Department of Cell and Molecular Biology, Karolinska Institute, Stockholm, Sweden.
Nat Methods. 2022 May;19(5):560-566. doi: 10.1038/s41592-022-01446-x. Epub 2022 Apr 25.
Single-cell sequencing methods rely on molecule-counting strategies to account for amplification biases, yet no experimental strategy to evaluate counting performance exists. Here, we introduce molecular spikes-RNA spike-ins containing built-in unique molecular identifiers (UMIs) that we use to identify critical experimental and computational conditions for accurate RNA counting in single-cell RNA-sequencing (scRNA-seq). Using molecular spikes, we uncovered impaired RNA counting in methods that were not informative for cellular RNA abundances due to inflated UMI counts. We further leverage molecular spikes to improve estimates of total endogenous RNA amounts in cells, and introduce a strategy to correct experiments with impaired RNA counting. The molecular spikes and the accompanying R package UMIcountR ( https://github.com/cziegenhain/UMIcountR ) will improve the validation of new methods, better estimate and adjust for cellular mRNA amounts and enable more indepth characterization of RNA counting in scRNA-seq.
单细胞测序方法依赖于分子计数策略来解释扩增偏差，但不存在评估计数性能的实验策略。在这里，我们引入了分子 Spike-RNA 嵌合体，其中包含内置的独特分子标识符 (UMI)，我们使用这些 Spike 来识别单细胞 RNA 测序 (scRNA-seq) 中准确 RNA 计数的关键实验和计算条件。使用分子 Spike，我们发现由于 UMI 计数膨胀，对于细胞 RNA 丰度没有信息的方法，RNA 计数受损。我们进一步利用分子 Spike 来提高细胞内总内源性 RNA 量的估计，并引入了一种纠正 RNA 计数受损实验的策略。分子 Spike 和随附的 R 包 UMIcountR（https://github.com/cziegenhain/UMIcountR）将改进新方法的验证，更好地估计和调整细胞 mRNA 量，并能够更深入地分析 scRNA-seq 中的 RNA 计数。
