Comparative Transcriptomic Analysis of the Hematopoietic System between Human and Mouse by Single Cell RNA Sequencing

Cells. 2021 Apr 21;10(5):973. doi: 10.3390/cells10050973.

Abstract

(1) Background: mouse models are fundamental to the study of hematopoiesis, but comparisons between mouse and human in single cells have been limited in depth. (2) Methods: we constructed a single-cell resolution transcriptomic atlas of hematopoietic stem and progenitor cells (HSPCs) of human and mouse, from a total of 32,805 single cells. We used Monocle to examine the trajectories of hematopoietic differentiation, and SCENIC to analyze gene networks underlying hematopoiesis. (3) Results: After alignment with Seurat 2, the cells of mouse and human could be separated by same cell type categories. Cells were grouped into 17 subpopulations; cluster-specific genes were species-conserved and shared functional themes. The clustering dendrogram indicated that cell types were highly conserved between human and mouse. A visualization of the Monocle results provided an intuitive representation of HSPC differentiation to three dominant branches (Erythroid/megakaryocytic, Myeloid, and Lymphoid), derived directly from the hematopoietic stem cell and the long-term hematopoietic stem cells in both human and mouse. Gene regulation was similarly conserved, reflected by comparable transcriptional factors and regulatory sequence motifs in subpopulations of cells. (4) Conclusions: our analysis has confirmed evolutionary conservation in the hematopoietic systems of mouse and human, extending to cell types, gene expression and regulatory elements.

Keywords: cross-species analysis; gene regulatory network; hematopoiesis; single-cell RNA sequencing.

Publication types

  • Comparative Study
  • Research Support, N.I.H., Intramural

MeSH terms

  • Animals
  • Cell Lineage
  • Evolution, Molecular
  • Gene Expression Regulation
  • Hematopoiesis*
  • Hematopoietic Stem Cells* / cytology
  • Hematopoietic Stem Cells* / metabolism
  • Humans
  • Mice
  • Single-Cell Analysis / methods*
  • Transcriptome*