Sampling time-dependent artifacts in single-cell genomics studies

Abstract

Abstract Robust protocols and automation now enable large-scale single-cell RNA and ATAC sequencing experiments and their application on biobank and clinical cohorts. However, technical biases introduced during sample acquisition can hinder solid, reproducible results, and a systematic benchmarking is required before entering large-scale data production. Here, we report the existence and extent of gene expression and chromatin accessibility artifacts introduced during sampling and identify experimental and computational solutions for their prevention.

Keywords

• Single-cell
• Biobank
• RNA sequencing
• Peripheral blood mononuclear cells
• PBMC
• Chronic lymphocytic leukemia