In one paragraphArticle in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registryThe trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
3 · Its place in the literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
19 authors.
Yilin YangDepartment of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA.ORCID 0009-0009-8301-3918 Paul R HessDepartment of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Sijia HuangPenn Institute of Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA.ORCID 0000-0002-4680-9484 Marcos G TenecheSanford Burnham Prebys Medical Discovery Institute, Cancer Genome and Epigenetics Program, La Jolla, CA, USA.ORCID 0000-0003-4785-7987 Hanzhi WangDepartment of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
Karl N MillerSanford Burnham Prebys Medical Discovery Institute, Cancer Genome and Epigenetics Program, La Jolla, CA, USA.ORCID 0000-0002-6076-3136 Andrew E DavisSanford Burnham Prebys Medical Discovery Institute, Cancer Genome and Epigenetics Program, La Jolla, CA, USA.ORCID 0000-0002-7797-1556 Charlene MicianoDepartment of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA.
Kelly Yichen LiCenter for Data Science and Artificial Intelligence, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA.ORCID 0000-0001-7377-5729 Sainath MamdeCenter for Epigenomics, University of California, San Diego, La Jolla, CA, USA.
Kevin YipSanford Burnham Prebys Medical Discovery Institute, Cancer Genome and Epigenetics Program, La Jolla, CA, USA.ORCID 0000-0001-5516-9944 Qian YangDepartment of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA.
Elizabeth SmootDepartment of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA.ORCID 0009-0007-5837-1794 Allen WangDepartment of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA.
Bradley JohnsonDepartment of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA.ORCID 0000-0002-7443-7227 Peter D AdamsSanford Burnham Prebys Medical Discovery Institute, Cancer Genome and Epigenetics Program, La Jolla, CA, USA.ORCID 0000-0002-0684-1770 Nancy R ZhangDepartment of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA.ORCID 0000-0002-0880-5749 Funding
Spatial Mapping Senescent Cells Across the Mouse Lifespan by Multiplex Transcriptomics and EpigenomicsU54AG079758 · NIA · SANFORD BURNHAM PREBYS MEDICAL DISCOVERY INSTITUTE · PI PETER D. ADAMS · 2022 to 2026
$12.1MMultiomic single cell and spatial interrogation of mechanisms in cellular adaptation to stressR01GM149671 · NIGMS · UNIVERSITY OF PENNSYLVANIA · PI Sydney Shaffer, Nancy R Zhang · 2024 to 2026
$1.6MMultiomic methods for the characterization of cellular agingR56AG081351 · NIA · UNIVERSITY OF PENNSYLVANIA · PI JOHNSON, F. BRAD, ZHANG, NANCY R · 2024 to 2024
$400kNIA NIH HHS R56 AG081351NIA NIH HHS U54 AG079758NIGMS NIH HHS R01 GM149671
6 · The paper itselfAbstract
Characterizing cellular aging is essential for understanding age-related diseases. While tissue-level studies reveal broad age-associated changes, they often reflect compositional shifts rather than cell-level reprogramming. The cellular damage hypothesis posits that aging involves the accumulation of DNA, chromatin, and other damage across molecular layers, increasing transcriptional entropy. Existing supervised methods for detecting cellular senescence yield cell type-specific senescence scores but rely on labeled data and lack generalizability. Here, we introduce a first-principles framework for quantifying transcriptional entropy in single cells as each cell's deviation from a transcriptomic manifold, capturing breakdown of transcriptional coordination. This unsupervised approach identifies aging-affected cell types and distinguishes two cellular aging mechanisms: loss of expression precision and activation of stress-response pathways in high entropy cells. Applied to Tabula Muris Senis and SenNet Multiome datasets, transcriptional entropy correlates with chromatin-based mitotic age and highlights regenerative tissue compartments as most affected by aging.
Identifiers
PMID41648477
PMCPMC12871850
What Socratic holds
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