OneSC

OneSC simulates cell state transitions using systems of stochastic differential equations governed by a regulatory network of core transcription factors, enabling in silico perturbation experiments from scRNA-seq data.

Key Features:

• Boolean Network Inference: OneSC prioritizes generating Boolean networks that produce faithful cell state transitions and terminal cell states mimicking real biological systems.
• Dynamical Simulation: The platform simulates cell state transitions using systems of stochastic differential equations governed by a regulatory network of core transcription factors.
• In Silico Perturbation: OneSC enables computational perturbation experiments to predict cell fate decision biases of transcription factor perturbations.
• Synthetic Cell Generation: The platform generates in silico synthetic single-cell expression profiles that recapitulate temporal differentiation trajectories.

Scientific Applications:

• Myeloid Differentiation: OneSC inferred a core transcription factor network from mouse myeloid progenitor scRNA-seq data and simulated four differentiation trajectories into erythrocytes, megakaryocytes, granulocytes, and monocytes.
• Cell Fate Prediction: In silico perturbations of core transcription factor networks predict cell fate decision biases that match previous experimental observations.
• Developmental Biology: Computational modeling of cell state transitions enables rapid and cheap perturbation experiments compared to laboratory approaches.
• Cancer Biology: The platform supports modeling of cell state transitions relevant to cancer progression and heterogeneity.
• Cell Fate Engineering: OneSC facilitates the design and prediction of engineered cell state transitions through computational simulation.

Methodology:

OneSC uses scRNA-seq data to infer Boolean regulatory networks of core transcription factors, then simulates cell state transitions using systems of stochastic differential equations governed by these networks to generate synthetic single-cell expression profiles.