OneSC: A computational platform for recapitulating cell state transitions.

Area of Science:

• Developmental Biology
• Computational Biology
• Systems Biology

Background:

• Computational modeling of cell state transitions is crucial for understanding developmental biology, cancer biology, and cell fate engineering.
• Single-cell RNA sequencing (scRNA-seq) provides high-resolution temporal snapshots of cell states during transitions.
• Existing methods often struggle to faithfully mimic biological cell state transitions and steady states.

Purpose of the Study:

• To present OneSC, a novel platform for simulating synthetic cells across developmental trajectories.
• To develop a computational model based on stochastic differential equations and core transcription factor (TF) regulatory networks.
• To generate Boolean networks that accurately reproduce cell state transitions and steady states.

Main Methods:

• OneSC utilizes systems of stochastic differential equations governed by core TF regulatory networks.
• The platform prioritizes generating Boolean networks for faithful cell state transition and steady-state mimicry.
• Applied to mouse myeloid progenitor scRNA-seq data to infer a core TF network.

Main Results:

• Dynamical simulations using the inferred network accurately recapitulated four myeloid differentiation trajectories.
• Generated synthetic single-cell expression profiles that mimic differentiated cell states (erythrocytes, megakaryocytes, granulocytes, monocytes).
• In silico perturbations of the core network accurately predicted TF perturbation-induced cell fate biases.

Conclusions:

• OneSC provides a powerful tool for in silico simulation of cell state transitions and developmental trajectories.
• The platform's ability to predict TF perturbation effects offers valuable insights for experimental design.
• OneSC advances computational approaches in developmental and cancer biology by enabling rapid, cost-effective perturbation experiments.