Advancing biological understanding of cellular senescence with multi-omics

Abstract

Cellular senescence is a complex biological process that plays a pathophysiological role in aging and age-related diseases. The biological understanding of senescence at the cellular and tissue levels remains incomplete due to the lack of specific biomarkers, as well as the relative rarity of senescent cells, their phenotypic heterogeneity and dynamic features. This review provides a comprehensive overview of multi-omics approaches for the characterization and biological understanding of cellular senescence. The technical capability and challenges of each approach are discussed, and practical guidelines are provided for selecting tools for identifying, characterizing and spatially mapping senescent cells. The importance of computational analyses in multi-omics research, including senescent cell identification, signature detection, and interactions of senescent cells with microenvironments, is highlighted. Moreover, tissue-specific case studies and experimental design considerations for individual organs are presented. Finally, future directions and the potential impact of multi-omics approaches on the biological understanding of cellular senescence are discussed.

Cellular senescence is a stable cell fate marked by essentially irreversible cell cycle arrest, apoptosis resistance, and widespread remodeling of the secretory and metabolic landscape. A hallmark feature of this state is the senescence associated secretory phenotype (SASP)¹, leading to the release of heterogeneous signaling moieties that are either cell-free (i.e., secretome) and/or within extracellular vesicles (EVs) that can remodel tissue architecture, serve as biomarkers and influence systematic physiology^2–6. Senescent cells accumulate with age, partly due to declining immune-mediated clearance⁷. Yet, their frequency and impact vary across tissues and individuals^8,9, and several modifiable factors can influence them, e.g., exercise and diet. Although traditionally viewed as post-mitotic, senescent cells—particularly persistent ones—can re-enter the cell cycle upon acquiring oncogenic mutations¹⁰. Recent evidence suggests that such mutation-harboring transiently senescent cells may serve as a source of both primary and recurrent cancers. Preclinical studies demonstrate that genetic and pharmacological ablation of senescent cells can extend the healthspan and/or lifespan¹¹, underscoring their therapeutic implications.

Despite this promise, identifying senescent cells in vivo remains a central challenge. While senescent cells share a non-proliferative capacity, they can be triggered by a variety of intrinsic and extrinsic stressors, including telomere attrition, DNA damage, oncogene activation, and inflammatory signaling¹². As a result, their phenotypes are highly heterogeneous and context-dependent—varying across tissues, cell types, and time since senescence initiation. Beyond cell cycle arrest and SASP expression, senescent cells may also exhibit persistent DNA damage responses, metabolic alterations, chromatin remodeling, and epigenetic reprogramming. These features do not appear uniformly and often escape detection by traditional target-specific assays.

Given this complexity, no single molecular marker is sufficient to define senescence across contexts. The field has increasingly turned to multi-omics approaches to resolve the diverse molecular layers of senescence at high resolution. These approaches span spatial profiling technologies, e.g., transcriptomics, proteomics, epigenomics, and metabolomics. In this review, we provide a comprehensive overview of multi-omics technologies and computational analyses to offer practical guidelines for cellular senescence research, organized by the biological features they capture and their applications to in vivo tissue analysis. We also discuss key barriers to senescent cells detection and classification and introduce emerging concepts such as senotypes—functionally distinct subtypes of senescent cells defined by integrative molecular signatures (Box 1). Additionally, we will highlight organ-specific, single-cell and spatial omics considerations across 16 tissues (Table S1), and present two detailed case studies on skeletal muscle and adipose tissue.

Box 1 |. Defining senotypes through multi-modal integration.

The concept of senotypes—senescent cell subtypes with distinct molecular and functional profiles—represents a critical conceptual shift in the field. Traditionally, senescence has been viewed as a binary cell fate defined by proliferative arrest and SASP production. However, recent studies suggest that senescent cells vary widely in transcriptional identity¹², immune interactions¹⁸⁸, secretory output¹⁵⁸, chromatin state⁹⁴, and metabolic activity^161,162. These variations are shaped by cell lineage, tissue microenvironment, inducing stimuli, and time since senescence initiation.

Integrating genomic, epigenomic, transcriptomic, proteomic, metabolomic, and spatial data will help define senotypes in a rigorous, reproducible, and biologically meaningful way. Senotypes will not only serve as a classification system for senescence research but also provide a framework for:

• Understanding how different senescent cells contribute to aging and disease

• Identifying selective vulnerabilities for therapeutic clearance (e.g., senolytics)

• Predicting tissue-specific responses to senescence modulation

As this concept gains traction, researchers may begin to categorize senescent cells by senotype, in the same way that immunologists distinguish T cell subsets or cancer biologists define molecular tumor subtypes. Ultimately, senotypes may become the foundation for a precision medicine approach to targeting cellular senescence in aging and disease.

Barriers to defining and detecting senescent cells in vivo

A non-universally conserved combination of molecular and phenotypic traits defines senescent cells. This heterogeneity, both within and across tissues, poses several major challenges for identifying senescent cells under physiological conditions.

Lack of universal biomarkers

One of the most fundamental issues for detecting senescent cells in vivo is the lack of universal biomarkers. While p16^INK4a and p21^CIP1 are frequently used as indicators of senescent cells, their expression is highly context-dependent and can occur in non-senescent states^11,13. Similarly, the SASP is variable, with distinct profiles depending on the senescence trigger, tissue environment, and time since onset¹. Expression of SASP components, such as interleukin-6 (IL-6)¹⁴ or matrix metalloproteinases (MMPs)¹⁵, may differ widely between replicative and stress-induced senescence models. Complicating matters further, commonly used transcriptomic methods such as 3′ single-cell RNA sequencing (scRNA-seq) cannot reliably distinguish between transcripts from loci like CDKN2A¹⁶, which encodes both p16^INK4a and p14^ARF.

