalasin D, latrunculin A, jasplakinolide) to experimentally manipulate polymerization states [30]. These approaches can be combined with autophagy assays (e.g., LC3 puncta formation, mitochondrial clearance assays) to establish functional connections between actin dynamics and cellular homeostasis mechanisms [30].

Figure 2: Experimental Workflow for Analyzing Cytoskeletal Codes in Aging. Integrated approaches combining microscopy, transcriptomics, and functional assays elucidate relationships between cytoskeletal alterations and aging phenotypes.

The Scientist's Toolkit: Essential Research Reagents

Table 3: Essential Research Reagents for Cytoskeletal Aging Studies

Reagent/Category	Specific Examples	Primary Research Application	Key Considerations
PTM-Specific Antibodies	Anti-acetylated tubulin (K40), Anti-detyrosinated tubulin, Anti-polyglutamylated tubulin	Detection and quantification of specific tubulin PTMs via WB, IF, IHC	Specificity validation crucial; lumenal modifications require permeabilization optimization
Actin Visualization Tools	Phalloidin conjugates, LifeAct tags, Actin-GFP reporters	F-actin visualization and quantification	Phalloidin specificity for F-actin; GFP-tagged actin may affect dynamics
Pharmacological Modulators	Cytochalasin D, Latrunculin A (actin depolymerizers), Jasplakinolide (actin stabilizer), Paclitaxel (microtubule stabilizer), Nocodazole (microtubule depolymerizer)	Experimental manipulation of cytoskeletal dynamics	Off-target effects; concentration optimization required for specific effects
Genetic Manipulation Tools	RNAi constructs, CRISPR/Cas9 systems, Overexpression vectors for PTM enzymes (αTAT1, HDAC6, etc.)	Targeted manipulation of PTM pathways	Compensation mechanisms; cell-type specific effects
Transcriptomic Analysis Platforms	Bulk RNA-seq, Single-cell RNA-seq, Spatial transcriptomics	Comprehensive gene expression profiling of aging processes	Computational expertise required; integration with protein-level data
WH-4-025	WH-4-025, MF:C39H38F3N7O5, MW:741.8 g/mol	Chemical Reagent	Bench Chemicals
Safimaltib	Safimaltib, CAS:2230273-76-2, MF:C20H11F6N5O2, MW:467.3 g/mol	Chemical Reagent	Bench Chemicals

Therapeutic Targeting and Future Perspectives

Targeting Cytoskeletal PTMs for Age-Related Diseases

The growing understanding of cytoskeletal codes in aging has opened promising therapeutic avenues for age-related diseases. For neurological conditions, HDAC6 inhibitors have shown particular promise by increasing tubulin acetylation and restoring axonal transport deficits observed in multiple neurodegenerative models [29]. In Alzheimer's models, HDAC6 inhibition reduces tau levels, improves memory, and ameliorates cognitive defects [29]. Similarly, microtubule-stabilizing compounds like paclitaxel have demonstrated neuroprotective effects in Parkinson's models, reversing dopaminergic neuron death despite challenges with blood-brain barrier penetration [29] [27].

Novel delivery approaches for cytoskeletal-targeting compounds are actively being developed. Nanosuspension formulations and nasal administration routes for paclitaxel show promise in overcoming the blood-brain barrier, potentially enabling more effective neurological applications [27]. For actin-related pathologies, the discovery that pharmacological disruption of actin polymerization in aged animals can reverse brain aging phenotypes suggests exciting therapeutic possibilities [30].

Beyond neurological applications, targeting tubulin PTMs represents a promising strategy for cardiac conditions. In heart failure models, modulating tubulin detyrosination and acetylation can improve cardiomyocyte contractility and mechanical properties [28]. The development of PTM-specific therapeutic agents that avoid the side effects of broad cytoskeletal disruption used in cancer chemotherapy represents an important frontier in aging medicine [28].

Future Research Directions and Unanswered Questions

Despite significant advances, key questions remain regarding cytoskeletal codes in aging. The developmental components of age-related cytoskeletal disorders represent a particularly intriguing area—how do mutations or PTMs in cytoskeletal proteins with essential developmental functions lead specifically to late-onset degeneration? What compensatory mechanisms operate during early life stages that are lost during aging? [25].

The crosstalk between different PTM types and between cytoskeletal systems requires further elucidation. How do tubulin PTMs influence actin dynamics and vice versa? How do PTMs integrate with the "MAP code" to generate specialized microtubule functions in different cellular compartments? [25]. The recent discovery that infolding formin 2 (INF2) regulates microtubule acetylation through both microtubule stabilization and control of α-TAT1 transcription illustrates the complex regulatory networks involved [29].

From a methodological perspective, developing tools to manipulate specific PTM types with spatiotemporal precision represents an important technological frontier. Optogenetic approaches, subcellularly-targeted enzymatic systems, and nanoparticle-based delivery of PTM modifiers could enable more precise dissection of cytoskeletal code functions in aging [24]. Similarly, advancing transcriptomic technologies—particularly single-cell and spatial transcriptomics—will provide unprecedented resolution for understanding how cytoskeletal alterations contribute to tissue aging heterogeneity [32] [31].

As the field progresses, integrating cytoskeletal aging research with broader geroscience efforts will be essential. Understanding how cytoskeletal PTMs interact with other hallmarks of aging—including genomic instability, loss of proteostasis, mitochondrial dysfunction, and altered nutrient sensing—will provide a more comprehensive picture of aging biology and potentially identify synergistic intervention points [26] [31]. The remarkable capacity of cytoskeletal-targeting interventions to reverse age-related cellular changes in model organisms suggests that pursuing this research direction may yield transformative approaches for promoting healthy human aging.

Computational and Transcriptomic Methods for Deciphering Cytoskeletal Alterations

The cytoskeleton, a dynamic network of filamentous proteins, is fundamental to cellular integrity, function, and viability. Comprising microfilaments, intermediate filaments, and microtubules, this structural framework maintains cellular shape, enables intracellular transport, and facilitates critical processes like phagocytosis [2]. Recent transcriptomic studies have revealed that the dysregulation of cytoskeletal genes is a common mechanism underpinning several age-related pathologies. With aging being a primary risk factor for chronic diseases, understanding these transcriptional alterations provides crucial insights into disease mechanisms and potential therapeutic interventions [2]. The application of advanced machine learning (ML) techniques, particularly Support Vector Machines (SVM) combined with Recursive Feature Elimination (RFE), has emerged as a powerful approach for identifying subtle but biologically significant cytoskeletal gene signatures from high-dimensional transcriptomic data. This guide objectively compares the performance of this methodology against other feature selection and classification techniques within the context of age-related disease research.

Comparative Performance of ML Techniques in Cytoskeletal Gene Identification

Classifier Performance Benchmarking

A foundational study directly compared the performance of five machine learning classifiers in identifying cytoskeletal genes associated with five age-related diseases: Hypertrophic Cardiomyopathy (HCM), Coronary Artery Disease (CAD), Alzheimer's Disease (AD), Idiopathic Dilated Cardiomyopathy (IDCM), and Type 2 Diabetes Mellitus (T2DM). The SVM classifier consistently outperformed other algorithms across all conditions [2].

Table 1: Comparative Classifier Accuracy for Cytoskeletal Gene Identification

Disease	SVM	Random Forest	k-NN	Decision Tree	Gaussian Naive Bayes
HCM	Highest Accuracy	Lower Accuracy	Lower Accuracy	Lower Accuracy	Lower Accuracy
CAD	Highest Accuracy	Lower Accuracy	Lower Accuracy	Lower Accuracy	Lower Accuracy
AD	Highest Accuracy	Lower Accuracy	Lower Accuracy	Lower Accuracy	Lower Accuracy
IDCM	Highest Accuracy	Lower Accuracy	Lower Accuracy	Lower Accuracy	Lower Accuracy
T2DM	Highest Accuracy	Lower Accuracy	Lower Accuracy	Lower Accuracy	Lower Accuracy

The superior performance of SVM is attributed to its capability to handle large feature spaces, manage high-dimensional data such as gene expression values, and effectively identify outliers, making it particularly suited for transcriptomic analyses [2].

SVM-RFE vs. Other Feature Selection Methods

When paired with Recursive Feature Elimination (RFE), SVM was further evaluated against other feature selection techniques. The SVM-RFE pipeline was compared to models using LASSO and ANOVA for feature selection [2].

Table 2: SVM-RFE vs. Alternative Feature Selection Methods (F1-Score)

Disease	SVM-RFE	LASSO	ANOVA
HCM	Highest F1-Score	Lower F1-Score	Lower F1-Score
CAD	Highest F1-Score	Lower F1-Score	Lower F1-Score
AD	Highest F1-Score	Lower F1-Score	Lower F1-Score
IDCM	97.47%	98.14%	Lower F1-Score
T2DM	Highest F1-Score	Lower F1-Score	Lower F1-Score

SVM-RFE achieved the highest F1-score for four of the five diseases, with LASSO showing a slight advantage for IDCM. This demonstrates that SVM-RFE is a robust and generally superior approach for selecting the most discriminative cytoskeletal genes [2].

Experimental Protocols and Workflows

Core SVM-RFE Workflow for Cytoskeletal Gene Identification

The following diagram illustrates the generalized experimental workflow for identifying cytoskeletal gene signatures using SVM-RFE, as applied in age-related disease research [2].

Detailed Methodological Framework

The implementation of SVM-RFE for cytoskeletal gene identification involves several critical steps:

• Gene Compilation: The process begins with compiling a comprehensive set of 2,304 cytoskeletal genes from the Gene Ontology term GO:0005856, which encompasses genes encoding microfilaments, intermediate filaments, microtubules, and related regulatory proteins [2].
• Data Preprocessing: Transcriptomic data undergoes rigorous preprocessing, including normalization and batch effect correction using tools like the Limma package, to ensure comparability across samples [2] [33].
• SVM-RFE Execution: The SVM-RFE algorithm recursively removes the least important features (genes) based on their contribution to the SVM model's classification accuracy between patient and control groups. This iterative process continues until an optimal subset of genes with the highest discriminatory power is identified [2].
• Validation and Integration: The genes selected by SVM-RFE are cross-validated through differential expression analysis (using DESeq2 or Limma) to confirm their statistical significance and biological relevance. The overlapping genes from both methods form the high-confidence cytoskeletal signature [2].

Key Cytoskeletal Gene Signatures in Age-Related Diseases

Identified Cytoskeletal Biomarkers

The application of the SVM-RFE framework has successfully identified specific cytoskeletal genes associated with major age-related diseases [2].

Table 3: SVM-RFE Identified Cytoskeletal Genes in Age-Related Diseases

Disease	Identified Cytoskeletal Genes	Biological Function
Hypertrophic Cardiomyopathy (HCM)	ARPC3, CDC42EP4, LRRC49, MYH6	Actin polymerization, cytoskeletal regulation, sarcomeric function
Coronary Artery Disease (CAD)	CSNK1A1, AKAP5, TOPORS, ACTBL2, FNTA	Kinase activity, protein anchoring, cytoskeletal structure
Alzheimer's Disease (AD)	ENC1, NEFM, ITPKB, PCP4, CALB1	Neuronal intermediate filaments, calcium signaling, synaptic plasticity
Idiopathic Dilated Cardiomyopathy (IDCM)	MNS1, MYOT	Sarcomeric integrity, Z-disc stability
Type 2 Diabetes (T2DM)	ALDOB	Metabolic-cytoskeletal crosstalk

Analytical Pathway for Cytoskeletal Gene Signature Discovery

The analytical process from raw data to biological insight involves multiple steps of computational analysis and interpretation, as shown in the following pathway.

Successful implementation of SVM-RFE for cytoskeletal transcriptomics requires specific computational tools and biological resources.

Table 4: Essential Research Reagents and Computational Tools

Category	Item	Application/Function
Computational Tools	SVM-RFE (sigFeature R package)	Feature selection and gene ranking from transcriptomic data [34] [2]
	Limma / DESeq2	Differential expression analysis and data normalization [2] [33]
	Seurat / Scanpy	Single-cell RNA-seq analysis and visualization [35] [36]
	WGCNA	Co-expression network analysis for pathway identification [37]
Biological Resources	Cytoskeletal Gene Set (GO:0005856)	Reference list of 2,304 cytoskeletal genes for analysis [2]
	Age-Related Disease Transcriptomes	Publicly available datasets (e.g., GEO, TCGA) for training and validation [2] [7]
Validation Methods	External Dataset ROC Analysis	Independent performance validation of identified gene signatures [2]
	CellChat / NetworkAnalyst	Pathway interaction and network analysis [37]

The integration of SVM with Recursive Feature Elimination represents a robust and superior methodology for identifying cytoskeletal gene signatures in age-related diseases. Benchmarking studies demonstrate that SVM classifiers achieve the highest accuracy among common ML algorithms, and the SVM-RFE pipeline outperforms other feature selection methods like LASSO and ANOVA for most age-related conditions. The identified cytoskeletal genes, including ARPC3, ENC1, and MYH6, illuminate the profound role of cytoskeletal dysregulation in neurodegeneration, cardiovascular diseases, and metabolic disorders. This computational framework provides researchers with a powerful approach to uncover novel diagnostic biomarkers and therapeutic targets, ultimately advancing our understanding of the mechanistic links between cytoskeletal integrity and human aging.

Table 1: Core Cytoskeletal Genes Dysregulated in Age-Related Pathologies

Gene / Component	Associated Age-Related Disease(s)	Direction of Change	Primary Analytical Method	Key Functional Implication
TUBA1A (Tubulin)	General Brain Ageing [7]	Downregulated	snRNA-seq	Loss of microtubular structure, impaired transport
TUBB3 (Tubulin)	General Brain Ageing [7], Thyroid Ageing [4]	Downregulated	snRNA-seq, Correlation Analysis	Neuronal microtubule destabilization
NF-L (Neurofilament)	Sensory Axon Ageing [3] [38]	Upregulated	Immunostaining / Transcriptomics	Altered axonal caliber, potential transport deficits
NF-M (Neurofilament)	Sensory Axon Ageing [3] [38]	Upregulated	Immunostaining / Transcriptomics	Altered axonal caliber, potential transport deficits
NF-H (Neurofilament)	Sensory Axon Ageing [3] [38]	Upregulated	Immunostaining / Transcriptomics	Altered axonal caliber, potential transport deficits
Actin	Sensory Axon Ageing [3] [38]	Upregulated	Immunostaining	Altered structural plasticity, synaptic signaling
Multiple Cytoskeletal Genes	HCM, CAD, AD, IDCM, T2DM [39]	Mixed Dysregulation	SVM Machine Learning	Potential biomarkers and drug targets for multiple diseases

Detailed Experimental Protocols for Cytoskeletal DEG Identification

Integrative Machine Learning and Differential Expression Analysis

This protocol outlines the computational framework used to identify cytoskeletal genes associated with a range of age-related diseases, including Hypertrophic Cardiomyopathy (HCM), Coronary Artery Disease (CAD), and Alzheimer's disease (AD) [39].

• Objective: To investigate transcriptional changes of cytoskeletal genes and their regulators in five age-related diseases and identify potential biomarkers.
• Methodology:
  • Data Acquisition: Transcriptomic datasets from studies on HCM, CAD, AD, Idiopathic Dilated Cardiomyopathy (IDCM), and Type 2 Diabetes Mellitus (T2DM) are collected.
  • Differential Expression Analysis: Standard bioinformatic pipelines (e.g., DESeq2, limma) are employed to identify genes with statistically significant expression changes between disease and control samples.
  • Machine Learning Modeling: Multiple machine-learning algorithms are trained and validated on the expression data. The Support Vector Machine (SVM) classifier has been reported to achieve the highest accuracy in this specific application [39].
  • Gene Selection: The model identifies the most informative genes for classification. This study pinpointed 17 genes involved in the cytoskeleton's structure and regulation as associated with age-related diseases [39].
  • Validation: The potential of the identified cytoskeletal genes as biomarkers and drug targets is assessed.
• Key Output: A ranked list of high-confidence cytoskeletal DEGs with diagnostic and therapeutic potential for age-related pathologies.

Single-Nucleus RNA Sequencing of Human Prefrontal Cortex

This protocol describes a high-resolution approach to map transcriptomic changes, including cytoskeletal gene expression, across different cell types in the ageing human brain [7].

• Objective: To identify cell-type-specific gene-expression changes in the human prefrontal cortex across the lifespan, from infancy to centenarian.
• Methodology:
  • Tissue Collection: Fresh-frozen human prefrontal cortex samples are obtained from neurotypical donors across a wide age range.
  • Nuclei Isolation: Nuclei are isolated from the tissue to enable single-nucleus RNA sequencing (snRNA-seq).
  • Library Preparation and Sequencing: Droplet-based snRNA-seq libraries are prepared and sequenced.
  • Bioinformatic Analysis:
    • Clustering and Annotation: Nuclei are clustered based on gene expression profiles and annotated into cell types (e.g., excitatory neurons, inhibitory neurons, microglia, oligodendrocytes).
    • Differential Expression: Expression changes between age groups (e.g., elderly vs. adult) are calculated for each cell type.
• Key Finding: This study revealed a widespread downregulation of core cytoskeletal genes during brain ageing across multiple cell types. For example, TUBA1A was downregulated in all 13 brain cell types analyzed, and TUBB3 was downregulated in 12 out of 13 cell types [7].

Quantitative Immunostaining of Human Sensory Axons

This protocol uses histological and imaging techniques to quantify changes in cytoskeletal protein levels and axonal morphology directly in human tissue [3] [38].

• Objective: To investigate alterations in cytoskeletal content and sensory axon caliber in human skin biopsies during ageing.
• Methodology:
  • Biopsy Collection: 3 mm punch skin biopsies are obtained from the proximal (thigh) and distal (ankle) leg of healthy volunteers.
  • Tissue Fixation and Sectioning: Biopsies are fixed and cryo-sectioned into thin slices.
  • Immunofluorescence Staining: Sections are stained with antibodies targeting specific cytoskeletal components:
    • Neurofilament subunits (NfL, NfM, NfH)
    • Neuronal tubulin (TUBB3)
    • Actin
  • Image Acquisition and Quantification: Confocal microscopy is used to acquire high-resolution images. Quantitative analysis measures fluorescence intensity (reflecting protein mass) and axon diameter.
• Key Finding: This approach demonstrated an age-dependent increase in all major cytoskeletal components (neurofilaments, microtubules, and actin) in peripheral sensory axons [3] [38]. The increase in axon diameter was found to be gender-specific, evident only in males [3] [38].

Comparative Data Analysis of Cytoskeletal Alterations

Table 2: Cross-Tissue Comparison of Cytoskeletal Dysregulation in Ageing

Tissue / System	Experimental Model	Key Cytoskeletal Findings	Consistency & Notes
Human Prefrontal Cortex	snRNA-seq (Infancy to Centenarian) [7]	Downregulation of TUBA1A, TUBB3, TUBA4A, TUBB	High consistency across 13 different brain cell types.
Human Sensory Axons	Skin Biopsies / Immunostaining (Ages 23-79) [3] [38]	Upregulation of Neurofilaments (NfL, NfM, NfH), TUBB3, Actin	Contrasts with brain transcriptome; suggests post-transcriptional regulation or PNS/CNS differences.
Human Thyroid Gland	RNA-seq (TCGA/GTEx data) [4]	Cytoskeletal proteins enriched in age-correlated pathways	Associated with pathways like hypertrophic cardiomyopathy.
General Age-Related Diseases	Computational Framework (Machine Learning) [39]	Identification of 17 cytoskeletal genes as biomarkers for HCM, CAD, AD, IDCM, T2DM	Provides a holistic, multi-disease perspective.

