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OneSC: a computational platform for recapitulating cell state transitions.

Da Peng1, Patrick Cahan1,2,3

  • 1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, United States.

Bioinformatics (Oxford, England)
|November 21, 2024
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Summary
This summary is machine-generated.

OneSC simulates cell state transitions using computational models trained on single-cell RNA sequencing data. This platform accurately predicts cell fate decisions and recapitulates developmental trajectories, aiding in biological research.

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Area of Science:

  • Developmental biology
  • Cancer biology
  • Cell fate engineering
  • Computational biology

Background:

  • Computational modeling of cell state transitions is crucial for in silico perturbation experiments.
  • Single-cell RNA sequencing (scRNA-seq) provides high-resolution temporal snapshots of cell states.
  • Existing methods often struggle to faithfully mimic real biological systems.

Purpose of the Study:

  • To present OneSC, a platform for simulating cell state transitions.
  • To generate Boolean networks that accurately reflect biological cell fate decisions.
  • To validate the platform using mouse myeloid progenitor differentiation data.

Main Methods:

  • Developed OneSC, a Python package utilizing stochastic differential equations and regulatory networks.
  • Inferred a core transcription factor (TF) network from scRNA-seq data.
  • Performed in silico perturbations of the inferred network.

Main Results:

  • OneSC successfully simulated mouse myeloid progenitor differentiation trajectories.
  • The platform generated synthetic single-cell expression profiles mimicking erythrocyte, megakaryocyte, granulocyte, and monocyte fates.
  • In silico TF perturbations accurately predicted experimental cell fate biases.

Conclusions:

  • OneSC provides a robust platform for modeling cell state transitions and predicting TF perturbation effects.
  • The tool aids in understanding and engineering cell fate decisions.
  • OneSC facilitates rapid and cost-effective in silico experimentation.