Evidence map›Paper›PMID 38045252›Full record

ArticlebioRxiv : the preprint server for biology2025

Single-cell and spatial detection of senescent cells using DeepScence.

Yilong Qu, Beijie Ji, Runze Dong, Liangcai Gu, Cliburn Chan, Jichun Xie, Carolyn Glass, Xiao-Fan Wang, Andrew B Nixon, Zhicheng Ji

Open access · greenAbstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. 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, 10 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors at 2 institutions in 1 country.

Yilong QuDepartment of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA.
Beijie JiDepartment of Statistical Science, Duke University, Durham, NC, USA.
Runze DongDepartment of Biochemistry, University of Washington, Seattle, WA, USA.
Liangcai GuDepartment of Biochemistry, University of Washington, Seattle, WA, USA.ORCID 0000-0002-8232-6025
Cliburn ChanDepartment of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA.
Jichun XieDepartment of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA.
Carolyn GlassDepartment of Pathology, Duke University School of Medicine, Durham, NC, USA.
Xiao-Fan WangDepartment of Pharmacology and Cancer Biology, Duke University School of Medicine, Durham, NC, USA.
Andrew B NixonDepartment of Medicine, Duke University School of Medicine, Durham, NC, USA.
Zhicheng JiDepartment of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA.
Duke University · USUniversity of Washington · US

Funding

The Duke Senescent Cell Evaluations in Normal Tissues (SCENT) Mapping CenterU54AG075936 · NIA · DUKE UNIVERSITY · PI CHAN, CLIBURN C · 2021 to 2025
$12.7M
PIXEL-seq-based spatial, multi-omic profiling for senescent cell mapping with single-cell resolutionUH3CA268096 · NCI · UNIVERSITY OF WASHINGTON · PI GU, LIANGCAI · 2023 to 2024
$1.7M
Computational methods for in situ spatial transcriptomicsR35GM154865 · NIGMS · DUKE UNIVERSITY · PI Zhicheng Ji · 2024 to 2026
$1.2M
NCI NIH HHS UH3 CA268096NIA NIH HHS U54 AG075936NIGMS NIH HHS R35 GM154865
6 · The paper itself

Abstract

Accurately identifying senescent cells is essential for studying their spatial and molecular features. We developed DeepScence, a method based on deep neural networks, to identify senescent cells in single-cell and spatial transcriptomics data. DeepScence is based on CoreScence, a senescence-associated gene set we curated that incorporates information from multiple published gene sets. We demonstrate that DeepScence can accurately identify senescent cells in single-cell gene expression data collected both

Identifiers

PMID38045252
PMCPMC10690237
OpenAlexW4388911429

What OpenQuestion holds

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LicenceCC BY
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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 OpenQuestion graph.