Identifying transposable element expression dynamics and heterogeneity during development at the single-cell level with a processing pipeline scTE

Jiangping He,Yuhao Li,Qiang Zhuang,Li Sun,Isaac A Babarinde,Gang Ma,Yuanjie Wei,Andrew P Hutchins,Junjie Shi,Shuyang Xu,Jiekai Chen,Ruhai Chen

doi:10.1038/s41467-021-21808-x

Abstract

Transposable elements (TEs) make up a majority of a typical eukaryote’s genome, and contribute to cell heterogeneity in unclear ways. Single-cell sequencing technologies are powerful tools to explore cells, however analysis is typically gene-centric and TE expression has not been addressed. Here, we develop a single-cell TE processing pipeline, scTE, and report the expression of TEs in single cells in a range of biological contexts. Specific TE types are expressed in subpopulations of embryonic stem cells and are dynamically regulated during pluripotency reprogramming, differentiation, and embryogenesis. Unexpectedly, TEs are expressed in somatic cells, including human disease-specific TEs that are undetectable in bulk analyses. Finally, we apply scTE to single-cell ATAC-seq data, and demonstrate that scTE can discriminate cell type using chromatin accessibly of TEs alone. Overall, our results classify the dynamic patterns of TEs in single cells and their contributions to cell heterogeneity.

Highlights

Transposable elements (TEs) make up a majority of a typical eukaryote’s genome, and contribute to cell heterogeneity in unclear ways
We first demonstrate scTE’s capabilities through an analysis of mouse embryonic stem cells, which is one of the best characterized models for TE expression, as the expression of the endogenous retrovirus (ERV) MERVL marks a small population of cells in embryonic stem cell (ESC) cultures that are totipotent20,21. scTE accurately recovers the expected pattern of heterogeneous MERVL expression
We designed an algorithm in which TE reads are allocated to TE metagenes based on the TE type-specific sequence

Summary

Introduction

Transposable elements (TEs) make up a majority of a typical eukaryote’s genome, and contribute to cell heterogeneity in unclear ways. Single-cell sequencing technologies are powerful tools to explore cells, analysis is typically gene-centric and TE expression has not been addressed. As TEs pose unique challenges in quantification, due to their degeneracy and multiple genomic copies, a prerequisite to understand TEs at the single cell level is a tool to quantify the hundreds to millions of copies of repetitive elements within the genome. To this end, we developed scTE, an algorithm that quantifies TE expression in single-cell sequence data. We gain insight into complex TE expression patterns in mammalian development and human diseases

Methods

Results

Conclusion