Evidence map›Paper›PMID 39574111›Full record

ArticleGenome biology2024

scStateDynamics: deciphering the drug-responsive tumor cell state dynamics by modeling single-cell level expression changes.

Wenbo Guo, Xinqi Li, Dongfang Wang, Nan Yan, Qifan Hu, Fan Yang, Xuegong Zhang, Jianhua Yao, Jin Gu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genome biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Computational modeling of single-cell dynamics data.Briefings in bioinformatics · 2025
    Review
  3. Article
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

9 authors.

Wenbo GuoMOE Key Lab of Bioinformatics, Department of Automation, BNRIST Bioinformatics Division, Tsinghua University, Beijing, China.
Xinqi LiMOE Key Lab of Bioinformatics, Department of Automation, BNRIST Bioinformatics Division, Tsinghua University, Beijing, China.
Dongfang WangBiomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, China.
Nan YanMOE Key Lab of Bioinformatics, Department of Automation, BNRIST Bioinformatics Division, Tsinghua University, Beijing, China.
Qifan HuMOE Key Lab of Bioinformatics, Department of Automation, BNRIST Bioinformatics Division, Tsinghua University, Beijing, China.
Fan YangAI Lab, Shenzhen, Tencent, China.
Xuegong ZhangMOE Key Lab of Bioinformatics, Department of Automation, BNRIST Bioinformatics Division, Tsinghua University, Beijing, China.
Jianhua YaoAI Lab, Shenzhen, Tencent, China. jianhuayao@tencent.com.
Jin GuMOE Key Lab of Bioinformatics, Department of Automation, BNRIST Bioinformatics Division, Tsinghua University, Beijing, China. jgu@tsinghua.edu.cn.ORCID 0000-0003-3968-8036

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding tumor cell heterogeneity and plasticity is crucial for overcoming drug resistance. Single-cell technologies enable analyzing cell states at a given condition, but catenating static cell snapshots to characterize dynamic drug responses remains challenging. Here, we propose scStateDynamics, an algorithm to infer tumor cell state dynamics and identify common drug effects by modeling single-cell level gene expression changes. Its reliability is validated on both simulated and lineage tracing data. Application to real tumor drug treatment datasets identifies more subtle cell subclusters with different drug responses beyond static transcriptome similarity and disentangles drug action mechanisms from the cell-level expression changes.

Indexed as

AlgorithmsNeoplasmsSingle-Cell AnalysisAntineoplastic AgentsDrug Resistance, NeoplasmGene Expression ProfilingGene Expression Regulation, NeoplasticHumansTranscriptomeAntineoplastic AgentsDrug responseDynamicsSingle-cellTumor

Identifiers

PMID39574111
PMCPMC11583649

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.