Gene regulatory network approach to decipher key transcriptional dynamics driving differentiation of induced pluripotent stem cells to neural lineage.

Stem cell based approaches are increasingly studied as models for understanding neurodegenerative diseases. Despite progress, a unified mechanistic understanding of human iPSCs to neural commitment remains incomplete, due to variability in methodologies and experimental conditions, highlighting the need for a more standardized mechanistic framework. This study investigates the transcriptional landscape and regulatory mechanisms underlying the differentiation of induced pluripotent stem cells (iPSCs) towards neural commitment using three orthogonal methodologies across four scRNA seq datasets. In brief, each dataset was processed through a standard Seurat pipeline followed by identification of differentially expressed genes (DEGs) via the Wilcoxon rank-sum test. The significant transcription factors (TFs) were cross validated against the human TF catalogue and ranked accordingly. We identified 15 TFs up-regulated across datasets, and 54 TFs upregulated in three out of four datasets. Among these, ASCL1 emerged as the top candidate. The downregulation of LIN28A and SOX2 is consistent with silencing of the pluripotency network. The second part of the study involved the construction of a co -expression network through a consensus of the four datasets. The network yielded 24 co-expression modules which prioritised hub transcription factors of neural commitment. Module trait correlations were computed with WGCNA, identifying NCAM1 module as top hub. RTN regulon interference and master regulator analysis was applied as a third analysis method. The intersection resulted in identification of LCOR, MEIS3 and EBF1 as the top intramodular hub genes regulators. Current study offers a comprehensive map of key transcriptional dynamics and regulatory nodes involved in iPSC-to-NSC differentiation. This study offers insights into neural development and identifies prioritised candidates for experimental follow-up.