Inferring Boolean Networks from Single-Cell Human Embryo Datasets

Abstract

This study aims to understand human embryonic development and cell fate determination, specifically in relation to trophectoderm (TE) maturation. We utilize single-cell transcriptomics (scRNAseq) data to develop a framework for inferring computational models that distinguish between two developmental stages. Our method selects pseudoperturbations from scRNAseq data since actual perturbations are impractical due to ethical and legal constraints. These pseudo-perturbations consist of input-output discretized expressions, for a limited set of genes and cells. By combining these pseudo-perturbations with prior-regulatory networks, we can infer Boolean networks that accurately align with scR-NAseq data for each developmental stage. Our publicly available method was tested with several benchmarks, proving the feasibility of our approach. Applied to the real dataset, we infer Boolean network families, corresponding to the medium and late TE developmental stages. Their structures reveal contrasting regulatory pathways, offering valuable biological insights and hypotheses within this domain.