Evidence map›Paper›PMID 38798365›Full record

ArticlebioRxiv : the preprint server for biology2024

Single-cell morphology encodes functional subtypes of senescence in aging human dermal fibroblasts.

Pratik Kamat, Nico Macaluso, Chanhong Min, Yukang Li, Anshika Agrawal, Aaron Winston, Lauren Pan, Bartholomew Starich, Teasia Stewart, Pei-Hsun Wu and 3 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. 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

5 · Who and what money

Authors and funding

13 authors.

Pratik KamatDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA.ORCID 0000-0002-2028-6136
Nico MacalusoDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA.ORCID 0000-0002-1648-1588
Chanhong MinInstitute for Nanobiotechnology, Johns Hopkins University, Baltimore, Maryland, USA.
Yukang LiInstitute for Nanobiotechnology, Johns Hopkins University, Baltimore, Maryland, USA.
Anshika AgrawalDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
Aaron WinstonDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
Lauren PanDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
Bartholomew StarichDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA.ORCID 0000-0002-0006-0236
Teasia StewartDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
Pei-Hsun WuDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
Jean FanDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.ORCID 0000-0002-0212-5451
Jeremy WalstonDepartment of Geriatric Medicine and Gerontology, Johns Hopkins School of Medicine, Baltimore, Maryland, USA.ORCID 0000-0002-6965-2723
Jude M PhillipDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA.ORCID 0000-0002-2999-5254

Funding

Technological Assessment and Solutions Core - RC4P30AG021334 · NIA · JOHNS HOPKINS UNIVERSITY · PI Jeremy D Walston · 2003 to 2026
$33.2M
Computational methods for delineating subcellular and cellular spatial transcriptional heterogeneity along developmental trajectoriesR35GM142889 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI FAN, JEAN · 2021 to 2025
$2.0M
Three-dimensional maps of senescence in the human pancreasUG3CA275681 · NCI · JOHNS HOPKINS UNIVERSITY · PI WU, PEI-HSUN · 2022 to 2023
$1.1M
NCI NIH HHS UG3 CA275681NIA NIH HHS P30 AG021334NIGMS NIH HHS R35 GM142889
6 · The paper itself

Abstract

Cellular senescence is an established driver of aging, exhibiting context-dependent phenotypes across multiple biological length-scales. Despite its mechanistic importance, profiling senescence within cell populations is challenging. This is in part due to the limitations of current biomarkers to robustly identify senescent cells across biological settings, and the heterogeneous, non-binary phenotypes exhibited by senescent cells. Using a panel of primary dermal fibroblasts, we combined live single-cell imaging, machine learning, multiple senescence induction conditions, and multiple protein-based senescence biomarkers to show the emergence of functional subtypes of senescence. Leveraging single-cell morphologies, we defined eleven distinct morphology clusters, with the abundance of cells in each cluster being dependent on the mode of senescence induction, the time post-induction, and the age of the donor. Of these eleven clusters, we identified three

Indexed as

agingbiomarkerscell morphologycellular senescencefunctional subtypes

Identifiers

PMID38798365
PMCPMC11118441

What OpenQuestion 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 OpenQuestion graph.