scSTAR reveals hidden heterogeneity with a real-virtual cell pair structure across conditions in single-cell RNA sequencing data

Jie Hao, Jiawei Zou, Jiaqiang Zhang, Ke Chen, Duojiao Wu, Wei Cao, Guoguo Shang, Jean Y H Yang, KongFatt Wong-Lin, Hourong Sun, Zhen Zhang, Xiangdong Wang, Wantao Chen, Xin Zou

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)
13 Downloads (Pure)

Abstract

Cell-state transition can reveal additional information from single-cell ribonucleic acid (RNA)-sequencing data in time-resolved biological phenomena. However, most of the current methods are based on the time derivative of the gene expression state, which restricts them to the short-term evolution of cell states. Here, we present single-cell State Transition Across-samples of RNA-seq data (scSTAR), which overcomes this limitation by constructing a paired-cell projection between biological conditions with an arbitrary time span by maximizing the covariance between two feature spaces using partial least square and minimum squared error methods. In mouse ageing data, the response to stress in CD4+ memory T cell subtypes was found to be associated with ageing. A novel Treg subtype characterized by mTORC activation was identified to be associated with antitumour immune suppression, which was confirmed by immunofluorescence microscopy and survival analysis in 11 cancers from The Cancer Genome Atlas Program. On melanoma data, scSTAR improved immunotherapy-response prediction accuracy from 0.8 to 0.96.
Original languageEnglish
Article numberbbad062
Pages (from-to)1-13
Number of pages14
JournalBriefings in Bioinformatics
Volume24
Issue number2
Early online date22 Feb 2023
DOIs
Publication statusPublished online - 22 Feb 2023

Bibliographical note

Funding Information:
National Natural Science Foundation of China (82170045 to J.H., 31800253 to K.C., 81771672 to D.J.W. and 81672745 to W.C.); Special Fund for Scientific Research of Shanghai Landscaping & City Appearance Administrative Bureau (G222410 to K.C., J.H. and X.Z.); Translational Medicine Cross Research Fund of Shanghai Jiao Tong University (ZH2018QNB29 to J.H.); Natural Science Foundation of Shanghai (16ZR1417900 to X.Z.); Shanghai Pujiang program (16PJ1405200 to X.Z. and 16PJ1405100 to J.H.); Shanghai Sailing Program (17YF1410400 to K.C.); The Innovative Research Team of High-level Local Universities in Shanghai (SHSMU-ZLCX20212301 to J.H., W.T.C.).

Publisher Copyright:
© The Author(s) 2023. Published by Oxford University Press. All rights reserved.

Keywords

  • Molecular Biology
  • Information Systems
  • scRNA-seq
  • partial least square regression
  • cell state dynamics

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