Integrating single-cell and bulk transcriptomes identifies neuronal heat-stress-associated candidate genes in a reef-building coral

Abstract

Cell-type-specific molecular responses are obscured in bulk RNA sequencing, whereas generating matched single-cell datasets across environmental conditions remains costly and technically challenging, particularly in non-model organisms. Here, we present a workflow that integrates existing bulk and single-cell RNA-seq resources to prioritize cell-type-associated environmental-response genes. We demonstrate the workflow by examining heat-stress-associated neuronal genes in the reef-building coral Stylophora pistillata. Neuronal genes were first identified based on cell type-specific expression in single-cell transcriptomic data and then intersected with differentially expressed genes from bulk RNA-seq datasets under heat-stress. In parallel, we applied a deconvolution approach to infer cell type-specific expression changes directly from bulk data, independently capturing neuronal transcriptional responses. This integrated pipeline reduced thousands of heat-responsive genes previously identified in bulk RNA-seq studies to nine heat-associated, neuron-enriched candidates. Eight of the nine candidates showed their highest mean expression in the same neuronal subcluster. Targeted sequence-based characterization supported the identification of a TRPA-family ion channel, two calmodulin/centrin-family Ca2+-binding proteins, and a Class A/rhodopsin-like GPCR of unresolved ligand specificity. Two additional candidates were predicted secreted proteins belonging to conserved coral protein families, while the molecular functions of the remaining candidates remained unresolved. Their coordinated regulation suggests heat-associated remodeling of neuronal sensory and intracellular signaling processes. Our workflow provides a scalable framework for extracting cell type-associated molecular responses from existing transcriptomic resources and prioritizing candidates for further investigation. More broadly, it offers a generalizable strategy for investigating cell type-specific gene expression responses across organisms, cell types, and environmental conditions without requiring new single-cell datasets.