Evidence map›Paper›PMID 42485183›Full record

ArticleSTAR protocols2026

Protocol for classifying cellular senescence from single-cell and single-nucleus RNA sequencing data.

Anina N Lund, Ryan C Thompson, Brian H Kopell, Girish N Nadkarni, Eric J Nestler, Eric E Schadt, Alexander W Charney, Noam D Beckmann

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in STAR protocols, 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
1 · What the graph read from it

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 registry

The 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 literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Anina N LundIcahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA. Electronic address: anina.lund@mssm.edu.
Ryan C ThompsonIcahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Brian H KopellIcahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Girish N NadkarniIcahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Eric J NestlerIcahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Eric E SchadtIcahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Alexander W CharneyIcahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA. Electronic address: alexander.charney@mssm.edu.
Noam D BeckmannIcahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA. Electronic address: noam.beckmann@mssm.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cellular senescence is a cell state characterized by stable cell-cycle arrest accompanied by coordinated molecular, metabolic, and functional changes. Here, we present a protocol to classify senescent cells from single-cell or single-nucleus RNA sequencing data using gene expression activity scoring of a senescence gene set provided by the user. We describe steps for data-driven optimization of estimated proportion of senescent cells per cell type via enrichment testing. We then detail procedures for differential expression analysis to define senescence-associated transcriptional programs. For complete details on the use and execution of this protocol, please refer to Lund et al.

Indexed as

BioinformaticsGenomicsSystems biology

Identifiers

PMID42485183
PMCPMC13396706

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.