A Targeted Multi-omic Analysis Approach Measures Protein Expression and Low-Abundance Transcripts on the Single-Cell Level
A Targeted Multi-omic Analysis Approach Measures Protein Expression and Low-Abundance Transcripts on the Single-Cell Level
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DOI：
10.1016/j.celrep.2020.03.063
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发表时间：
2020-04-07
期刊：
影响因子：
8.8
通讯作者：
Prlic, Martin
中科院分区：
文献类型：
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作者：
Mair, Florian;Erickson, Jami R.;Prlic, Martin
高通量单细胞RNA测序（scRNA-seq）已成为评估免疫细胞异质性的常用工具。最近，开发了RNA和蛋白质表达的组合测量，通常称为通过测序的转录组和表位的细胞索引（CITE-seq）。蛋白质表达数据沿着转录组数据的获取解决了仅评估转录本所固有的一些限制，而且几乎使每个单细胞所需的测序读取深度加倍。此外，仍然缺乏可视化组合转录-蛋白质数据集的分析工具。在这里，我们描述了一种靶向转录组学方法，该方法在一个实验中结合了对400多个基因的分析和对2 × 10（4）个细胞上40多个蛋白质的同时测量。与全转录组方法相比，这种靶向方法仅需要约十分之一的读取深度，同时保留对低丰度转录物的高灵敏度。为了分析这些多组学数据集，我们通过非线性随机嵌入（One-SENSE）调整了一维soli表达，以便在单细胞水平上直观地可视化蛋白质-转录本关系。
High-throughput single-cell RNA sequencing (scRNA-seq) has become a frequently used tool to assess immune cell heterogeneity. Recently, the combined measurement of RNA and protein expression was developed, commonly known as cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq). Acquisition of protein expression data along with transcriptome data resolves some of the limitations inherent to only assessing transcripts but also nearly doubles the sequencing read depth required per single cell. Furthermore, there is still a paucity of analysis tools to visualize combined transcript-protein datasets. Here, we describe a targeted transcriptomics approach that combines an analysis of over 400 genes with simultaneous measurement of over 40 proteins on 2 x 10(4) cells in a single experiment. This targeted approach requires only about one-tenth of the read depth compared to a whole-transcriptome approach while retaining high sensitivity for low abundance transcripts. To analyze these multi-omic datasets, we adapted one-dimensional soli expression by nonlinear stochastic embedding (One-SENSE) for intuitive visualization of protein-transcript relationships on a single-cell level.