Single-cell transcriptomics unveils gene regulatory network plasticity

Background: Single-cell RNA sequencing (scRNA-seq) plays a pivotal role in our understanding of cellular heterogeneity. Current analytical workflows are driven by categorizing principles that consider cells as individual entities and classify them into complex taxonomies. Results: We devise a conceptually different computational framework based on a holistic view, where single-cell datasets are used to infer global, large-scale regulatory networks. We develop correlation metrics that are specifically tailored to single-cell data, and then generate, validate, and interpret single-cell-derived regulatory networks from organs and perturbed systems, such as diabetes and Alzheimer’s disease. Using tools from graph theory, we compute an unbiased quantification of a gene’s biological relevance and accurately pinpoint key players in organ function and drivers of diseases. Conclusions: Our approach detects multiple latent regulatory changes that are invisible to single-cell workflows based on clustering or differential expression analysis, significantly broadening the biological insights that can be obtained with this leading technology.This work has received funding from the Ministerio de Ciencia, Innovación y Universidades (SAF2017-89109-P; AEI/FEDER, UE). Core funding is from the ISCIII and the Generalitat de Catalunya. We acknowledge support of the Spanish Ministry of Economy, Industry and Competitiveness(MEIC) to the EMBL partnership, the Centro de Excelencia Severo Ochoa, the CERCA Programme /Generalitat de Catalunya, the Spanish Ministry of Economy, Industry and Competitiveness (MEIC)through the Instituto de Salud Carlos III and the Generalitat de Catalunya through Departament deSalut and Departament d’Empresa i Coneixement. We also acknowledge the Co-financing by theSpanish Ministry of Economy, Industry and Competitiveness (MEIC) with funds from the EuropeanRegional Development Fund (ERDF) corresponding to the 2014-2020 Smart Growth OperatingProgram