Intracellular and Intercellular Gene Regulatory Network Inference From Time-Course Individual RNA-Seq.

Makoto Kashima,Yuki Shida,Takashi Yamashiro,Hiromi Hirata,Hiroshi Kurosaka

doi:10.3389/fbinf.2021.777299

Makoto Kashima, Yuki Shida + Show 3 more

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https://doi.org/10.3389/fbinf.2021.777299

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Gene regulatory network (GRN) inference is an effective approach to understand the molecular mechanisms underlying biological events. Generally, GRN inference mainly targets intracellular regulatory relationships such as transcription factors and their associated targets. In multicellular organisms, there are both intracellular and intercellular regulatory mechanisms. Thus, we hypothesize that GRNs inferred from time-course individual (whole embryo) RNA-Seq during development can reveal intercellular regulatory relationships (signaling pathways) underlying the development. Here, we conducted time-course bulk RNA-Seq of individual mouse embryos during early development, followed by pseudo-time analysis and GRN inference. The results demonstrated that GRN inference from RNA-Seq with pseudo-time can be applied for individual bulk RNA-Seq similar to scRNA-Seq. Validation using an experimental-source-based database showed that our approach could significantly infer GRN for all transcription factors in the database. Furthermore, the inferred ligand-related and receptor-related downstream genes were significantly overlapped. Thus, the inferred GRN based on whole organism could include intercellular regulatory relationships, which cannot be inferred from scRNA-Seq based only on gene expression data. Overall, inferring GRN from time-course bulk RNA-Seq is an effective approach to understand the regulatory relationships underlying biological events in multicellular organisms.

Highlights

Regulation of gene expression is a fundamental factor that controls cellular events such as proliferation and differentiation
To our knowledge, this study is the first to report Gene regulatory network (GRN) inference based on the combination of individual bulk RNA-Seq and pseudo-time analysis
Unlike scRNA-Seq, which can elucidate transcriptomic dynamics in a certain cellular event such as proliferation and differentiation, bulk RNA-Seq could provide a mixture of various transcriptomic dynamics regarding the cellular events occurring in an embryo (Chasman and Roy, 2017)

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Regulation of gene expression is a fundamental factor that controls cellular events such as proliferation and differentiation. Gene regulatory network (GRN) inference based on time-course data has garnered considerable attention in single-cell RNA-Seq (scRNA-Seq). Transcriptomic heterogeneity of cells due to asynchronous progression of cellular events enables us to infer regulatory relationships of genes. First, dimensional reduction of scRNA-Seq data provides a trajectory of cellular events such as differentiation and proliferation (Treutlein et al, 2014; Haghverdi et al, 2016). As scRNA-Seq with pseudo-time is a dense timecourse observation of cellular events, gene regulatory networks can be inferred by comparing the timing of gene upregulation and downregulation along pseudo-time (Matsumoto et al, 2017; Aalto et al, 2020)

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