Signaling Pathways and Workflow Diagrams

Cytoskeletal Dysregulation in Brain Ageing Pathway

Experimental Workflow for Cytoskeletal DEG Identification

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Reagents and Resources for Cytoskeletal Ageing Research

Research Reagent / Tool	Function / Target	Application Example in Ageing Studies
Anti-Neurofilament Antibodies (NfL, NfM, NfH)	Immunostaining of neuronal intermediate filaments	Quantifying age-dependent increase in axonal neurofilament mass in human skin biopsies [3] [38].
Anti-TUBB3 (β-III Tubulin) Antibody	Immunostaining of neuron-specific microtubules	Demonstrating increased microtubule mass in ageing sensory axons [3] [38].
Anti-Actin Probes / Phalloidin	Staining of filamentous (F-) actin	Visualizing and quantifying actin cytoskeleton alterations in ageing axons [3] [38].
Single-Nucleus RNA-seq Kits (10x Genomics)	Cell-type-specific transcriptome profiling	Identifying downregulation of cytoskeletal housekeeping genes (TUBA1A, TUBB3) across cell types in the ageing brain [7].
SVM Classifier Algorithms (e.g., in R/Python)	Machine learning-based biomarker discovery	Selecting 17 high-confidence cytoskeletal genes associated with multiple age-related diseases from transcriptomic data [39].
Microtubule-Stabilizing Agents (e.g., Paclitaxel derivatives)	Experimental therapeutic intervention	Low concentrations tested for protecting neurons against Aβ toxicity and cytoskeletal dystrophy in Alzheimer's models [40].
Angiotensin II human TFA	Angiotensin II human TFA, MF:C52H72F3N13O14, MW:1160.2 g/mol	Chemical Reagent
Aficamten	Aficamten, CAS:2364554-48-1, MF:C18H19N5O2, MW:337.4 g/mol	Chemical Reagent

Integrative multi-omics approaches are transforming our understanding of complex biological systems, particularly in aging and age-related diseases. By combining transcriptomic and metabolomic data, researchers can now construct comprehensive systems-level models that reveal how molecular changes manifest in physiological decline. This integrated perspective is especially valuable for investigating cytoskeletal alterations that occur during aging, as these changes often involve complex interactions between gene expression patterns, metabolic states, and cellular structure. The cytoskeleton, comprising microtubules, actin filaments, and intermediate filaments, maintains cellular homeostasis, caliber, polarity, and transport—functions that frequently deteriorate with age and contribute to neurodegenerative conditions [25] [24].

This guide examines how transcriptomics and metabolomics are being combined to advance research into age-related cytoskeletal changes, comparing experimental approaches, their applications, and the biological insights they generate. We present standardized methodologies, comparative data analyses, and visualization tools to help researchers select appropriate strategies for their specific investigations into aging and cytoskeletal integrity.

Experimental Approaches and Methodologies

Standardized Workflows for Multi-Omics Integration

Table 1: Core Methodologies for Integrative Transcriptomics and Metabolomics

Research Focus	Transcriptomics Approach	Metabolomics Approach	Integration Methods	Key Reference Model
Oxidative Stress in Muscle Tissue	RNA sequencing identifying differentially expressed genes (DEGs)	UHPLC-MS non-targeted metabolomics profiling	KEGG pathway mapping of both DEGs and differential metabolites	Common carp (Cyprinus carpio) exposed to Hâ‚‚Oâ‚‚ [41]
Stem Cell Aging	RNA-seq for gene expression profiling	LC-MS non-targeted metabolomics with multivariate analysis	Integrated pathway analysis identifying lipid metabolism genes	Rat bone marrow mesenchymal stem cells (BMSCs) [42]
Aging with Type 2 Diabetes	mRNA expression profiling with GeneSpring software	HPLC/Q-TOF MS with PLS-DA and OPLS models	IMPaLA web tool for integrated pathway analysis	Aging mouse model with T2DM [43]
Traditional Chinese Medicine Anti-Aging	RNA-seq combined with metabolomics	Metabolomic profiling of aging-related changes	GO and KEGG enrichment analyses of multi-omics data	Human subjects treated with Bushen Kangshuai Granules [44]

Detailed Experimental Protocol for Multi-Omics Analysis

A representative protocol for integrated transcriptomics and metabolomics studies, synthesized from multiple research approaches, involves these critical stages:

1. Experimental Design and Sample Preparation:

• Utilize appropriate biological models (in vivo models like common carp [41] or mice [43], in vitro systems like rat BMSCs [42], or human clinical samples [44])
• Implement controlled interventions (e.g., Hâ‚‚Oâ‚‚ exposure for oxidative stress [41], high-carbohydrate diet for T2DM induction [43])
• Include appropriate control groups and sufficient biological replicates (typically n≥5 per group)
• For cytoskeletal aging studies, consider neural tissues or stem cells relevant to neurodegenerative processes

2. Transcriptomic Profiling:

• Extract high-quality RNA using standardized kits (e.g., Trio RNA-Seq kit)
• Perform RNA sequencing using next-generation sequencing platforms (Illumina recommended)
• Identify differentially expressed genes (DEGs) with thresholds of |FC| ≥ 2 and p ≤ 0.05 [43]
• Conduct functional enrichment analysis using GO and KEGG databases [41] [44]

3. Metabolomic Profiling:

• Employ metabolite extraction with pre-chilled ternary solvent (methanol/acetonitrile/water, 2:2:1) [41]
• Analyze using UHPLC-MS (Agilent 1290 Infinity LC) or HPLC/Q-TOF MS systems [41] [43]
• Process data with multivariate statistical methods (PCA, OPLS-DA)
• Identify significantly altered metabolites (VIP > 1 and p < 0.05) [42]

4. Data Integration and Pathway Analysis:

• Use integrated analysis tools (IMPaLA, MetPA) to identify pathways significantly enriched in both datasets [43]
• Construct correlation networks between DEGs and differential metabolites
• Validate key findings through targeted experiments (e.g., gene overexpression, metabolic inhibition)

Key Research Applications and Findings

Metabolic Pathways in Cellular Aging

Integrated multi-omics approaches have revealed conserved metabolic alterations during aging across different biological models. In rat bone marrow mesenchymal stem cells (BMSCs), aging was characterized by 23 significantly altered metabolites predominantly involved in glycerophospholipid metabolism, linoleic acid metabolism, and biosynthesis of unsaturated fatty acids [42]. Transcriptomic analysis identified 590 differentially expressed genes in young versus old BMSCs, with KEGG enrichment showing particularly strong responses in metabolism-related pathways [42].

Table 2: Conserved Metabolic Alterations in Aging Across Models

Biological Model	Key Altered Metabolic Pathways	Transcriptional Changes	Functional Implications
Rat BMSCs [42]	Glycerophospholipid metabolism, Linoleic acid metabolism, Biosynthesis of unsaturated fatty acids	590 DEGs including Scd, Scd2, Dgat2, Fads2, Lpin1	Impaired stem cell function, altered differentiation potential
Common Carp Muscle [41]	Oxidative phosphorylation, Adipocytokine signaling, PPAR signaling	470 upregulated, 451 downregulated DEGs	Muscle quality deterioration, metabolic dysregulation
Aging Mice with T2DM [43]	Glucose, fat, and amino acid metabolism, Insulin resistance	2,486 downregulated, 3,131 upregulated mRNAs	Hepatic metabolic dysfunction, glycogen accumulation
Human Subjects (BKG Treatment) [44]	PI3K-AKT signaling, Sphingolipid metabolism, ECM-receptor interaction	Reversal of 70 age-related gene expressions	Improved aging symptoms, reduced oxidative stress

Cytoskeletal Alterations and Molecular Connections

Research into cytoskeletal alterations during aging has benefited significantly from multi-omics approaches. The cytoskeleton—composed of microtubules, intermediate filaments, and actin filaments—undergoes significant post-translational modifications (PTMs) during aging that affect neuronal function and contribute to neurodegenerative diseases [25] [24]. These PTMs establish a "cytoskeletal code" that endows the cytoskeletal scaffold with specific and local functionality, particularly important in neurons with long processes that depend on the cytoskeleton for axonal transport, structural elements, and synaptic plasticity [24].

Integrated analyses have revealed that microtubule PTMs such as acetylation and tyrosination/detyrosination influence each other and affect progression of age-related conditions. Site-specific phosphorylation of tau (a microtubule-associated protein) may lead to conformational changes that can be either beneficial for normal neurite development or contribute to pathological states when dysregulated [25]. These findings illustrate the dual nature of cytoskeletal PTMs in both physiological aging and disease progression.

Signaling Pathways in Aging and Cytoskeletal Integrity

Multi-omics studies have consistently identified several key signaling pathways that connect metabolic changes with cytoskeletal alterations during aging. The PPAR signaling pathway was significantly enriched in oxidative stress models, indicating metabolic dysregulation that can impact cytoskeletal organization [41]. The PI3K-AKT signaling pathway emerges as a crucial regulator in both aging and cytoskeletal organization, with its downregulation associated with anti-aging effects in traditional Chinese medicine studies [44].

Oxidative phosphorylation pathways show elevated activity under oxidative stress conditions [41], which can directly affect cytoskeletal integrity through reactive oxygen species-mediated damage to cytoskeletal components. Additionally, sphingolipid metabolism has been identified as an upregulated pathway in anti-aging interventions [44], with sphingolipids playing important roles in membrane structure and cytoskeletal interactions.

The Scientist's Toolkit: Essential Research Solutions

Table 3: Key Research Reagent Solutions for Multi-Omics Studies

Product Category	Specific Examples	Key Applications	Technical Considerations
RNA Sequencing Kits	Trio RNA-Seq (NuGEN), CORALL Total RNA-Seq (Lexogen)	Transcriptome analysis, especially for low-abundance transcripts	Suited for different input quantities, compatibility with downstream analyses [45]
Spatial Transcriptomics Platforms	10x Genomics Visium, Akoya PhenoImager systems	Tissue context preservation, spatial gene expression mapping	Compatibility with FFPE or fresh frozen tissues, resolution capabilities [46]
Mass Spectrometry Systems	Agilent 1290 Infinity LC, HPLC/Q-TOF MS	Non-targeted metabolomics, metabolite identification	Sensitivity, resolution, compatibility with different sample types [41] [43]
Data Analysis Software	MetPA, IMPaLA, GeneSpring, SIMCA-P	Integrated pathway analysis, multivariate statistics	Compatibility with various data formats, visualization capabilities [42] [43]
Model Systems	Common carp, Mouse T2DM models, Rat BMSCs	Studying aging, oxidative stress, metabolic disorders	Relevance to human biology, ethical considerations, practical handling [41] [42] [43]
MYF-01-37	MYF-01-37, MF:C15H17F3N2O, MW:298.30 g/mol	Chemical Reagent	Bench Chemicals
Inarigivir ammonium	Inarigivir ammonium, MF:C20H29N8O10PS, MW:604.5 g/mol	Chemical Reagent	Bench Chemicals

Comparative Analysis of Multi-Omics Approaches

The selection of appropriate multi-omics strategies depends heavily on research goals, sample types, and analytical priorities. Spatial transcriptomics technologies are particularly valuable for cytoskeletal aging studies as they preserve tissue architecture context while assessing gene expression, with the market projected to grow from USD 410.46 million in 2024 to approximately USD 1,569.03 million by 2034 [46]. This approach allows researchers to correlate cytoskeletal alterations with specific tissue regions or cell types.

For aging studies focused on metabolism-cytoskeleton interactions, non-targeted metabolomics combined with RNA-seq provides the most comprehensive coverage of molecular changes. This approach successfully identified key lipid metabolism genes (Scd, Scd2, Dgat2, Fads2) in aging rat BMSCs and revealed their connection to altered glycerophospholipid metabolites [42]. The integration of these datasets through pathway analysis tools like IMPaLA enables identification of biologically meaningful patterns that would remain hidden in single-omics approaches [43].

Emerging approaches incorporating AI and machine learning are further enhancing multi-omics integration, with foundation models trained on massive, multimodal datasets providing new insights into cancer biology that could be extended to aging research [47]. These technologies enable researchers to probe complex molecular relationships, potentially identifying novel connections between metabolic states and cytoskeletal integrity during aging.

The cytoskeleton is a fundamental network of intracellular filamentous proteins that maintains cellular shape, enables intracellular transport, and regulates signal transduction [2] [48]. Decades of research have established that the dynamic nature of the cytoskeleton is associated with downstream signaling events that regulate cellular aging and neurodegeneration [2]. With advancing age, cytoskeletal integrity declines, leading to dysfunctional cellular processes that contribute to numerous chronic disorders [2] [8]. This case study examines a novel computational framework that has identified 17 cytoskeletal genes associated with five age-related diseases, providing new insights into transcriptional dysregulation of cytoskeletal genes in disease pathology and presenting potential biomarkers for therapeutic development [2].

Computational Framework and Methodological Workflow

The study employed an integrative computational approach combining machine learning-based models with differential expression analysis to identify cytoskeletal gene biomarkers across five age-related diseases: Hypertrophic Cardiomyopathy (HCM), Coronary Artery Disease (CAD), Alzheimer's Disease (AD), Idiopathic Dilated Cardiomyopathy (IDCM), and Type 2 Diabetes Mellitus (T2DM) [2]. The framework was designed to investigate transcriptional changes in cytoskeletal genes and their regulators, leveraging multiple analytical techniques to ensure robust identification of disease-associated genes [2].

Detailed Experimental Protocols

The methodological workflow consisted of several standardized computational biology protocols:

• Gene List Curation: Researchers retrieved the cytoskeletal gene list from the Gene Ontology Browser (ID: GO:0005856), containing 2,304 genes encompassing microfilaments, intermediate filaments, microtubules, and related polymeric filamentous structures [2].

• Transcriptome Data Acquisition and Preprocessing: Transcriptome data were retrieved for all five analyzed diseases from public repositories. For HCM, two different datasets were combined to increase the number of control samples. Batch effect correction and normalization were performed using the Limma package in R [2].

• Machine Learning Classification: Five different algorithms were utilized to build classification models based on normalized expression values of cytoskeletal genes: Decision Trees (DTs), Random Forest (RF), k-nearest Neighbors (k-NN), Gaussian Naive Bayes (GNB), and Support Vector Machines (SVMs). Five-fold cross-validation was used to assess model accuracy [2].

• Feature Selection: Recursive Feature Elimination (RFE) was employed alongside the SVM classifier to select the most discriminative gene signatures differentiating patients from normal samples. RFE recursively removed features with a definite step, built models with remaining features, and calculated accuracy [2].

• Differential Expression Analysis: DESeq2 was used for the T2DM dataset and the Limma package for HCM, AD, CAD, and IDCM datasets to identify differentially expressed genes (DEGs) between patient and normal samples, with a focus on cytoskeletal genes [2].

• Validation and Performance Metrics: The performance of identified candidate genes was validated using Receiver Operating Characteristic (ROC) analysis on external datasets. Multiple evaluation metrics were calculated, including accuracy, F1-score, recall, precision, balanced accuracy, True Positives (TP), True Negatives (TN), False Positives (FP), False Negatives (FN), Positive Predictive Value (PPV), and Negative Predictive Value (NPV) [2].

Experimental Workflow Visualization

Performance Comparison of Machine Learning Models

Classifier Performance Across Diseases

The study evaluated five machine learning algorithms for classifying disease states based on cytoskeletal gene expression profiles. The Support Vector Machines (SVM) classifier demonstrated superior performance across all five age-related diseases compared to other algorithms [2]. The SVM classifier is particularly well-suited for gene expression data due to its ability to handle large feature spaces and datasets while effectively identifying outliers [2]. This finding aligns with previous research indicating SVM's capability to detect delicate patterns in complex diseases [2].

Table 1: Machine Learning Classifier Performance Comparison

Algorithm	HCM Accuracy	CAD Accuracy	AD Accuracy	IDCM Accuracy	T2DM Accuracy	Overall Suitability
SVM	Highest	Highest	Highest	Highest	Highest	Excellent for gene expression data, handles large feature spaces well
Random Forest	Lower	Lower	Lower	Lower	Lower	Moderate, effective for feature importance but less accurate
k-NN	Lower	Lower	Lower	Lower	Lower	Moderate, sensitive to dataset scale and dimensionality
Decision Trees	Lower	Lower	Lower	Lower	Lower	Lower, prone to overfitting on complex biological data
Gaussian Naive Bayes	Lower	Lower	Lower	Lower	Lower	Lower, assumes feature independence which rarely holds in genomics

Feature Selection Method Comparison

The study compared multiple feature selection techniques paired with the SVM model to identify the most relevant cytoskeletal genes. Based on F1-score performance, RFE paired with SVM outperformed other methods for HCM, CAD, AD, and T2DM. However, LASSO achieved a slightly higher F1-score of 98.14% compared to RFE's 97.47% for IDCM [2].

Table 2: Feature Selection Technique Performance

Feature Selection Method	HCM Performance	CAD Performance	AD Performance	IDCM Performance	T2DM Performance
RFE with SVM	Highest F1-score	Highest F1-score	Highest F1-score	F1-score: 97.47%	Highest F1-score
LASSO	Lower F1-score	Lower F1-score	Lower F1-score	F1-score: 98.14%	Lower F1-score
ANOVA	Lower F1-score	Lower F1-score	Lower F1-score	Lower F1-score	Lower F1-score

RFE-Selected Gene Performance Metrics

The RFE-SVM approach identified a compact subset of cytoskeletal genes that effectively discriminated between patients and normal samples across all five diseases. The performance metrics demonstrated high predictive accuracy and reliability [2].

Table 3: Performance Metrics of RFE-Selected Cytoskeletal Genes

Disease	Number of RFE-Selected Features	Accuracy	F1-Score	Recall	Precision	Balanced Accuracy	AUC
HCM	4	High	High	High	High	High	High
CAD	5	High	High	High	High	High	High
AD	5	High	High	High	High	High	High
IDCM	2	High	High	High	High	High	High
T2DM	1	High	High	High	High	High	High

Identified Cytoskeletal Gene Biomarkers and Their Disease Associations

Specific Gene-Disease Associations

The computational framework identified 17 cytoskeletal genes with significant associations to the five age-related diseases. The study highlighted distinct gene signatures for each disease condition [2]:

• Hypertrophic Cardiomyopathy (HCM): ARPC3, CDC42EP4, LRRC49, MYH6
• Coronary Artery Disease (CAD): CSNK1A1, AKAP5, TOPORS, ACTBL2, FNTA
• Alzheimer's Disease (AD): ENC1, NEFM, ITPKB, PCP4, CALB1
• Idiopathic Dilated Cardiomyopathy (IDCM): MNS1, MYOT
• Type 2 Diabetes Mellitus (T2DM): ALDOB

Overlapping Cytoskeletal Genes Across Multiple Diseases

The analysis revealed several cytoskeletal genes that were shared across multiple age-related diseases, suggesting common pathways in cytoskeletal dysregulation [2]:

Table 4: Cytoskeletal Genes Shared Across Multiple Age-Related Diseases

Gene Symbol	Associated Diseases	Potential Significance
ANXA2	AD, IDCM, T2DM	Common cytoskeletal regulator across neurodegenerative, cardiac, and metabolic diseases
TPM3	AD, CAD, T2DM	Tropomyosin family member implicated in multiple age-related conditions
SPTBN1	AD, CAD, HCM	Spectrin beta chain with roles in cellular structure and mechanical stability
MAP1B	AD, T2DM	Microtubule-associated protein linking neurodegeneration and metabolic dysfunction
RRAGD	AD, T2DM	Small GTPase connecting cytoskeletal organization with nutrient sensing
RPS3	AD, T2DM	Ribosomal protein with potential cytoskeletal interactions in multiple diseases
JAKMIP1	AD, CAD	Janus kinase and microtubule-interacting protein connecting signaling and structure
ABLIM3	AD, CAD	Actin-binding LIM protein family member
PDE4B	AD, CAD	Phosphodiesterase with potential cytoskeletal interactions

Additionally, the study identified 20 overlapping cytoskeletal genes specifically shared between Alzheimer's Disease and Idiopathic Dilated Cardiomyopathy, indicating particularly strong cytoskeletal connections between neurodegenerative and cardiac pathologies in aging [2].

Biological Significance and Mechanistic Insights

Cytoskeletal Dysregulation in Cellular Aging

The identified genes participate in critical cytoskeletal functions that become dysregulated during aging. The cytoskeleton consists of three major filament types: actin filaments (5-9 nm diameter), microtubules (25 nm outer diameter), and intermediate filaments (approximately 10 nm) [48]. These structures maintain cellular mechanical properties, facilitate intracellular transport, and enable cellular motility [48]. Age-related alterations in cytoskeletal dynamics disrupt these essential functions, contributing to disease pathogenesis [2] [8].

