IQCELL: A platform for predicting the effect of gene perturbations on developmental trajectories using single-cell...

Area of Science:

• Computational Biology
• Developmental Biology
• Genomics

Background:

• Single-cell RNA sequencing (scRNA-seq) data is increasingly available for diverse developmental systems.
• Inferring gene regulatory networks (GRNs) directly from scRNA-seq data presents a significant opportunity.
• Understanding developmental programs requires robust methods for GRN inference and simulation.

Purpose of the Study:

• To introduce IQCELL, a novel platform for inferring, simulating, and studying executable logical GRNs from scRNA-seq data.
• To enable the simulation of hypotheses governing developmental programs.
• To accelerate the design of strategies for controlling stem cell fate.

Main Methods:

• Description of the IQCELL platform architecture.
• Application of IQCELL to scRNA-seq datasets from mouse T-cell and red blood cell development.
• Implementation of an IQCELL gene selection pipeline for identifying candidate genes without prior knowledge.

Main Results:

• IQCELL inferred over 74% of known causal gene interactions in the studied datasets.
• Dynamic simulations using the inferred GRNs qualitatively recapitulated known gene perturbation effects.
• GRN simulations based on IQCELL-identified candidate genes yielded results comparable to curated gene lists.

Conclusions:

• The IQCELL platform provides a versatile tool for inferring and simulating executable GRNs.
• IQCELL facilitates the study of dynamic biological systems and aids in understanding developmental processes.
• The platform supports the identification of key regulatory genes and aids in stem cell research.