Single nucleus transcriptomics data integration recapitulates the major cell types in human liver.

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-seq (snRNA-seq) data from Chromium 10X Genomics and Drop-seq for a human liver sample. We utilized state of the art bioinformatics tools to perform 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 form 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 (iHSC) 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 iHSC that highlights the potential of our approach. We suggest snRNA-seq integrative approaches could be utilized to design larger and cost-effective studies.