Recent research on Profilin 1 (Pfn1), a cytoskeletal regulator that decreases significantly in aged human microglia, demonstrates how cytoskeletal disruption can trigger cellular senescence [8]. Pfn1 ablation disrupts actin-microtubule coupling, leading to collapse of microglial morphodynamics and failure to respond to brain injury [8]. This cytoskeletal disruption triggers a senescence-associated secretory phenotype (SASP) driven by the ERK/NF-κB signaling axis, resulting in synaptic decline [8].

Signaling Pathways Involving Identified Cytoskeletal Genes

The diagram illustrates how cytoskeletal genes identified in this study interface with known pathways in age-related diseases. Environmental stressors like microgravity can trigger FGFR2 upregulation, leading to cytoskeletal rearrangement that disrupts autophagic flux [49]. Similarly, deficiency of cytoskeletal regulators like Pfn1 disrupts actin-microtubule coupling, activating ERK/NF-κB signaling and promoting cellular senescence [8]. The identified cytoskeletal genes function within these pathways, contributing to disease pathology when dysregulated.

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 5: Key Research Reagent Solutions for Cytoskeletal Age-Related Disease Studies

Reagent/Material	Specific Examples	Research Function	Application in Current Study
Gene Ontology Databases	GO:0005856 (Cytoskeleton)	Provides standardized gene annotations and functional classifications	Curated list of 2,304 cytoskeletal genes for analysis [2]
Transcriptome Datasets	Disease-specific RNA-seq data	Enables gene expression profiling and differential expression analysis	Source data for machine learning classification and DEG identification [2]
Machine Learning Frameworks	SVM, Random Forest, k-NN	Classifies disease states based on gene expression patterns	Identified discriminative cytoskeletal gene signatures for each disease [2]
Differential Expression Tools	DESeq2, Limma Package	Identifies statistically significant expression changes between conditions	Determined cytoskeletal genes differentially expressed in patients vs controls [2]
Feature Selection Algorithms	Recursive Feature Elimination (RFE)	Selects most informative gene subsets while reducing dimensionality	Identified minimal gene sets maintaining high classification accuracy [2]
Validation Datasets	External cohort data	Tests generalizability of identified biomarkers	ROC analysis validated performance on independent datasets [2]
Lurbinectedin-d3	Lurbinectedin-d3, MF:C41H44N4O10S, MW:787.9 g/mol	Chemical Reagent	Bench Chemicals

Discussion and Research Implications

This computational framework successfully identified 17 cytoskeletal genes associated with five age-related diseases using an integrative approach of machine learning and differential expression analysis. The SVM classifier demonstrated superior performance for cytoskeletal gene-based classification, and RFE feature selection identified compact, informative gene subsets with high predictive accuracy [2].

The findings provide a holistic overview of transcriptionally dysregulated cytoskeletal genes in age-related diseases, highlighting both disease-specific patterns and shared cytoskeletal alterations across conditions [2]. The overlapping genes between Alzheimer's Disease and Idiopathic Dilated Cardiomyopathy suggest particularly strong cytoskeletal connections between neurodegenerative and cardiac pathologies [2].

These results have significant implications for both basic research and therapeutic development. The identified cytoskeletal genes represent potential biomarkers for disease detection and progression monitoring. Furthermore, they may serve as novel therapeutic targets for interventions aimed at mitigating cytoskeletal dysfunction in age-related diseases. Future research should validate these findings in additional patient cohorts and investigate the mechanistic roles of these genes in cytoskeletal maintenance and age-related pathological processes.

The study demonstrates the power of computational approaches in identifying biologically significant patterns in complex disease datasets, providing a framework that can be extended to investigate cytoskeletal genes in other age-related conditions.

The quest for precise and early diagnosis of age-related diseases represents a frontier in modern medical research. Transcriptomics, the large-scale study of all RNA molecules in a cell, has emerged as a powerful tool for identifying molecular signatures that can be developed into diagnostic biomarkers. When framed within the context of cytoskeletal alterations—a process increasingly recognized as central to cellular aging and dysfunction—this approach gains particular promise. The cytoskeleton, a dynamic network of filamentous proteins, is essential for maintaining cellular shape, integrity, and intracellular transport. Recent research has established that transcriptional dysregulation of cytoskeletal genes is a common feature in numerous age-related pathologies, offering a novel avenue for diagnostic development [2] [50]. This guide objectively compares the leading computational and experimental methodologies used to transform transcriptomic findings, particularly those related to cytoskeletal alterations, into robust diagnostic biomarker panels.

Comparative Analysis of Transcriptomic Biomarker Discovery Approaches

The transformation of raw transcriptomic data into reliable biomarkers employs diverse methodologies, each with distinct strengths, limitations, and optimal use cases. The table below provides a structured comparison of the primary approaches used in the field.

Table 1: Comparison of Transcriptomic Biomarker Discovery Methodologies

Methodology	Core Principle	Best-Suited Applications	Key Advantages	Documented Limitations
Differential Expression (DE) Analysis [51] [52]	Identifies genes with statistically significant expression differences between sample groups (e.g., disease vs. control).	Initial screening for candidate biomarkers; studies with well-defined, distinct phenotypes.	- Conceptual simplicity- Extensive, well-established tools (DESeq2, edgeR)- Direct interpretation of results	- Focuses on individual genes, missing systemic biology- Sensitive to sample size and statistical thresholds- May overlook subtle but coordinated changes
Weighted Gene Co-expression Network Analysis (WGCNA) [52]	Identifies modules of highly correlated genes and links them to sample traits, based on systems biology principles.	Uncovering underlying biological networks and complex phenotypes; identifying "hub genes" with key regulatory roles.	- Captures higher-order biological organization- More robust to outliers- Identifies functionally related gene modules	- Computationally intensive- Requires larger sample sizes for reliability- Complex interpretation of network biology
Machine Learning (ML)-Based Feature Selection [53] [2]	Uses algorithms (e.g., SVM, Random Forest) to identify the most informative gene subsets for classifying samples.	Building multi-gene predictive models for disease classification; handling high-dimensional data.	- Optimizes for predictive accuracy- Can model complex, non-linear interactions- Integrates well with clinical data	- High risk of overfitting without rigorous validation- "Black box" nature can obscure biological interpretability- Performance is highly dependent on data quality and quantity
Integrated WGCNA + DE Analysis [52]	A two-step method where the entire dataset is first used for WGCNA, and then key modules are analyzed for DE.	Comprehensive biomarker discovery that balances systems-level insight with individual gene-level changes.	- Preserves network topology for more accurate hub gene identification- More nuanced functional interpretation- Improved biomarker candidacy over DE alone	- More complex workflow than either method alone- Still requires validation of candidate genes

Cytoskeletal Gene Signatures in Age-Related Diseases: A Focused Look

The cytoskeleton's central role in cellular homeostasis makes its transcriptional regulation a rich source of biomarkers. Recent studies have successfully identified specific cytoskeletal genes associated with major age-related diseases using the methodologies compared above.

Table 2: Experimentally-Validated Cytoskeletal Biomarker Candidates in Age-Related Diseases

Disease	Identified Cytoskeletal Genes	Discovery Methodology	Reported Performance
Alzheimer's Disease (AD) [2]	ENC1, NEFM, ITPKB, PCP4, CALB1	SVM with Recursive Feature Elimination (RFE) & Differential Expression	High classification accuracy between AD and control samples.
Coronary Artery Disease (CAD) [2]	CSNK1A1, AKAP5, TOPORS, ACTBL2, FNTA	SVM with Recursive Feature Elimination (RFE) & Differential Expression	Genes accurately discriminated CAD patients from healthy controls.
Hypertrophic Cardiomyopathy (HCM) [2]	ARPC3, CDC42EP4, LRRC49, MYH6	SVM with Recursive Feature Elimination (RFE) & Differential Expression	Identified gene set enabled high-accuracy patient classification.
Coronary Artery Disease with Obstructive Sleep Apnea (CADOSA) [54]	S100A12, MMP9	Differential Expression (edgeR/DESeq2) & Machine Learning	AUC = 0.83 (S100A12) and 0.78 (MMP9) for comorbidity diagnosis.
Ageing Human Prefrontal Cortex [7]	TUBB3, TUBA1A, VAMP2	Single-nucleus RNA Sequencing (snRNA-seq)	Widespread downregulation across multiple brain cell types.

The downregulation of essential cytoskeletal genes like TUBB3 and TUBA1A during brain aging points to a universal loss of cellular structural integrity, a process that may precede or facilitate neurodegeneration [7]. Furthermore, the successful identification of genes like S100A12 and MMP9 for diagnosing the complex comorbidity CADOSA demonstrates the power of transcriptomics to disentangle multifaceted disease states [54].

Experimental Protocols: From Sample to Biomarker Panel

Translating a tissue sample into a validated biomarker panel requires a meticulous, multi-stage workflow. The following protocols detail the key experimental and analytical steps.

Sample Processing and RNA Sequencing

Objective: To obtain high-quality transcriptomic data from patient samples.

• Sample Collection: Collect target tissue or peripheral blood mononuclear cells (PBMCs) from matched patient and control cohorts. For PBMCs, use Ficoll-Paque density gradient centrifugation for isolation within two hours of blood draw [54].
• RNA Extraction: Use TRIzol or similar reagents for total RNA extraction. Remove genomic DNA contamination with DNase I treatment.
• Quality Control: Assess RNA integrity and purity using an Agilent Bioanalyzer (RIN > 7.0) and Nanodrop (260/280 ratio ~1.8-2.1) [54].
• Library Preparation & Sequencing: Construct sequencing libraries using platform-specific kits (e.g., for DNBSEQ-T7 or Illumina platforms). Sequence to generate 150 bp paired-end reads, which provide sufficient length for accurate alignment and quantification.

Computational Analysis & Biomarker Identification

Objective: To process raw sequencing data and identify a robust diagnostic gene signature.

• Data Preprocessing:
  • Quality Trimming: Use tools like fastp to remove adapter sequences and low-quality bases [54].
  • Alignment & Quantification: Align clean reads to a reference genome (e.g., GRCh38) using aligners like STAR. Generate gene-level counts with tools like featureCounts [54].
• Data Normalization & Correction: Normalize raw counts to correct for technical biases (e.g., library size) using methods like TPM or FPKM. Apply batch correction algorithms (e.g., ComBat, Limma) if samples were processed in multiple batches [51].
• Biomarker Discovery: Apply one or more of the methodologies from Table 1.
  • For DE Analysis: Use DESeq2 or edgeR to identify genes with an absolute fold change > 1.5 and an adjusted p-value < 0.05 [54] [51].
  • For WGCNA: Construct a co-expression network from the entire dataset. Identify modules correlated with the disease trait and select hub genes within significant modules for further validation [52].
  • For ML-Based Selection: Train a classifier (e.g., SVM, CatBoost) on the expression matrix. Use feature selection techniques like RFE or SHAP analysis to rank and select the most important genes for classification [53] [2].
• Functional Validation: Confirm the biological relevance of candidate biomarkers through:
  • Independent Cohort Validation: Measure the expression of the final biomarker panel in a separate, unseen cohort of patients to assess generalizability [51] [53].
  • Orthogonal Assays: Use quantitative RT-PCR to technically confirm RNA-seq results for the top candidates [54].
  • Pathway Analysis: Perform Gene Ontology (GO) or KEGG enrichment analysis to interpret the biomarker panel in the context of biological pathways, such as those related to cytoskeletal integrity [54] [51].

Diagram Title: Transcriptomic Biomarker Discovery Workflow

Successful biomarker discovery relies on a suite of well-characterized reagents and computational resources.

Table 3: Research Reagent Solutions for Transcriptomic Biomarker Development

Tool / Resource	Function	Application in Biomarker Discovery
Luminex xMAP Multiplex Immunoassays [53]	Simultaneously quantify multiple protein biomarkers in serum/plasma.	High-throughput validation of protein biomarkers identified from transcriptomic data (e.g., CA19-9, GDF15).
TRIzol Reagent [54]	Monophasic solution for effective RNA isolation from cells and tissues.	Preserves RNA integrity during extraction from PBMCs or tissue samples, a critical first step.
DESeq2 / edgeR [54] [51]	Statistical software packages for analyzing RNA-seq count data.	Identify differentially expressed genes between case and control groups; core tools for initial biomarker screening.
WGCNA R Package [52]	Algorithm for constructing weighted gene co-expression networks.	Discovers modules of coordinately expressed genes and identifies hub genes as high-value biomarker candidates.
Support Vector Machine (SVM) [2]	A supervised machine learning model for classification and regression.	Effectively classifies patient samples and selects discriminative gene features from high-dimensional transcriptomic data.
Polly by Elucidata [51]	Cloud platform with curated, analysis-ready multi-omics data.	Accelerates biomarker validation by allowing comparison of candidate genes with public transcriptomic datasets.

The development of diagnostic panels from transcriptomic findings is a multifaceted process that benefits significantly from an integrated approach. No single methodology is universally superior; rather, the convergence of evidence from differential expression, network analysis, and machine learning provides the most robust foundation for biomarker development. Focusing on biologically coherent processes such as cytoskeletal alterations offers a powerful strategy to narrow the candidate pool and enhance the biological interpretability of the resulting diagnostic panels. As protocols become more standardized and computational tools more sophisticated, the pipeline from transcriptomic data to clinically actionable biomarkers will become increasingly efficient, paving the way for earlier diagnosis and personalized treatment strategies for age-related diseases.

Addressing Challenges in Cytoskeletal Transcriptomics: Sex-Dimorphism, Technical Noise, and Data Integration

Technical variability introduced through batch effects presents a significant challenge in transcriptomic studies of age-related diseases. These unwanted variations arising from differences in experimental conditions, sequencing platforms, or processing times can obscure genuine biological signals and compromise the identification of true biomarkers. For researchers investigating cytoskeletal alterations in age-related diseases, where transcriptional changes may be subtle yet biologically critical, effective normalization and batch effect correction are particularly essential. This guide objectively compares the performance of leading strategies and provides detailed experimental protocols to enhance data robustness in transcriptomics research.

Understanding Batch Effects and Normalization in Transcriptomics

The Nature of Technical Variability

Batch effects represent systematic technical variations that affect groups of samples processed together, potentially masking biological effects of interest or generating false-positive correlations [55]. In transcriptomic studies of aging, these artifacts can emerge at multiple levels: sample collection, preparation, sequencing runs, or even across different laboratories. The complexity of experimental procedures in large-scale studies often leads to batch effects confounded with various factors of interest, challenging the reproducibility and reliability of research findings [56].

For cytoskeletal research in age-related diseases, where studies may analyze samples collected over extended periods, batch effects can be particularly problematic. Research has demonstrated that cytoskeletal genes, including those encoding tubulins (TUBA1A, TUBB3, TUBB) and calmodulins (CALM2, CALM3), show age-associated expression patterns that could easily be obscured by technical variability if not properly addressed [7].

Normalization Strategies Across Experimental Stages

RNA-seq normalization adjusts raw transcriptomic data to account for technical factors that may mask actual biological effects. Different normalization approaches target specific types of variability across three primary stages [57]:

Within-sample normalization enables comparison of gene expression within an individual sample by adjusting for transcript length and sequencing depth. Common methods include:

• FPKM/RPKM: Corrects for library size and gene length but exhibits limitations in between-sample comparisons [57].
• TPM: Similar to FPKM but provides more stable values across samples, suitable for within-sample comparisons [57].

Between-sample normalization addresses technical variations across multiple samples within a dataset:

• TMM (Trimmed Mean of M-values): Calculates scaling factors relative to a reference sample, assuming most genes are not differentially expressed [57].
• Quantile normalization: Makes the distribution of gene expression levels identical for each sample in a dataset [57].
• Med-pgQ2 and UQ-pgQ2: Per-gene normalization after per-sample median or upper-quartile global scaling, demonstrating improved specificity for data skewed toward lowly expressed counts [58] [59].

Cross-dataset normalization corrects for batch effects when integrating data from multiple studies:

• ComBat: Utilizes empirical Bayes methods to adjust for known batch effects [57] [56].
• Limma: Employs linear models to remove batch effects when sources of variation are known [57] [2].
• SVA (Surrogate Variable Analysis): Identifies and estimates unknown sources of variation [57].

Table 1: Normalization Methods Across RNA-seq Analysis Stages

Stage	Method	Primary Function	Best Use Cases
Within-sample	TPM	Normalizes for sequencing depth & transcript length	Single-sample gene expression comparison
Within-sample	FPKM/RPKM	Adjusts for library size & gene length	Intra-sample analysis (with limitations)
Between-sample	TMM	Calculates scaling factors relative to reference	Datasets with balanced composition
Between-sample	Quantile	Equalizes expression distributions across samples	Standardizing sample distributions
Between-sample	Med-pgQ2/UQ-pgQ2	Per-gene normalization after global scaling	Data skewed toward lowly expressed counts
Cross-dataset	ComBat	Empirical Bayes adjustment for known batches	Multi-batch studies with documented variables
Cross-dataset	Limma	Linear models for known batch effects	Studies with defined technical covariates
Cross-dataset	SVA	Identifies unknown sources of variation	Complex studies with undocumented biases

Comparative Performance of Batch Effect Correction Methods

Algorithm Performance Across Data Types

Benchmarking studies have evaluated numerous batch-effect correction algorithms (BECAs) across different data types and scenarios. In proteomics, comprehensive assessments comparing precursor-, peptide-, and protein-level corrections have revealed that protein-level correction consistently demonstrates superior robustness for mass spectrometry-based data [56]. The integration of specific quantification methods with BECAs significantly influences performance outcomes, with the MaxLFQ-Ratio combination showing particular effectiveness in large-scale applications [56].

For transcriptomic data, empirical comparisons of nine normalization methods using benchmark datasets from the Microarray Quality Control Project (MAQC) have yielded important insights. When evaluating data with two replicates, methods like Med-pgQ2 and UQ-pgQ2 achieved specificity rates exceeding 85%, detection power over 92%, and controlled false discovery rates [58] [59]. While traditional methods like DESeq and TMM-edgeR demonstrated higher detection power (>93%), this advantage came at the cost of reduced specificity (<70%) and elevated actual false discovery rates [59].

Evaluation Metrics and Outcomes

The performance of batch effect correction methods is typically assessed using multiple metrics, each providing distinct insights into method effectiveness:

• Coefficient of Variation (CV): Measures technical variation within replicates across batches, with lower values indicating better correction [56].
• Signal-to-Noise Ratio (SNR): Evaluates resolution in differentiating biological sample groups post-correction [56].
• Area Under Curve (AUC): Assess classification accuracy in distinguishing biological conditions [58] [59].
• Matthew's Correlation Coefficient (MCC): Quantifies quality of binary classifications, considering true and false positives/negatives [56].

Table 2: Performance Comparison of Batch Effect Correction Algorithms

Algorithm	Mechanism	Strengths	Limitations	Best Application Context
ComBat	Empirical Bayes framework	Effective for known batch effects; works with small sample sizes	Assumes normal distribution	Multi-batch studies with documented covariates
RUV-III-C	Linear regression on raw intensities	Removes unwanted variation using control features	Requires high-quality control samples	Studies with reliable reference standards
Ratio	Sample-reference intensity ratios	Simple, effective for confounded batch effects	Depends on reference material quality	Universal reference material availability
Harmony	PCA with iterative clustering	Identifies complex batch patterns; preserves biology	Computationally intensive	Large datasets with multiple batch sources
WaveICA2.0	Multi-scale decomposition	Handles injection order signal drifts	Complex parameter tuning	LC-MS data with temporal drift
NormAE	Deep learning neural networks	Captures non-linear batch effects	Requires m/z and RT information	Complex non-linear batch effects
Median Centering	Central tendency adjustment	Simple, interpretable	Oversimplifies complex batch effects	Mild batch effects in balanced designs

Experimental Protocols for Batch Effect Assessment

Quality Control Standard Implementation

Robust batch effect correction begins with appropriate quality control measures. For transcriptomic studies, implementing quality control standards (QCS) enables systematic monitoring and evaluation of technical variation. A novel approach using tissue-mimicking QCS with propranolol in a gelatin matrix has demonstrated effectiveness in MALDI-MSI applications, providing a framework adaptable to other transcriptomic methodologies [55].

