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Gene regulatory network inference with popInfer reveals dynamic regulation of hematopoietic stem cell quiescence upon diet restriction and aging

View ORCID ProfileMegan K. Rommelfanger, Marthe Behrends, Yulin Chen, Jonathan Martinez, Martin Bens, Lingyun Xiong, View ORCID ProfileK. Lenhard Rudolph, View ORCID ProfileAdam L. MacLean
doi: https://doi.org/10.1101/2023.04.18.537360
Megan K. Rommelfanger
1Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
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  • ORCID record for Megan K. Rommelfanger
Marthe Behrends
2Research Group on Stem Cell and Metabolism Aging, Leibniz Institute on Aging, Fritz Lipmann Institute (FLI), Jena, Germany
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Yulin Chen
2Research Group on Stem Cell and Metabolism Aging, Leibniz Institute on Aging, Fritz Lipmann Institute (FLI), Jena, Germany
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Jonathan Martinez
1Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
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Martin Bens
3Core Facility Next Generation Sequencing, Leibniz Institute on Aging, Fritz Lipmann Institute (FLI), Jena, Germany
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Lingyun Xiong
1Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
4Department of Stem Cell Biology and Regenerative Medicine, Broad-CIRM Center, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
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K. Lenhard Rudolph
2Research Group on Stem Cell and Metabolism Aging, Leibniz Institute on Aging, Fritz Lipmann Institute (FLI), Jena, Germany
5Medical Faculty, Jena University Hospital, Friedrich Schiller University, Jena, Germany
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  • For correspondence: macleana@usc.edu lenhard.rudolph@leibniz-fli.de
Adam L. MacLean
1Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
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  • For correspondence: macleana@usc.edu lenhard.rudolph@leibniz-fli.de
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Abstract

Inference of gene regulatory networks (GRNs) can reveal cell state transitions from single-cell genomics data. However, obstacles to temporal inference from snapshot data are difficult to overcome. Single-nuclei multiomics data offer means to bridge this gap and derive temporal information from snapshot data using joint measurements of gene expression and chromatin accessibility in the same single cells. We developed popInfer to infer networks that characterize lineage-specific dynamic cell state transitions from joint gene expression and chromatin accessibility data. Benchmarking against alternative methods for GRN inference, we showed that popInfer achieves higher accuracy in the GRNs inferred. popInfer was applied to study single-cell multiomics data characterizing hematopoietic stem cells (HSCs) and the transition from HSC to a multipotent progenitor cell state during murine hematopoiesis across age and dietary conditions. From networks predicted by popInfer, we discovered gene interactions controlling entry to/exit from HSC quiescence that are perturbed in response to diet or aging.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/maclean-lab/popInfer

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted April 20, 2023.
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Gene regulatory network inference with popInfer reveals dynamic regulation of hematopoietic stem cell quiescence upon diet restriction and aging
Megan K. Rommelfanger, Marthe Behrends, Yulin Chen, Jonathan Martinez, Martin Bens, Lingyun Xiong, K. Lenhard Rudolph, Adam L. MacLean
bioRxiv 2023.04.18.537360; doi: https://doi.org/10.1101/2023.04.18.537360
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Gene regulatory network inference with popInfer reveals dynamic regulation of hematopoietic stem cell quiescence upon diet restriction and aging
Megan K. Rommelfanger, Marthe Behrends, Yulin Chen, Jonathan Martinez, Martin Bens, Lingyun Xiong, K. Lenhard Rudolph, Adam L. MacLean
bioRxiv 2023.04.18.537360; doi: https://doi.org/10.1101/2023.04.18.537360

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