scGRNom: a computational pipeline of integrative multi-omics analyses for predicting cell-type disease genes and regulatory networks

Abstract

Strong phenotype-genotype associations have been reported across brain diseases. However, understanding underlying gene regulatory mechanisms remains challenging, especially at the cellular level. To address this, we integrated the multi-omics data at the cellular resolution of the human brain: cell-type chromatin interactions, epigenomics and single cell transcriptomics, and predicted cell-type gene regulatory networks linking transcription factors, distal regulatory elements and target genes (e.g., excitatory and inhibitory neurons, microglia, oligodendrocyte). Using these cell-type networks and disease risk variants, we further identified the cell-type disease genes and regulatory networks for schizophrenia and Alzheimer's disease. The celltype regulatory elements (e.g., enhancers) in the networks were also found to be potential pleiotropic regulatory loci for a variety of diseases. Further enrichment analyses including gene ontology and KEGG pathways revealed potential novel cross-disease and disease-specific molecular functions, advancing knowledge on the interplays among genetic, transcriptional and epigenetic risks at the cellular resolution between neurodegenerative and neuropsychiatric diseases. Finally, we summarized our computational analyses as a general-purpose pipeline for predicting gene regulatory networks via multi-omics data.Recent analyses have also revealed that brain disease risk variants are located in non-coding regulatory elements (e.g., enhancers) and that the risk genes likely have cell-type specific effects including neuronal and non-neuronal types [25,26]. In addition, recent single-cell studies 68 TFs,

Illuminating links between cis-regulators and trans-acting variants in the human prefrontal cortex

“…For instance, our mediation analysis indicated that RORA (a nuclear receptor TF) is a mediator for the trans-eGene RNASEL, which encodes mammalian endoribonuclease. Based on single-cell multi-omics data [15], we found that RORA regulates the gene RNASEL specifically in neuronal cell types [15]. As shown in Fig.…”

“…As a result, we built a mediator-trans-cis-QTL Gene Regulatory Network (GRN) using mediators (TFs) and trans-network (TGs) cis-network results; this regulatory network links SNPs to mediators to the trans-genes that are regulated by them, respectively. Next, we utilized two types of predicted cell-type GRNs from scGRNom [15] for the major brain cell types: excitatory neurons (Ex1, Ex2, Ex3e, Ex4, Ex5, Ex6a, Ex6b, Ex8, and Ex9), inhibitory neurons (In1a, In1b, In1c, In3, In4a, In4b, In6a, In6b, In7, and In8), microglia, and oligodendrocytes. The first predicted GRN type corresponds to cell-type open chromatin regions from scATAC-seq data.…”

NetREm Network Regression Embeddings reveal cell-type transcription factor coordination for gene regulation

“…RUNX1 binds to numerous Mic-specific genes [20]. Coordination between SPI1 and MEF2C plays pivotal roles in Mic enhancer landscapes [21]. Several TF-TF links, like SPI1-RUNX1 in AD Mic and FOXO3-MEF2C and MEF2A-MEF2C in all 4 TF-TF coordination networks, are annotated AD PPI links by the Contextual PPI [18].…”

“…Lake et al [46] provides data for 4 CNS cell types: ExNs, InNs, Mic, Oligo. This data undergoes preprocessing according to methods outlined in Jin et al [21], which includes removing genes expressed in <100 cells, normalization and scaling via Seurat [78], imputing single-cell expression using the MAGIC algorithm. The final data comprises 15,694 genes across 13,709 ExNs, 6,045 InNs, 317 Microglia, and 2,657 Oligodendrocyte cells.…”

“…State-of-the-art tools (e.g. BEELINE [17], SCENIC [18], GRNBoost [19], Signac [20], scGRNom [21]) build cell-type GRNs using co-expression, information-theoretic, machine learningbased, and/or multiomics integration techniques to infer potential cell-type-specific relationships among individual candidate TFs and their TGs that they help regulate (e.g. TF-TG links).…”