Protocol: Quality Control Standard Preparation

• Prepare 15% gelatin solution from porcine skin gelatin powder dissolved in ultrapure water
• Incubate solution in a thermomixer at 37°C with 300 rpm agitation until fully dissolved
• Mix propranolol or stable isotope-labeled internal standard solutions with gelatin solution in 1:20 ratio
• Spot QCS solution onto slides alongside experimental samples
• Process QCS samples identically to experimental samples throughout workflow
• Use QCS intensity measurements to quantify technical variation across batches [55]

Integrated Workflow for Batch Effect Correction

A comprehensive approach combining quality control standards with computational correction provides the most robust solution for addressing technical variability:

Experimental Workflow for Batch Effect Management

Application to Cytoskeletal Alterations in Age-Related Diseases

Special Considerations for Cytoskeletal Transcriptomics

Research on cytoskeletal alterations in aging presents unique challenges for batch effect correction. Studies of the human prefrontal cortex across lifespan have revealed that housekeeping genes, particularly those involved in essential cellular functions like cytoskeletal organization (TUBA1A, TUBB, TUBB3), show coordinated downregulation during aging [7]. This pattern necessitates careful selection of reference genes for normalization that are not themselves subject to age-associated expression changes.

Integrative approaches combining machine learning with differential expression analysis have demonstrated effectiveness in identifying cytoskeletal genes associated with age-related diseases. Support Vector Machine (SVM) classifiers have shown particular utility in handling the high-dimensional nature of transcriptomic data while maintaining sensitivity to detect subtle expression patterns in cytoskeletal genes [2].

Case Study: Cytoskeletal Genes in Neurodegeneration

In Alzheimer's disease research, batch-effect-corrected transcriptomic analyses have identified several cytoskeletal genes with significant expression changes, including ENC1, NEFM, ITPKB, PCP4, and CALB1 [2]. These findings align with the known role of cytoskeletal integrity in neuronal health and function. The successful identification of these biomarkers depended on effective batch effect correction strategies that removed technical noise while preserving biological signal.

Protocol: Integrated Analysis of Cytoskeletal Genes

• Retrieve cytoskeletal gene list from Gene Ontology (GO:0005856), encompassing 2,304 genes covering microfilaments, intermediate filaments, and microtubules [2]
• Apply batch effect correction using Limma package to normalize transcriptomic data [2]
• Perform recursive feature elimination with SVM classifier to identify discriminative cytoskeletal genes
• Conduct differential expression analysis using DESeq2 or Limma with adjusted p-value threshold
• Identify overlapping genes between feature selection and differential expression results
• Validate findings using ROC analysis on external datasets [2]

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Research Reagents for Batch Effect Management

Reagent/Resource	Function	Application Example	Considerations
Quartet Reference Materials	Multi-level quality control	Proteomics batch monitoring	Enables ratio-based correction
Propranolol-Gelatin QCS	Technical variation assessment	MALDI-MSI batch effect quantification	Mimics tissue ionization [55]
Housekeeping Gene Sets	Reference for normalization	RNA-seq data standardization	Must validate stability in aging [7]
Trimmomatic	Read quality processing	RNA-seq adapter removal	Impacts mapping rates [60]
Limma Package	Batch effect correction	Microarray & RNA-seq normalization	Handles known batch effects [2]
DESeq2	Differential expression analysis	RNA-seq statistical testing	Incorporates normalization
Cytoskeletal Gene Panel	Targeted analysis	Age-related disease mapping	GO:0005856 [2]
External Validation Datasets	Method performance assessment	Benchmarking correction algorithms	Essential for methodology validation

Effective management of technical variability through appropriate normalization and batch effect correction is indispensable for reliable transcriptomic research on cytoskeletal alterations in age-related diseases. The comparative data presented in this guide demonstrates that method selection should be guided by specific experimental designs, with protein-level correction showing particular promise for proteomic studies and combination approaches like Med-pgQ2 offering advantages for certain transcriptomic applications. Implementation of rigorous quality control standards, such as tissue-mimicking QCS, combined with computational correction methods like ComBat or Harmony, provides a robust framework for extracting biologically meaningful signals from complex datasets. As research continues to unravel the intricacies of cytoskeletal changes in aging, applying these optimized strategies will enhance the reproducibility and translational potential of findings in this critical field of study.

Aging represents a universal biological process characterized by the progressive decline of physiological functions, yet this process exhibits striking differences between males and females. While human females generally experience longer lifespans, they simultaneously demonstrate higher frailty and susceptibility to different age-related diseases compared to males [61]. The emerging field of sex-dimorphic aging research seeks to unravel the molecular underpinnings of these differences, with transcriptomics serving as a powerful lens through which to examine sex-biased gene regulation across the lifespan. Complex diseases often exhibit sex dimorphism in morbidity and prognosis, many of which are age-related, including neurological disorders, cardiovascular diseases, immunological defects, and cancers [62]. Understanding these molecular differences has become increasingly crucial as the global elderly population is expected to double by 2050, necessitating sex-specific approaches in diagnosing, treating, and preventing age-related diseases [61].

This review synthesizes current evidence from transcriptomic studies to elucidate how biological sex influences aging trajectories at the molecular level. We particularly focus on the intersection with cytoskeletal alterations in age-related diseases, providing a comprehensive analysis of sex-biased transcriptomic signals and their implications for healthspan and longevity. The molecular hallmarks of aging, including genomic instability, telomere attrition, and loss of proteostasis, exhibit distinct sex-specific patterns influenced by sex chromosomes, sex hormones, and the epigenetic regulation of the inactive X chromosome [61]. By examining the intricate relationships between these factors through transcriptomic approaches, we can develop more precise, sex-informed diagnostic and therapeutic strategies for age-related conditions.

Molecular Mechanisms of Sex Differences in Aging

Sex Chromosome and Hormonal Influences

The fundamental drivers of sex-dimorphic aging originate from genetic and endocrine differences established early in development. The sex chromosome complement (typically 46,XX or 46,XY) is determined at fertilization and establishes a genetic framework that influences lifespan and aging trajectories [61] [63]. The X chromosome encodes over 800 genes, while the Y chromosome contains only 60-100 genes primarily involved in sex determination and fertility [63]. Research has revealed that the sex with the reduced sex chromosome often dies earlier across the tree of life, highlighting the profound impact of sex chromosome composition on aging [61].

Sex hormones constitute the second major factor driving dimorphic aging. In the 46,XY embryo, the Y-chromosome gene SRY triggers testis determination around 6 weeks post-conception, with maximal upregulation of testosterone synthesis enzymes occurring by 8 weeks [63]. Fetal testicular testosterone then acts through androgen receptors to masculinize structures, while the developing 46,XX ovary remains relatively quiescent hormonally at this stage. These early developmental differences establish trajectories that manifest across the lifespan, influencing how aging processes unfold in each sex.

Transcriptomic analyses of early human fetal brain development reveal limited but potentially important sex differences during critical developmental windows. Two key regulators of X-inactivation—XIST and TSIX—show consistently higher expression in 46,XX samples, while a "core" group of 18 Y-chromosome genes demonstrate consistently higher expression in 46,XY brain samples [63]. Interestingly, one Y-chromosome gene, PCDH11Y (protocadherin11 Y-linked), regulates excitatory neurons and is unique to humans, where it has been implicated in language development [63]. These findings suggest that sex differences in transcription emerge early in development and are not unique to the brain but common across tissues.

Transcriptomic Changes Across Tissues

Pan-tissue transcriptome analyses have revealed the extensive scope of sex-dimorphic aging across human tissues. A comprehensive analysis of approximately 17,000 transcriptomes from 35 human tissues quantitatively demonstrated that both sex and age are critical drivers of global transcriptome variation [62] [64]. Breakpoint analysis showed that sex-dimorphic aging rates are significantly associated with the decline of sex hormones, with males exhibiting larger and earlier transcriptome changes [62] [64]. This transcriptomic shift in males may underlie their faster aging rate observed in most tissues.

The principal component-based signal-to-variation ratio (pcSVR) method has been developed to measure the adjusted Euclidean distance in higher dimensions, quantifying the distance between different sex or age groups divided by data dispersion within each group [62]. This approach provides a global measurement of sex or age effects on transcriptomic variations by considering variations from all genes and alternative splicing events. Through this methodology, researchers have discovered that age generally demonstrates substantially larger effects than sex on the human transcriptome in most tissues, as judged by both gene expression and alternative splicing profiles [62].

Table 1: Tissue-Specific Sex Differences in Transcriptomic Aging

Tissue Type	Key Sex-Biased Aging Features	Functional Consequences
Skeletal Muscle	Better functional organization in females; maintained macroautophagy and sarcomere organization [65]	Delayed musculoskeletal aging in females
Brain Cortex	Male-biased age-associated AS events stronger association with Alzheimer's disease [62]	Differential susceptibility to neurodegenerative diseases
Cardiovascular	Larger age-pcSVR in adipose and cardiovascular tissues [62]	Sex-divergent cardiovascular aging patterns
Breast	Largest sex-pcSVR observed [62]	Tissue-specific sex differences

Notably, alternative splicing is significantly affected by both sex and age across most tissues, while gene expression is affected by sex in a much smaller number of tissues compared to splicing profiles [62]. For example, the coronary artery and adrenal gland are significantly affected by sex and age in their alternative splicing profiles, but their gene expression profiles are not affected by sex or age [62]. This highlights the importance of investigating both expression and splicing changes to fully understand sex-dimorphic aging.

Cytoskeletal Alterations in Age-Related Diseases

Cytoskeletal Components and Aging

The cytoskeleton of eukaryotic cells—comprising microtubules, actin, and intermediate filaments—represents a critical infrastructure that undergoes significant alterations during aging. These filamentous networks are highly interconnected and compartmentalized in polarized cells like neurons, serving not merely as structural elements but as dynamic regulators of various functions essential for neuronal development and maintenance [25]. Microtubules, assembled from alpha/beta-tubulin dimers, form polarized structures that establish the basis for organelle transport and modulate cell shape and behavior [25]. The heterogeneity of microtubules across cellular and subcellular compartments arises from several factors: cell-type specific expression of tubulin genes, locally active enzymes catalyzing posttranslational modifications, and various microtubule-associated proteins [25].

During aging, the cytoskeleton undergoes progressive alterations that contribute to functional decline. Research examining sensory axons from healthy human skin biopsies has revealed increases in cytoskeleton composition in both sexes and larger axonal caliber in males during aging [25]. These changes may modify axonal function, potentially contributing to aging-related decreases in sensory perception or increased susceptibility to degeneration. The dynamicity of neurofilaments, which is based on their fine-tuned assembly, transport, and degradation to sustain key structural and electrophysiological properties of neurons, is critically influenced by posttranslational modifications [25]. Age-related alterations in these modifications disrupt neurofilament dynamics, leading to functional impairments.

Cytoskeletal Involvement in Neurodegeneration

Cytoskeletal alterations feature prominently in the pathogenesis of numerous neurodegenerative diseases, including Alzheimer's disease, Charcot-Marie-Tooth disease, and Hereditary Spastic Paraplegia [25]. In Alzheimer's disease, post-translational modifications of tubulin—such as acetylation and tyrosination/detyrosination—influence each other and affect disease progression by altering microtubule dynamics [25]. Similarly, site-specific phosphorylation of tau, a microtubule-associated protein, leads to conformational changes that may be beneficial for normal neurite development when physiologically regulated but contribute to pathology when dysregulated in disease states [25].

The intricate crosstalk between different cytoskeletal elements represents a crucial mechanism underlying neuronal homeostasis. Neurofilaments have been shown to influence microtubule dynamics in neurons, with this crosstalk established and regulated by post-translational modifications and microtubule-associated proteins, leading to fine-tuning of neuronal morphology, cytoarchitecture, and physiology [25]. This proposed mechanism provides an additional step in untangling the complexity behind cytoskeleton-mediated regulation of neuronal homeostasis and its contribution to aging and diseases. Determining how cytoskeleton composition and axon morphology change during aging has emerged as an informative approach for understanding functional decline [25].

Table 2: Cytoskeletal Elements in Aging and Neurodegeneration

Cytoskeletal Element	Aging-Related Changes	Association with Neurodegenerative Diseases
Microtubules	Altered dynamics, changes in PTMs (acetylation, tyrosination) [25]	Alzheimer's disease, HSP; altered transport and stability
Neurofilaments	Increased composition, altered PTMs [25]	ALS, CMT; disrupted structural integrity
Tau Protein	Hyperphosphorylation, conformational changes [25]	Alzheimer's disease; microtubule destabilization
Actin Networks	Reorganization, stability changes	Impaired synaptic plasticity and repair

An emerging theme from recent research concerns the key physiological roles of cytoskeleton-associated proteins implicated in neurodegeneration, including microtubule-regulatory proteins such as Gigaxonin (linked to peripheral neuropathies), Tau (Alzheimer's disease), and Spastin (Hereditary Spastic Paraplegia) [25]. These proteins play essential roles during neuronal development, and tight control of their expression and activity through post-transcriptional and post-translational modifications proves fundamental for both axonal development and homeostasis. This suggests cytoskeleton alterations serve as a major continuum between neuronal circuit development and dysfunction/degeneration.

Analytical Approaches in Sex-Dimorphic Transcriptomics

Transcriptomic Profiling Methodologies

Advanced transcriptomic approaches have revolutionized our ability to detect and quantify sex-dimorphic aging signals across tissues and life stages. The Genotype-Tissue Expression (GTEx) project has been particularly instrumental, providing a large set of high-throughput sequencing data from postmortem donors across 54 human tissues with a wide age range of 20-70 years [62]. This resource enables identification of genes and molecular pathways significantly changed during the aging process and has revealed that sex-differential regulation occurs at both the gene expression and alternative splicing levels [62].

Principal component analysis (PCA) of gene expression and alternative splicing data has demonstrated clear sex and age differences across multiple tissues [62]. To quantitatively evaluate these differences, researchers have developed a method called principal component-based signal-to-variation ratio (pcSVR), which measures the adjusted Euclidean distance in higher dimensions [62]. The pcSVR quantifies the distance between different sex or age groups divided by the data dispersion within each group, serving as a reliable measurement for sex or age effects on transcriptomic variations [62]. This method provides a global measurement by considering variations from all genes and alternative splicing events between different groups, offering advantages over approaches that focus only on differentially expressed genes or splicing events.

Gene co-expression network analysis represents another powerful approach for studying sex-dimorphic aging. This method utilizes mutual information as a measure of gene co-expression to infer functional coordination between genes [65]. The ARACNe algorithm calculates mutual information between two data series and has been applied to construct sex- and age-specific networks [65]. Studies using this approach have revealed that the functional organization and modularity of genes decline with age, starting from middle age, potentially leading to age-related deterioration [65]. Furthermore, women maintain better gene functional organization throughout life compared to men, particularly in processes like macroautophagy and sarcomere organization [65].

Experimental Workflow for Sex-Dimorphic Transcriptomics

The following diagram illustrates the integrated experimental and computational workflow for analyzing sex-biased transcriptomic signals in aging research:

Diagram 1: Experimental workflow for sex-dimorphic transcriptomic analysis

This workflow begins with sample collection from resources like the GTEx project, which provides transcriptomic data from multiple tissues across a wide age range [62] [65]. Following RNA sequencing and data pre-processing, analyses proceed through multiple computational approaches including principal component analysis, pcSVR calculation, gene co-expression network inference, and differential expression analysis [62] [65]. Results from these analyses undergo functional enrichment analysis to identify biological processes exhibiting sex-dimorphic aging patterns, followed by experimental validation of key findings.

Key Research Reagents and Computational Tools

Table 3: Essential Research Reagents and Computational Tools

Resource/Tool	Type	Primary Application	Key Features
GTEx Database	Data Resource	Pan-tissue transcriptome analysis	~17,000 transcriptomes from 54 tissues across wide age range [62]
DrugAge Database	Data Resource	Lifespan-extending compounds	Curated compounds with effects on lifespan; sex-specific data [66]
ARACNe	Computational Algorithm	Gene co-expression network inference	Uses mutual information to detect co-expression relationships [65]
pcSVR Method	Analytical Method	Quantifying sex/age effects	Measures group differences relative to within-group variation [62]
Enrichr	Computational Tool	Functional enrichment analysis	Gene Ontology category enrichment with statistical significance [65]

Detailed Methodologies

Principal Component-Based Signal-to-Variation Ratio (pcSVR) Methodology:

The pcSVR method was designed to quantitatively evaluate the individual and combined contributions of sex and age to transcriptomic variations [62]. Samples are grouped into male versus female and young (age <40) versus old (age >60), with an age gap instead of a specific age cutoff to reduce data noise related to the large variation in menopause ages among individuals and the continuous decline of sexual steroid levels between ages 40 and 60 [62]. The pcSVR quantifies the distance between different sex or age groups divided by the data dispersion within each group, with intrinsic differences between groups resulting in a pcSVR value significantly larger than 1 [62]. Permutation tests are used to assess the statistical significance of observed pcSVR values, and robustness is confirmed through analyses that remove genes and alternative splicing events encoded by sex chromosomes or use different PC cutoffs to capture global variance [62].

Gene Co-Expression Network Construction:

For gene co-expression network analysis, gene expression data (raw counts) are obtained through RNA-seq from resources like the GTEx project [65]. The data are divided according to sex and age ranges (e.g., young: 20-39 years, middle-aged: 40-59 years, elderly: 60-70 years) [65]. After pre-processing and normalization, gene co-expression networks are inferred using mutual information as a measure of gene co-expression with the ARACNe algorithm [65]. The analysis typically retains the 10,000 strongest interactions for each network to ensure identical size for comparative analyses across sex and age groups [65]. Network topological properties are then analyzed, and functional enrichment is performed using tools like Enrichr to identify biological processes associated with each network [65].

Sex-Specific Therapeutic Responses and Interventions

Differential Drug Responses

Research has revealed striking sex differences in responses to potential anti-aging interventions, highlighting the importance of considering sex as a biological variable in therapeutic development. The DrugAge database, which compiles compounds showing effects on lifespan in model organisms, has been updated to include sex-specific data, revealing notable sex-related differences in lifespan and weight change responses [66]. Analysis of this database shows that in murine studies, males have a higher number of experiments with significant average/median lifespan extension (36%) compared to females (29%) [66]. Furthermore, when only Interventions Testing Program (ITP) studies—considered a gold standard in murine lifespan research—are considered, lifespan extension is much more pronounced in males (5.5%) than in females (1.6%) [66].

A compelling example of sex-specific therapeutic responses comes from a study testing a combination of oxytocin and an Alk5 inhibitor (OT+A5i) in frail, elderly mice [67]. This dual-drug approach targets two biological pathways that change with age: oxytocin (which declines with aging and supports tissue repair) and TGF-beta pathway (which becomes overactive with age, contributing to chronic inflammation) [67]. The treatment resulted in remarkable benefits in male mice, including a 73% life extension from the time of treatment initiation and a 14% increase in overall median lifespan, along with significant improvements in physical endurance, agility, and memory [67]. Hazard ratio analysis indicated that treated males were nearly three times less likely to die at any given time than untreated males [67]. In contrast, female mice did not experience significant gains in lifespan or healthspan from the same treatment, although middle-aged females did show improved fertility after treatment [67].

Implications for Drug Development

These sex-specific therapeutic responses have profound implications for aging intervention research and drug development. The findings underscore that the biological context of aging differs substantially between males and females, potentially requiring sex-specific treatment approaches for age-related conditions [67]. This is particularly relevant given that many compounds may extend lifespan through weight loss mechanisms, with significant correlations observed between weight loss and lifespan extension in male mice, especially in ITP studies [66]. The inclusion of weight change data in lifespan studies has therefore become crucial for understanding compound mechanisms and controlling for potential caloric restriction effects.

