Transfer learning in a biomaterial fibrosis model identifies in vivo senescence heterogeneity and contributions to vascularization and matrix production across species and diverse pathologies
Open accessAbstract
Cellular senescence is a state of permanent growth arrest that plays an important role in wound healing, tissue fibrosis, and tumor suppression. Despite senescent cells' (SnCs) pathological role and therapeutic interest, their phenotype in vivo remains poorly defined. Here, we developed an in vivo-derived senescence signature (SenSig) using a foreign body response-driven fibrosis model in a p16-CreER T2;Ai14 reporter mouse. We identified pericytes and "cartilage-like" fibroblasts as senescent and defined cell type-specific senescence-associated secretory phenotypes (SASPs). Transfer learning and senescence scoring identified these two SnC populations along with endothelial and epithelial SnCs in new and publicly available murine and human data single-cell RNA sequencing (scRNAseq) datasets from diverse pathologies. Signaling analysis uncovered crosstalk between SnCs and myeloid cells via an IL34-CSF1R-TGFβR signaling axis, contributing to tissue balance of vascularization and matrix production. Overall, our study provides a senescence signature and a computational approach that may be broadly applied to identify SnC transcriptional profiles and SASP factors in wound healing, aging, and other pathologies.
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Existing senescence signatures and computational tools, including SenMayo, hUSI, SenSig, and SenePy, perform variably across biological contexts, likely because they capture different facets of the phenotype depending on how they were derived and validated.
“Although several valuable senescence signatures and computational tools have been developed, including SenMayo, hUSI, SenSig, and SenePy,14–17 their performance can vary across biological contexts, likely because they capture distinct facets of the senescence phenotype depending on how they were derived and validated.18”
From SenCat: Cataloging human cell senescence through multi-omic profiling of multiple senescent primary cell types (2026) · cites this paper as [15]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredThe senescent state in the SenCat models was confirmed by evaluating multiple senescence markers or phenotypes, following SenNet consortium guidelines.
“The senescent state was confirmed by evaluating multiple senescence markers or phenotypes, consistent with guidelines from the SenNet consortium.14–17,20”
From SenCat: Cataloging human cell senescence through multi-omic profiling of multiple senescent primary cell types (2026) · cites this paper as [15]
The section on generating an in vivo senescence signature reports evaluating several markers and phenotypes, including p16, p21, SASP factors, and growth arrest. However, the paper describes its own reporter-mouse and SenSig models; it does not identify SenCat models or say that its evaluation followed SenNet consortium guidelines (no page number is available in our copy). (LLM, conf=0.82)
Experimental — accuracy not yet measuredThose earlier models show strong cross-dataset generalizability and robust scoring, yet they often come from mixed multi-study collections or narrower biological settings, which may restrict how well they capture the full range of senescent states across cell types and triggers.
“These models demonstrate impressive cross-dataset generalizability and scoring robustness, but they are often derived either from heterogeneous multi-study collections or from narrower biological settings, which can limit the extent to which they capture the full diversity of senescent states across cell types and triggers.14–17,20”
From SenCat: Cataloging human cell senescence through multi-omic profiling of multiple senescent primary cell types (2026) · cites this paper as [15]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredEarlier work has produced RNA signatures of cellular senescence from various collections of human in vitro data.
“recent efforts have identified transcriptomic signatures of cellular senescence derived from a different panels of human in vitro datasets.14–17,20”
From SenCat: Cataloging human cell senescence through multi-omic profiling of multiple senescent primary cell types (2026) · cites this paper as [15]
The discussion (section labeled “1 3”; page unavailable) says earlier studies sequenced senescent cells cultured in vitro to derive signatures across several forms of senescence, and the results describe three in vitro-derived signatures from the literature. Our copy does not establish that the datasets used to derive those signatures were human, so it supports the claim except for that qualifier. (LLM, conf=0.82)
Experimental — accuracy not yet measuredSenCat adds value beyond earlier computational and ML methods through its broad atlas, its in-house generation under tightly controlled and consistent conditions, its combination of RNA and protein data, and its capacity to derive both shared and context-limited senescence programs within one framework.
“the added value of SenCat lies in the breadth of the underlying atlas, the fact that it was entirely generated under strictly controlled and consistent settings in-house, the integration of transcriptomic and proteomic modalities, and the ability to derive both shared and context-restricted senescence-associated programs from a common framework.”
