1.
2.
3.
4.
Alon, U. Network motifs: theory and experimental approaches. Nat. Rev.
Genet. 8, 450–461 (2007).
Peter, I. S. & Davidson, E. H. Implications of developmental gene regulatory
networks inside and outside developmental biology. Curr. Topics Dev. Biol.
117, 237–251 (2016).
Iacono, G., Massoni-Badosa, R. & Heyn, H. Single-cell transcriptomics unveils
gene regulatory network plasticity. Genome Biol. 20, 1–20 (2019).
Liu, Y.-Y., Slotine, J.-J. & Barabási, A.-L. Controllability of complex networks.
Nature 473, 167–173 (2011).
32.
33.
34.
Kobayashi, K., Maeda, K., Tokuoka, M., Mochizuki, A. & Satou, Y. Controlling
cell fate specification system by key genes determined from network structure.
IScience 4, 281–293 (2018).
Mercatelli, D., Scalambra, L., Triboli, L., Ray, F. & Giorgi, F. M. Gene
regulatory network inference resources: A practical overview. Biochim.
Biophys. Acta (BBA)-Gene Regulatory Mech. 1863, 194430 (2020).
Delgado, F. M. & Gómez-Vela, F. Computational methods for gene regulatory
networks reconstruction and analysis: A review. Artif. Intel. Med. 95, 133–145
(2019).
Oki, S. et al. Chip-atlas: a data-mining suite powered by full integration of
public chip-seq data. EMBO Rep. 19, e46255 (2018).
Huynh-Thu, V. A., Irrthum, A., Wehenkel, L. & Geurts, P. Inferring
regulatory networks from expression data using tree-based methods. PloS One
5, e12776 (2010).
Marbach, D. et al. Wisdom of crowds for robust gene network inference. Nat.
Methods 9, 796–804 (2012).
Pratapa, A., Jalihal, A. P., Law, J. N., Bharadwaj, A. & Murali, T.
Benchmarking algorithms for gene regulatory network inference from singlecell transcriptomic data. Nat. Methods 17, 147–154 (2020).
Seçilmiş, D. et al. Knowledge of the perturbation design is essential for
accurate gene regulatory network inference. Sci. Rep. 12, 16531 (2022).
Dixit, A. et al. Perturb-seq: dissecting molecular circuits with scalable singlecell rna profiling of pooled genetic screens. Cell 167, 1853–1866 (2016).
Datlinger, P. et al. Pooled crispr screening with single-cell transcriptome
readout. Nat. Methods 14, 297–301 (2017).
Replogle, J. M. et al. Mapping information-rich genotype-phenotype
landscapes with genome-scale perturb-seq. Cell. (2022).
Yang, L. et al. scmageck links genotypes with multiple phenotypes in singlecell crispr screens. Genome Biol. 21, 1–14 (2020).
Cannoodt, R., Saelens, W., Deconinck, L. & Saeys, Y. Spearheading future
omics analyses using dyngen, a multi-modal simulator of single cells. Nat.
Commun. 12, 1–9 (2021).
Li, M. & Belmonte, J. C. I. Ground rules of the pluripotency gene regulatory
network. Nat. Rev. Genet. 18, 180–191 (2017).
Young, R. A. Control of the embryonic stem cell state. Cell 144, 940–954
(2011).
Takahashi, K. & Yamanaka, S. A decade of transcription factor-mediated
reprogramming to pluripotency. Nat. Rev. Mol. Cell Biol. 17, 183–193 (2016).
Yamamoto, T. et al. Differentiation potential of pluripotent stem cells
correlates to the level of chd7. Sci. Rep. 8, 1–12 (2018).
Miyazaki, K. et al. Generation of progesterone-responsive endometrial stromal
fibroblasts from human induced pluripotent stem cells: role of the wnt/ctnnb1
pathway. Stem Cell Rep. 11, 1136–1155 (2018).
Raina, K., Dey, C., Thool, M., Sudhagar, S. & Thummer, R. P. An insight into
the role of utf1 in development, stem cells, and cancer. Stem Cell Rev. Rep. 17,
1280–1293 (2021).
Szklarczyk, D. et al. The STRING database in 2021: customizable
protein–protein networks, and functional characterization of user-uploaded
gene/measurement sets. Nucleic Acids Res. 49, D605–D612 (2021).
Giurgiu, M. et al. CORUM: the comprehensive resource of mammalian
protein complexes—2019. Nucleic Acids Res. 47, D559–D563 (2019).