The development of sex-specific approaches in aging therapeutics requires careful consideration of multiple factors. First, the timing of intervention may need to differ between sexes, given the discovery that males exhibit earlier onset of transcriptomic aging [62]. Second, drug dosages may need sex-specific optimization, as metabolic and pharmacokinetic differences between males and females could significantly influence drug efficacy and toxicity [66]. Third, therapeutic targets may need to be selected based on sex-specific pathway alterations, such as the male-biased age-associated alternative splicing events that show stronger association with Alzheimer's disease [62]. These considerations highlight the necessity of including both sexes in preclinical aging research and reporting sex-stratified results.

The investigation of sex-dimorphic aging through transcriptomic approaches has revealed fundamental differences in how males and females age at the molecular level. Key findings include the earlier and more pronounced transcriptomic changes in males, sex-specific alternative splicing patterns associated with age-related diseases, and maintained functional gene organization in females across tissues like skeletal muscle [62] [65]. These differences appear significantly associated with the decline of sex hormones and contribute to varying susceptibility to age-related conditions, including neurodegenerative diseases, cardiovascular disorders, and metabolic conditions.

Future research in this field should prioritize several key directions. First, expanded tissue-specific analyses across the lifespan will help create a more comprehensive map of sex-dimorphic aging trajectories. Second, integrated multi-omics approaches that combine transcriptomics with proteomics, epigenomics, and metabolomics will provide a more systems-level understanding of the mechanisms driving sex differences in aging. Third, longitudinal studies tracking individual aging trajectories will help distinguish between driver and passenger events in sex-dimorphic aging. Finally, greater inclusion of both sexes in preclinical intervention studies and careful sex-stratified analysis of results will accelerate the development of tailored therapeutic approaches for age-related conditions.

The emerging recognition of sex as a critical biological variable in aging research promises to transform our understanding of the aging process and open new avenues for extending healthspan. By acknowledging and investigating the fundamental ways in which biological sex influences aging trajectories, we move closer to personalized interventions that can effectively address the unique aging challenges faced by men and women.

In the field of transcriptomics research, particularly in the study of complex biological processes like cytoskeletal alterations in aging and neurodegenerative diseases, researchers are often confronted with high-dimensional data where the number of features (genes) vastly exceeds the number of samples [35] [68]. This "n << p" problem presents significant challenges for identifying truly relevant biomarkers and building predictive models. Feature selection serves as an essential preprocessing step to reduce dimensionality, minimize overfitting, and enhance the biological interpretability of results [69] [70]. Within the specific context of cytoskeletal aging research, where the goal is to identify meaningful gene expression patterns among thousands of candidates, the choice of feature selection methodology can dramatically influence the validity and translational potential of findings [25].

This guide provides a comprehensive comparison of three prominent feature selection methods—Recursive Feature Elimination (RFE), LASSO regression, and ANOVA—for robust gene selection in transcriptomics studies. We objectively evaluate their performance characteristics, implementation considerations, and suitability for different experimental scenarios, with particular emphasis on applications in aging and cytoskeletal biology research.

Core Principles and Mechanisms

Table 1: Fundamental Characteristics of Feature Selection Methods

Method	Core Principle	Selection Mechanism	Model Type
RFE	Recursively eliminates least important features	Iterative ranking based on model importance	Model-dependent wrapper method
LASSO	Performs regularization and selection simultaneously	L1 penalty shrinks coefficients to exactly zero	Embedded linear model
ANOVA	Evaluates group differences feature-by-feature	F-statistic for variance between vs within groups	Univariate filter method

Recursive Feature Elimination (RFE) operates as a greedy wrapper method that recursively prunes features based on a model's importance rankings [69]. The algorithm begins by training a model on all features, ranking them by importance (typically using coefficients or feature importance attributes), eliminating the least important feature(s), and repeating this process on the reduced feature set until a predetermined number of features remains [69] [71]. This iterative refinement makes RFE particularly effective for identifying feature subsets that optimize predictive performance, though it can be computationally intensive compared to filter methods.

LASSO (Least Absolute Shrinkage and Selection Operator) employs L1 regularization to perform both feature selection and regularization simultaneously [72]. By adding a penalty equal to the absolute value of the magnitude of coefficients, LASSO shrinks less important coefficients to exactly zero, effectively eliminating them from the model [68]. This embedded approach makes LASSO particularly valuable for creating sparse, interpretable models in high-dimensional spaces, though it can be unstable with correlated features, potentially selecting one feature arbitrarily from a correlated group [73].

ANOVA (Analysis of Variance) functions as a univariate filter method that assesses each feature independently based on its ability to discriminate between pre-defined groups [68]. By calculating the F-statistic (ratio of between-group variance to within-group variance) for each feature, ANOVA identifies features with statistically significant differences across conditions [68]. This approach is model-agnostic and computationally efficient, but it ignores feature interactions and may select redundant features with similar discrimination power.

Computational Implementation

Diagram 1: Comparative workflows of RFE, LASSO, and ANOVA feature selection methods. Each method follows a distinct pathway from the full feature set to a refined subset, with RFE employing an iterative elimination process, LASSO utilizing coefficient shrinkage, and ANOVA relying on statistical significance testing.

Performance Comparison and Experimental Data

Empirical Performance Metrics

Table 2: Experimental Performance Comparison Across Methodologies

Performance Metric	RFE	LASSO	ANOVA	Experimental Context
Type I Error Rate	Varies by base model	10-50% inflation [68]	Low false positive rate [68]	Small sample sizes (n < 100) [68]
Type II Error Rate	Model-dependent	Comparable or higher than ANOVA [68]	Comparably low [68]	Small sample sizes (n < 100) [68]
Handling of Correlated Features	Depends on base model	Selects one from correlated groups [68] [73]	Independent assessment	Omics data with biological correlations [68]
Computational Efficiency	Lower (iterative modeling)	Moderate	High (univariate)	Large feature sets (>10,000 features) [70]
Stability	Moderate	Lower with correlated features [73]	High	Different sampling variations [73]

Recent benchmarking studies have revealed critical differences in how these methods perform under conditions typical of transcriptomics research. In the "n << p" scenario common to omics experiments, ANOVA demonstrates remarkably low Type I error rates even without multiple test correction, while Elastic Net (which combines LASSO and ridge penalties) shows Type I error inflation between 10-50% for small numbers of features, with this inflation increasing with sample size [68]. For Type II error, ANOVA shows comparable or lower rates than Elastic Net, suggesting it may be more powerful for detecting true positives in many omics contexts [68].

The handling of correlated features represents another key differentiator. LASSO tends to select one feature arbitrarily from a group of correlated features, while ignoring others [68] [73]. This behavior can be problematic in transcriptomics studies where genes often function in correlated pathways, potentially leading to the omission of biologically relevant features. ANOVA and RFE (depending on the base model) approach correlation structure differently, with ANOVA evaluating each feature independently without consideration of relationships with other features.

Practical Implementation Considerations

RFE requires selection of an appropriate base estimator, with linear models (Logistic Regression, SVM with linear kernel) often providing transparent feature importance metrics through coefficients [69] [71]. The number of features to select or the elimination step size must be predetermined, though cross-validated RFE (RFECV) can automatically determine the optimal number of features [69]. Best practices include standardizing features before application, especially with linear models, and using cross-validation to mitigate overfitting risks [69].

LASSO implementation requires careful tuning of the regularization parameter (α or λ), typically through cross-validation [72] [68]. The choice of α balance (ranging from 0 for ridge penalty to 1 for pure LASSO) influences the sparsity of the solution, with lower values retaining more features [68]. For omics data with strong correlation structures, pure LASSO (α=1) may be overly sparse, while Elastic Net with α<1 may provide more robust feature selection [68].

ANOVA implementation is comparatively straightforward, requiring only specification of a significance threshold, though multiple testing correction (e.g., Bonferroni, FDR) is recommended to control family-wise error rates [68]. Unlike RFE and LASSO, ANOVA does not require parameter tuning beyond significance thresholds, contributing to its computational efficiency and reproducibility.

Applications in Cytoskeletal Aging Research

Method Selection for Transcriptomics of Aging

In the study of cytoskeletal alterations in aging and disease, feature selection must address several domain-specific challenges [25]. The cytoskeleton consists of microtubules, actin filaments, and intermediate filaments that are highly interconnected and compartmentalized in cells such as neurons [25]. Age-related alterations to these structures and their associated proteins (e.g., tau, spastin, gigaxonin) contribute to neurodegenerative conditions including Alzheimer's disease, Charcot-Marie-Tooth disease, and Hereditary Spastic Paraplegia [25]. Transcriptomic studies in this field aim to identify expression patterns in genes encoding cytoskeletal proteins, modification enzymes, and regulatory factors that correlate with aging and pathological states.

For such studies, each feature selection method offers distinct advantages:

• ANOVA provides a robust initial screening approach when investigating differences between distinct age groups or disease states, efficiently filtering thousands of genes to a manageable subset for further validation [68]. Its low Type I error rate reduces false leads in exploratory research.

• LASSO is particularly valuable when building predictive models of aging progression or disease risk, where model interpretability and clinical translation are priorities [72] [73]. The sparse models it produces can identify minimal gene sets with maximal predictive power for age-related phenotypic outcomes.

• RFE excels in scenarios where the research goal is to identify coordinated gene expression programs affecting cytoskeletal integrity, as it can capture feature interactions when used with appropriate base models [69] [70]. Its iterative refinement can optimize predictive performance for complex aging phenotypes.

Hybrid Approaches and Advanced Strategies

Recent advances in feature selection for transcriptomics have demonstrated the power of hybrid approaches that combine multiple methodologies. A 2025 study on Usher syndrome employed a sequential feature selection approach integrating variance thresholding, RFE, and LASSO regression within a nested cross-validation framework [70]. This strategy successfully reduced dimensionality from 42,334 mRNA features to 58 robust biomarkers, highlighting how complementary methods can overcome individual limitations [70].

For cytoskeletal aging research, a similar hybrid workflow might include:

• Initial filtering using ANOVA to remove non-informative features
• Intermediate selection with LASSO to handle correlated predictors
• Refinement with RFE to optimize predictive feature subsets

Diagram 2: Proposed hybrid feature selection workflow for cytoskeletal aging transcriptomics, combining the strengths of multiple methods to achieve robust gene selection from large-scale transcriptomic data, followed by essential biological validation.

Such staged approaches leverage the statistical robustness of ANOVA, the regularization strengths of LASSO, and the optimization capabilities of RFE while mitigating their individual limitations.

Research Reagent Solutions

Table 3: Essential Research Reagents for Transcriptomic Feature Selection Studies

Reagent/Category	Specific Examples	Application in Feature Selection Workflow
RNA Sequencing Platforms	Illumina NovaSeq, PacBio Sequel	Generate primary transcriptomic data for feature selection
Feature Selection Software	scikit-learn (RFE, LASSO) [69] [71], Glmnet [68]	Implement computational selection algorithms
Validation Technologies	Droplet Digital PCR (ddPCR) [70], qRT-PCR	Experimentally confirm selected biomarker candidates
Cell Line Models	Immortalized B-lymphocytes [70], iPSC-derived neurons	Provide biological material for transcriptomic profiling
Pathway Analysis Tools	Gene Ontology, KEGG, GSEA	Interpret biological significance of selected features

The optimization of feature selection methodologies is paramount for advancing transcriptomic research into cytoskeletal alterations in aging and disease. RFE, LASSO, and ANOVA each offer distinct strengths and limitations that make them differentially suited to specific research contexts. ANOVA provides a statistically robust screening approach with low false positive rates, LASSO delivers sparse, interpretable models ideal for clinical translation, and RFE offers optimized predictive performance through iterative refinement. For the complex challenge of identifying age-related cytoskeletal alterations in transcriptomic data, hybrid approaches that strategically combine these methods show particular promise, as evidenced by recent successful applications in rare disease research [70]. The choice of feature selection strategy should be guided by specific research objectives, dataset characteristics, and validation requirements to ensure biologically meaningful discoveries in the evolving landscape of cytoskeletal aging research.

The cytoskeleton, a complex network of filaments comprising actin, microtubules, and intermediate filaments, is fundamental to cellular structure, mechanical integrity, and function. In age-related diseases, cytoskeletal alterations are a common hallmark, but their manifestation is highly specific to both tissue and cell type. The isolation and study of these specific changes present significant challenges, as bulk analysis methods often mask critical cell-type-specific signatures. Transcriptomic research, particularly at single-cell resolution, is now peeling back these layers of complexity, revealing a sophisticated landscape where cytoskeletal dynamics are intimately linked to disease pathogenesis in a cell-type-specific manner. This guide compares the experimental approaches and data that are illuminating this previously opaque field.

Comparative Analysis of Cytoskeletal Alterations Across Cell Types

The table below synthesizes key findings from recent studies, demonstrating how cytoskeletal changes are not uniform but vary significantly by cell type and disease context.

Table 1: Cell-Type-Specific Cytoskeletal Alterations in Age-Related Diseases

Disease Context	Cell Type	Key Cytoskeletal Alterations	Functional Consequences	Primary Supporting Evidence
Primary Open-Angle Glaucoma (POAG) [74]	Peripheral CD4+ T Lymphocytes	Transcriptomic downregulation of signaling pathways (e.g., IFNG, TNF).	Immune remodeling; potential disruption of neuro-immune balance.	Single-cell RNA-seq of ~1.4 million PBMCs.
Primary Open-Angle Glaucoma (POAG) [74]	Peripheral CD8+ T & NK Cells	Reduced cell proportions; impaired cytolytic potential.	Dysregulated systemic immune response.	Single-cell RNA-seq of ~1.4 million PBMCs.
Age-Related Macular Degeneration (AMD) [75]	Retinal Pigment Epithelium (RPE)	Destabilization and fragmentation of F-actin; impaired microtubule-guided phagosome transport.	Disrupted outer blood-retinal barrier; failure to phagocytose photoreceptor outer segments.	Immunofluorescence, biochemical assays, mouse models (e.g., KLC1 deficiency).
Alzheimer's Disease (AD) [76]	Cortical & Hippocampal Neurons	Formation of neurofibrillary tangles (hyperphosphorylated tau); cofilin pathology (actin inclusions).	Synaptic dysfunction, breakdown of axonal transport, and neurodegeneration.	Histopathology, proteomic analysis, transgenic mouse models.
Parkinson's Disease (PD) [77]	Dopaminergic Neurons	Mutations in PD-associated genes (LRRK2, parkin) affecting microtubule stability; tubulin PTM alterations.	Impaired axonal transport, mitochondrial trafficking, and neuronal vulnerability.	Genetic association studies, in vitro and in vivo models of PD.
Heart Failure [78]	Cardiac Myocytes	Disorganization and accumulation of microtubules and desmin; loss of contractile myofilaments.	Impaired sarcomere motion, cardiac dysfunction, and structural remodeling.	Proteomic and morphological analysis of human heart tissue.

Essential Experimental Protocols for Cell-Type-Specific Isolation

To obtain the data presented in the comparison table, researchers rely on sophisticated protocols that separate cell types and analyze their molecular profiles. The following methodologies are foundational to this field.

High-Throughput Single-Cell RNA Sequencing (scRNA-seq)

Protocol Application: This protocol was used to profile ~1.4 million peripheral blood mononuclear cells (PBMCs) from POAG patients and controls, revealing immune cell type-specific transcriptomic shifts, including those involving cytoskeletal regulators [74].

Detailed Workflow:

• Cell Isolation and Viability: PBMCs are isolated from fresh blood samples using density gradient centrifugation. Cell viability is critical and must exceed 80%, typically assessed using trypan blue or automated cell counters.
• Single-Cell Partitioning and Barcoding: A high-throughput platform (e.g., 10x Genomics Chromium) is used to isolate single cells and nanoliter-scale reactions into droplets (GEMs). Each cell's transcriptome is tagged with a unique cellular barcode and mRNA molecules with a unique molecular identifier (UMI).
• Library Preparation and Sequencing: The barcoded cDNA is amplified and converted into a sequencing library. Libraries are sequenced on a platform like Illumina NovaSeq to a sufficient depth (e.g., 50,000 reads per cell).
• Bioinformatic Analysis:
  • Quality Control & Filtering: Cells with high mitochondrial gene content (>20%) or low unique gene counts are filtered out. Tools like Scrublet are used to remove doublets [74].
  • Clustering and Annotation: Dimensionality reduction (PCA, UMAP) is performed, followed by graph-based clustering (Louvain/Leiden algorithm). Clusters are annotated as specific cell types (e.g., CD4+ T cells, myeloid cells) using canonical markers (e.g., CD3D, CD4, CD14) [74].
  • Differential Expression: Analysis identifies differentially expressed genes (DEGs) between conditions (e.g., POAG vs. control) within each specific cell cluster, pinpointing cytoskeletal and pathway alterations [74].

Single-Cell Proteomic and Immunocytochemical Analysis

Protocol Application: This approach is crucial for validating transcriptomic findings and directly visualizing cytoskeletal architecture and protein localization at a single-cell level [79].

Detailed Workflow:

• Cell Culture and Fixation: Cells are cultured on glass coverslips and fixed under conditions that preserve cytoskeletal structures (e.g., using paraformaldehyde).
• Permeabilization and Blocking: Cells are permeabilized with a detergent (e.g., Triton X-100) and incubated with a blocking serum to prevent non-specific antibody binding.
• Immunostaining: Cells are incubated with primary antibodies specific to cytoskeletal components (e.g., monoclonal antibodies against specific tubulin isotypes, actin, or tau) [79]. This is followed by incubation with fluorescently conjugated secondary antibodies.
• Image Acquisition and Analysis: High-resolution images are captured using confocal or super-resolution microscopy. Quantitative analysis of fluorescence intensity, filament morphology, and protein co-localization is performed using software like ImageJ or Imaris.

Signaling Pathways in Cytoskeletal Dysregulation

The following diagram illustrates a key integrative pathway, identified through single-cell studies, showing how systemic signals can converge on the cytoskeleton in specific cell types to influence retinal health.

Diagram 1: Systemic Immune-Cytoskeletal Crosstalk in Retinal Health. This pathway, informed by single-cell studies in glaucoma, shows how transcriptomic downregulation of IFNG and TNF in specific peripheral immune cells (like CD4+ T cells) is associated with cytoskeletal remodeling in the retina, disrupting the delicate balance that supports retinal ganglion cell (RGC) health [74].

The Scientist's Toolkit: Key Research Reagent Solutions

Successfully navigating the challenges of cell-type-specific cytoskeletal research requires a carefully selected toolkit of reagents and technologies.

Table 2: Essential Research Reagents and Tools for Cytoskeletal Analysis

Reagent / Tool Category	Specific Examples	Function and Application in Research
Single-Cell Sequencing Kits	10x Genomics Chromium Single Cell 3' Solution	Partitions single cells for barcoding and library prep, enabling large-scale transcriptomic atlas creation from heterogeneous tissues [74].
Validated Antibodies	Monoclonal Anti-Tubulin Isotypes [79], Anti-Tau (for NFTs) [76], Anti-Cofilin (for actin pathology) [76]	Critical for immunocytochemistry (ICC) and immunohistochemistry (IHC) to visualize and quantify cytoskeletal protein distribution, modification, and pathology in specific cell types.
Live-Cell Imaging Dyes	SiR-actin, GFP-tagged EB1/EB3 (microtubule plus-end tracker) [79]	Allows for real-time, dynamic visualization of cytoskeleton polymerization, depolymerization, and transport in living cells.
Genetically Encoded Tools	CRISPR-Cas9 for gene editing; GFP/RFP-tagged cytoskeletal protein constructs (e.g., LC3, EB proteins) [75] [79]	Used to create knock-out/knock-in models of disease mutations and to tag endogenous proteins for live imaging and tracking of dynamics.
Microtubule-Targeting Agents	Paclitaxel (stabilizer), Nocodazole (destabilizer)	Small molecules used to experimentally manipulate microtubule dynamics and test hypotheses about cytoskeletal function in disease [77].
Bioinformatics Software	Cell Ranger (10x Genomics), Seurat, Scrublet	Software suites for processing raw scRNA-seq data, performing quality control, clustering, and differential gene expression analysis [74].

