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  • 标题:Phenotypically supervised single-cell sequencing parses within-cell-type heterogeneity
  • 本地全文:下载
  • 作者:Kevin Chen ; Kivilcim Ozturk ; Ryne L. Contreras
  • 期刊名称:iScience
  • 印刷版ISSN:2589-0042
  • 出版年度:2021
  • 卷号:24
  • 期号:1
  • 页码:1-27
  • DOI:10.1016/j.isci.2020.101991
  • 语种:English
  • 出版社:Elsevier
  • 摘要:SummaryTo better understand cellular communication driving diverse behaviors, we need to uncover the molecular mechanisms of within-cell-type functional heterogeneity. While single-cell RNA sequencing (scRNAseq) has advanced our understanding of cell heterogeneity, linking individual cell phenotypes to transcriptomic data remains challenging. Here, we used a phenotypic cell sorting technique to ask whether phenotypically supervised scRNAseq analysis (pheno-scRNAseq) can provide more insight into heterogeneous cell behaviors than unsupervised scRNAseq. Using a simple 3Din vitrobreast cancer (BRCA) model, we conducted pheno-scRNAseq on invasive and non-invasive cells and compared the results to phenotype-agnostic scRNAseq analysis. Pheno-scRNAseq identified unique and more selective differentially expressed genes than unsupervised scRNAseq analysis. Functional studies validated the utility of pheno-scRNAseq in understanding within-cell-type functional heterogeneity and revealed that migration phenotypes were coordinated with specific metabolic, proliferation, stress, and immune phenotypes. This approach lends new insight into the molecular systems underlying BRCA cell phenotypic heterogeneity.Graphical abstractDisplay OmittedHighlights•Graph-based clusters do not identify collective migration phenotypes•Collective invasion is associated with stress response and proliferation•Non-invasive behavior is associated with plasticity and immunomodulation•Signatures corresponding to migration phenotypes are conserved across speciesCell Biology; Complex System Biology; Transcriptomics
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