Feng, W. et al. Chd7 is indispensable for mammalian brain development
through activation of a neuronal differentiation programme. Nat. Commun. 8,
1–14 (2017).
Verberne, E. A. et al. Jarid2 haploinsufficiency is associated with a clinically
distinct neurodevelopmental syndrome. Genet. Med. 23, 374–383 (2021).
Shakya, A. et al. Pluripotency transcription factor oct4 mediates stepwise
nucleosome demethylation and depletion. Mol. Cell. Biol. 35, 1014–1025
(2015).
Chitalia, V. C. et al. Jade-1 inhibits wnt signalling by ubiquitylating β-catenin
and mediates wnt pathway inhibition by pvhl. Nat. Cell Biol. 10, 1208–1216
(2008).
Yamamoto, M. et al. The prdm14–ctbp1/2–prc2 complex regulates
transcriptional repression during the transition from primed to naïve
pluripotency. J. Sci. 133, jcs240176 (2020).
Schnetz, M. P. et al. Chd7 targets active gene enhancer elements to modulate
es cell-specific gene expression. PLoS Genet. 6, e1001023 (2010).
Aalto, A., Viitasaari, L., Ilmonen, P., Mombaerts, L. & Gonçalves, J. Gene
regulatory network inference from sparsely sampled noisy data. Nat.
Commun. 11, 1–9 (2020).
Fiedler, B., Mochizuki, A., Kurosawa, G. & Saito, D. Dynamics and control at
feedback vertex sets. i: Informative and determining nodes in regulatory
networks. J. Dyn. Differ. Eq. 25, 563–604 (2013).
Mochizuki, A., Fiedler, B., Kurosawa, G. & Saito, D. Dynamics and control at
feedback vertex sets. ii: A faithful monitor to determine the diversity of
molecular activities in regulatory networks. J. Theor. Biol. 335, 130–146
(2013).
COMMUNICATIONS BIOLOGY | (2023)6:1290 | https://doi.org/10.1038/s42003-023-05594-4 | www.nature.com/commsbio
13
ARTICLE
COMMUNICATIONS BIOLOGY | https://doi.org/10.1038/s42003-023-05594-4
35. Kobayashi, K., Maeda, K., Tokuoka, M., Mochizuki, A. & Satou, Y. Using
linkage logic theory to control dynamics of a gene regulatory network of a
chordate embryo. Sci. Rep. 11, 1–11 (2021).
36. Collier, A. J. et al. Genome-wide screening identifies polycomb repressive
complex 1.3 as an essential regulator of human naïve pluripotent cell
reprogramming. Sci. Adv. 8, eabk0013 (2022).
37. Tzelepis, K. et al. A crispr dropout screen identifies genetic vulnerabilities and
therapeutic targets in acute myeloid leukemia. Cell Rep. 17, 1193–1205 (2016).
38. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184,
3573–3587 (2021).
39. Akiba, T., Sano, S., Yanase, T., Ohta, T. & Koyama, M. Optuna: A nextgeneration hyperparameter optimization framework. In Proceedings of the
25rd ACM SIGKDD International Conference on Knowledge Discovery and
Data Mining (2019).
40. Huynh-Thu, V. A. & Geurts, P. dyngenie3: dynamical genie3 for the inference
of gene networks from time series expression data. Sci. Rep. 8, 1–12 (2018).
41. Barry, T., Wang, X., Morris, J. A., Roeder, K. & Katsevich, E. Sceptre improves
calibration and sensitivity in single-cell crispr screen analysis. Genome Biol.
22, 1–19 (2021).
42. Wang, L. Single-cell normalization and association testing unifying crispr
screen and gene co-expression analyses with normalisr. Nat. Commun. 12,
1–13 (2021).
43. Ishikawa, M. masastat/renge. https://doi.org/10.5281/zenodo.10114567.
[Computer software].
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
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Open Access This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative
Commons licence, and indicate if changes were made. The images or other third party
material in this article are included in the article’s Creative Commons licence, unless
indicated otherwise in a credit line to the material. If material is not included in the
article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from
the copyright holder. To view a copy of this licence, visit http://creativecommons.org/
licenses/by/4.0/.
© 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
14
COMMUNICATIONS BIOLOGY | (2023)6:1290 | https://doi.org/10.1038/s42003-023-05594-4 | www.nature.com/commsbio
...