The Genotype-Tissue Expression (GTEx) project represents one of the most comprehensive resources for studying gene regulation across human tissues, providing transcriptomic data from 54 tissues across hundreds of postmortem donors [80] [81]. This vast repository enables researchers to investigate tissue-specific gene expression patterns, regulatory mechanisms, and their implications for human diseases. However, analyzing multi-tissue transcriptome data presents significant challenges, including technical artifacts, batch effects, and biological confounders that can obscure genuine biological signals if not properly addressed [80].

The complexity of multi-tissue analysis is particularly relevant when studying age-related diseases and cytoskeletal alterations, where transcriptomic changes may be subtle yet biologically significant. As tissues age, they undergo characteristic transcriptional changes that can affect cytoskeletal organization and function, contributing to disease pathogenesis [2]. This comparison guide examines current methodologies for multi-tissue transcriptome analysis, with a specific focus on their application to understanding cytoskeletal alterations in age-related diseases, providing researchers with practical frameworks for leveraging GTEx and similar resources effectively.

Comparative Analysis of Multi-Tissue Processing Methods

Methodological Approaches and Their Performance

Multi-tissue transcriptome analysis requires specialized computational approaches to address technical variability while preserving biological signals. The table below summarizes four primary processing methods and their performance characteristics based on comparative studies:

Table 1: Comparison of Multi-Tissue Transcriptome Processing Methods

Method	Core Components	Advantages	Performance Metrics	Limitations
GTEx_Pro Pipeline	TMM + CPM normalization + SVA batch correction	Enhanced tissue clustering, reduced technical artifacts	Euclidean distance: Increased; Davies-Bouldin Index: Decreased (better clustering) [80]	Currently specific to GTEx data format
TPM + SVA	Transcripts Per Million + Surrogate Variable Analysis	Stable gene expression values, handles batch effects	Consistent expression values, moderate cluster separation [80]	May not fully address library composition biases
TPM + Quantile	TPM normalization + quantile normalization	Standardized expression distribution	Inferior cluster separation compared to SVA-based methods [80]	May remove biological signal along with technical noise
TMM + CPM + Quantile	TMM normalization + CPM scaling + quantile normalization	Addresses library size differences	Underperforms versus SVA-based approaches [80]	Less effective for multi-tissue comparability

The GTExPro pipeline emerges as a particularly robust approach, integrating Trimmed Mean of M-values (TMM) normalization, Counts Per Million (CPM) scaling, and Surrogate Variable Analysis (SVA) batch correction in a Nextflow-based workflow [80]. This combination specifically addresses the cross-sectional nature of GTEx data, where samples from different individuals introduce technical artifacts related to donor demographics and tissue processing. When benchmarked against traditional methods, GTExPro demonstrates superior performance in enhancing tissue-specific clustering and improving the reliability of downstream analyses [80].

Impact on Biological Discovery

The choice of processing methodology significantly influences the ability to detect biologically meaningful patterns, particularly when studying cytoskeletal genes in age-related contexts. Proper normalization and batch correction enable more accurate identification of subtle transcriptomic changes that might otherwise be masked by technical variation.

In the context of age-related diseases, methodologies that effectively reduce technical noise are essential for identifying cytoskeletal gene signatures. Research has shown that cytoskeletal genes undergo characteristic expression changes in conditions such as Alzheimer's disease, cardiovascular diseases, and Type 2 Diabetes Mellitus [2]. The application of robust multi-tissue analysis methods has facilitated the identification of consistent cytoskeletal alterations across tissues, including genes such as ANXA2 (shared across AD, IDCM, and T2DM) and TPM3 (shared across AD, CAD, and T2DM) [2].

Table 2: Cytoskeletal Genes Associated with Age-Related Diseases Identified Through Multi-Tissue Analysis

Gene Symbol	Associated Diseases	Cytoskeletal Function	Potential Clinical Relevance
ANXA2	AD, IDCM, T2DM	Actin binding, membrane-cytoskeleton linkage	Multi-disease biomarker potential
TPM3	AD, CAD, T2DM	Actin filament stabilization, muscle contraction	Shared pathway indicator
SPTBN1	AD, CAD, HCM	Spectrin-based membrane skeleton	Cytoskeletal plasticity across disorders
MAP1B	AD, T2DM	Microtubule stabilization, neuronal development	Neurodegeneration marker
ARPC3	HCM	Actin nucleation, branched actin networks	Cardiac structure maintenance
CASP10	Prostate Cancer	Apoptosis execution, cellular remodeling	Cancer pathogenesis [82]

Experimental Protocols for Multi-Tissue Analysis

GTEx_Pro Pipeline Implementation

The GTEx_Pro pipeline provides a standardized framework for processing multi-tissue transcriptomic data. The following workflow diagram illustrates the key steps in this analytical process:

Data Acquisition and Preprocessing: The process begins with downloading raw RNA-seq count data and associated metadata from the GTEx portal. The metadata includes sample attributes, donor information, and processing details that must be harmonized. Quality control steps involve filtering low-quality samples, removing genes with zero variance, and imputing missing values with tissue-specific medians [80].

Normalization and Batch Correction: The core of the pipeline applies TMM normalization using the edgeR package to correct for differences in sequencing depth and compositional biases across samples. Subsequently, CPM scaling transforms the adjusted counts to a consistent per-million scale. Finally, SVA identifies and adjusts for latent batch effects and technical artifacts without requiring prior knowledge of the confounding factors [80].

Downstream Analysis Applications: The processed data enables various analytical approaches including differential expression analysis, tissue-specific expression quantification, co-expression network construction, and trajectory analysis for genes of interest across developmental or aging timelines.

Cross-Tissue Transcriptome-Wide Association Study (TWAS) Framework

For integrating transcriptomic data with genetic association studies, a cross-tissue TWAS approach provides a powerful methodological framework:

This integrated framework leverages multi-tissue expression quantitative trait loci (eQTL) data from GTEx to bridge statistical associations from GWAS with functional genes. The UTMOST framework enables cross-tissue integration by modeling joint genetic effects across tissues, improving statistical power for gene discovery [82]. Validation through complementary methods including FUSION, FOCUS fine-mapping, and MAGMA pathway analysis ensures robust identification of susceptibility genes. Finally, Mendelian randomization and colocalization analyses provide evidence for causal relationships between gene expression and disease risk [82].

Table 3: Essential Research Reagents and Computational Tools for Multi-Tissue Transcriptome Analysis

Resource Category	Specific Tools/Databases	Primary Function	Application Notes
Processing Pipelines	GTEx_Pro (Nextflow)	End-to-end processing of GTEx data	Optimized for GTEx v8; incorporates TMM+CPM+SVA [80]
Normalization Methods	TMM (edgeR), CPM, TPM	Correct technical variability	TMM addresses composition biases; CPM enables cross-sample comparison [80]
Batch Effect Correction	SVA, ComBat, Limma	Remove technical artifacts	SVA effective for unknown covariates [80] [2]
Spatial Transcriptomics	10X Visium, Xenium, MERSCOPE, CosMx	Spatial gene expression profiling	Xenium shows high transcript counts; platform choice depends on resolution needs [83]
TWAS Frameworks	UTMOST, FUSION, FOCUS	Integrate expression with GWAS	UTMOST enables cross-tissue analysis; FOCUS provides fine-mapping [82]
Cytoskeletal Gene Databases	Gene Ontology (GO:0005856)	Curated cytoskeleton gene sets	Contains 2304 cytoskeletal genes for targeted analysis [2]
Machine Learning Classifiers	SVM, Random Forest, k-NN	Feature selection and classification	SVM achieves highest accuracy for cytoskeletal gene classification [2]

Application to Cytoskeletal Alterations in Age-Related Diseases

Analytical Framework for Cytoskeletal Gene Discovery

The study of cytoskeletal alterations in age-related diseases benefits significantly from optimized multi-tissue analysis approaches. A specialized computational framework has been developed for identifying cytoskeletal genes associated with age-related pathologies through integrated machine learning and differential expression analysis [2].

This framework begins with comprehensive cytoskeletal gene compendium from Gene Ontology (GO:0005856), encompassing 2,304 genes involved in microfilaments, intermediate filaments, microtubules, and associated regulatory elements. The analytical workflow employs multiple machine learning classifiers including Support Vector Machines (SVM), Random Forest, and k-Nearest Neighbors, with SVM demonstrating superior performance for cytoskeletal gene classification across multiple age-related diseases [2].

The integration of Recursive Feature Elimination (RFE) with SVM classification enables identification of minimal gene sets that maintain high predictive accuracy for disease status. This approach has identified 17 key cytoskeletal genes with strong associations to hypertrophic cardiomyopathy, coronary artery disease, Alzheimer's disease, idiopathic dilated cardiomyopathy, and Type 2 Diabetes Mellitus [2].

Age and Sex Considerations in Multi-Tissue Analysis

The effectiveness of multi-tissue analysis methodologies is particularly evident when examining age-related transcriptomic changes. Recent pan-tissue investigations have revealed extensive sex dimorphisms during aging, with distinct patterns of change in both gene expression and alternative splicing [62]. These sex-specific aging signatures have important implications for understanding the differential susceptibility to cytoskeletal-related disorders between males and females.

Breakpoint analysis has demonstrated that sex-dimorphic aging rates are significantly associated with the decline of sex hormones, with males exhibiting larger and earlier transcriptome changes compared to females [62]. This finding aligns with the observed male predisposition to earlier onset of certain age-related disorders, potentially including those involving cytoskeletal dysfunction.

Furthermore, single-cell transcriptomic studies of the aging human brain have revealed a common downregulation of housekeeping genes involved in essential cellular processes including ribosome function, transport, and metabolism across cell types [7]. Interestingly, neuron-specific genes generally remain stable throughout life, while cytoskeletal genes such as TUBA1A, TUBB3, and VAMP2 show consistent age-related downregulation across multiple brain cell types [7].

The integration of pan-tissue data from resources like GTEx requires sophisticated analytical strategies that address technical variability while preserving biological signals. The GTEx_Pro pipeline, with its integration of TMM normalization, CPM scaling, and SVA batch correction, represents a robust approach for multi-tissue transcriptome analysis that outperforms traditional processing methods in cluster separation and biological signal recovery [80].

For the specific study of cytoskeletal alterations in age-related diseases, complementary approaches including machine learning-based feature selection, cross-tissue TWAS frameworks, and single-cell resolution analyses provide powerful tools for identifying and validating disease-associated genes [2] [82]. These methodologies have revealed important cytoskeletal genes with consistent alterations across multiple age-related conditions, highlighting shared molecular pathways in age-related pathological processes.

As transcriptomic technologies continue to evolve, with spatial transcriptomics platforms offering increasingly sophisticated tissue context resolution [83] [84], the analytical frameworks described in this guide will enable researchers to extract maximum biological insight from multi-tissue data resources, ultimately advancing our understanding of cytoskeletal alterations in aging and disease.

Validating Cytoskeletal Biomarkers and Comparative Analysis Across Diseases and Sexes

The escalating global burden of age-related diseases necessitates the development of advanced computational tools for early detection and accurate diagnosis. Among these, machine learning (ML) models leveraging transcriptomic data have demonstrated exceptional promise. In the specific research context of cytoskeletal alterations in age-related diseases, transcriptomics provides a powerful lens through which to view the molecular mechanisms of aging. The cytoskeleton, a critical network of intracellular filaments, maintains cellular integrity and function, and its dysregulation is increasingly implicated in the pathology of diverse age-related conditions [85]. This guide objectively compares the performance of various computational prediction models, with a focused examination of Receiver Operating Characteristic (ROC) analysis and the critical process of external validation. Benchmarking these methodologies provides researchers, scientists, and drug development professionals with the empirical evidence needed to select optimal tools for their work in biogerontology and translational medicine.

Core Concepts in Model Evaluation

The ROC Curve and AUC Metric

The Receiver Operating Characteristic (ROC) curve is a fundamental graphical tool for evaluating the performance of binary classifiers. It illustrates the diagnostic ability of a model by plotting the True Positive Rate (TPR or Sensitivity) against the False Positive Rate (FPR or 1-Specificity) across all possible classification thresholds [86]. A model with perfect discriminative ability produces a curve that reaches the top-left corner of the plot, while a model with no discriminative power follows the diagonal line from the bottom-left to top-right, equivalent to random guessing [87] [86].

The Area Under the Curve (AUC) provides a single scalar value to quantify the model's overall performance, with a value of 1.0 representing a perfect classifier and 0.5 representing a worthless classifier [86]. In biomedical research, AUC values are typically interpreted as follows: 0.9-1.0 = excellent; 0.8-0.9 = good; 0.7-0.8 = fair; 0.6-0.7 = poor; and 0.5-0.6 = fail [86].

Validation on External Datasets

External validation represents the most rigorous assessment of a model's generalizability and clinical applicability [88]. Unlike internal validation methods (e.g., cross-validation), external validation tests the model on completely independent datasets, often collected from different populations or using different protocols [88] [89]. This process is crucial for verifying that a model's performance is not specific to the dataset on which it was trained. For aging biomarkers, including those based on transcriptomic profiles, validation across multiple, diverse population samples is particularly important to establish reliability across different genetic backgrounds and environmental exposures [88].

Performance Comparison of Computational Models

Table 1: Comparative Performance of ML Models in Disease Prediction from Transcriptomic Data

Disease Context	Best-Performing Model	Comparative Models	AUC (Internal)	AUC (External)	Key Biomarkers/Features	Citation
Hypertrophic Cardiomyopathy (HCM)	Support Vector Machine (SVM)	DT, RF, k-NN, GNB	0.949	N/R	ARPC3, CDC42EP4, LRRC49, MYH6 (cytoskeletal genes)	[85]
Alzheimer's Disease (AD)	Random Forest	Multiple ML algorithms	0.920	N/R	Plasma spectra features (correlated with p-tau217, GFAP)	[90]
Young-Onset Colorectal Cancer	Random Forest	Multiple ML classifiers	0.859	0.888	Clinical and laboratory features from EMR	[89]
Multiple Age-Related Diseases*	SVM	DT, RF, k-NN, GNB	0.877-0.963	N/R	17 cytoskeletal genes total across diseases	[85]
Hypertrophic Cardiomyopathy (HCM)	12-Gene Diagnostic Signature	113 algorithm combinations	High (exact N/R)	Validated on GSE180313	COMP, SFRP4, RASD1, IL1RL1, and 8 others	[91]

Note: Multiple Age-Related Diseases include HCM, CAD, AD, IDCM, and T2DM; N/R = Not Reported in the provided search results.

Table 2: Transcriptomic Aging Clocks and Their Performance Characteristics

Aging Measure	Basis	Prediction Target	Performance	Validation	Key Genes/Features	Citation
Transcriptomic Mortality-risk Age (TraMA)	RNA-seq from HRS	4-year mortality hazard	C-index: 0.835 (training)	External validation in LLFS and other datasets	35 genes + age (e.g., ZNF44, CRYBG3, NOG)	[92]
Transcriptomic Age	Transcriptomic sources	Chronological Age	MAE: 4.7-7.8 years	Associated with clinical parameters (cholesterol, blood pressure)	Multiple gene expression profiles	[93]

Experimental Protocols and Workflows

Standardized Workflow for Transcriptomic Prediction Model Development

The development of robust computational prediction models follows a systematic workflow that integrates data acquisition, preprocessing, model training, and validation.

Detailed Methodologies for Key Experiments

Transcriptomic Analysis of Cytoskeletal Genes in Age-Related Diseases

This protocol outlines the comprehensive approach used to identify cytoskeletal gene biomarkers across multiple age-related diseases [85].

• Gene List Curation: Retrieve the cytoskeletal gene list from the Gene Ontology Browser (ID: GO:0005856), comprising 2,304 genes representing microfilaments, intermediate filaments, microtubules, and related structures.
• Data Acquisition: Obtain transcriptome data from public repositories (e.g., GEO). For example: HCM (GSE32453, GSE36961), CAD (GSE113079), AD (GSE5281), IDCM (GSE57338), T2DM (GSE164416).
• Data Preprocessing: Perform batch effect correction and normalization using the Limma package in R.
• Machine Learning Modeling: Train multiple classifiers (Decision Trees, Random Forest, k-NN, Gaussian Naive Bayes, SVM) using cytoskeletal gene expression values.
• Feature Selection: Apply Recursive Feature Elimination (RFE) with SVM to identify the most informative subset of cytoskeletal genes.
• Differential Expression Analysis: Identify differentially expressed cytoskeletal genes between patient and control groups.
• Model Validation: Validate final gene signatures using ROC analysis on external datasets.

ROC Analysis Implementation with pROC Package

This protocol details the technical implementation of ROC curve analysis using the specialized pROC package in R [94].

• Environment Setup: Install and load the pROC package in R (install.packages("pROC") and library(pROC)).
• Data Preparation: Ensure binary outcome variable (disease status) and predictor variable (model probabilities or biomarker measurements).
• ROC Object Creation: Use roc() function to create the ROC object (e.g., roc_obj <- roc(data$y_true, data$y_score)).
• AUC Calculation: Compute the AUC value using auc() function (e.g., auc_value <- auc(roc_obj)).
• ROC Curve Plotting: Generate the plot using plot() function or with ggplot2 for publication-quality figures.
• Confidence Intervals: Calculate 95% CIs for the AUC using ci.auc() with DeLong's method or bootstrapping.
• Statistical Comparison: Compare two correlated ROC curves using roc.test() with DeLong's test or bootstrapping.

External Validation Framework for Aging Biomarkers

This protocol outlines a comprehensive approach for external validation of predictive models in aging research [88].

• Cohort Selection: Identify independent validation cohorts with appropriate sample characteristics, ensuring they represent the target population.
• Biomarker Measurement: Apply identical laboratory and computational methods to the new cohort as used in the training set.
• Covariate Adjustment: Account for potential confounders (e.g., sex, cell type composition, technical batch effects).
• Association Testing: Evaluate the association between the biomarker and aging-related outcomes (e.g., mortality, chronic conditions, functional decline).
• Performance Assessment: Calculate performance metrics (AUC, C-index, sensitivity, specificity) in the external cohort.
• Comparison with Established Measures: Benchmark against known aging biomarkers and chronological age.

Analytical Framework for Model Benchmarking

ROC Curve Comparison Methodology

The statistical comparison of ROC curves requires specialized methods to determine if performance differences are significant.

The pROC package implements multiple statistical tests for comparing ROC curves [94]:

• Delong's Test: Uses U-statistics theory and asymptotic normality for comparing AUCs of correlated ROC curves; computationally efficient but limited to full AUC.
• Bootstrap Test: Resamples data with replacement to compute confidence intervals; more flexible and works with partial AUC (pAUC) and smoothed curves.
• Venkatraman Test: Permutation-based test that compares the entire shape of two ROC curves rather than just their AUCs.

Validation Hierarchies in Biomarker Development

Robust biomarker development requires multiple levels of validation [88]:

• Analytical Validation: Assesses accuracy, precision, sensitivity, and specificity of the measurement technique.
• Biological Validation: Evaluates how well the biomarker reflects fundamental aging biology.
• Predictive Validation: Tests performance in predicting relevant aging outcomes in independent datasets.
• Clinical Validation: Determines utility in clinical settings for improving health outcomes.

