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RENGE infers gene regulatory networks using time-series single-cell RNA-seq data with CRISPR perturbations

Ishikawa, Masato Sugino, Seiichi Masuda, Yoshie Tarumoto, Yusuke Seto, Yusuke Taniyama, Nobuko Wagai, Fumi Yamauchi, Yuhei Kojima, Yasuhiro Kiryu, Hisanori Yusa, Kosuke Eiraku, Mototsugu Mochizuki, Atsushi 京都大学 DOI:10.1038/s42003-023-05594-4

2023.12.28

概要

Single-cell RNA-seq analysis coupled with CRISPR-based perturbation has enabled the inference of gene regulatory networks with causal relationships. However, a snapshot of single-cell CRISPR data may not lead to an accurate inference, since a gene knockout can influence multi-layered downstream over time. Here, we developed RENGE, a computational method that infers gene regulatory networks using a time-series single-cell CRISPR dataset. RENGE models the propagation process of the effects elicited by a gene knockout on its regulatory network. It can distinguish between direct and indirect regulations, which allows for the inference of regulations by genes that are not knocked out. RENGE therefore outperforms current methods in the accuracy of inferring gene regulatory networks. When used on a dataset we derived from human-induced pluripotent stem cells, RENGE yielded a network consistent with multiple databases and literature. Accurate inference of gene regulatory networks by RENGE would enable the identification of key factors for various biological systems.

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Acknowledgements

This study was supported by JST CREST Grant number JPMJCR1922 (M.E., A.M. and

K.Y.), JSPS KAKENHI Grant numbers JP17K20146 and JP21H04959 (K.Y.), JP20K15714

and JP22K06237 (Y.T), 17K00398 and 20K12059 (H.K.), 19H05670 and 19H03196

(A.M.), the Mitsubishi foundation (K.Y.), Takeda Science Foundation (Y.T.), Kato

Memorial Bioscience Foundation (Y.T.), and Joint Usage/Research Center program of

Institute for Life and Medical Sciences, Kyoto University (A.M. and H.K.). Sequencing

was supported by Single-cell Genome Information Analysis Core (SignAC) at WPIASHBi, Kyoto University. Computations were performed using the super-computing

resource provided by Human Genome Center, the Institute of Medical Science, the

University of Tokyo (http://sc.hgc.jp/shirokane.html). M.I. would like to thank Naoto

Yamaguchi for helpful comments regarding development of RENGE.

design of CRISPR. Y.M., S.S., Y.T., and K.Y. performed CRISPR and iPSC experiments.

Y.S. and N.T. performed scRNA-seq experiments. M.I. F.W. and M.E. processed and

analyzed the data. M.I. and K.Y. wrote the manuscript with assistance from other

authors.

Competing interests

The authors declare no competing interests.

Additional information

Supplementary information The online version contains supplementary material

available at https://doi.org/10.1038/s42003-023-05594-4.

Correspondence and requests for materials should be addressed to Masato Ishikawa.

Peer review information Communications Biology thanks Mikel Hernaez and the other,

anonymous, reviewer(s) for their contribution to the peer review of this work. Primary

Handling Editor: George Inglis. A peer review file is available.

Reprints and permission information is available at http://www.nature.com/reprints

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© The Author(s) 2023

Author contributions

M.I. designed and implemented RENGE and performed analyses using RENGE. H.K.,

A.M., Y.Y. and Y.K. contributed to developing RENGE. M.E. contributed to experimental

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