Computational strategies and challenges in characterizing senescent cells

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 and CDKN1A, or DNA damage indicators including γH2AX, 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-6 (ref. ), IL-1β and matrix metalloproteinases, 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 models, which can accommodate partial and progressive expression of senescence-associated programs. This model of senescence is similar to the long-accepted concept of the 'field of cancerization' in cancer research. This conceptualization and modeling are analogous to the concept of a tensor field on physics adapted to multiomics analysis.

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. Furthermore, although classically senescent cells are often rare, cells with pre-senescent processes are much more frequent and can be studied with multiomic 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 in vivo) senescence identification, we can use these markers to anchor the mapping of 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.

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 using curated senescence gene sets, such as CellAge, SenMayo, SenSig and SenePy. Gene set enrichment analysis 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 nonproliferative 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 and Metacell-2 (ref. ) group statistically indistinguishable transcriptomic profiles into metacells, effectively reducing noise while preserving cell type resolution. Similarly, PAGODA identifies coordinated expression variability in predefined pathways or gene sets, allowing detection of stable and dynamic expression programs. Imputation methods aim to infer missing expression values by leveraging data from similar cells or low-dimensional structures.

Probabilistic models, such as scImpute, SAVER, SAVER-X and VIPER, estimate true expression levels by modeling dropout as a statistical process. Alternative strategies, including DrImpute and MAGIC, borrow information from neighboring cells, while deep learning approaches, for example, scVI, SAUCIE and DCA, 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 inference. This may be especially true for senescent cells, the unique transcriptional and regulatory features of which can be diluted or obscured by smoothing algorithms not designed for rare or transitional cell states. Spatial transcriptomic technologies, for example, MERFISH and sequential fluorescence in situ hybridization (seqFISH)+, capture transcript localization but are limited by target gene throughput. Computational methods, such as gimVI, Tangram, Harmony, Seurat and SpaGE, 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 cells (MuSCs) and epithelial dysplasia. 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 histone H3 Lys9 trimethylation (H3K9me3) and formation of senescence-associated heterochromatin foci. The expression of LINE-1 ORF1p, a retrotransposon element, has been implicated in senescent cells. 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 features, suggesting that these marks reflect functionally relevant state transitions. In single-cell epigenomic data, such as single-cell assay for transposase-accessible chromatin sequencing (ATAC-seq), aggregation can be applied at the level of transcription factor (TF) 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 features or coaccessible regions. 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.

Aside from 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) 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 and spatial gene expression studies have transformed senescence from a monolithic concept into a dynamic, heterogeneous process with context-dependent roles. Although combining multiple markers remains essential, the field now emphasizes cell type-specific signatures, functional subtypes and advanced computational tools (for example, SenePy, SenPred) to decode senescent cell complexity. In addition, cells can acquire senescence indirectly via paracrine signaling, creating heterogeneous microenvironments. Morphological and transcriptional clustering reveals senescence subtypes with distinct drug sensitivities. Together, spatial multiomics 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.

While all cells are influenced by their microenvironment, senescence is distinct in that it is a 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 proteases (the SASP), remodel the extracellular matrix and modulate the 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, enabling the localization of rare senescent cell populations across diverse tissues.

Machine learning methods have further enhanced spatial omics analysis by modeling tissue architecture as structured graphs. Graph neural networks and Gaussian process models have been used to extract spatial neighborhoods for clustering, visualization and differential expression analysis tools, such as DIALOGUE, NCEM and SIMVI, and infer multicellular signaling programs by modeling transcriptional outputs as functions of neighboring cell identities. Regression-based models, such as MISTy and SVCA, 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 (for example, XFuse, iStar) further enhances cell segmentation and single-cell spatial gene expression mapping from multi-cell mapping (for example, Visium), supporting studies of senescent cell-microenvironment interactions.

High-throughput whole-slide imaging, multiplexed immunohistochemistry and artificial intelligence (AI)-driven image analysis now allow direct inference of senescence features from morphological data. For example, multiplex immunohistochemistry of aging tissue can reveal cells coexpressing epigenetic and senescence markers (for example, p16, p21) while aligning these with gene expression profiles from spatial transcriptomics. These integrated approaches refine the morphomolecular definition of senescent cells and open new opportunities for AI-guided diagnosis in age-related diseases.

Computational inference of cell-cell communication networks is also advancing rapidly. Tools, such as NicheNet and Domino, 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 colocalization, allowing researchers to distinguish functional from nonfunctional 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 STARmap allow 3D transcriptomic profiling, while CODA uses deep learning to reconstruct tissue volumes from serial hematoxylin and eosin images. These tools enable digital senescence atlases of entire organs and can incorporate senescent cell niche interactions across various resolutions.

Together, spatial multiomics 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.

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, expanded CRISPR-compatible CITE-seq as well as multiplexed 4i imaging, 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 machine learning techniques, such as variational autoencoders and optimal transport, have been developed to identify perturbation responses in single-cell data.

Gene regulatory network (GRN) inference methods provide another lens through which to interrogate senescence control. Tools such as CellOracle integrate time-resolved scRNA-seq with prior 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. An integrated multiomic approach has revealed the cellular senescence epigenetic landscape and primary regulatory elements. Complementing this, SCENIC+ uses multiomics data, including scRNA-seq and single-cell ATAC-seq (scATAC-seq), to identify 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 (for example, loss of H3K9me3, lamin B1 downregulation, epigenetic erosion) 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.

Multiomics 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 Seurat, LIGER and GLUE, align scRNA-seq and scATAC-seq datasets into a shared latent space. Deep learning frameworks such as DeepMAPS 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 multiomics technologies, such as SM-Omics and spatial ATAC-RNA-seq, profile gene expression and chromatin in adjacent sections. New barcoding strategies aim to measure multiple modalities in the same tissue slice. Deep learning models now incorporate morphological features to predict expression states or identify novel subtypes missed by standard clustering. Integrating transcriptomic, epigenomic, proteomic and spatial modalities along with tissue morphology and temporal trajectories allows reconstruction of high-resolution senescence landscapes.

In sum, 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.