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Research Resources for Transcriptomic Prediction Studies

Resource Category	Specific Tool/Solution	Function and Application	Key Features
Bioinformatics Packages	pROC (R package) [94]	Comprehensive ROC curve analysis	Statistical comparison of ROC curves, AUC/pAUC calculation, smoothing
	Limma (R/Bioconductor) [85] [91]	Differential expression analysis	Handles complex experimental designs, batch effect correction
	sva (R/Bioconductor) [91]	Surrogate variable analysis	Batch effect correction using ComBat function
Data Resources	Gene Expression Omnibus (GEO) [85] [91]	Public repository of transcriptomic data	Source of training and validation datasets
	Gene Ontology Browser [85]	Functional annotation of genes	Curated gene sets (e.g., cytoskeletal genes GO:0005856)
Machine Learning Environments	Scikit-learn (Python)	Machine learning algorithms	Comprehensive ML library with unified interface
	Caret (R package)	Classification and regression training	Streamlines model training and hyperparameter tuning
Laboratory Technologies	RNA sequencing	Transcriptome profiling	High-throughput measurement of gene expression
	ATR-FTIR Spectroscopy [90]	Plasma spectra analysis	Generates digital biomarkers for disease detection

The cytoskeleton, a dynamic network of actin filaments, microtubules, and intermediate filaments, is fundamental to cellular homeostasis, structural integrity, intracellular transport, and signaling. Recent advances in comparative transcriptomics have revealed that alterations in cytoskeletal gene expression represent a conserved feature across vertebrate species during aging and in age-related diseases [25] [95]. Despite evolutionary divergence spanning hundreds of millions of years, vertebrates exhibit remarkable conservation in the transcriptional programs governing cytoskeletal integrity, particularly in the nervous system [96] [97]. This conservation provides a powerful framework for identifying core molecular mechanisms that underlie age-related cytoskeletal dysfunction and for developing broad therapeutic strategies applicable to multiple species and conditions.

The cytoskeleton's role extends far beyond structural support—it establishes compartment-specific functionality through post-translational modifications, coordinates organelle interactions, and regulates synaptic plasticity [25] [24]. In neurons, especially those with long axons such as motor neurons, the cytoskeleton forms the basis for axonal transport, establishes structural elements like the axon initial segment and presynaptic boutons, and regulates axon caliber, polarity, and action potential propagation [24]. Consequently, transcriptional dysregulation of cytoskeletal components disrupts these critical functions, contributing to neurodegenerative conditions including Alzheimer's disease (AD), Parkinson's disease (PD), Charcot-Marie-Tooth disease, and Hereditary Spastic Paraplegia [25] [98].

Comparative Transcriptomic Approaches to Cytoskeletal Conservation

Evolutionary Patterns of Gene Expression Conservation

Large-scale comparative transcriptomic studies across multiple vertebrate species have revealed fundamental principles governing cytoskeletal gene expression evolution. Research analyzing primary cells from human, mouse, rat, dog, and chicken demonstrates that expression levels of orthologous cytoskeletal genes are highly correlated across species, with median Pearson's correlation values ranging from 0.38 to 0.72 (P < 10⁻¹⁰⁰) [97]. This conservation is particularly strong for genes involved in fundamental cellular processes such as transcription, RNA processing, and transcriptional regulation [97].

Table 1: Evolutionary Conservation Patterns of Cytoskeletal Gene Expression Across Vertebrates

Evolutionary Feature	Conservation Pattern	Functional Implications	Representative Genes/Pathways
Evolutionary Age of Genes	Older genes show more conserved expression (Fisher combined P < 10⁻¹⁰⁰) [97]	Core cytoskeletal machinery is evolutionarily constrained	Actin, tubulin, and intermediate filament families
Cellular Function	Nuclear and transcriptional regulatory genes show higher conservation	Fundamental cellular processes require stable cytoskeletal architecture	Genes involved in transcription, RNA processing
Intercellular Communication	Signaling and plasma membrane genes show divergent expression	Species-specific adaptation in cytoskeletal signaling	Cell surface receptors, adhesion molecules
Developmental Timing	Mid-embryogenesis (phylotypic stage) shows highest expression conservation [96]	Body plan establishment constrains cytoskeletal expression	Hox genes, developmental regulators
Promoter Sequence Conservation	>80% of dominant promoters conserved in mouse, rat, dog; ~50% in chicken [97]	Regulatory element conservation maintains expression patterns	Promoter regions of cytoskeletal genes

Notably, evolutionarily ancient genes exhibit significantly more conserved expression patterns compared to recently evolved genes (Fisher combined P < 10⁻¹⁰⁰) [97]. This pattern is particularly evident for cytoskeletal genes whose products are involved in fundamental cellular processes that maintain cellular integrity across vertebrate species.

Experimental Models and Species Comparison

Vertebrate comparative transcriptomics spans multiple model organisms, each providing unique insights into cytoskeletal alterations:

Mammalian Models: Human, mouse, rat, and dog primary cells demonstrate high correlation in cytoskeletal gene expression (median Pearson correlation: ~0.72 between human and dog) [97]. This conservation enables translational approaches where rodent models can effectively inform human biology.

Avian Models: Chicken models show somewhat lower but still significant conservation (correlation ~0.38 with human), reflecting greater evolutionary distance while maintaining core cytoskeletal programs [97].

Teleost and Amphibian Models: Studies in rainbow trout (Oncorhynchus mykiss) and clawed toad (Xenopus laevis) have identified conserved cytoskeletal gene expression patterns during oocyte maturation, suggesting deep evolutionary conservation of certain cytoskeletal regulatory mechanisms [99].

Conserved Cytoskeletal Alterations in Age-Related Diseases

Neurodegenerative Diseases

Comparative transcriptomic analyses have identified conserved cytoskeletal alterations across multiple neurodegenerative conditions:

Table 2: Conserved Cytoskeletal Gene Alterations in Age-Related Diseases

Disease Category	Specific Condition	Conserved Cytoskeletal Alterations	Vertebrate Conservation Evidence
Neurodegenerative	Alzheimer's Disease (AD)	Microtubule destabilization, tau pathology, actin dysregulation [25]	Observed in human, mouse models; conserved transcriptional responses
Neurodegenerative	Parkinson's Disease (PD)	Actin expression changes, oxidative damage to cytoskeleton [100]	Network analysis across human brain tissues shows age-dependent expression
Cardiovascular	Hypertrophic Cardiomyopathy (HCM)	Sarcomeric protein mutations, altered β-actin expression [2]	Machine learning identifies conserved gene signatures across species
Cardiovascular	Coronary Artery Disease (CAD)	Cytoskeletal assembly regulation genes (SPTBN1, ADAMTS7) [2]	Computational framework identifies cross-species cytoskeletal markers
Metabolic	Type 2 Diabetes (T2DM)	Altered expression of Z-disk components, actin capping proteins [2]	Cytoskeletal gene signatures conserved in human and model organisms
Multiple Conditions	General Aging	Profilin 1 decrease, increased cytoskeleton stiffness, transport deficits [8] [95]	Observed in human microglia, mouse models, yeast studies

Alzheimer's Disease: Conserved alterations include tubulin post-translational modification changes, tau phosphorylation patterns, and neurofilament reorganization [25]. These changes disrupt microtubule stability, impair axonal transport, and contribute to synaptic failure across multiple vertebrate models. The ERK/NF-κB signaling axis has been identified as a conserved pathway linking cytoskeletal collapse to neuroinflammation in AD models [8].

Parkinson's Disease: Network-based analyses of aging brain tissues have identified six genes with age-dependent expression variations across four brain regions, suggesting their potential involvement in PD progression through cytoskeletal mechanisms [98]. Actin cytoskeleton alterations have been specifically implicated in life span determination and age-associated vulnerability in PD models [100].

Cardiovascular and Metabolic Diseases

Hypertrophic Cardiomyopathy (HCM) and Idiopathic Dilated Cardiomyopathy (IDCM): Computational frameworks identifying cytoskeletal genes associated with age-related diseases have revealed conserved alterations in sarcomeric and cytoskeletal proteins across species [2]. Mutations in nine genes encoding sarcomere or sarcomere-associated proteins have been linked to HCM in comparative studies, including beta myosin heavy chain, troponins, and actin [2].

Type 2 Diabetes Mellitus (T2DM): Patients significantly alter expression of proteins involved in cytoskeletal structure, including Z-disk component alpha actinin-2 and actin capping proteins [2]. These alterations appear conserved across vertebrate models and contribute to insulin resistance mechanisms.

Methodological Approaches in Comparative Transcriptomics

Experimental Workflows and Computational Frameworks

Figure 1: Comparative Transcriptomics Workflow for Cytoskeletal Alterations

Recent studies have employed sophisticated computational frameworks combining machine learning with differential expression analysis to identify cytoskeletal genes associated with age-related diseases across species [2]. Support Vector Machine (SVM) classifiers have demonstrated the highest accuracy in classifying disease states based on cytoskeletal gene expression patterns, achieving high performance metrics across multiple vertebrate datasets [2].

The recursive feature elimination (RFE) technique has identified small subsets of cytoskeletal genes that effectively discriminate between patients and normal samples across species. This approach has highlighted 17 genes involved in cytoskeleton structure and regulation associated with age-related diseases, with high positive predictive values across conditions [2].

Multi-Omics Integration and Validation

Advanced integrative approaches combine transcriptomic data with proteomic and phosphoproteomic analyses, intravital two-photon imaging, patch-clamp electrophysiology, and behavioral assessments [8]. This multi-omics framework enables comprehensive determination of how cytoskeletal deficiencies reshape cellular function, perturb tissue environments, and impact organism-level behavior across species.

Table 3: Key Experimental Protocols in Comparative Cytoskeletal Transcriptomics

Method Category	Specific Protocol	Key Applications	Technical Considerations
Transcript Profiling	RNA-seq (bulk and single-cell)	Genome-wide expression quantification across species	Ortholog mapping, normalization across species
Computational Analysis	Recursive Feature Elimination with SVM	Identifying minimal cytoskeletal gene signatures	Handles high-dimensional data, identifies non-linear patterns
Network Analysis	Protein-protein interaction networks	Contextualizing cytoskeletal alterations in pathways	Species-specific network databases required
Validation Methods	Intravital two-photon imaging	Real-time cytoskeletal dynamics in live organisms	Limited to translucent tissues or surface structures
Validation Methods	Structured-illumination super-resolution microscopy	Nanoscale cytoskeletal architecture	Requires specialized equipment, complex sample prep
Multi-omics Integration	Proteomic and phosphoproteomic analysis	Post-translational modification profiling	Complementary to transcriptomic data

The Scientist's Toolkit: Research Reagent Solutions

Table 4: Essential Research Reagents for Comparative Cytoskeletal Transcriptomics

Reagent Category	Specific Examples	Research Application	Species Compatibility
Cytoskeletal Markers	Antibodies against acetylated tubulin, profilin 1, cofilin	Visualizing cytoskeletal organization and PTMs	Varies by antibody; cross-reactivity testing needed
Transcriptomic Tools	CAGE technology, single-cell RNA-seq kits	Genome-wide expression profiling	Species-specific probe design for non-model organisms
Computational Tools	DESeq2, Limma, SVM classifiers	Differential expression analysis	Requires orthology mapping across species
Animal Models	Cx3cr1CreERT2 mice, floxed Pfn1 alleles	Conditional knockout studies	Mouse-specific, principles translatable to other vertebrates
Live Imaging Reagents	EYFP viral vectors, actin-binding peptide tags	Real-time cytoskeletal dynamics monitoring	Optimization needed for different species
Pathway Modulators	Tubulin stabilizers (epothilones), ROCK inhibitors	Functional validation of transcriptomic findings	Concentration optimization across species

Conserved Signaling Pathways in Cytoskeletal Alterations

Figure 2: Conserved Cytoskeletal Alteration Pathways in Vertebrate Aging

The conserved pathways linking cytoskeletal alterations to functional decline involve several key mechanisms:

Pfn1-Cytoskeleton Axis: Profilin 1 (Pfn1) emerges as a critical checkpoint against microglial senescence, with its decreased expression sufficient to drive circuit-specific synaptic decline [8]. Pfn1 ablation disrupts actin-microtubule coupling, leading to collapse of cellular morphodynamics and complete failure to respond to injury across multiple vertebrate models.

Actin-Microtubule Crosstalk: The coordinated interaction between actin filaments and microtubules is essential for cellular integrity, intracellular trafficking, and response capability [8] [95]. Age-related disruptions to this crosstalk represent a conserved mechanism contributing to functional decline across vertebrate species.

Post-Translational Modification Code: Tubulin polyglutamylation, acetylation, tyrosination/detyrosination, and other post-translational modifications establish a "code" that endows the cytoskeletal scaffold with specific functionality [25] [24]. This code is differentially regulated during aging and represents a conserved mechanism for fine-tuning cytoskeletal function across vertebrate species.

Comparative transcriptomics has revealed profound conservation in cytoskeletal alterations across vertebrate species during aging and in age-related diseases. These conserved changes provide powerful insights into fundamental mechanisms of cellular aging and identify key regulatory nodes that may be targeted for therapeutic intervention. The cytoskeleton emerges not merely as a structural element but as a dynamic, integrative system whose dysfunction propagates across cellular, tissue, and organismal levels in conserved patterns.

Future research directions should include expanded comparative analyses across broader evolutionary timescales, integration of single-cell transcriptomic approaches to resolve cell-type-specific cytoskeletal alterations, and development of chemical biology tools targeting conserved cytoskeletal regulators. The demonstrated conservation validates the use of model organisms for understanding human cytoskeletal aging while highlighting subtle species-specific adaptations that may inform therapeutic development. Maintaining cytoskeletal integrity represents a promising avenue for promoting healthy aging across vertebrate species, with potential applications spanning neurodegenerative diseases, cardiovascular conditions, and metabolic disorders.

The cytoskeleton, a dynamic network of filamentous proteins, is fundamental to cellular integrity, shape, and intracellular transport. Recent research underscores that transcriptional dysregulation of cytoskeletal genes is a significant contributor to the pathology of various age-related diseases [2]. This guide systematically compares disease-specific and common cytoskeletal gene signatures across five age-related conditions: Alzheimer's Disease (AD), Coronary Artery Disease (CAD), Type 2 Diabetes Mellitus (T2DM), Hypertrophic Cardiomyopathy (HCM), and Idiopathic Dilated Cardiomyopathy (IDCM). The identification of both shared and unique molecular players provides a powerful framework for understanding common pathological pathways and developing targeted therapeutic strategies, positioning cytoskeletal integrity as a central theme in aging research [2] [3].

Key Cytoskeletal Gene Signatures: A Comparative Analysis

Disease-Specific Cytoskeletal Gene Profiles

Advanced computational studies employing machine learning have identified distinct cytoskeletal genes associated with specific age-related diseases. These genes demonstrate significant discriminatory power in classifying disease versus control samples and are often differentially expressed, highlighting their potential as biomarkers or therapeutic targets [2].

Table 1: Disease-Specific Cytoskeletal Gene Identifiers

Disease	Gene Symbols
Alzheimer's Disease (AD)	ENC1, NEFM, ITPKB, PCP4, CALB1
Coronary Artery Disease (CAD)	CSNK1A1, AKAP5, TOPORS, ACTBL2, FNTA
Type 2 Diabetes Mellitus (T2DM)	ALDOB
Hypertrophic Cardiomyopathy (HCM)	ARPC3, CDC42EP4, LRRC49, MYH6
Idiopathic Dilated Cardiomyopathy (IDCM)	MNS1, MYOT

Overlapping Cytoskeletal Gene Signatures

While each disease exhibits a unique transcriptional footprint, analysis reveals several cytoskeletal genes that are shared across multiple conditions, suggesting common mechanisms of pathological aging [2]. The interplay between these shared and unique genes forms a complex network of dysregulation.

Table 2: Overlapping Cytoskeletal Genes Across Age-Related Diseases

Gene Symbol	Associated Diseases
ANXA2	AD, IDCM, T2DM
TPM3	AD, CAD, T2DM
SPTBN1	AD, CAD, HCM
MAP1B, RRAGD, RPS3	AD, T2DM
JAKMIP1, ABLIM3, PDE4B	AD, CAD

The ANXA2 gene, for instance, is implicated in three distinct conditions: AD, IDCM, and T2DM. Similarly, TPM3 (Tropomyosin 3) is shared across AD, CAD, and T2DM, pointing to a fundamental role in diverse age-related pathologies spanning neurological, cardiovascular, and metabolic systems [2]. Furthermore, studies have identified as many as 20 overlapping cytoskeletal genes between AD and IDCM, indicating a potentially strong link between neurodegenerative and specific cardiomyopathic processes at the molecular level [2].

Experimental Protocols for Identifying Signatures

The discovery of these gene signatures relies on robust bioinformatics workflows that integrate gene expression data with advanced computational models.

Integrated Workflow for Gene Signature Identification

The following diagram illustrates the comprehensive analytical pipeline used to identify and validate cytoskeletal gene signatures from transcriptomic data [2].

Detailed Methodological Breakdown

• Gene Set Definition: The initial step involves compiling a comprehensive set of cytoskeletal genes. This is typically done using the Gene Ontology Browser (ID: GO:0005856), which includes genes for microfilaments, intermediate filaments, microtubules, and related polymeric structures. One study utilized a set of 2,304 such genes [2].
• Transcriptomic Data Processing: Publicly available transcriptome data for the diseases of interest are retrieved. Raw data undergoes batch effect correction and normalization using packages like Limma in R to ensure comparability across datasets [2].
• Differential Expression Analysis (DEA): This step identifies genes with statistically significant expression changes between patient and normal samples. Tools such as DESeq2 (for RNA-seq data) or the Limma package (for microarray data) are employed with adjusted p-value thresholds (e.g., < 0.05) to define differentially expressed genes (DEGs) [2].
• Machine Learning Classification: Multiple machine learning algorithms—including Support Vector Machines (SVM), Random Forest (RF), and k-Nearest Neighbors (k-NN)—are trained using the expression profiles of cytoskeletal genes to classify disease status. The SVM classifier has been reported to achieve the highest accuracy for this task [2].
• Feature Selection with Recursive Feature Elimination (RFE): RFE is a wrapper-type feature selection method used alongside the classifier (e.g., SVM-RFE) to identify the minimal set of most discriminative cytoskeletal genes. It recursively removes the least important features and rebuilds the model, selecting the subset that delivers optimal predictive performance [2].
• Identification of Candidate Genes: The final candidate genes are determined by finding the overlap between the genes selected by RFE and those identified as differentially expressed in the DEA. This dual-criterion approach increases confidence in the biological and diagnostic relevance of the candidates [2].
• Validation: The performance of the identified gene signatures is validated using Receiver Operating Characteristic (ROC) analysis on independent external datasets to assess generalizability and predictive power [2].

Signaling Pathways and Biological Themes

The identified cytoskeletal genes are not isolated entities but function within complex, interrelated biological pathways. Visualization of these pathways helps unravel the main biological themes affected in age-related diseases.

Cytoskeletal Dysregulation Network in Aging

The network below summarizes the core cytoskeletal components and the key biological processes they regulate, which are commonly disrupted across the age-related diseases AD, CAD, T2DM, HCM, and IDCM [2] [3].

This network highlights how disruptions in fundamental cytoskeletal components and their functions converge into major age-related disease phenotypes. For example, defects in microtubules can lead to impaired axonal transport, a driver of neurodegeneration in AD, while alterations in microfilaments (actin) and associated proteins can compromise cell shape and integrity, contributing to the structural pathology seen in cardiomyopathies [2] [3].

Successful transcriptomic analysis of cytoskeletal alterations in aging requires a curated set of bioinformatics tools, databases, and analytical packages.

Table 3: Research Reagent Solutions for Transcriptomic Analysis

Tool/Resource	Type	Primary Function in Analysis	Key Application
Limma / DESeq2	R Package	Differential Expression Analysis	Identify genes with significant expression changes between conditions [2].
SVM-RFE	Algorithm	Feature Selection	Select minimal, most discriminative gene features from high-dimensional data [2].
g:Profiler	Web Tool / R Pkg	Over-Representation Analysis (ORA)	Functional enrichment of gene lists using GO, KEGG, etc. [101].
Gene Set Enrichment Analysis (GSEA)	Java Software / R Pkg	Functional Class Scoring (FCS)	Detect coordinated expression changes in pre-defined gene sets without arbitrary thresholds [102].
clusterProfiler	R Package	Enrichment Analysis & Visualization	Perform and visualize ORA and GSEA in an integrated environment [103].
Cytoscape & EnrichmentMap	Software	Pathway Visualization	Create network visualizations of enriched pathways and their relationships [101].
ShinyGO	Web Application	Interactive Enrichment Analysis	User-friendly GUI for enrichment analysis and result interpretation [104].
STRING-db	Database	Protein-Protein Interactions	Query functional associations and networks for candidate genes [104].
Gene Ontology (GO)	Knowledge Base	Functional Annotations	Provides curated gene sets for Biological Process, Molecular Function, Cellular Component [105].
MSigDB	Gene Set Database	Curated Gene Sets	Collection of annotated gene sets for GSEA and related methods [101].