From SenCat: Cataloging human cell senescence through multi-omic profiling of multiple senescent primary cell types (2026) · cites this paper as [15]
The Discussion (page not available) says the paper’s SenSig method identifies shared features of senescence while allowing tissue- and pathology-specific analysis. It does not establish those capabilities for SenCat, nor does it support the claimed broad, entirely in-house atlas generated under consistent conditions or integration of transcriptomic and proteomic data. The methods describe an RNA-based SenSig signature instead. (LLM, conf=0.84)
Experimental — accuracy not yet measured
The study identified several putative senescent hepatocyte clusters based on senescence gene expression, SASP factors, and established senescence signatures.
“We then identified several PS hepatocyte clusters based on senescence gene expression, SASP factors, and established senescence signatures (STAR Methods).40,41”
From Cellular senescence in human liver under normal aging and cancer (2026) · cites this paper as [41]
In “SenSig identifies distinct senescent phenotypes in human idiopathic pulmonary fibrosis and cancer” (page unavailable), the source reports identifying putative senescent clusters using a senescence signature and discusses SASP factors. Those clusters are from lung fibrosis and cancer datasets, not hepatocytes; the source does not support attributing the identification of hepatocyte clusters to this study. (LLM, conf=0.85)
Experimental — accuracy not yet measuredML-defined senescent populations were enriched for other senescence signatures, with some lineage-specific disparities.
“the ML-defined senescent populations were enriched for other senescence signatures,12–18 although with some lineage-specific disparities for some lists (Figure 4G)”
From SenCat: Cataloging human cell senescence through multi-omic profiling of multiple senescent primary cell types (2026) · cites this paper as [15]
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- Authors
- Cherry, C., Andorko, J. I., Krishnan, K., Mejías, J. C., Nguyen, H. H., Stivers, K. B., Gray-Gaillard, E. F., Ruta, A., Han, J., Hamada, N., Hamada, M., Sturmlechner, I., Trewartha, S., Michel, J. H., Davenport Huyer, L., Wolf, M. T., Tam, A. J., Peña, A. N., Keerthivasan, S., Le Saux, C. J., Fertig, E. J., Baker, D. J., Housseau, F., van Deursen, J. M., Pardoll, D. M., & Elisseeff, J. H.
- Affiliations
- Johns Hopkins University (US)
- Mayo Clinic (US)
- National Cancer Institute (US)
- Bristol-Myers Squibb (United States) (US)
- Corresponding author
- Elisseeff, Jennifer H.
- ORCID
- Andorko, James I.
- Krishnan, Kavita
- Mejías, Joscelyn C.
- Nguyen, Helen Hieu
- Stivers, Katlin B.
- Gray-Gaillard, Elise F.
- Ruta, Anna
- Han, Jin
- Sturmlechner, Ines
- Trewartha, Shawn
- Michel, John H.
- Wolf, Matthew T.
- Tam, Ada J.
- Peña, Alexis N.
- Keerthivasan, Shilpa
- Fertig, Elana J.
- Baker, Darren J.
- Housseau, Franck
- Pardoll, Drew M.
- Elisseeff, Jennifer H.
- Venue
- GeroScience
- Published
- 2023-04-20
- Volume
- 45
- Issue
- 4
- Pages
- 2559-2587
- ISSN
- 2152-4041 (ISSN-L)
- 2509-2723
- PMC
- PMC10651581
- OpenAlex
- W4366462717
- Cited by
- 66
- References
- 103
- Citations by year
- 2026: 24 · 2025: 16 · 2024: 17 · 2023: 9
- FWCI
- 11.66
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- GeroScience (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion, other-oa)
- Funders
- U.S. Department of Defense (W81XWH-17-1-0627, W81XWH-14-1-0285)
- Office of Strategic Coordination (DP1AR076959)
- Bloomberg~Kimmel Institute for Cancer Immunotherapy, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University
- Johns Hopkins University
- Bristol-Myers Squibb
- National Cancer Institute (U01CA253403)
- Foundation for the National Institutes of Health (R01 AG057493, 1T32AG058527-01, 5T32CA153952-08)
- National Science Foundation (DGE-1746891)
- Topics
- Health Sciences › Medicine › Physiology › 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, senescent cells, senescence-associated secretory phenotype, fibrosis, foreign body reaction, pericytes, endothelial cell senescence, vascularization, extracellular matrix synthesis, wound healing, aging, transfer learning, single-cell RNA sequencing
- MeSH terms
- Machine Learning, Aging, Animals, Fibroblasts, Humans, Phenotype, Cellular Senescence, Mice
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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