Single-cell time series analysis reveals the dynamics of HSPC response to inflammation

Author:

Bouman Brigitte J12,Demerdash Yasmin345,Sood Shubhankar345,Grünschläger Florian456,Pilz Franziska34,Itani Abdul R345ORCID,Kuck Andrea34,Marot-Lassauzaie Valérie17ORCID,Haas Simon148910,Haghverdi Laleh1ORCID,Essers Marieke AG3411ORCID

Affiliation:

1. Berlin Institute for Medical Systems Biology, Max Delbrück Center in the Helmholtz Association, Berlin, Germany

2. Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany

3. Division Inflammatory Stress in Stem Cells, German Cancer Research Center (DKFZ), Heidelberg, Germany

4. Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM gGMBH), Heidelberg, Germany

5. Faculty of Biosciences, University of Heidelberg, Heidelberg, Germany

6. Division of Stem Cells and Cancer, Deutsches Krebsforschungszentrum (DKFZ) and DKFZ–ZMBH Alliance, Heidelberg, Germany

7. Charité-Universitätsmedizin, Berlin, Germany

8. Department of Hematology, Oncology and Cancer Immunology, Campus Benjamin Franklin, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany

9. German Cancer Consortium (DKTK), Heidelberg, Germany

10. Berlin Institute of Health (BIH) at Charité – Universitätsmedizin Berlin, Berlin, Germany

11. DKFZ-ZMBH Alliance, Heidelberg, Germany

Abstract

Hematopoietic stem and progenitor cells (HSPCs) are known to respond to acute inflammation; however, little is understood about the dynamics and heterogeneity of these stress responses in HSPCs. Here, we performed single-cell sequencing during the sensing, response, and recovery phases of the inflammatory response of HSPCs to treatment (a total of 10,046 cells from four time points spanning the first 72 h of response) with the pro-inflammatory cytokine IFNα to investigate the HSPCs’ dynamic changes during acute inflammation. We developed the essential novel computational approaches to process and analyze the resulting single-cell time series dataset. This includes an unbiased cell type annotation and abundance analysis post inflammation, tools for identification of global and cell type-specific responding genes, and a semi-supervised linear regression approach for response pseudotime reconstruction. We discovered a variety of different gene responses of the HSPCs to the treatment. Interestingly, we were able to associate a global reduced myeloid differentiation program and a locally enhanced pyroptosis activity with reduced myeloid progenitor and differentiated cells after IFNα treatment. Altogether, the single-cell time series analyses have allowed us to unbiasedly study the heterogeneous and dynamic impact of IFNα on the HSPCs.

Funder

Deutsche Forschungsgemeinschaft

Bundesministerium für Bildung und Forschung

Publisher

Life Science Alliance, LLC

Subject

Health, Toxicology and Mutagenesis,Plant Science,Biochemistry, Genetics and Molecular Biology (miscellaneous),Ecology

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