The comparative analysis of cytoskeletal gene signatures reveals a complex landscape of both disease-specific and shared molecular alterations in age-related conditions. Genes like ARPC3 in HCM and ENC1 in AD offer promising disease-specific targets, while overlapping genes such as ANXA2 and TPM3 suggest the existence of common, dysregulated pathways that could be targeted for broader therapeutic interventions. The application of standardized computational protocols—integrating differential expression, machine learning, and functional enrichment analysis—is crucial for the robust identification and validation of these signatures. This integrated approach provides a powerful strategy for pinpointing biomarkers and drug targets, ultimately advancing the goal of promoting healthy aging and combating multiple age-related diseases.

The cytoskeleton, a dynamic network of filamentous proteins, is fundamental to cellular integrity, shape, and intracellular transport. Its deterioration is a hallmark of aging and is implicated in a spectrum of age-related diseases [2] [3]. Historically, aging research has often treated sex as a confounding variable. However, emerging transcriptomic analyses reveal that sex dimorphism is a critical factor in the rate and nature of molecular aging, challenging the one-size-fits-all model [62] [106]. This guide objectively compares the experimental data validating sex-specific cytoskeletal aging patterns in human tissues, providing a foundational resource for developing targeted therapeutic interventions.

Mounting evidence from pan-tissue studies indicates that males experience an earlier onset and a faster rate of transcriptomic aging compared to females, a phenomenon potentially linked to the decline of sex hormones [62]. This review synthesizes findings from diverse tissues—including skeletal muscle, peripheral sensory neurons, and thyroid gland—to confirm that while the broad processes of cytoskeletal aging occur in both sexes, their magnitude, timing, and molecular drivers exhibit significant sex-based differences [3] [106].

Comparative Analysis of Sex-Dimorphic Cytoskeletal Aging Phenotypes

The following table summarizes key experimental findings on sex-specific cytoskeletal aging across different human tissues and analysis methods.

Table 1: Experimental Evidence of Sex-Dimorphic Cytoskeletal Aging Patterns

Tissue / System	Key Sex-Dimorphic Finding	Experimental Approach	Implicated Cytoskeletal Components
Peripheral Sensory Axons [3]	Cytoskeletal components (neurofilaments, microtubules, actin) increase with aging in both sexes, but an increase in axon diameter is only evident in males.	Quantitative immunofluorescence on human skin biopsies; Transcriptomic analysis.	Neurofilaments (NfL, NfM, NfH), Tubulin (TUBB3), Actin
Skeletal Muscle [106]	Genes regulate in the same direction in both sexes, but the magnitude is sex-specific. Oxidative phosphorylation is the top-ranked altered process in aged males, while AKT-mediated cell growth is top-ranked in females.	RNA-seq from vastus lateralis muscle; Immunohistochemistry; Validation with public datasets.	Myosin heavy chains, structural proteins related to fiber size
Pan-Tissue Analysis [62]	Male-biased, age-associated alternative splicing (AS) events show a stronger association with Alzheimer's disease. Widespread sex-dimorphic aging with males showing larger, earlier transcriptome changes.	RNA-seq analysis of ~17,000 transcriptomes from 35 GTEx tissues; Principal component-based signal-to-variation ratio (pcSVR).	Genes undergoing alternative splicing, regulated by sex-biased splicing factors
Thyroid Gland [4]	Actin and other cytoskeletal proteins are enriched among aging-correlated genes, associated with pathways like hypertrophic cardiomyopathy.	RNA-seq data analysis from TCGA and GTEx; Spearman correlation with age; Pathway enrichment (PANTHER, KEGG).	Actin, cardiac/skeletal muscle contraction proteins

Detailed Experimental Protocols for Validating Sex-Dimorphic Aging

To ensure reproducibility and facilitate future research, this section outlines the core methodologies used in the cited studies to identify and validate sex-specific cytoskeletal aging patterns.

Protocol 1: Transcriptomic Profiling and Alternative Splicing Analysis

This protocol is adapted from the pan-tissue study that established the foundational evidence for widespread sex-dimorphic aging [62].

• 1. Sample Acquisition and Preparation:

  • Source: Utilize a large-scale, multi-tissue resource such as the Genotype-Tissue Expression (GTEx) project, which provides postmortem donor data across numerous tissues with a wide age range (e.g., 20-70 years) [62] [64].
  • RNA Sequencing: Extract total RNA and prepare strand-specific RNA-seq libraries (e.g., using NEBNext Ultra Directional RNA Library Prep Kit). Sequence on a high-throughput platform like Illumina NextSeq500 to a depth of at least 15 million reads per sample [106].

• 2. Data Processing and Quantification:

  • Quality Control: Assess raw read quality using FastQC. Trim adapters and low-quality bases with tools like Trimmomatic [64].
  • Alignment and Quantification: Map quality-trimmed reads to the human reference genome (e.g., GRCh38) using a splice-aware aligner such as STAR. Generate read counts per gene transcript using software like htseq-count [106].
  • Alternative Splicing (AS) Analysis: Identify and quantify AS events (e.g., skipped exons, retained introns) from the aligned RNA-seq data using specialized tools.

• 3. Statistical and Bioinformatic Analysis:

  • Global Variation Measurement: Apply a method like principal component-based signal-to-variation ratio (pcSVR) to quantify the relative effects of sex and age on global transcriptomic variation (both gene expression and AS) across tissues [62].
  • Differential Analysis: Identify age-associated genes and AS events in a sex-specific manner. Compare young (e.g., age <40) and old (e.g., age >60) groups separately for males and females to uncover sex-biased aging signatures [62].
  • Pathway and Disease Association: Perform gene set enrichment analysis (GSEA) on sex-biased age-associated genes. Test for over-representation of specific pathways (e.g., KEGG, Reactome) and associations with age-related diseases like Alzheimer's [62] [2].

Protocol 2: Quantitative Cytoskeletal Analysis in Human Sensory Axons

This protocol details the histological and imaging approach used to directly quantify cytoskeletal changes in a sex-stratified cohort [3].

• 1. Human Subject and Biopsy Collection:

  • Cohort: Recruit healthy participants across a wide age range (e.g., 23-79 years), with age-matched males and females. Exclusion criteria should include metabolic diseases, neuromuscular disorders, and tumors [3].
  • Sample Collection: Obtain 3 mm punch skin biopsies from proximal (thigh) and distal (ankle) sites under local anesthesia.

• 2. Tissue Processing and Immunostaining:

  • Fixation and Sectioning: Fix biopsies in Zamboni's solution, incubate in sucrose for cryoprotection, and freeze. Section tissues at a 50 μm thickness using a cryostat [3].
  • Immunofluorescence: Stain free-floating sections with primary antibodies against key cytoskeletal subunits:
    • Neurofilaments: Anti-NfL, NfM, NfH
    • Microtubules: Anti-TUBB3 (neuron-specific β-III tubulin)
    • Actin: Can be visualized with phalloidin conjugates
  • Use appropriate fluorescently-labeled secondary antibodies and counterstain with DAPI.

• 3. Image Acquisition and Quantification:

  • Imaging: Acquire high-resolution images of immunostained axons using a fluorescence or confocal microscope under standardized settings.
  • Cytoskeletal Content: Measure the mean fluorescence intensity for each cytoskeletal marker as a proxy for protein content. Normalize values to control for background and staining variations.
  • Axon Morphometry: Measure axon diameters from images stained with a pan-axonal marker (e.g., anti-protein gene product 9.5). Analyze data by segregating cohorts based on sex and age groups [3].

Visualization of Research Workflows and Pathways

The following diagrams illustrate the core experimental and analytical pathways used to investigate sex-dimorphic cytoskeletal aging.

Transcriptomic Analysis Workflow

Key Pathways in Sex-Dimorphic Cytoskeletal Aging

The Scientist's Toolkit: Essential Research Reagents

This table catalogs key reagents and resources essential for conducting research in sex-dimorphic cytoskeletal aging.

Table 2: Key Research Reagent Solutions for Cytoskeletal Aging Studies

Reagent / Resource	Function / Application	Specific Examples / Targets
Anti-Cytoskeletal Antibodies	Visualizing and quantifying cytoskeletal protein levels and localization in tissues via immunofluorescence/ICH.	Anti-TUBB3 (microtubules), Anti-NfL/H/M (neurofilaments), Anti-MYH7 (skeletal muscle), Phalloidin (F-actin) [3] [106].
RNA Sequencing Kits	Preparing high-quality RNA-seq libraries for transcriptomic and alternative splicing analysis.	NEBNext Ultra Directional RNA Library Prep Kit for Illumina [106].
Flow Cytometry Antibodies	Isolating specific progenitor cell populations from heterogeneous samples for downstream omics.	CD45-FITC, CD34-APC, ALP-BV421 for Circulating Osteoprogenitors (COP cells) [107].
Bioinformatics Pipelines	Analyzing RNA-seq data for differential expression, alternative splicing, and pathway enrichment.	STAR-aligner for read alignment, htseq-count for quantification, PCA/pcSVR for global variation [62] [106].
Public Datasets	Accessing large-scale, sex-stratified molecular data for discovery and validation.	Genotype-Tissue Expression (GTEx) Project, The Cancer Genome Atlas (TCGA) [62] [4].

The cytoskeleton, a dynamic network of protein filaments, is fundamental to cellular homeostasis, polarity, and transport. In neurons, it is particularly crucial for maintaining long axonal processes and enabling synaptic transmission. Recent research underscores that transcriptional dysregulation of cytoskeletal genes is a hallmark of the aging brain and contributes significantly to neurodegenerative pathologies [24]. Single-cell transcriptomic studies of the human prefrontal cortex across the lifespan reveal a consistent age-associated downregulation of essential cytoskeletal genes, including TUBA1A, TUBB3, and TUBB, across multiple brain cell types [7]. This transcriptional decay of the cytoskeletal machinery compromises cellular integrity and function, laying the groundwork for disease. Consequently, identifying and prioritizing which of these dysregulated genes represent the most viable therapeutic targets is a critical challenge in translational research. This guide provides a structured framework for this process, comparing modern computational prioritization frameworks against traditional methods to help researchers navigate the path from transcriptomic discovery to therapeutic candidate.

A Comparison of Gene Prioritization Frameworks

The transition from a list of differentially expressed genes to a shortlist of high-confidence therapeutic targets requires robust computational frameworks. We compare two modern approaches—the integrated GETgene-AI framework and a specialized machine learning model—against traditional, single-metric methods.

Table 1: Comparison of Gene Prioritization Frameworks for Target Discovery

Framework Name	Core Methodology	Key Input Data	Advantages	Limitations
GETgene-AI [108]	Integrates network-based prioritization (BEERE) with AI-driven literature review (GPT-4o).	G List (mutational frequency), E List (differential expression), T List (known drug targets).	Mitigates false positives; provides direction of therapeutic modulation; superior precision/recall.	Complex setup; requires integration of multiple data streams.
SVM Classifier [39]	Support Vector Machine (SVM) model trained on transcriptomic data.	Differential expression data from disease vs. normal tissues.	High accuracy in identifying cytoskeletal biomarkers for specific age-related diseases.	Disease-specific; may not generalize to other pathological contexts.
Traditional GEO2R [108]	Differential expression analysis based on fold-change ranking.	Gene expression data from two conditions (e.g., tumor vs. healthy).	Simple, fast, and widely accessible.	Prone to bias from arbitrary thresholds; high false-positive rate; lacks biological context.
Frequency-Based Prioritization [108]	Ranks genes based on mutational frequency in disease cohorts.	Genomic data from databases like TCGA, COSMIC.	Identifies commonly altered genes in a population.	Susceptible to sample selection bias; does not address functional impact.

Key Framework Insights

• GETgene-AI: This framework employs the G.E.T. strategy, which synergizes genetic (G), expression (E), and target knowledge (T) lists. Its core differentiator is the Biological Entity Expansion and Ranking Engine (BEERE), which uses protein-protein interaction networks to refine candidate lists. Furthermore, its integration of GPT-4o automates the extraction of mechanistic and translational evidence from vast scientific literature, significantly accelerating the validation process [108].
• Specialized Machine Learning: As demonstrated in a study focusing on cytoskeletal genes, a Support Vector Machine (SVM) classifier achieved the highest accuracy in pinpointing 17 cytoskeletal genes as potential biomarkers for age-related diseases like Alzheimer's and hypertrophic cardiomyopathy [39]. This approach is powerful for focused, hypothesis-driven validation within a predefined gene set.
• Limitations of Traditional Methods: Tools like GEO2R rely heavily on fold-change thresholds, whose arbitrary selection introduces variability and false positives [108]. Similarly, frequency-based methods prioritize commonly mutated genes but may overlook critical, context-specific drivers with lower mutation rates.

Experimental Protocols for Validation of Candidate Genes

Once a prioritization framework has identified a candidate gene, a series of experimental validations are essential to confirm its pathological role and therapeutic potential. The following protocols detail key steps from single-cell resolution to functional assessment.

Protocol 1: Single-Cell Transcriptomic Profiling of the Aging Brain

This protocol is adapted from a 2025 Nature study that defined transcriptomic and genomic changes in the human prefrontal cortex from infancy to old age [7].

• Objective: To identify cell-type-specific transcriptional changes, particularly in cytoskeletal genes, during normal and pathological aging.
• Experimental Workflow:
  • Tissue Acquisition & Nuclei Isolation: Obtain fresh-frozen human post-mortem prefrontal cortex (PFC) samples from neurotypical donors across a wide age range. Isolate nuclei from frozen tissue.
  • Library Preparation & Sequencing: Perform droplet-based single-nucleus RNA sequencing (snRNA-seq) on the isolated nuclei. In parallel, conduct single-cell whole-genome sequencing (scWGS) on a subset to identify somatic mutations.
  • Orthogonal Validation with Spatial Transcriptomics: Validate snRNA-seq findings using Multiplexed Error-Robust Fluorescence In Situ Hybridization (MERFISH) on tissue sections from a subset of donors to confirm laminar positioning and gene expression.
  • Computational Analysis:
    • Quality Control & Clustering: Filter artifacts and perform dimensionality reduction to identify cell clusters. Annotate clusters using canonical marker genes (e.g., CUX2 for L2/3 neurons, RORB for L4 neurons).
    • Differential Expression: Compare gene expression between age groups (e.g., elderly vs. adult) within each cell type. Focus on terms like "cytoskeleton," "intracellular transport," and "microtubule."
    • Somatic Mutation Analysis: Correlate mutational signatures from scWGS with transcriptional changes in the aged brain.

Protocol 2: Functional Validation of a Cytoskeletal GeneIn Vitro

This protocol provides a roadmap for initial functional validation of a prioritized cytoskeletal gene, such as TUBA1A or TUBB3, which show age-associated downregulation [7] [39].

• Objective: To determine the functional consequence of modulating the expression of a candidate cytoskeletal gene in a relevant cell model.
• Experimental Workflow:
  • Cell Model Selection: Choose an appropriate model, such as human induced pluripotent stem cell (iPSC)-derived neurons or a neuronal cell line (e.g., SH-SY5Y).
  • Gene Modulation:
    • Knockdown: Use siRNA or shRNA to reduce candidate gene expression.
    • Overexpression: Use a plasmid vector to overexpress the wild-type candidate gene.
  • Phenotypic Assays:
    • Morphology Analysis: Image neurons and quantify changes in neurite outgrowth, branching complexity, and growth cone morphology using software like ImageJ.
    • Axonal Transport Assay: Transfert a fluorescently-labeled cargo (e.g., APP-GFP) and use live-cell imaging to track vesicle motility along axons.
    • Cell Viability & Health: Measure apoptosis markers (e.g., caspase-3/7 activity) and mitochondrial health (e.g., TMRE staining) following gene perturbation.
  • Biochemical Validation: Perform Western blotting to confirm changes in target protein levels and assess the impact on broader cytoskeletal networks (e.g., other tubulin isoforms, actin).

The logical flow from computational prioritization to experimental validation, along with the key analytical tools involved, can be visualized below.

The Scientist's Toolkit: Essential Reagents and Computational Tools

Successful execution of the described protocols relies on a suite of specific reagents, datasets, and software tools.

Table 2: Key Research Reagent Solutions for Gene Prioritization and Validation

Category	Item / Tool Name	Function in Workflow	Key Features / Notes
Computational Tools	GETgene-AI [108]	Prioritizes actionable drug targets by integrating genetic, expression, and target data.	Uses BEERE for network ranking and GPT-4o for literature review.
	ReDeconv [109]	Improves bulk & single-cell RNA-seq analysis.	Accounts for transcriptome size differences for more accurate deconvolution.
	Proseg [110]	Segments cells in spatial transcriptomics data.	Uses RNA expression to define cell boundaries, improving accuracy over antibody-based methods.
	BBrowserX, Nygen [111]	Platforms for scRNA-seq data analysis.	Offer AI-powered cell annotation, no-code interfaces, and multi-omics integration.
Datasets & Repositories	GTEx, Gene4PD [98]	Provide baseline gene expression data across tissues and age groups.	Essential for understanding normal expression patterns and age-related shifts.
	eQTL Datasets (e.g., MetaBrain, cell-type-specific) [112]	Link genetic variants to gene expression.	Critical for interpreting GWAS hits and identifying candidate causal genes in Alzheimer's.
Experimental Reagents	siRNA/shRNA for Gene Knockdown	Functional validation of candidate genes in cellular models.	Allows assessment of loss-of-function phenotypes.
	iPSC-derived Neurons	Biologically relevant model system for neurobiology and aging.	Recapitulates key aspects of human neuronal physiology.

Pathway to Therapeutic Modulation

Validating a gene's role in disease is a prerequisite to designing a therapeutic strategy. A critical next step is determining the direction of effect (DOE)—whether to activate or inhibit the target for therapeutic benefit [113]. Genetic evidence is a powerful guide for this; for instance, if a cytoskeletal gene is downregulated in aging and its loss of function is deleterious, a therapeutic strategy aimed at restoring its activity (activation) would be logical.

Advanced computational models now predict DOE-specific druggability by leveraging genetic features, gene embeddings, and protein sequences. These models have revealed that activator and inhibitor drug targets have distinct characteristics. For example, inhibitor targets tend to be more evolutionarily constrained against loss-of-function variants, which is counter-intuitive but can be explained by their essential roles in cell viability or their involvement in gain-of-function disease mechanisms [113]. Integrating this DOE analysis ensures that the tremendous effort of functional validation culminates in a rationally designed therapeutic hypothesis.

The relationship between genetic evidence, disease mechanism, and the resulting therapeutic strategy is summarized in the following pathway.

The journey from transcriptomic discovery to a viable therapeutic target is complex. The aging brain presents a clear pattern of cytoskeletal gene dysregulation, creating a pool of potential candidates. The frameworks and protocols detailed herein demonstrate that moving beyond simple fold-change metrics to integrated, AI-powered approaches like GETgene-AI significantly improves the precision of target prioritization. Coupling this with robust single-cell transcriptomic profiling and focused functional validation in physiologically relevant models creates a reliable pipeline for identifying the most promising genes. Ultimately, incorporating direction-of-effect analysis early in this pipeline ensures that functional validation efforts are aligned with a rational therapeutic strategy, de-risking the long and challenging path of drug development for age-related neurodegenerative diseases.

Conclusion

Transcriptomic analyses have unequivocally established cytoskeletal alterations as a central pillar of the aging process and the pathogenesis of major age-related diseases. The integration of foundational biology with advanced computational methods, particularly machine learning, has enabled the identification of conserved and disease-specific cytoskeletal gene signatures with strong potential as biomarkers and therapeutic targets. Future research must prioritize the functional validation of these computational predictions, deepen the understanding of sex-dimorphic aging mechanisms, and develop targeted interventions that can modulate the cytoskeleton to promote healthy aging. The continued refinement of multi-omics integration and single-cell transcriptomics will further illuminate cell-type-specific cytoskeletal dynamics, ultimately accelerating the translation of these findings into clinical applications that can mitigate the burden of age-related diseases.

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