Cited by 141Telomeres, Telomerase, and SenescenceSingle-cell and spatial transcriptomicsGenetics, Aging, and Longevity in Model Organisms

The DOI matches its Crossref record; 108 of the 110 references it cites were found in a registry or an index; 66 of the 81 sources its claims cite were checked for support.

Spatial mapping of cellular senescence: emerging challenges and opportunities

Open access

Abstract

Cellular senescence is a well-established driver of aging and age-related diseases. There are many challenges to mapping senescent cells in tissues such as the absence of specific markers and their relatively low abundance and vast heterogeneity. Single-cell technologies have allowed unprecedented characterization of senescence; however, many methodologies fail to provide spatial insights. The spatial component is essential, as senescent cells communicate with neighboring cells, impacting their function and the composition of extracellular space. The Cellular Senescence Network (SenNet), a National Institutes of Health (NIH) Common Fund initiative, aims to map senescent cells across the lifespan of humans and mice. Here, we provide a comprehensive review of the existing and emerging methodologies for spatial imaging and their application toward mapping senescent cells. Moreover, we discuss the limitations and challenges inherent to each technology. We argue that the development of spatially resolved methods is essential toward the goal of attaining an atlas of senescent cells.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

This review from the SenNet imaging working group argues that mapping senescent cells in tissues requires spatially resolved, multi-marker approaches because no single marker is specific and senescent cells are rare and heterogeneous. It surveys low-plex to high-plex imaging, spatial transcriptomics, image analysis, and deep learning, emphasizing limitations and opportunities toward a senescence atlas.

Key findings

  • Senescent cells lack a stand-alone specific marker; commonly used markers such as SA-β-Gal, p16INK4a, and p21CIP1 can appear in non-senescent contexts, so the field favors multi-marker detection. Senescent cells may be present below 5% in aged tissues, making rare-cell detection difficult.
  • Spatial context is essential because senescent cells communicate with neighbors through the SASP, influencing immune recruitment, proliferation, angiogenesis, inflammation, epithelial-to-mesenchymal transition, tissue repair, and wound healing.
  • High-plex spatial proteomics can measure roughly 10–60 proteins with tagged antibodies, barcoded platforms may exceed 100 targets, and mass-spectrometry platforms can use up to 40 metal-conjugated antibodies; however, platforms differ in resolution, throughput, tissue area, antibody validation needs, and whether they destroy the sample.
  • Spatial transcriptomics spans targeted FISH-based methods with hundreds of genes (for example, Xenium panels of 300–400 genes plus up to 100 custom probes), unbiased Visium with spots around 50 μm and 100 μm spacing over a 6.5 × 6.5 mm area (version 2: 11 × 11 mm), and ultra-high-resolution methods such as Seq-Scope, Stereo-seq, and PIXEL-seq reaching up to about 23 unique transcripts per μm². Deep learning applied to H&E and DAPI images predicted senescence; breast tissue from 67 healthy individuals scored 460,000 nuclei, and skin biopsies from more than 160 individuals showed predicted dermal senescence increasing with age.

Methods

  • Narrative review by the SenNet imaging mapping working group, synthesizing existing and emerging spatial imaging, spatial omics, image-analysis, and deep-learning methods for detecting senescent cells.
  • No primary experimental cohort or new data are reported; the work draws on cited literature and consortium examples, with tables comparing platforms and figures illustrating workflows.
  • The main analysis is a qualitative comparison of technologies by plex level, resolution, throughput, tissue compatibility, and suitability for rare senescent cells.

Limitations

  • Stated: no universal senescence marker exists; markers are nonspecific; senescent cells are rare (below 5% in aged tissues) and heterogeneous; antibody validation is problematic, especially for mouse p16INK4a; platform constraints include limited tissue area, low throughput, long acquisition, cost, autofluorescence, fixation effects, destructive mass imaging, and inability to resolve single cells for some methods.
  • Stated: computational challenges include segmentation errors, deconvolution difficulty distinguishing senescent from non-senescent cells within a cell type, lack of annotated reference scRNA-seq datasets, and unknown benchmark performance for senescent-cell deconvolution.
  • Evident from the text: as a review, it provides no new experimental validation or systematic benchmarking; many proposed integrations and deep-learning pipelines are described as future or in-progress rather than fully validated.

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Integrity signals

Registry

Crossref · 10.1038/s43587-023-00446-6

Cites

108 of 108 references verified0 citation errors · 0 not checkable

Statistics

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Verification

Claims the paper makes

34 Own interpretation · 27 No basis given · 22 Supported · 14 Partly supported · 5 No full text of the sources · 4 Supported so far · 1 Not supported · accuracy not yet measured

Show all 50 claims

  • It may state two separate claims as one. 41
  • Probably not a substantial claim. 23
57 of 57
  • The paper relies on this without support.

    Cellular senescence is a driver of aging and age-related diseases.

    “Cellular senescence is a well-established driver of aging and age-related diseases.”
  • The paper relies on this without support.

    Cells bearing senescence-associated markers accumulate in most tissues during aging and age-related diseases.

    “Cells bearing senescence-associated markers have been shown to accumulate in most tissues during aging and age-related diseases.”
  • The paper relies on this without support.

