Integration of single-cell RNA-Seq and CyTOF data characterises heterogeneity of rare cell subpopulations
Integration of single-cell RNA-Seq and CyTOF data characterises heterogeneity of rare cell subpopulations
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DOI：
10.12688/f1000research.121829.1
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
E. Repapi;D. Agarwal;G. Napolitani;David Sims;Stephen S. Taylor
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文献类型：
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作者：
E. Repapi;D. Agarwal;G. Napolitani;David Sims;Stephen S. Taylor
背景：随着高度复用的多组学方法的出现，同时测量细胞蛋白质和单细胞数据的转录组已经成为一种令人兴奋的新可能性。然而，质量细胞术（CyTOF）是一种完善的、负担得起的蛋白质组学数据分析技术，它非常适合发现和表征非常罕见的细胞亚群，具有丰富的公开可用数据集。方法：我们使用两个公开的数据集，展示并评估了来自匹配和未匹配样本的单细胞RNA-Seq和CyTOF数据集的多模式整合。结果：我们证明了将注释良好的CyTOF数据与单细胞RNA测序相结合可以帮助以高精度识别和注释细胞群。此外，我们表明，该集成可以为匹配和不匹配的数据集提供与当前来自CITE-Seq的抗体衍生标签（ADT）金标准相当的蛋白质标记的输入测量。使用这种方法，我们使用公开可用的数据在高分辨率下鉴定和转录表征了罕见的CD11c阳性B细胞亚群，并且我们在单个细胞设置中揭示了其异质性，而无需提前对细胞进行分类，以一种以前不可能的方式。结论：该方法为以统一和公正的方式使用可用的蛋白质组学和转录组学数据集提供了框架，以协助正在进行和未来的细胞表征和生物标志物鉴定研究。
Background: The simultaneous measurement of cellular proteins and transcriptomes of single cell data has become an exciting new possibility with the advent of highly multiplexed multi-omics methodologies. However, mass cytometry (CyTOF) is a well-established, affordable technique for the analysis of proteomic data, which is well suited for the discovery and characterisation of very rare subpopulations of cells with a wealth of publicly available datasets. Methods: We present and evaluate the multimodal integration of single cell RNA-Seq and CyTOF datasets coming from both matched and unmatched samples, using two publicly available datasets. Results: We demonstrate that the integration of well annotated CyTOF data with single cell RNA sequencing can aid in the identification and annotation of cell populations with high accuracy. Furthermore, we show that the integration can provide imputed measurements of protein markers which are comparable to the current gold standard of antibody derived tags (ADT) from CITE-Seq for both matched and unmatched datasets. Using this methodology, we identify and transcriptionally characterise a rare subpopulation of CD11c positive B cells in high resolution using publicly available data and we unravel its heterogeneity in a single cell setting without the need to sort the cells in advance, in a manner which had not been previously possible. Conclusions: This approach provides the framework for using available proteomic and transcriptomic datasets in a unified and unbiased fashion to assist ongoing and future studies of cellular characterisation and biomarker identification.