Single-cell analysis identifies dynamic gene expression networks that govern B cell development and transformation

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Abstract

Integration of external signals and B-lymphoid transcription factor activities organise B cell lineage commitment through alternating cycles of proliferation and differentiation, producing a diverse repertoire of mature B cells. We use single-cell transcriptomics/proteomics to identify differentially expressed gene networks across B cell development and correlate these networks with subtypes of B cell leukemia. Here we show unique transcriptional signatures that refine the pre-B cell expansion stages into pre-BCR-dependent and pre-BCR-independent proliferative phases. These changes correlate with reciprocal changes in expression of the transcription factor EBF1 and the RNA binding protein YBX3, that are defining features of the pre-BCR-dependent stage. Using pseudotime analysis, we further characterize the expression kinetics of different biological modalities across B cell development, including transcription factors, cytokines, chemokines, and their associated receptors. Our findings demonstrate the underlying heterogeneity of developing B cells and characterise developmental nodes linked to B cell transformation.

Original languageEnglish (US)
Article number6843
JournalNature communications
Volume12
Issue number1
DOIs
StatePublished - Dec 2021

Bibliographical note

Funding Information:
We thank G. Hubbard, A. Rost, and N. Keller, for mouse and technical assistance, J. Motl and P. Champoux for cell sorting and Flow Cytometry Core Facility maintenance at the University of Minnesota (5P01AI035296), D. George for providing Ybx3−/− bone marrow, E. Stanley, J. Daniels, and Dr. K. Beckman and the University of Minnesota Genomics Center for 10X genomics single-cell capture and sequencing, Dr. Meinrad Busslinger for Pax5+/− mice, Dr. Rudolf Grosschedl for Ebf1+/− mice, Dr. Tim Ley for Ybx3-/- mice, and Drs. J. Pereira, T. Lebien, and D. Owen for review and comments on the paper. The authors acknowledge the Minnesota Supercomputing Institute (MSI) at the University of Minnesota for providing resources that contributed to the research results reported within this paper. This work was supported by an individual predoctoral F30 fellowship from the NIH (F30CA232399) and T32 training grant (T32 GM008244), R.D.L, and. NIH grants R01AI124512, R01AI147540, and R01CA232317, M.A.F.

Publisher Copyright:
© 2021, The Author(s).

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural

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