Time-Varying Gene Expression Network Analysis Reveals Conserved Transition States in Hematopoietic Differentiation between Human and Mouse.

Shouguo Gao, Ye Chen, Zhijie Wu, Sachiko Kajigaya, Xujing Wang, Neal S Young

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Article in Genes, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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6 authors.

Shouguo GaoHematopoiesis and Bone Marrow Failure Laboratory, Hematology Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA.

Ye ChenDepartment of Mathematics and Statistics, Northern Arizona University, Flagstaff, AZ 86011, USA.

Zhijie WuHematopoiesis and Bone Marrow Failure Laboratory, Hematology Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA.

Sachiko KajigayaHematopoiesis and Bone Marrow Failure Laboratory, Hematology Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA.

Xujing WangDivision of Diabetes, Endocrinology, and Metabolic Diseases (DEM), National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD 20817, USA.

Neal S YoungHematopoiesis and Bone Marrow Failure Laboratory, Hematology Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

(1) Background: analyses of gene networks can elucidate hematopoietic differentiation from single-cell gene expression data, but most algorithms generate only a single, static network. Because gene interactions change over time, it is biologically meaningful to examine time-varying structures and to capture dynamic, even transient states, and cell-cell relationships. (2) Methods: a transcriptomic atlas of hematopoietic stem and progenitor cells was used for network analysis. After pseudo-time ordering with Monocle 2, LOGGLE was used to infer time-varying networks and to explore changes of differentiation gene networks over time. A range of network analysis tools were used to examine properties and genes in the inferred networks. (3) Results: shared characteristics of attributes during the evolution of differentiation gene networks showed a "U" shape of network density over time for all three branches for human and mouse. Differentiation appeared as a continuous process, originating from stem cells, through a brief transition state marked by fewer gene interactions, before stabilizing in a progenitor state. Human and mouse shared hub genes in evolutionary networks. (4) Conclusions: the conservation of network dynamics in the hematopoietic systems of mouse and human was reflected by shared hub genes and network topological changes during differentiation.

Indexed as

Gene Regulatory NetworksHematopoietic SystemAlgorithmsCell DifferentiationHumansTranscriptomesingle-cell RNA sequencetime-varying networktransition state during differentiation

Identifiers

PMID36292775

DOI10.3390/genes13101890

PMCPMC9601530

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