A computational approach to identify cellular heterogeneity and tissue-specific gene regulatory networks

Abstract

Background: The heterogeneity of cells across tissue types represents a major challenge for studying biological mechanisms as well as for therapeutic targeting of distinct tissues. Computational prediction of tissue-specific gene regulatory networks may provide important insights into the mechanisms underlying the cellular heterogeneity of cells in distinct organs and tissues.

Results: Using three pathway analysis techniques, gene set enrichment analysis (GSEA), parametric analysis of gene set enrichment (PGSEA), alongside our novel model (HeteroPath), which assesses heterogeneously upregulated and downregulated genes within the context of pathways, we generated distinct tissue-specific gene regulatory networks. We analyzed gene expression data derived from freshly isolated heart, brain, and lung endothelial cells and populations of neurons in the hippocampus, cingulate cortex, and amygdala. In both datasets, we found that HeteroPath segregated the distinct cellular populations by identifying regulatory pathways that were not identified by GSEA or PGSEA. Using simulated datasets, HeteroPath demonstrated robustness that was comparable to what was seen using existing gene set enrichment methods. Furthermore, we generated tissue-specific gene regulatory networks involved in vascular heterogeneity and neuronal heterogeneity by performing motif enrichment of the heterogeneous genes identified by HeteroPath and linking the enriched motifs to regulatory transcription factors in the ENCODE database.

Conclusions: HeteroPath assesses contextual bidirectional gene expression within pathways and thus allows for transcriptomic assessment of cellular heterogeneity. Unraveling tissue-specific heterogeneity of gene expression can lead to a better understanding of the molecular underpinnings of tissue-specific phenotypes.

Keywords: Endothelial cells; Endothelial heterogeneity; Gene expression; Gene set enrichment; Neuronal heterogeneity; Neurons; Pathway analysis; Systems biology; Therapeutic targets; Tissue specificity; Transcription factor binding motifs; Transcriptional networks; Vascular biology.

Fig. 1

a Tissue-specific transcriptomic profiling. First, the gene expression data is preprocessed and normalized. Then, the gene expression data and gene set data are integrated together. Each KEGG pathway is statistically evaluated using the traditional algorithms GSEA, PGSEA, and the novel HeteroPath algorithm to identify tissue-specific pathways. Next, the tissue-specific gene regulatory networks are constructed by identification of heterogeneous genes and their regulatory transcription factors as determined by motif enrichment analysis using the ENCODE database. b The HeteroPath algorithm for identifying heterogeneous pathways and gene sets. HeteroPath aims to find the pathways/gene sets that are not only differentially expressed from the global median gene expression value but also appear to be responsible for the regulation of distinct cell types

Fig. 2

Comparison of enriched pathways a The significantly enriched experimental sets and canonical pathways in mouse endothelial cells were inferred by HeteroPath, GSEA, and PGSEA. Top 10 enrichment scores, p-values, numbers of significant gene sets, and percentage of unique gene sets are shown. b The significantly enriched experimental sets and canonical pathways in mouse neurons were inferred by HeteroPath, GSEA, and PGSEA. Top 10 enrichment scores, p-values, numbers of significant gene sets, and percentage of unique gene sets are shown. c ROC curves for the HeteroPath algorithm using the endothelial cell dataset. fc = fold-change; AUC= area under curve.  d ROC curves for the HeteroPath algorithm using the neurons dataset fc = fold-change; AUC= area under curve

Fig. 3

Endothelial cell heterogeneity. a Heat map of heterogeneous pathways identified by HeteroPath from Brain, Lung, and Heart endothelial cells. The orange to yellow to white gradient represents increasing expression of the pathway with orange representing minimal expression while the white represents high expression of the pathway. Upregulated tissue-specific pathways are highlighted in colored boxes. b, c The results of enriched PGSEA and GSEA pathways from Brain, Lung, and Heart endothelial cells. The orange to yellow to white gradient represents increasing expression of the pathway with orange representing minimal expression while the white represents high expression of the pathway. d A Venn diagram displaying the number of overlapping and unique KEGG pathways identified by HeteroPath, PGSEA, and GSEA

Fig. 4

Neuronal heterogeneity a Heat map representation of heterogeneous pathways identified by HeteroPath from hippocampal, cingulate cortex, and amygdala neurons. The orange to yellow to white gradient represents increasing expression of the pathway with orange representing minimal expression while the white represents high expression of the pathway. Upregulated tissue-specific pathways are highlighted in colored boxes. b, c The results of enriched PGSEA and GSEA pathways from hippocampal, cingulate cortex, and amygdala neurons. The orange to yellow to white gradient represents increasing expression of the pathway with orange representing minimal expression while the white represents high expression of the pathway. d A Venn diagram displaying the number of overlapping and unique KEGG pathways identified by HeteroPath, PGSEA, and GSEA

Fig. 5

Gene regulatory networks for HeteroPath tissue-specific pathways a The heat map shows the normalized mRNA expression level in Brain, Lung, and Heart endothelial cells for the heterogeneous genes of the Wnt signaling pathway. b Wnt signaling gene regulatory network including upregulated transcription factors which bind motifs in the promoter region of brain-specific heterogeneous elements. c The heat map shows the normalized mRNA expression level in hippocampal, cingulate cortex, and amygdala neurons for the heterogeneous genes of the oxidative phosphorylation pathway d Oxidative phosphorylation gene regulatory network including upregulated transcription factors which bind motifs in the promoter region of hippocampal-specific heterogeneous elements