PreprintCited by 83Gene Regulatory Network AnalysisSingle-cell and spatial transcriptomicsGenomics and Chromatin Dynamics

CellOracle: Dissecting cell identity via network inference and in silico gene perturbation

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Kamimoto, Kenji, Hoffmann, Christy M., Morris, Samantha A.·2020·bioRxiv (Cold Spring Harbor Laboratory)

Abstract

Summary: Here, we present CellOracle, a computational tool that integrates single-cell transcriptome and epigenome profiles to infer gene regulatory networks (GRNs), critical regulators of cell identity. Leveraging inferred GRNs, we simulate gene expression changes in response to transcription factor (TF) perturbation, enabling network configurations to be interrogated in silico , facilitating their interpretation. We validate the efficacy of CellOracle to recapitulate known regulatory changes across hematopoiesis, correctly predicting the outcomes of well-characterized TF perturbations. Integrating CellOracle analysis with lineage tracing of direct reprogramming reveals distinct network configurations underlying different reprogramming failure modes. Furthermore, analysis of GRN reconfiguration along successful reprogramming trajectories identifies new factors to enhance target cell yield, uncovering a role for the AP-1 subunit Fos, with the hippo signaling effector, Yap1. Together, these results demonstrate the efficacy of CellOracle to infer and interpret cell-type-specific GRN configurations, at high-resolution, promoting new mechanistic insights into the regulation and reprogramming of cell identity.

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Crossref · 10.1101/2020.02.17.947416

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  • Recent work from the Morris lab has shown that inferred eGRNs can be used to predict the effect of a TF perturbation.

    “Recent work from the Morris lab72 has shown that inferred eGRNs can be used to predict the effect of a TF perturbation.”

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Authors
Kamimoto, K., Hoffmann, C. M., & Morris, S. A.
Affiliations
Washington University in St. Louis (US)
Corresponding author
Morris, Samantha A.
Venue
bioRxiv (Cold Spring Harbor Laboratory)
Publisher
openRxiv
Published
2020-02-17
OpenAlex
W3006301383
Cited by
81
References
84
Citations by year
2025: 3 · 2024: 7 · 2023: 14 · 2022: 32 · 2021: 21 · 2020: 4
Type
posted content
Language
en
Open access
Locations
bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
Funders
  • Silicon Valley Community Foundation (HCA2-A-1708- 02799)
  • Paul G. Allen Frontiers Group
  • Japan Society for the Promotion of Science
  • National Institute of General Medical Sciences (GM126112, R01-GM126112)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
Keywords
gene regulatory network inference, cell identity, transcription factor perturbation, single-cell transcriptomics, single-cell epigenomics, hematopoiesis, lineage tracing, FOS, YAP, Hippo signaling pathway

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