Technical and sampling biases

Beyond these molecular issues, senescent cells are rare and fragile, making them technically difficult to isolate, dissociate, and profile. Their enlarged morphology and sensitivity to mechanical stress often lead to underrepresentation in single-cell suspensions, particularly in high-throughput workflows. As a result, key senescence features may be missed during sample processing. Additionally, senescent cells share transcriptional overlap with other non-proliferative cell states, including quiescent¹⁷ and terminally differentiated cells¹⁸, confounding the specificity of computational classification.

Loss of spatial context

Bulk and dissociated-cell methods lack the resolution needed to determine the tissue localization or niche effects of senescent cells. Although spatial transcriptomics and proteomics platforms are emerging, many suffer from limited gene coverage, signal dropout in poor-quality tissue regions, and batch effects that complicate data integration. Resolution also varies across platforms—from ~50 μm (Visium¹⁹) to submicron-scale (Seq-Scope²⁰, Pixel-Seq²¹)—affecting the ability to resolve individual cells and/or subcellular compartments.

Cross-modality integration

A further obstacle is the need for cross-modality validation. Senescence is a complex phenotype that spans multiple layers—transcriptional, epigenetic, metabolic, morphological—none of which can fully define the state on its own. Standardized definitions, consistent marker panels, and shared data structures are critical to compare results across laboratories or technologies. Algorithms that integrate multiple modalities using harmonized analytic pipelines enable more effective definition and characterization of organ-, tissue-, and cell-type-specific senescence signatures.

Toward a standardized framework

To overcome these challenges, multi-omics approaches must be complemented by rigorous cross-validation across tissues, technologies, and experimental models. The concept of senescence as a uniform phenotype is giving way to a more nuanced framework in which senescent cells are defined by distinct, measurable molecular signatures that reflect their origin, function, and microenvironment. This evolving framework is being formalized through the emerging concept of senotypes (see Box 1 for details).

Defining senescence through multi-omics as a framework for tissue-scale discovery

Senescent cells are defined not by a single hallmark but by a complex interplay of features—including cell cycle arrest, SASP production, DNA damage, and chromatin remodeling—that vary widely by tissue type, cell lineage, and inducing stimulus. These diverse features form the molecular foundation for the emerging concept of senotypes—senescent cell subtypes with distinct transcriptional, spatial, and functional identities (see also Box 1). Recent advances in multi-modal omics technologies (Fig. 1) have enabled the systematic dissection of these heterogeneous features across scales, offering the potential to classify senescent cells into senotypes in a rigorous and reproducible manner. These technologies allow systematic characterization of senescent cells based on gene expression, protein abundance, spatial distribution, metabolic function, and chromatin organization. Below, we review core omics technologies in context-specific senescence features they interrogate.

Fig. 1. Overview of technologies used by SenNet Consortium.

On the left, bulk- and single- cell approaches are listed that focus on protein (Flow cytometry, CyTOF), mRNA (scRNA-seq, scCITE-seq), and gDNA (scATAC-seq, 3D genome analysis). On the right, spatial multi-omics approaches provide molecular data with spatial context. Protein-based methods capture spatial distribution of proteins, while RNA techniques capture spatial transcriptomics. Multi-modality platforms can capture transcriptomics, proteomics, and beyond with information of spatial organization. These approaches are linked (shown in the centre), illustrating how they integrate single-cell resolution with spatial information for comprehensive tissue analysis. Created in BioRender. https://BioRender.com/qf6spm8. Abbreviations: ATAC-seq – Assay for Transposase-Accessible Chromatin using sequencing; ChIP-seq – Chromatin Immunoprecipitation sequencing; CODEX – Co-Detection by Indexing; CUT&Tag – Cleavage Under Targets and Tagmentation; CyTOF – Cytometry by Time-Of-Flight; DBiT-seq – Deterministic Barcoding in Tissue for Spatial Omics Sequencing; gDNA – Genomic DNA; High-C – High-throughput Chromosome Conformation Capture; IMC – Imaging Mass Cytometry; MERFISH – Multiplexed Error-Robust Fluorescence In Situ Hybridization; MIBI – Multiplexed Ion Beam Imaging; MINA – Microscopic Imaging of Nucleic Acids; mRNA – Messenger RNA; NINA – Non-Invasive Nuclear Architecture; sc-CITE-seq – Single-cell Cellular Indexing of Transcriptomes and Epitopes by sequencing; smFISH – Single Molecule Fluorescent In Situ Hybridization; SPRITE – Split-Pool Recognition of Interactions by Tag Extension; TSA-seq – Tyramide Signal Amplification sequencing.

Bulk- and single-cell multi-omics

Single-cell RNA-seq (scRNA-seq) enables high-throughput profiling of rare cell populations, including senescent cells that may represent <0.5% of a given tissue^22,23. Transcriptional profiling enables detection of stress-induced genes, such as CDKN2A and CDKN1A (p21^CIP1)²⁴, although both markers can be expressed in non-senescent contexts. Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq) is an antibody-based single-cell technique that enables the simultaneous quantification of surface protein expression and transcriptomic profiles within the same cell²⁵. This dual-modality platform provides orthogonal validation of senescence markers and enhances resolution in distinguishing senescent cell subtypes. Proteomic studies are common at the bulk level but remain technically challenging at the single-cell level; however, ongoing innovations in single-cell mass spectrometry (MS) are poised to increase resolution. Single-cell MS-based proteomics²⁶ and secretome profiling²⁷ can further quantify SASP components. However, the modest and variable expression of markers including p16^INK4a and technical challenges with dissociating senescent cells²⁸, necessitate orthogonal approaches, such as senescence-associated β-galactosidase (SA-β-gal) staining²⁹, detection of DNA damage response foci³⁰ (e.g., γH2AX, 53BP1), or imaging mass cytometry and spatial transcriptomics for in situ localization³¹. In vivo SASP profiles may also diverge substantially from those observed in vitro³².

