CellOracle: Dissecting cell identity via network inference and in silico gene perturbation
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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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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.”
From Enhancer grammar of liver cell types and hepatocyte zonation states (2022) · cites this paper as [72]
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Functional inference of gene regulation using single-cell multi-omics
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Modeling gene regulatory networks using neural network architectures
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Next-Generation Lineage Tracing and Fate Mapping to Interrogate Development
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Self-citationMachine learning for perturbational single-cell omics
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An Integrated Epigenomic and Transcriptomic Map of Mouse and Human αβ T Cell Development
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PerturbNet predicts single-cell responses to unseen chemical and genetic perturbations
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Probing cell identity hierarchies by fate titration and collision during direct reprogramming
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Reprogramming cellular identity in vivo
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Single-Cell Epigenomics Reveals Mechanisms of Cancer Progression
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An integrated pipeline for mammalian genetic screening
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Learning Single-Cell Perturbation Responses using Neural Optimal TransportPreprint
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Brassinosteroid gene regulatory networks at cellular resolutionPreprint
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GAGER: gene regulatory network assisted gene expression restorationPreprint
Chowdhury, M. Z. U. S., Any, S. S., Samee, M. A. H., & Rahman, A. H. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.27.625595
- 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
- green
- doi.org
- Licence
- 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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