Decoding nucleosome positions with ATAC-seq data at single-cell level

Abstract

AbstractAs the basal bricks, the dynamics and arrangement of nucleosomes orchestrate the higher architecture of chromatin in a fundamental way, thereby affecting almost all nuclear biology processes. Thanks to its rather simple protocol, ATAC-seq has been rapidly adopted as a major tool for chromatin-accessible profiling at both bulk and single-cell level. However, to picture the arrangement of nucleosomes per se remains a challenge with ATAC-seq. In the present work, we introduce a novel ATAC-seq analysis toolkit, named deNOPA, to predict nucleosome positions. Assessments showed that deNOPA not only outperformed state-of-the-art tools, but it is the only tool able to predict nucleosome position precisely with ultrasparse ATAC-seq data. The remarkable performance of deNOPA was fueled by the reads from short fragments, which compose nearly half of sequenced reads and are normally discarded from nucleosome position detection. However, we found that the short fragment reads enrich information on nucleosome positions and that the linker regions were predicted by reads from both short and long fragments using Gaussian smoothing. We applied deNOPA to a single-cell ATAC-seq dataset and deciphered the intrapopulation heterogeneity of the human erythroleukemic cell line (K562). Last, using deNOPA, we showed that the dynamics of nucleosome organization may not directly couple with chromatin accessibility in the cis-regulatory regions when human cells respond to heat shock stimulation. Our deNOPA provides a powerful tool with which to analyze the dynamics of chromatin at nucleosome position level in the single-cell ATAC-seq age.