    Visium provides robust spatially resolved whole-transcriptome evaluation at relatively low resolution and discontinuous detection area.

    “The company 10x Genomics currently offers spatial barcoding-based transcript detection (Visium) in flash-frozen and FFPE tissues, providing robust spatially resolved whole-transcriptome evaluation at the cost of relatively low resolution and discontinuous detection area (barcoded spot diameter, ~50 μm; 100 μm between spots; 6.5 × 6.5-mm tissue-capture area).”
  • The paper relies on this without support.

    Senescent cells are typically rare in tissues.

    “This would allow for integration of transcriptomic, proteomic and epigenomic data for the positive identification of senescent cells, which are typically rare.”
  • The paper relies on this without support.

    Many senescence-associated transcripts, such as those encoding cell cycle-dependent kinase inhibitors, are not very abundantly expressed.

    “Furthermore, many of the senescence-associated transcripts, such as those encoding cell cycle-dependent kinase inhibitors, are not very abundantly expressed; therefore, high-resolution and high-capture output are essential for spatial analysis of cell senescence.”
  • The paper relies on this without support.

    Reference scRNA-seq datasets with annotated senescent cells are lacking in many types of tissues.

    “Third, reference scRNA-seq datasets with annotated senescent cells are lacking in many types of tissues.”
  • The paper relies on this without support.

    The performance of deconvoluting senescent cells is largely unknown.

    “Finally, although there are studies benchmarking the performance of deconvolution methods in a more general setting88, the performance of deconvoluting senescent cells is largely unknown.”
  • The paper relies on this without support.

    The accuracy for individual nuclei is difficult to establish in tissues.

    “While the accuracy for individual nuclei is difficult to establish in tissues, deep learning predictors can offer overall assessment of senescence in tissues along with differential analysis across ages, medical conditions or other factors.”
  • The paper relies on this without support.

    Senescence is a cell state with temporal and phenotypic heterogeneous characteristics that depend on cell type, organ, stimulus and physiologic and temporal conditions.

    “While markers can help to identify specific cell types, senescence is a cell state with temporal and phenotypic heterogeneous characteristics that will depend on cell type, organ, stimulus and physiologic and temporal conditions.”
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Metadata

Authors
Gurkar, A. U., Gerencser, A. A., Mora, A. L., Nelson, A. C., Zhang, A. R., Lagnado, A. B., Enninful, A., Benz, C., Furman, D., Beaulieu, D., Jurk, D., Thompson, E. L., Wu, F., Rodriguez, F., Barthel, G., Chen, H., Phatnani, H., Heckenbach, I., Chuang, J. H., Horrell, J., Petrescu, J., Alder, J. K., Lee, J. H., Niedernhofer, L. J., Kumar, M., Königshoff, M., Bueno, M., Sokka, M., Scheibye-Knudsen, M., Neretti, N., Eickelberg, O., Adams, P. D., Hu, Q., Zhu, Q., Porritt, R. A., Dong, R., Peters, S., Victorelli, S., Pengo, T., Khaliullin, T., Suryadevara, V., Fu, X., Bar-Joseph, Z., Ji, Z., & Passos, J. F.
Affiliations
  • University of Pittsburgh (US)
  • Buck Institute for Research on Aging (US)
  • The Ohio State University (US)
  • Duke University (US)
  • Mayo Clinic (US)
  • Yale University (US)
  • University of Minnesota (US)
  • Carnegie Mellon University (US)
  • Columbia University Irving Medical Center (US)
  • The Jackson Laboratory for Genomic Medicine (US)
  • Brown University (US)
  • University of Michigan (US)
  • Stanford University (US)
  • Sanford Burnham Prebys Medical Discovery Institute (US)
  • University of California San Diego (US)
  • University of Washington (US)
Corresponding author
Passos, João F.
Venue
Nature Aging
Published
2023-07-03
Volume
3
Issue
7
Pages
776-790
ISSN
2662-8465 (ISSN-L)
OpenAlex
W4382989269
Cited by
141
References
110
Citations by year
2026: 43 · 2025: 46 · 2024: 44 · 2023: 8
FWCI
3.88
Type
journal article
Language
en
Open access
Locations
  • Nature Aging (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • The Mouseion at the JAXlibrary (Jackson Laboratory) (submittedVersion)
Funders
  • Novo Nordisk Fonden (NNF17OC0027812)
  • National Institutes of Health
  • National Institute on Aging (U54AG075941, U54AG075932, U54AG076041, U54AG076040, U54AG079758, U54AG075931, U54AG075936)
  • NIH Office of the Director
  • National Cancer Institute (UG3CA268091, UG3CA268202, UG3CA268112, UH3CA268103, UG3CA268103, UG3CA268096, UH3CA268091, U24CA268108)
  • Common Fund (U24CA268108)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Telomeres, Telomerase, and Senescence
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
Keywords
cellular senescence, spatial imaging, senescence markers, single-cell technologies, senescence heterogeneity, extracellular space, aging, age-related diseases
MeSH terms
Aging, Animals, Humans, Longevity, United States, Cellular Senescence, Mice

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