Besides, senescence is associated with alterations in nuclear structure and chromatin organization³³. Key features include reduced LAMINB1³⁴ expression and altered lamina- and nucleolus-associated domains (LADs³⁵ and NADs^36,37). Bulk or single-cell 3D genomics methods such as Hi-C³⁸, GAM³⁹, SPRITE⁴⁰, or ChIA-PET⁴¹ can reveal genome-wide or protein-mediated contact probabilities, and TSA-seq⁴² maps 3D genome organization relative to nuclear compartments. But these 3D genomic approaches lack spatial localization.

Spatial multi-omics

Spatial landscape is paramount to senescence biology. To guide the reader through the rapidly evolving technological landscape, we organize this section around four major categories of spatial profiling: (i) imaging-based transcriptomics and proteomics, (ii) sequencing-based spatial transcriptomics, (iii) spatial 3D genomics, and (iv) MS based spatial proteomics and metabolomics. This structure reflects both the experimental logic and the biological relevance of each modality in capturing spatial heterogeneity in senescence. Senescent cells exert paracrine effects on neighboring cells through SASP, reshape tissue architecture, and modulate immune surveillance. Thus, spatial multi-omics technologies preserve the physical location of molecular measurements and are indispensable for senescence research.

Imaging-based multi-omics

Technologies such as Multiplexed Error-Robust Fluorescence In Situ Hybridization (MERFISH)⁴³, Single-Molecule Fluorescence In Situ Hybridization (smFISH)⁴⁴, RNAscope⁴⁵, Xenium⁴⁶, CosMx Spatial Molecular Imaging⁴⁷, and Multiplexed Imaging of Nucleome Architectures (MINA)⁴⁸ achieve subcellular or single-cell resolution through multiplexed RNA hybridization and imaging. While gene panels are currently targeted and instrumentation-intensive, detection sensitivity of the aforementioned technologies matches or exceeds scRNA-seq, and platforms are expanding rapidly. Co-Detection by Indexing (CODEX)⁴⁹ and 4i imaging⁵⁰ enables sub-cellular spatial proteomics mapping. Thus, these platforms can characterize the spatial heterogeneity of senescent cells and are particularly useful for identifying senescent microenvironments enriched in SASP factors.

Sequencing-based spatial transcriptomics

Next generation sequencing (NGS)-based spatial transcriptomics platforms fall under a category of sequencing-based technologies that enable transcriptome-wide profiling with spatial resolution. Platforms such as Visium¹⁹ and deterministic barcoding in tissue for spatial omics sequencing (DBiT-seq)⁵¹ perform untargeted, transcriptome-wide mapping by barcoding spatial locations and sequencing captured RNA. Visium captures barcoded mRNA transcripts on spatially organized 55-μm spots embedded in a capture slide, enabling region-specific transcriptome profiling, while Visium HD improves upon this with higher spatial granularity (2 μm)⁵². DBiT-seq introduces spatial barcodes using orthogonally flowing microfluidic channels, achieving resolution down to 10 μm⁵¹. These platforms link gene expression to histologically annotated tissue regions, allowing correlation of transcriptomic profiles with tissue architecture. However, these powerful methods have limitations. For example, Visium¹⁹ and similar array-based systems⁵¹ may suffer from signal dropout in degraded or low-quality tissue regions. Thus, these platforms are more suitable to define spatial heterogeneity of senescent cells in intact tissues.

To achieve higher resolution, recent innovations such as Slide-Seq⁵³, Seq-Scope²⁰, Seq-Scope-X⁵⁴ and Pixel-Seq²¹, and Open-ST^55–57, generate high-density barcoded arrays via random DNA cluster generation or patterned substrates, enabling submicron resolution spatial barcoding. These platforms are especially valuable for detecting rare senescent cells and resolving localized transcriptional changes associated with senescence, such as spatial SASP gradients and senescent cell-immune interactions. Slide-Seq⁵³ and related high-resolution approaches often require expensive custom fabrication and have relatively low RNA capture efficiencies. Ongoing efforts are addressing these constraints through innovations such as polony gel stamping²¹, which reduces fabrication cost, and chemical strategies to increase RNA yield and probe hybridization efficiency. These advances will be crucial for broadening access to high-resolution spatial transcriptomics in senescence research.

Spatial 3D genomics

imaging-based techniques, such as chromatin tracing⁵⁸ and MINA⁴⁸, combine chromatin tracing, RNA detection, and immunostaining in the same cells, enabling joint analysis of nuclear architecture and gene expression. These approaches allow integration of 3D genome topology, gene expression, and nuclear landmarks in tissue context^59,60, helping us understand gene dysregulation in cellular senescence.

MS-based spatial proteomics and metabolomics

MS imaging (MSI) provides molecular maps of proteins, metabolites, lipids and post-translational modifications across tissues⁶¹. With resolution down to 2 μm, MSI reveals sub-cellular and cellular metabolic heterogeneity and complements transcriptomic methods⁶². MSI can reveal metabolic shifts or post-translational modifications that distinguish senescent cells from non-senescent cells. Specific applications of MSI in senescence research include identifying lipid droplet accumulation, altered mitochondrial metabolites, and SASP-associated small molecules in fibrotic or aged tissues. Senescent niches are anatomical sites enriched in senescent cells and SASP effects (e.g., fibrotic lesions⁶³). Coupled with histological annotation and unsupervised clustering, MSI is especially valuable in delineating senescent niches and tissue-level remodeling.

