Identification of phenotype-specific networks from paired gene expression-cell shape imaging data

Abstract

The morphology of breast cancer cells is often used as an indicator of tumour severity and prognosis. Additionally, morphology can be used to identify more fine-grained, molecular developments within a cancer cell, such as transcriptomic changes and signaling pathway activity. Delineating the interface between morphology and signaling is important to understand the mechanical cues that a cell processes in order to undergo epithelial-to-mesenchymal transition and consequently metastase. However, the exact regulatory systems that define these changes remain poorly characterised. In this study, we employ a network-systems approach to integrate imaging data and RNA-seq expression data. By constructing a cell-shape signaling network from shape correlated gene expression modules and their upstream regulators, we found central roles for development pathways such as Wnt and Notch as well as evidence for the fine control of NFkB signaling by numerous kinase and transcriptional regulators. Further analysis of our network implicates the small GTPase, Rap1 as a potential mediator between the sensing of mechanical stimuli and regulation of NFkB activity. Overall, our analysis provides mechanistic information on the interplay between cell signalling, gene regulation and cell morphology and our approach is generalisable to other cell phenotypes.

Inter-gastruloid heterogeneity revealed by single cell transcriptomics time course: implications for organoid based perturbation studies

“…To investigate the origins of the intergastruloid heterogeneity, we used Weighted Gene Co-Expression Network Analysis (WGCNA) to find biological properties significantly associated with our phenotype of interesty (Barker et al, 2022; Langfelder and Horvath, 2008). For this we first ran single cell WGCNA (Feregrino and Tschopp, 2021) on only the day 3 MULTI-seq cells.…”