Single nucleus transcriptomics data integration recapitulates the major cell types in human liver. Issue 2 (28th November 2020) - Diamanti, Klev; Inda Díaz, Juan Salvador; Raine, Amanda; Pan, Gang; Wadelius, Claes; Cavalli, Marco
Abstract : Aim: The aim of this study was to explore the benefits of data integration from different platforms for single nucleus transcriptomics profiling to characterize cell populations in human liver. Methods: We generated single‐nucleus RNA sequencing data from Chromium 10X Genomics and Drop‐seq for a human liver sample. We utilized state of the art bioinformatics tools to undertake a rigorous quality control and to integrate the data into a common space summarizing the gene expression variation from the respective platforms, while accounting for known and unknown confounding factors. Results: Analysis of single nuclei transcriptomes from both 10X and Drop‐seq allowed identification of the major liver cell types, while the integrated set obtained enough statistical power to separate a small population of inactive hepatic stellate cells that was not characterized in either of the platforms. Conclusions: Integration of droplet‐based single nucleus transcriptomics data enabled identification of a small cluster of inactive hepatic stellate cells that highlights the potential of our approach. We suggest single‐nucleus RNA sequencing integrative approaches could be utilized to design larger and cost‐effective studies.