Together, these technologies form a convergent framework for studying senescence at tissue scale. The integration of multi-omics promises to resolve the molecular complexity of senescent cells and uncover new biomarkers, therapeutic targets, and senotypes. Coordinated, cross-platform efforts are now underway to construct comprehensive senescence atlases that can serve as a reference senescence researchers (see Box 2).

Box 2 |. The SenNet Consortium.

Cellular senescence is a complex and context-dependent cell fate that unfolds across multiple molecular layers—from chromatin organization to secretory signaling. Multi-omics approaches provide an unprecedented opportunity to characterize this complexity at scale and in situ.

The NIH Common Fund’s Cellular Senescence Network (SenNet, https://sennetconsortium.org/) is a transdisciplinary initiative launched in 2021 to systematically identify, characterize, and spatially map senescent cells senescent cells across human and mouse tissues throughout the lifespan. SenNet’s overarching mission is to construct a reference atlas of cellular senescence that captures the phenotypic, molecular, and spatial heterogeneity of senescent cells in vivo.

Rather than assuming a universal signature, SenNet embraces the biological complexity of senescence by leveraging multi-modal profiling—including single-cell transcriptomics, spatial proteomics, secretome analysis, chromatin tracing, 3D genomics, and metabolomics—to uncover diverse molecular states and functional properties of senescent cells

Key goals of the consortium include:

• Generating harmonized, multi-omics data across 18 reference tissues¹³⁶ in human and mouse.

• Defining senescent cell subtypes (“senotypes”) through integrative analysis.

• Mapping the spatial distribution of senescent cells in situ, at cellular and subcellular resolution.

• Developing scalable technologies and computational frameworks for senescence detection, classification, and visualization.

• Building open-access atlases and tools for the broader aging, cancer, and regenerative medicine communities.

By standardizing protocols, enabling cross-laboratory reproducibility, and providing openly available datasets, SenNet is establishing a critical foundation for senescence-focused discovery and therapeutic development. These efforts are complementary to, but broader than, any single technological approach, and provide a unifying framework for interpreting the diverse manifestations of cellular senescence in health and disease.

Computational strategies and challenges in characterizing senescent cells

Computational challenges in defining senescence

Cellular senescence is a highly complex and dynamic cell state that lacks a unified molecular definition. While senescent cells are classically described by essentially permanent cell cycle arrest, resistance to apoptosis, and acquisition of a SASP, these features vary markedly across tissues, cell types, and biological contexts. Unlike terminal differentiation or apoptosis, senescence does not represent a singular or easily dichotomized fate. Instead, senescent cells span a continuum of phenotypic states that evolve over time and are influenced by intrinsic genetic programs and extrinsic environmental cues.

From a computational perspective, this biological heterogeneity presents a foundational challenge: how to define and classify senescent cells using high-dimensional omics data when the “ground truth” of what constitutes senescence is itself variable and context-dependent. Widely used markers, such as CDKN2A (p16^INK4a) and CDKN1A (p21^CIP1), or DNA-damage indicators including γH2AX⁶⁴, are not universally expressed across all senescent cells and may be undetectable in certain single-cell platforms due to low transcript abundance. Similarly, SASP molecules, such as IL-6¹⁴, IL-1β⁶⁵, and MMPs¹⁵, are dynamic, condition-specific, and regulated at multiple levels—complicating their use as universal identifiers.

Conventional clustering and classification approaches, which rely on discrete groupings of cells with shared molecular features, often fail to capture this spectrum. Senescence is better conceptualized as a trajectory or latent continuum, along which cells gradually accumulate hallmark features, such as chromatin reorganization, transcriptional reprogramming, mitochondrial dysfunction, and SASP activity. This realization has led to a shift toward probabilistic and trajectory-aware models⁶⁶, which can accommodate partial and progressive expressions of senescence-associated programs. This model of senescence is similar to concept of “field of cancerization” long-accepted in cancer research⁶⁷. This conceptualization and modeling are analogous to the concept of a tensor field on physics adapted to multi-omics analysis^68,69.

A “Field of Senescence” approach considers that cells may exist in intermediate states and these intermediate-state cells spread spatially in tissues and interact with one another in complex ways involving multiple molecular species. And although classically senescent cells are often rare, cells with pre-senescent processes are much more frequent and can be studied with multi-omics approaches using a variety of functional lenses. Finally, because an in vivo senescent gold standard does not exist, but there is a rich knowledge of markers and pathways involved in hard core (in vitro or vivo) senescence identification, we can use these markers to anchor the mapping of the senescence field human tissues thus enabling a more nuanced investigation of senescence.

Accordingly, computational frameworks must incorporate multimodal data, temporal dynamics, and tissue-specific priors. These approaches move beyond binary classification and instead infer senescence as a probabilistic state shaped by molecular and cellular signals across multiple omics layers. In the sections that follow, we explore how these challenges are being addressed through integrated single-cell analysis, spatial modeling, perturbation-based inference, and data integration.

Omics-based identification of senescent cells

Identifying senescent cells in high-dimensional omics data remains a critical bottleneck in senescence research. Senescent cells are transcriptionally heterogeneous and lack distinct clustering patterns in dimensionality reduction plots. To circumvent these limitations, computational workflows frequently rely on gene set enrichment analysis (GSEA)⁷⁰ using curated senescence gene sets, such as CellAge⁷¹, SenMayo⁷², SenSig⁷³, and SenePy⁷⁴. GSEA provides a pathway-level signature, quantifying enrichment of senescence programs analogous to approaches used for inferring cell cycle states. However, this method depends on the quality and completeness of the input gene set and may struggle to distinguish senescence from related non-proliferative states, such as quiescence or differentiation.

