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.

中文翻译：

---

在单细胞基因组学研究中采样时间相关的伪影

强大的协议和自动化现在支持大规模单细胞 RNA 和 ATAC 测序实验及其在生物库和临床队列中的应用。然而，样本采集过程中引入的技术偏差会阻碍可靠、可重复的结果，在进入大规模数据生产之前需要进行系统的基准测试。在这里，我们报告了采样过程中引入的基因表达和染色质可及性伪影的存在和程度，并确定了预防它们的实验和计算解决方案。