Report

Teaser, summary, work performed and final results

Periodic Reporting for period 1 - Allelic Regulation (Revealing Allele-level Regulation and Dynamics using Single-cell Gene Expression Analyses)

Teaser

Cells of diploid organisms inherit one gene copy (allele) from each parent. Therefore, a gene can be expressed from both alleles (biallelic) or from only one allele (monoallelic). Although transcription from both alleles is detected for most genes in cell population...

Summary

Cells of diploid organisms inherit one gene copy (allele) from each parent. Therefore, a gene can be expressed from both alleles (biallelic) or from only one allele (monoallelic). Although transcription from both alleles is detected for most genes in cell population experiments, little is known about allele-specific expression in single cells and its phenotypic consequences. To answer fundamental questions about allelic transcription heterogeneity in single cells, this research program will focus on single-cell transcriptome analyses with allelic-origin resolution. To this end, we will investigate both clonally stable and dynamic random monoallelic expression across a large number of cell types, including cells from embryonic and adult stages. This research program will be accomplished with the novel single-cell RNA-seq method developed within my lab to obtain quantitative, genome-wide gene expression measurement. To distinguish between mitotically stable and dynamic patterns of allelic expression, we will analyze large numbers a clonally related cells per cell type, from both primary cultures (in vitro) and using transgenic models to obtain clonally related cells in vivo. The biological significance of the research program is first an understanding of allelic transcription, including the nature and extent of random monoallelic expression across in vivo tissues and cell types. These novel insights into allelic transcription will be important for an improved understanding of how variable phenotypes (e.g. incomplete penetrance and variable expressivity) can arise in genetically identical individuals. Additionally, the single-cell transcriptome analyses of clonally related cells in vivo will provide unique insights into the clonality of gene expression per se.

Work performed

Within this reporting period I am happy to report that we have finished aim 1 and have made good initial progress towards aims 2 and 3.

To resolve the clonality in random monoallelic gene expression using primary cells (aim 1), we used single-cell RNA-sequencing on primary fibroblasts (passage 2; clonally expanded) and on human T-cells for which we also obtained clonal information by inferring their T-cell receptor rearrangements. From this data, we could demonstrate that clonal random monoallelic expression for autosomal genes are scarce (in contrast to earlier papers) and that random monoallelic expression instead frequently arise as a consequence of stochastic gene expression. We went further to demonstrate that determinants for monoallelic expression resulting from stochastic gene expression is cell size, activation state and cell cycle state. This work was recently published in Nature Genetics (Reinius, Mold et al. 2016).

We have also made significant progress towards aim 2 and 3, where we will systematically analyse random monoallelic expression across in vivo cell types. This aim is using Confetti lineage tracing mouse models that we breed with Cast mice to simultaneously allow for lineage tracing and allelic single-cell gene expression analyses. The current status is that we have acquired the Confetti mouse, we have bred it into the accurate background and are now starting to make the experimental F1 offspring where it is also mated with Cast. We are therefore ready to start addressing the more ambitious aims 2 and 3.

Final results

The research in this project is deepening our understanding for how our genetic material is regulated, and will have broad impact on biology and medicine. The stochastic and regulated forms of allelic gene expression regulation could impact on how disease symptoms manifest and therefore have implications for human genetic disorders.