A major technical obstacle in senescent cell analysis is the sparsity of single-cell data, particularly when studying low-abundance populations. Senescent cells are often underrepresented in dissociation-based protocols and exhibit elevated dropout rates across genomic modalities. This limits the ability of standard methods to detect them and necessitates tailored approaches for data aggregation and imputation. Aggregation methods address sparsity by summarizing expression patterns across biologically similar cells or gene modules. For instance, MetaCell (MC1)⁷⁵ and MetaCell-2 (MC2)⁷⁶ group statistically indistinguishable transcriptomic profiles into metacells, effectively reducing noise while preserving cell-type resolution. Similarly, PAGODA⁷⁷ identifies coordinated expression variability in predefined pathways or gene sets, allowing detection of stable and dynamic expression programs⁷⁷. Imputation methods aim to infer missing expression values by leveraging data from similar cells or low-dimensional structures.

Probabilistic models, such as scImpute⁷⁸, SAVER⁷⁹, SAVER-X⁸⁰, and VIPER⁸¹, estimate true expression levels by modeling dropout as a statistical process. Alternative strategies, including DrImpute⁸² and MAGIC⁸³, borrow information from neighboring cells, while deep learning approaches, e.g. scVI⁸⁴, SAUCIE⁸⁵, and DCA⁸⁶, learn latent representations that reconstruct underlying expression landscapes. Despite their utility, a recent benchmark study found that imputation does not necessarily improve downstream analyses, such as clustering or trajectory inference⁸⁷. This may be especially true for senescent cells, whose unique transcriptional and regulatory features can be diluted or obscured by smoothing algorithms not designed for rare or transitional cell states. Spatial transcriptomics technologies, e.g., MERFISH⁴³ and seqFISH+⁸⁸, capture transcript localization but are limited by target gene throughput. Computational methods, such as gimVI⁸⁹, Tangram⁹⁰, Harmony⁹¹, Seurat⁹², and SpaGE⁹³, address this by integrating spatial and scRNA-seq data to impute gene expression across tissue maps.

Some of these tools have been used in senescence studies; for example, MAGIC was applied to model aging muscle stem cells⁹⁴ and epithelial dysplasia⁹⁵. Yet these applications remain rare, and imputation frameworks have not been widely validated in the context of senescent cell detection. Indeed, standard smoothing-based approaches may obscure rare senescence-associated signatures—especially when markers are weakly expressed or restricted to narrow tissue niches. Moving forward, improving data recovery for senescence studies will require methods tailored for rare, heterogeneous, and transitional populations. This includes robust models that preserve dropout-informative signals, flexible aggregators that maintain subtype resolution, and uncertainty-aware imputation that accounts for biological variability rather than simply filling in zeros.

Epigenomic profiles provide additional orthogonal signals. Senescent cells exhibit features of chromatin remodeling, including, in at least some cases, loss of H3K9me3 and formation of senescence-associated heterochromatin foci (SAHF)^96,97. The expression of LINE-1-ORF-1p, a retrotransposon element, has been implicated in senescent cells⁹⁸. Computational tools that analyze DNA methylation and chromatin accessibility patterns are increasingly used to model these alterations. Notably, partial epigenetic reprogramming has been shown to reverse some senescence-associated chromatin features⁹⁹, suggesting that these marks reflect functionally relevant state transitions. In single-cell epigenomic data, such as single-cell assay for transposase-accessible chromatin (ATAC)-seq, aggregation can be applied at the level of transcription factor binding sites or gene-level accessibility. Tools, such as chromVAR and Cicero, infer regulatory activity and enhancer-promoter interactions by pooling accessibility data across genomic features¹⁰⁰ or co-accessible regions¹⁰¹. However, aggregation-based methods introduce trade-offs. Coarse graining may obscure subtle transitions or the domains in which senescent cells reside. This highlights the need for approaches that adaptively tune resolution to preserve biological heterogeneity or learning deep representations from paired modalities^102–104.

Besides transcriptomic and epigenomic signatures, mitochondrial and metabolic signatures also contribute to senescent cell identification. Dysregulation of oxidative phosphorylation, shifts in NAD+/NADH ratios, and secretion of inflammatory cytokines such as Macrophage Migration Inhibitory Factor (MIF)¹⁰⁵ are recurrent in senescent cells. Integrative models that incorporate mitochondrial content, transcriptional profiles, and surface marker data are better positioned to resolve senescent cells within heterogeneous tissue samples.

Recent scRNA-seq^11,106–108 and spatial gene expression¹⁰⁷ studies have transformed senescence from a monolithic concept into a dynamic, heterogeneous process with context-dependent roles. Though combining multiple markers remains essential, the field now emphasizes cell-type-specific signatures, functional subtypes, and advanced computational tools (e.g., SenePy⁷⁴, SenPred¹⁰⁹) to decode senescent cells complexity. In addition, cells can acquire senescence indirectly via paracrine signaling, creating heterogeneous microenvironments^110,111. Morphological and transcriptional clustering reveals senescence subtypes with distinct drug sensitivities¹¹². Together, spatial multi-omics and computational modeling of cell–cell interactions offer a powerful lens through which to study the systemic influence of senescent cells on tissue homeostasis, regeneration, and pathology.

Spatial context and cell–cell interaction analysis

While all cells are influenced by their microenvironment, senescence distinct in that it is cell-intrinsic state and a process that profoundly shapes its local tissue context. As senescent cells accumulate, they actively secrete a diverse array of cytokines and protease (the SASP), remodel the extracellular matrix, and modulate immune response. These potent and multifaceted microenvironmental effects are challenging to capture using dissociated single-cell data alone, necessitating spatially resolved approaches and context-aware computational models. Recent advances in spatial transcriptomics and spatially resolved proteomics now enable the mapping of senescent cells within their native tissue architecture. However, many of these technologies operate at multicellular resolution, limiting the inference of fine-grained cell–cell interactions. To address this, deconvolution algorithms have been developed to estimate cell-type contributions within each spatial pixel^90,113–116, enabling the localization of rare senescent cell populations across diverse tissues.

