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<p>Large-scale, unbiased single-cell genomics studies of complex developmental compartments, such as hematopoiesis, have inferred novel cell states and trajectories; however, further characterization has been hampered by difficulty isolating cells corresponding to discrete genomic states. To address this, we present a framework that integrates multimodal single-cell analyses (RNA, surface protein and chromatin) with high-dimensional flow cytometry and enables semiautomated enrichment and functional characterization of diverse cell states. Our approach combines transcription factor expression with chromatin activity to uncover hierarchical gene regulatory networks driving these states. We delineated and isolated rare bone marrow Lin<sup>−</sup>Sca<sup>−</sup>CD117<sup>+</sup>CD27<sup>+</sup> multilineage cell states (‘MultiLin’), validated predicted lineage trajectories and mapped differentiation potentials. Additionally, we used transcription factor activity on chromatin to trace and isolate multilineage progenitors undergoing multipotent to oligopotent lineage restriction. In the proposed model of steady-state hematopoiesis, discrete states governed developmental trajectories. This framework provides a scalable solution for isolating and characterizing novel cell states across different biological systems.</p>

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A unified multimodal single-cell framework reveals a discrete state model of hematopoiesis in mice

  • 期刊论文

  • 发表日期：

    2025年10月22日

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  • Kyle Ferchen,
  • Xuan Zhang,
  • Kairavee Thakkar,
  • Guangyuan Li,
  • David Bernardicius,
  • Sidharth Sen,
  • Priyanka Rawat,
  • Andre Olsson,
  • Sierra N. Bennett,
  • Crystal Potter,
  • Fred D. Finkelman,
  • Josh Croteau,
  • Samantha Morris,
  • Harinder Singh,
  • Nathan Salomonis,
  • H. Leighton Grimes

摘要

Large-scale, unbiased single-cell genomics studies of complex developmental compartments, such as hematopoiesis, have inferred novel cell states and trajectories; however, further characterization has been hampered by difficulty isolating cells corresponding to discrete genomic states. To address this, we present a framework that integrates multimodal single-cell analyses (RNA, surface protein and chromatin) with high-dimensional flow cytometry and enables semiautomated enrichment and functional characterization of diverse cell states. Our approach combines transcription factor expression with chromatin activity to uncover hierarchical gene regulatory networks driving these states. We delineated and isolated rare bone marrow Lin−Sca−CD117+CD27+ multilineage cell states (‘MultiLin’), validated predicted lineage trajectories and mapped differentiation potentials. Additionally, we used transcription factor activity on chromatin to trace and isolate multilineage progenitors undergoing multipotent to oligopotent lineage restriction. In the proposed model of steady-state hematopoiesis, discrete states governed developmental trajectories. This framework provides a scalable solution for isolating and characterizing novel cell states across different biological systems.