Transcription factors play a crucial role in gene expression regulation, directly interpreting the genome to influence cellular functions and processes. Understanding Transcriptional Regulatory Networks (TRNs) provides insights into gene expression dynamics and regulatory mechanisms, critical for comprehending biological processes and disease states. Through single-cell RNA sequencing (scRNA-seq), analyzing gene expression at single-cell resolution offers opportunities to elucidate cellular heterogeneity and expression at a higher resolution. This paper presents preliminary validation of scRegNet, a novel network inference pipeline designed for single-cell transcriptomic data analysis, integrating correlative and causal inference methods to construct TRNs. Here, the pipeline incorporates Pearson correlation and Granger causality testing along with a comprehensive TF-target gene database and trajectory inference algorithms. We demonstrate scRegNet's efficacy using a mouse pancreatic endocrinogenesis dataset, identifying key TFs associated with endocrine cell differentiation. Comparison with literature-based TFs validates scRegNet's accuracy, while novel TFs offer hypotheses for further experimental validation. Our results reveal benefits of integrating multiple causal inference methods and trajectory analyses for robust TRN inference across unique datasets. This study highlights scRegNet's potential as a versatile tool for deciphering gene regulatory mechanisms in diverse biological contexts, paving the way for future applications in single-cell transcriptomic research.

Glioblastoma multiforme (GBM) is an aggressive brain cancer without effectivetreatment options, leaving patient survival rates extremely low. HDAC1 knockdown was found to initiate an invasive phenotype in vivo, particularly within the BT145 human glioma stem cell (hGSC) line. Analysis through RNA sequencing (RNA-seq) gene expression and regulatory networks found both CEBPβ, a known transcription factor (TF) involved in cellular invasion, and the STAT3 pathway, a notorious genetic component of GBM, were differentially expressed in BT145 hGSCs after HDAC1 knockdown. Furthermore, overlap of genes regulated by CEBPβ and STAT3 indicate the CEBPβ/STAT3 pathway may be involved in the observed BT145- specific invasive phenotype. The SYstems Genetics Network AnaLysis (SYGNAL) pipeline was applied to construct sex-specific gene regulatory networks from The Cancer Genome Atlas (TCGA) GBM patient expression data. Unique bicluster eigengenes were discovered separately for all, female, and male patients. Through the application of these bicluster eigengenes to a GBM cohort with multiparametric magnetic resonance imaging (mpMRI) localized biopsies, sex-specific associations between bicluster expression, mpMRI readout, and hallmarks of cancer were determined. Distinctive cancer functions were revealed transcriptionally through bicluster expression, and connected to a unique mpMRI feature. Specifically, SPGRC mpMRI indicated a strong signal for both immune hallmarks (evading immune detection and tumor-promoting inflammation). At the same time, MD mpMRI displayed a tendency toward sustained angiogenesis, possibly signaling the formation of new blood vessels. Uncovering each mpMRI feature’s underlying biological processes enables improved GBM diagnosis and treatment utilizing an individualized, non-invasive approach.