Machine learning (ML) methods have further enhanced spatial omics analysis by modeling tissue architecture as structured graphs. Graph neural networks^117–119 and Gaussian process models^120,121 have been utilized to extract spatial neighborhoods for clustering, visualization, and differential expression analysis tools, such as DIALOGUE¹²², NCEM¹²³ and SIMVI¹¹⁹, infer multicellular signaling programs by modeling transcriptional outputs as functions of neighboring cell identities. Regression-based models, such as MISTy¹²⁴ and SVCA¹²⁵, quantify the contribution of spatial proximity and tissue gradients to gene expression variance. This allows researchers to disentangle intrinsic senescent gene programs from those induced by neighboring immune, stromal, or epithelial cells. Integration of high-resolution histological imaging with spatial transcriptomics (e.g., XFuse¹²⁶, iStar¹²⁷) further enhances cell segmentation and single-cell spatial gene expression mapping from multi-cell mapping (e.g., Visium¹⁹), supporting the studies of senescent cells–microenvironment interactions.

High-throughput whole-slide imaging (WSI)¹²⁸, multiplexed immunohistochemistry (IHC)¹²⁹, and artificial intelligence (AI)-driven image analysis¹³⁰ now allow direct inference of senescence features from morphological data¹³¹. For example, multiplex IHC^66,132–134 of aging tissue can reveal cells co-expressing epigenetic¹³⁵ and senescence markers (e.g., p16, p21)¹³⁶, while aligning these with gene expression profiles from spatial transcriptomics. These integrated approaches refine the morpho-molecular definition of senescent cells and open new opportunities for AI-guided diagnosis in age-related diseases^137–139.

Computational inference of cell–cell communication networks is also advancing rapidly. Tools, such as NicheNet¹⁴⁰ and Domino¹⁴¹, infer ligand-receptor signaling relationships by integrating expression data with curated interaction databases and downstream target activation. While these methods were originally developed for scRNA-seq, extensions now incorporate spatial co-localization, allowing researchers to distinguish functional from non-functional interactions in situ. Such innovations are critical to understanding how senescent cells engage in paracrine signaling, immune evasion, or tissue remodeling. Reconstructing senescent cells in 3D requires integrating multi-slice spatial data. Technologies such as STARmap¹⁴² allow 3D transcriptomic profiling, while CODA¹⁴³ uses deep learning to reconstruct tissue volumes from serial H&E images. These tools enable digital senescence atlases of entire organs and can incorporate senescent cell niche interactions across various resolutions.

Together, spatial multi-omics and computational modeling of cell–cell interactions offer a powerful lens through which to study the systemic influence of senescent cells on tissue homeostasis, regeneration, and pathology. These tools will be especially valuable for identifying tissue-specific senotypes and their niche-dependent functions within complex aging environments.

Functional perturbation and regulatory inference

Beyond observational profiling, perturbation experiments offer a powerful window into the causal mechanisms that govern Senescent cell states. Perturbation-based single-cell technologies, such as CRISPR screens, ECCITE-seq¹⁴⁴, as well as multiplexed 4i imaging⁵⁰, enable systematic investigation of how gene perturbations affect senescence phenotypes across transcriptomic and proteomic dimensions. These approaches help distinguish genes that are merely associated with senescence from those that actively regulate its induction, maintenance, or escape. Yet, analyzing perturbation or case-controlled single-cell data is challenging due to heterogeneous treatment responses, confounding factors, cell composition imbalances, and batch effects. To address these issues, computational methods leveraging ML techniques, such as variational autoencoders and optimal transport, have been developed to identify perturbation responses in single-cell data^50,144,145.

Gene regulatory network (GRN) inference methods provide another lens through which to interrogate senescence control¹⁴⁶. Tools such as CellOracle¹⁴⁷ integrate time-resolved scRNA-seq with prior transcription factors (TF)–target annotations to simulate in silico TF perturbations, identifying master regulators that drive state transitions. CellOracle has been used to predict regulatory switches in developmental systems and could similarly be applied to model transitions into and out of senescence—especially in reprogramming, senolysis, or senescence escape contexts. As integrated multi-omics approach has revealed cellular senescence epigenetic landscape and primary regulatory elements¹⁴⁸. Complementing this, SCENIC+¹⁴⁹ uses multi-omics data, including scRNA-seq and scATAC-seq, to infer TF activity and regulon dynamics in a spatially aware manner by integrating with spatial transcriptome 10X Visium data. This is particularly relevant for senescent cells, which often exhibit chromatin-level changes (e.g., loss of H3K9me3, LAMINB1 downregulation, epigenetic erosion^{98, 99}) that are not captured by transcriptional output alone. SCENIC+ can infer how TFs drive or maintain the senescent phenotype and can be combined with perturbation datasets to predict outcomes of TF inhibition or activation.

Multi-omics integration is critical for senescence research because no single modality captures the full complexity of senescent cell states. Transcription, chromatin remodeling, metabolic shifts, and spatial context all contribute orthogonal information that must be jointly analyzed to reconstruct senescent cell biology in vivo. Tools, such as Seurat¹⁵⁰, LIGER¹⁵¹, and GLUE¹⁵², align single-cell RNA and ATAC-seq datasets into a shared latent space. Deep learning frameworks such as DeepMAPS¹⁵³ model cell–gene relationships using graph-based architectures, enabling integrative clustering and GRN inference. These models can uncover subtype-specific senescence regulators by connecting epigenomic changes to transcriptional outputs. Spatial multi-omics technologies, such as SM-Omics¹⁵⁴ and spatial ATAC–RNA-seq¹⁵⁵, profile gene expression and chromatin in adjacent sections. New barcoding strategies aim to measure multiple modalities in the same tissue slice¹⁵⁵. Deep learning models now incorporate morphological features to predict expression states or identify novel subtypes missed by standard clustering¹⁵⁶. Integrating transcriptomic, epigenomic, proteomic, and spatial modalities along with tissue morphology and temporal trajectories allows reconstruction of high-resolution senescence landscapes.

