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Dissecting cell identity via network inference and in silico gene perturbation

Author

Listed:
  • Kenji Kamimoto

    (Washington University School of Medicine in St Louis
    Washington University School of Medicine in St Louis
    Washington University School of Medicine in St Louis)

  • Blerta Stringa

    (Washington University School of Medicine in St Louis
    Washington University School of Medicine in St Louis)

  • Christy M. Hoffmann

    (Washington University School of Medicine in St Louis
    Washington University School of Medicine in St Louis
    Washington University School of Medicine in St Louis)

  • Kunal Jindal

    (Washington University School of Medicine in St Louis
    Washington University School of Medicine in St Louis
    Washington University School of Medicine in St Louis)

  • Lilianna Solnica-Krezel

    (Washington University School of Medicine in St Louis
    Washington University School of Medicine in St Louis)

  • Samantha A. Morris

    (Washington University School of Medicine in St Louis
    Washington University School of Medicine in St Louis
    Washington University School of Medicine in St Louis)

Abstract

Cell identity is governed by the complex regulation of gene expression, represented as gene-regulatory networks1. Here we use gene-regulatory networks inferred from single-cell multi-omics data to perform in silico transcription factor perturbations, simulating the consequent changes in cell identity using only unperturbed wild-type data. We apply this machine-learning-based approach, CellOracle, to well-established paradigms—mouse and human haematopoiesis, and zebrafish embryogenesis—and we correctly model reported changes in phenotype that occur as a result of transcription factor perturbation. Through systematic in silico transcription factor perturbation in the developing zebrafish, we simulate and experimentally validate a previously unreported phenotype that results from the loss of noto, an established notochord regulator. Furthermore, we identify an axial mesoderm regulator, lhx1a. Together, these results show that CellOracle can be used to analyse the regulation of cell identity by transcription factors, and can provide mechanistic insights into development and differentiation.

Suggested Citation

  • Kenji Kamimoto & Blerta Stringa & Christy M. Hoffmann & Kunal Jindal & Lilianna Solnica-Krezel & Samantha A. Morris, 2023. "Dissecting cell identity via network inference and in silico gene perturbation," Nature, Nature, vol. 614(7949), pages 742-751, February.
  • Handle: RePEc:nat:nature:v:614:y:2023:i:7949:d:10.1038_s41586-022-05688-9
    DOI: 10.1038/s41586-022-05688-9
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    Cited by:

    1. Shilu Zhang & Saptarshi Pyne & Stefan Pietrzak & Spencer Halberg & Sunnie Grace McCalla & Alireza Fotuhi Siahpirani & Rupa Sridharan & Sushmita Roy, 2023. "Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets," Nature Communications, Nature, vol. 14(1), pages 1-25, December.
    2. Hao Li & Zebei Han & Yu Sun & Fu Wang & Pengzhen Hu & Yuang Gao & Xuemei Bai & Shiyu Peng & Chao Ren & Xiang Xu & Zeyu Liu & Hebing Chen & Yang Yang & Xiaochen Bo, 2024. "CGMega: explainable graph neural network framework with attention mechanisms for cancer gene module dissection," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    3. Gregory Farber & Yanhan Dong & Qiaozi Wang & Mitesh Rathod & Haofei Wang & Michelle Dixit & Benjamin Keepers & Yifang Xie & Kendall Butz & William J. Polacheck & Jiandong Liu & Li Qian, 2024. "Direct conversion of cardiac fibroblasts into endothelial-like cells using Sox17 and Erg," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    4. Kathryn Weinand & Saori Sakaue & Aparna Nathan & Anna Helena Jonsson & Fan Zhang & Gerald F. M. Watts & Majd Al Suqri & Zhu Zhu & Deepak A. Rao & Jennifer H. Anolik & Michael B. Brenner & Laura T. Don, 2024. "The chromatin landscape of pathogenic transcriptional cell states in rheumatoid arthritis," Nature Communications, Nature, vol. 15(1), pages 1-25, December.
    5. Xiaoying Wang & Maoteng Duan & Jingxian Li & Anjun Ma & Gang Xin & Dong Xu & Zihai Li & Bingqiang Liu & Qin Ma, 2024. "MarsGT: Multi-omics analysis for rare population inference using single-cell graph transformer," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    6. Nicolas Ledru & Parker C. Wilson & Yoshiharu Muto & Yasuhiro Yoshimura & Haojia Wu & Dian Li & Amish Asthana & Stefan G. Tullius & Sushrut S. Waikar & Giuseppe Orlando & Benjamin D. Humphreys, 2024. "Predicting proximal tubule failed repair drivers through regularized regression analysis of single cell multiomic sequencing," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    7. Peizhuo Wang & Xiao Wen & Han Li & Peng Lang & Shuya Li & Yipin Lei & Hantao Shu & Lin Gao & Dan Zhao & Jianyang Zeng, 2023. "Deciphering driver regulators of cell fate decisions from single-cell transcriptomics data with CEFCON," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    8. Yingtong Hou & Bo Lin & Tianyi Xu & Juan Jiang & Shuli Luo & Wanna Chen & Xinwen Chen & Yuanqi Wang & Guanrui Liao & Jianping Wang & Jiayuan Zhang & Xuyang Li & Xiao Xiang & Yubin Xie & Ji Wang & Sui , 2024. "The neurotransmitter calcitonin gene-related peptide shapes an immunosuppressive microenvironment in medullary thyroid cancer," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    9. Fangfang Yan & Akiko Suzuki & Chihiro Iwaya & Guangsheng Pei & Xian Chen & Hiroki Yoshioka & Meifang Yu & Lukas M. Simon & Junichi Iwata & Zhongming Zhao, 2024. "Single-cell multiomics decodes regulatory programs for mouse secondary palate development," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    10. Mingsen Li & Huizhen Guo & Bofeng Wang & Zhuo Han & Siqi Wu & Jiafeng Liu & Huaxing Huang & Jin Zhu & Fengjiao An & Zesong Lin & Kunlun Mo & Jieying Tan & Chunqiao Liu & Li Wang & Xin Deng & Guigang L, 2024. "The single-cell transcriptomic atlas and RORA-mediated 3D epigenomic remodeling in driving corneal epithelial differentiation," Nature Communications, Nature, vol. 15(1), pages 1-13, December.

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