Integrating bulk and single cell RNA-seq refines transcriptomic profiles of specific C. elegans neurons

Neuron-specific morphology and function are fundamentally tied to differences in gene expression across the nervous system. We previously generated a single cell RNA-seq dataset for every anatomical neuron class in the C. elegans hermaphrodite. Here we present a complementary set of bulk RNA-seq samples for 41 of the 118 neuron classes in C. elegans. We show that the bulk dataset captures both lowly expressed and noncoding RNAs that are missed in the single cell dataset, but also includes false positives due to contamination by other cell types. We present an integrated analytical strategy that effectively resolves both the low sensitivity of single cell RNA-seq data and the reduced specificity of bulk RNA-Seq. We show that this integrated dataset enhances the sensitivity and accuracy of transcript detection and quantification of differentially expressed genes. We propose that our approach provides a new tool for interrogating gene expression, by bridging the gap between old (bulk) and new (single cell) methodologies for transcriptomic studies. We suggest that these datasets will advance the goal of delineating the mechanisms that define neuronal morphology and connectivity in C. elegans.