Accurate estimation of pathway activity in single cells for clustering and differential analysis.

Inferring which and how biological pathways and gene sets change is a key question in many studies that utilize single-cell RNA sequencing. Typically, these questions are addressed by quantifying the enrichment of known gene sets in lists of genes derived from global analysis. Here we offer SiPSiC, a new method to infer pathway activity in every single cell. This allows more sensitive differential analysis and utilization of pathway scores to cluster cells and compute UMAP or other similar projections. We apply our method to COVID-19, lung adenocarcinoma and glioma data sets, and demonstrate its utility. SiPSiC analysis results are consistent with findings reported in previous studies in many cases, but SiPSiC also reveals the differential activity of novel pathways, enabling us to suggest new mechanisms underlying the pathophysiology of these diseases and demonstrating SiPSiC's high accuracy and sensitivity in detecting biological function and traits. In addition, we demonstrate how it can be used to better classify cells based on activity of biological pathways instead of single genes and its ability to overcome patient-specific artifacts.

Single-cell RNA sequencing identifies a paracrine interaction that may drive oncogenic notch signaling in human adenoid cystic carcinoma.

Salivary adenoid cystic carcinoma (ACC) is a rare, biologically unique biphasic tumor that consists of malignant myoepithelial and luminal cells. MYB and Notch signaling have been implicated in ACC pathophysiology, but in vivo descriptions of these two programs in human tumors and investigation into their active coordination remain incomplete. We utilize single-cell RNA sequencing to profile human head and neck ACC, including a comparison of primary ACC with a matched local recurrence. We define expression heterogeneity in these rare tumors, uncovering diversity in myoepithelial and luminal cell expression. We find differential expression of Notch ligands DLL1, JAG1, and JAG2 in myoepithelial cells, suggesting a paracrine interaction that may support oncogenic Notch signaling. We validate this selective expression in three published cohorts of patients with ACC. Our data provide a potential explanation for the biphasic nature of low- and intermediate-grade ACC and may help direct new therapeutic strategies against these tumors.