Taken together, perturbation-based modeling frameworks and GRN inference tools offer a functional perspective on senescence biology. They go beyond associations to ask how senescent states are regulated and which transcriptional circuits are necessary to maintain or exit those states. As large-scale perturbation atlases expand, these tools will help identify senotypes that are differentially sensitive to specific interventions.

Organ and cell-type-specific senescent signature detection

Senescent cells exhibit marked heterogeneity across tissues due to differences in developmental origin, cell composition, extracellular milieu, and exposure to age- or stress-induced damage. To investigate cellular senescence signatures, understanding organ-specific contexts is crucial due to the diverse biological functions and cellular compositions of different tissues. This underscores the importance of tailored approaches to studying senescence, as each tissue presents unique biological and technical challenges (Supplementary Table S1).

Below, we highlight two specific tissues, skeletal muscle and adipose tissue, as representative cases that exemplify the challenges and opportunities of applying multi-omics tools to senescence research. These tissues were selected not only for their physiological relevance but also because they present distinct technical barriers to senescent cell detection and classification—barriers that can be overcome through the integrated experimental approaches being adopted by SenNet (see Box 2) and the wider community.

Challenges and opportunities in skeletal muscle

Skeletal muscle makes up a large part of our bodies and is composed of multinucleated myofibers and diverse mononucleated cell types including muscle stem cells (MuSCs), fibro-adipogenic progenitors (FAPs), endothelial cells, and immune cells¹⁵⁷. Recent scRNA-seq and single-nucleus RNA-seq (snRNA-seq) studies have identified subsets of FAPs and myofibers with elevated expression of senescence markers, including CDKN2A (p16^INK4a), CDKN2B (p15^INK4b), and CDKN1A (p21^CIP1), as well as SASP-associated genes¹⁵⁸. However, MuSCs and myofibers are often underrepresented due to their quiescent state or large size, respectively, limiting detection of senescence hallmarks.

In response, recent approaches have used SA-β-gal staining with FACS to isolate SPiDER+ Senescent cells from regenerating muscle¹⁵⁹. These cells exhibit canonical senescent cell phenotypes including reduced LaminB1, elevated γH2AX, and secretion of inflammatory mediators that impair regeneration. Bulk RNA-seq from isolated myofibers also identified p21^high subpopulations enriched for TGF-β, Jak-STAT, and cytokine signaling¹⁵⁸. Though informative, these studies primarily rely on transcriptomic profiling. Recently, combining snRNA-seq and snATAC-seq has enabled parallel profiling of gene expression and chromatin accessibility in skeletal muscle, identifying senescent populations across myonuclei, muscle stem cells, and fibro-adipogenic progenitors^160,161,162. These data reveal heterogeneity in senescence states, regulation of SASP factors, and key transcription factors such as JUNB^161,162. Validation includes targeting p16, p21, and SA-β-gal activity in aged muscle stem cells^161,162. Integrated transcriptomic and proteomic data show that many age-related protein changes are regulated post-transcriptionally¹⁶³, linking senescence to altered mitochondrial function, proteostasis, and immune signaling, and separating primary aging effects from those of acute tissue injury, chronic inflammation or disuse.

Spatial transcriptomics has enabled high-resolution profiling of senescent cells in skeletal muscle across ageing, regeneration, and disease contexts. A recent study revealed age-associated accumulation of senescent-like MuSCs within injury zones, spatially adjacent to regeneration niches, through integrated snRNA-seq and spatial analysis¹⁶⁴. Combined spatial and single-cell transcriptomics further identified transient populations expressing Cdkn2a, Cdkn1a, and SASP factors during muscle repair and disease, localizing senescent cells within key regenerative microenvironments¹⁶⁵. In aged human muscle¹⁶⁶, digital spatial profiling confirmed discrete CDKN1A+ nuclei, supporting spatially restricted senescence. High-resolution platforms such as Seq-Scope provide subcellular mapping of gene expression, enabling future studies of senescence signatures at neuromuscular junctions and within muscle fibers¹⁶⁷. Collectively, these multi-omics and spatial approaches illuminate the spatial organization and context-dependent roles of senescent cells in muscle ageing and regeneration.

However, challenges remain. MuSCs are rare and often transcriptionally suppressed, myofibers are difficult to capture due to their size using scRNA-seq, and current spatial methods lack the resolution to distinguish clear boundaries between mononuclear cells and multinucleated myofibers. In addition, these stem or progenitor cells are plastic; adipose stromal cells may migrate into skeletal muscle upon injury and further contribute to FAP heterogeneity¹⁶⁸, making it difficult to track the spatiotemporal dynamics of muscle cell regeneration and aging. To resolve these limitations, future work must incorporate multi-modal approaches. For example, snRNA-seq enables analysis of fragile or large cells such as myofibers, while high-resolution spatial transcriptomics allows profiling of regeneration niches without dissociation, preserving tissue architecture^165,169,170. Epigenomic profiling^155,171 at single-cell or spatial level is essential to identify the underlying mechanism such as chromatin remodeling and DNA methylation controlling cellular heterogeneity and plasticity in senescent cells. Proteomic assays, including imaging mass cytometry¹⁷² and single-cell or spatial secretome mapping⁴⁹, will clarify the composition and localization of SASP factors. AI-driven segmentation algorithms trained specifically on muscle tissue architecture will enhance the cellular boundaries across various cell types. Integration with chromatin accessibility and DNA methylation profiling may further distinguish stable senescent cells from quiescent or injured states. These developments will be essential for understanding how cellular senescence drives muscle aging and regenerative decline.

