Functional module detection through integration of single-cell RNA sequencing data with protein–protein interaction networks

Recent advances in single-cell RNA sequencing (scRNA-seq) have allowed researchers to explore transcriptional function at a cellular level. In this study, we present scPPIN, a method for integrating single-cell RNA sequencing data with protein–protein interaction networks to detect active modules in cells of different transcriptional states. We achieve this by clustering RNA-sequencing data, identifying differentially expressed genes, constructing node-weighted protein–protein interaction networks, and finding the maximum-weight connected subgraphs with an exact Steiner-tree approach. As a case study, we investigate RNA-sequencing data from human liver spheroids but the techniques described here are applicable to other organisms and tissues. scPPIN allows us to expand the output of differential expressed genes analysis with information from protein interactions. We find that different transcriptional states have different subnetworks of the PPIN significantly enriched which represent biological pathways. In these pathways, scPPIN also identifies proteins that are not differentially expressed but of crucial biological function (e.g., as receptors) and therefore reveals biology beyond a standard differentially expressed gene analysis. Human primary hepatocytes from a mixture of 10 donors grown in a 3D spheroid, were purchased from InSphero AG (Switzerland) and maintained in the culture medial provided by the company. Single cell libraries were prepare with a 10X Genomics 3’ v2 kit and sequenced in an Illumina NextSeq 500. Sequencing data demultiplexing and alignment was carried out with CellRanger with default parameters. Single cell RNA sequencing of human primary liver spheroids from InSphero with chromium 10X 3' v2 kit.