Reconstructing gene regulatory networks in single-cell transcriptomic data analysis

Dai, Hao; Jin, Qiqi; Lin, Li; Chen, Luonan

doi:10.24272/j.issn.2095-8137.2020.215

Cited by 11 publications

(5 citation statements)

References 44 publications

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“…Recently, GRN inference based on the combination of scRNA-Seq and pseudo-time analysis has garnered considerable attention ( Dai et al, 2020 ). However, to our knowledge, this study is the first to report GRN inference based on the combination of individual bulk RNA-Seq and pseudo-time analysis.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2021

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“…Recently, GRN inference based on the combination of scRNA-Seq and pseudo-time analysis has attracted much attention 44 . However, to our knowledge, this study is the first to report GRN inference based on the combination of individual bulk RNA-Seq and pseudo-time analysis.…”

Section: Discussionmentioning

confidence: 99%

Intracellular and intercellular gene regulatory networks inference from time-course individual RNA-Seq

Kashima

Shida

Yamashiro

et al. 2021

Preprint

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Motivation: Recent technical advances in bulk RNA-Seq have enabled time-course RNA-Seq of whole individual embryos to understand the underlying molecular mechanisms. Thus, we hypothesized that gene regulatory networks (GRNs) inferred from time-course individual RNA-Seq during embryonic development reveal intercellular regulatory relationships involved in signaling pathways. Results: Time-course bulk RNA-Seq of individual mouse embryos in early development, followed by pseudo-time analysis and GRN inference, 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. Overall, the inferred GRN include intracellular as well as intercellular regulatory relationships, which cannot be inferred from scRNA-Seq. Thus, inferring GRN from time-course bulk RNA-Seq is a novel approach for understanding the regulatory relationships underlying cellular events.

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“…A large number of methods have been proposed to build TRNs using single-cell genomic data using both expression (scRNA-Seq) and chromatin accessibility data (scATAC-Seq), as discussed comprehensively elsewhere. [52][53][54][55][56][57][58][59] Singlecell technologies present tremendous opportunities for constructing TRNs. As opposed to bulk data where there are usually tens of samples as data points, there are thousands of cells as individual data points in the single-cell data.…”

Section: Transcription Factor Networkmentioning

confidence: 99%

Advances in computational and experimental approaches for deciphering transcriptional regulatory networks

Moeckel,

Mouratidis,

Chantzi

et al. 2024

BioEssays

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Understanding the influence of cis‐regulatory elements on gene regulation poses numerous challenges given complexities stemming from variations in transcription factor (TF) binding, chromatin accessibility, structural constraints, and cell‐type differences. This review discusses the role of gene regulatory networks in enhancing understanding of transcriptional regulation and covers construction methods ranging from expression‐based approaches to supervised machine learning. Additionally, key experimental methods, including MPRAs and CRISPR‐Cas9‐based screening, which have significantly contributed to understanding TF binding preferences and cis‐regulatory element functions, are explored. Lastly, the potential of machine learning and artificial intelligence to unravel cis‐regulatory logic is analyzed. These computational advances have far‐reaching implications for precision medicine, therapeutic target discovery, and the study of genetic variations in health and disease.

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Reconstructing gene regulatory networks in single-cell transcriptomic data analysis

Cited by 11 publications

References 44 publications

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

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

Intracellular and intercellular gene regulatory networks inference from time-course individual RNA-Seq

Advances in computational and experimental approaches for deciphering transcriptional regulatory networks

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