Challenges and opportunities in adipose tissue

Adipose tissue has emerged as a model tissue for systemic aging due to its early and depot-specific accumulation of senescent cells. Bulk transcriptomics¹⁷³ and proteomics¹⁷⁴ have shown that age-related transcriptional and metabolic changes in visceral adipose tissue precede those in other tissues. Subsequent adipose snRNA-seq^175,176 revealed a complex adipose environment. Indeed, the abundance of inflammatory cells¹⁷⁷ makes defining a senescent population that is distinct from classical CD45+ immune cells challenging. Elimination of the highly expressing p16 or p21 cells in mice using a genetic approach, or removal of senescent cells using senolytics, established that senescent cells do accumulate in visceral adipose depots with aging or obesity and that loss of such cells improves metabolic homeostasis and physical function^{11,108,178–181}.

Adipocytes are lipid-rich and fragile, making single-cell dissociation inefficient and leading to their underrepresentation in most scRNA-seq studies. However, snRNA-seq now permits broader inclusion of adipocytes and stromal populations, revealing depot-specific senescent cell enrichment¹⁷⁶ making adipose tissue a valuable source for senescent-cell mapping for several reasons. Firstly, adipose tissue contains multiple subpopulations of cells, including adipocytes, progenitor cells, macrophages and other immune cells^108,176. Secondly, using the SenMayo panel of transcriptomic biomarkers²⁵, it has been shown that aging epididymal and mesenteric murine adipose tissue depots positively correlate with senescence, while subcutaneous depots negatively correlate with senescence. Thirdly, limited spatial mapping of human and mouse adipose¹⁸² identified neighborhood-specific cell clustering propensities, alterations of innate and adaptive immune cells, and lipid-associated macrophages clustering around crown-like structures¹⁸³. Finally, computational meta-analysis revealed correlations with clinical states, body-mass index, and age¹⁸⁴.

Beyond snRNA-seq studies, a multi-omics study¹⁸⁵ of human mesenchymal stem cells (MSCs) from adipose and other tissues integrated single-cell transcriptomics with proteomic profiling, revealing progressive aging phenotypes marked by reduced PD-L1 expression and impaired T cell suppression. These findings highlight immune dysfunction as a hallmark of MSC senescence. In another study, integrated transcriptomic and proteomic analyses¹⁸⁶ of senescent endothelial cells (ECs) demonstrated that EC-derived SASP factors induce adipocyte senescence and impair insulin sensitivity by downregulating IRS-1 expression, linking vascular aging to adipose dysfunction.

Despite significant advances, several critical knowledge gaps remain in our understanding of adipose tissue senescence. The relative contributions of adipocyte-intrinsic versus stromal or immune cell senescence, the mechanisms driving depot-specific redistribution of adipose tissue during aging, and the heterogeneity of SASP factors across anatomical depots are all poorly defined. Single-cell omics have enabled the exploration of diverse cellular states, yet technical challenges, such as the fragility and size of adipocytes, limit the comprehensive capture of all relevant populations. Spatial omics approaches¹⁸⁷, while providing valuable insights into tissue architecture and cell–cell interactions, are similarly constrained by difficulties in tissue processing, as well as the large size and the typically low transcript abundance of many adipose cell types. Addressing these challenges will require continued methodological innovation, including the integration of high-throughput single-nucleus sequencing, advanced spatial transcriptomics, proteomics, lipidomics, and metabolomics. Such multi-omics strategies will be essential for mapping senescent cells and their microenvironments in situ, resolving the architecture of SASP-producing niches, and enabling simultaneous analysis of inflammatory mediators, lipid metabolism, and senescence burden within adipose tissue.

Outlook

The emergence of senescence as a fundamental biological process underlying aging, tissue remodeling, and disease has catalyzed a wave of technological and analytical innovation. Senescent cells are rare, heterogeneous, and context-dependent, posing significant challenges to reproducible detection, subtype classification, and functional interpretation. These challenges are further compounded by technical constraints, such as dissociation-induced stress responses, transcriptional dropout in fragile or quiescent cells, and limited spatial resolution in thick or lipid-rich tissues.

Multi-omics methods-ranging from single-nucleus transcriptomics and chromatin profiling to spatial proteomics and imaging mass cytometry, now enable detailed in situ characterization of senescent cells across diverse tissue contexts. However, progress in the field is not constrained by computational tools alone. Rather, it is the intersection of biological complexity, experimental limitations, and analytical scalability that defines the current frontier.

Future progress will require close coordination between experimental advances, such as improved barcoding chemistries, fixation protocols that preserve epitopes and RNA, and higher-throughput spatial omics platforms-and computational innovations, including semi-supervised learning, spatially aware graph models, and probabilistic frameworks that incorporate uncertainty.

With emerging multi-omic atlases of senescent cells across diverse human and mouse tissues, the field is poised to move from ad hoc marker panels toward a flexible and biologically grounded scaffold to encourage more refined study of senotypes. These efforts will clarify how senescent cells contribute to aging and chronic disease and provide new avenues for therapeutic targeting. Thus, senescence research is transitioning from a marker-limited paradigm to a multi-dimensional, tissue-aware systems biology framework, driven by the co-evolution of experimental platforms and computational models. The field’s future lies in this convergence, where mechanistic insight, spatial context, and predictive utility come together to shape next-generation senescence biology.

Fig. 2. Overview of computational multi-omics to detect, characterize and spatially map the location of senescent cell and their functional perturbation.

Created in BioRender. https://BioRender.com/qf6spm8.