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 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.

Automated summary

Integration of data from different platforms in single nucleus transcriptomics profiling allowed identification of major liver cell types and a small cluster of inactive hepatic stellate cells that were not characterized in either platform. This approach highlights the potential of single nucleus RNA sequencing integrative approaches and suggests the possibility of designing larger and cost-effective studies.