Department of Biostatistics, University of Florida, FL, USA.
Department of Comparative Biology and Experimental Medicine, University of Calgary, Calgary, AB, Canada.
Nucleic Acids Res. 2022 Jan 25;50(2):e12. doi: 10.1093/nar/gkab1071.
Considerable effort has been devoted to refining experimental protocols to reduce levels of technical variability and artifacts in single-cell RNA-sequencing data (scRNA-seq). We here present evidence that equalizing the concentration of cDNA libraries prior to pooling, a step not consistently performed in single-cell experiments, improves gene detection rates, enhances biological signals, and reduces technical artifacts in scRNA-seq data. To evaluate the effect of equalization on various protocols, we developed Scaffold, a simulation framework that models each step of an scRNA-seq experiment. Numerical experiments demonstrate that equalization reduces variation in sequencing depth and gene-specific expression variability. We then performed a set of experiments in vitro with and without the equalization step and found that equalization increases the number of genes that are detected in every cell by 17-31%, improves discovery of biologically relevant genes, and reduces nuisance signals associated with cell cycle. Further support is provided in an analysis of publicly available data.
研究人员投入了大量精力来改进实验方案，以降低单细胞 RNA 测序（scRNA-seq）数据中技术变异性和伪影的水平。我们在此证明，在汇集之前均等 cDNA 文库的浓度（单细胞实验中不一致执行的步骤）可以提高基因检测率、增强生物学信号，并减少 scRNA-seq 数据中的技术伪影。为了评估均等化对各种方案的影响，我们开发了 Scaffold，这是一个模拟 scRNA-seq 实验每个步骤的仿真框架。数值实验表明，均等化减少了测序深度和基因特异性表达变异性的差异。然后，我们在有和没有均等化步骤的情况下进行了一系列体外实验，发现均等化将每个细胞中检测到的基因数量增加了 17-31%，提高了对生物学相关基因的发现，并减少了与细胞周期相关的干扰信号。在对公开可用数据的分析中提供了进一步的支持。
