Catalina A Vallejos | bioRxiv Science

bioRxiv2020

Single-nucleus RNA-seq2 reveals a functional crosstalk between liver zonation and ploidy

Single-cell RNA-seq reveals the role of pathogenic cell populations in development and progression of chronic diseases. In order to expand our knowledge on cellular heterogeneity we have developed a single-nucleus RNA-seq2 method that allows deep characterization of nuclei isolated from frozen archived tissues. We have used this approach to characterize the transcriptional profile of individual hepatocytes with different levels of ploidy, and have discovered that gene expression in tetraploid mononucleated hepatocytes is conditioned by their position within the hepatic lobe. Our work has revealed a remarkable crosstalk between gene dosage and spatial distribution of hepatocytes.

genomics↗

bioRxiv2016

Beyond comparisons of means: understanding changes in gene expression at the single-cell level

Single-cell RNA sequencing (scRNA-seq) can be used to characterise differences in gene expression patterns between pre-specified populations of cells. Traditionally, differential expression tools are restricted to the study of changes in overall expression between cell populations. However, such analyses do not take full advantage of the rich information provided by scRNA-seq. In this article, we present a Bayesian hierarchical model which can be used to study changes in expression that lie beyond comparisons of means. In particular, our method can highlight genes that undergo changes in cell-to-cell heterogeneity between the populations but whose overall expression is preserved. Evidence supporting these changes is quantified using a probabilistic approach based on tail posterior probabilities, where a probability cut-off is calibrated through the expected false discovery rate. Our method incorporates a built-in normalisation strategy and quantifies technical artefacts by borrowing information from technical spike-in genes. Control experiments validate the performance of our approach. Finally, we compare expression patterns of mouse embryonic stem cells between different stages of the cell cycle, revealing substantial differences in cellular heterogeneity.

Genomics↗