Mercatelli Daniele, Balboni Nicola, Giorgio Francesca De, Aleo Emanuela, Garone Caterina, Giorgi Federico Manuel
Department of Pharmacy and Biotechnology, University of Bologna, 40126 Bologna, Italy.
Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy.
Methods Protoc. 2021 May 6;4(2):28. doi: 10.3390/mps4020028.
Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) is a recently established multimodal single cell analysis technique combining the immunophenotyping capabilities of antibody labeling and cell sorting with the resolution of single-cell RNA sequencing (scRNA-seq). By simply adding a 12-bp nucleotide barcode to antibodies (cell hashing), CITE-seq can be used to sequence antibody-bound tags alongside the cellular mRNA, thus reducing costs of scRNA-seq by performing it at the same time on multiple barcoded samples in a single run. Here, we illustrate an ideal CITE-seq data analysis workflow by characterizing the transcriptome of SH-SY5Y neuroblastoma cell line, a widely used model to study neuronal function and differentiation. We obtained transcriptomes from a total of 2879 single cells, measuring an average of 1600 genes/cell. Along with standard scRNA-seq data handling procedures, such as quality checks and cell filtering procedures, we performed exploratory analyses to identify most stable genes to be possibly used as reference housekeeping genes in qPCR experiments. We also illustrate how to use some popular R packages to investigate cell heterogeneity in scRNA-seq data, namely Seurat, Monocle, and slalom. Both the CITE-seq dataset and the code used to analyze it are freely shared and fully reusable for future research.
通过测序进行转录组和表位的细胞索引（CITE-seq）是一种最近建立的多模态单细胞分析技术，它将抗体标记和细胞分选的免疫表型分析能力与单细胞RNA测序（scRNA-seq）的分辨率相结合。通过简单地给抗体添加一个12个碱基的核苷酸条形码（细胞哈希），CITE-seq可用于在对细胞mRNA进行测序的同时对抗体结合的标签进行测序，从而通过在单次运行中对多个条形码样本同时进行scRNA-seq来降低成本。在这里，我们通过对广泛用于研究神经元功能和分化的SH-SY5Y神经母细胞瘤细胞系的转录组进行表征，来说明一个理想的CITE-seq数据分析工作流程。我们从总共2879个单细胞中获得了转录组，平均每个细胞测量1600个基因。除了标准的scRNA-seq数据处理程序，如质量检查和细胞过滤程序外，我们还进行了探索性分析，以确定最稳定的基因，这些基因可能在qPCR实验中用作参考管家基因。我们还说明了如何使用一些流行的R包来研究scRNA-seq数据中的细胞异质性，即Seurat、Monocle和slalom。CITE-seq数据集及其用于分析的代码均可免费共享，并且可完全重复用于未来的研究。
