Abstract
Droplet-based single cell transcriptome sequencing (scRNA-seq) technology, largely represented by the 10× Genomics Chromium system, is able to measure the gene expression from tens of thousands of single cells simultaneously. More recently, coupled with the cutting-edge Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq), the droplet-based system has allowed for immunophenotyping of single cells based on cell surface expression of specific proteins together with simultaneous transcriptome profiling in the same cell. Despite the rapid advances in technologies, novel statistical methods and computational tools for analyzing multi-modal CITE-Seq data are lacking. In this study, we developed BREM-SC, a novel Bayesian Random Effects Mixture model that jointly clusters paired single cell transcriptomic and proteomic data. Through simulation studies and analysis of public and in-house real data sets, we successfully demonstrated the validity and advantages of this method in fully utilizing both types of data to accurately identify cell clusters. In addition, as a probabilistic model-based approach, BREM-SC is able to quantify the clustering uncertainty for each single cell. This new method will greatly facilitate researchers to jointly study transcriptome and surface proteins at the single cell level to make new biological discoveries, particularly in the area of immunology.
Highlights
Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-Seq) is a recently developed revolutionary tool, which is the first technique that can measure single cell surface protein and mRNA expression level simultaneously in the same cell (1-3)
In CITE-Seq experiment, the abundance of RNA and surface marker is quantified by Unique Molecular Index (UMI) and Antibody-Derived Tags (ADT) respectively, for a common set of cells at the single cell resolution
We applied BREM-SC on two human peripheral blood mononuclear cells (PBMC) CITE-Seq datasets to assess the usefulness of our method in real application
Summary
Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-Seq) is a recently developed revolutionary tool, which is the first technique that can measure single cell surface protein and mRNA expression level simultaneously in the same cell (1-3). CITE-Seq allows for immunophenotyping of cells using existing single cell sequencing approaches (1), and it is fully compatible with droplet-based single cell RNA sequencing (scRNA-Seq) technology (e.g., 10X Genomics Chromium system (4)) and utilizes the discrete count of Antibody-Derived Tags (ADT) as the direct measurement of cell surface protein abundance This promising and popular technology provides an unprecedent opportunity for jointly analyzing transcriptome and surface proteins at the single cell level in a cost-effective way. In CITE-Seq experiment, the abundance of RNA and surface marker is quantified by Unique Molecular Index (UMI) and Antibody-Derived Tags (ADT) respectively, for a common set of cells at the single cell resolution These two data sources represent different but highly related and complementary biological components. Cell clustering based on scRNA-Seq could identify more cell types because of its higher dimensionality, it is less capable to distinguish highly similar cell types, such as CD4+ T cells and CD8+ T cells, due to a poor observed correlation between a mRNA and its translated protein expression in single cell (1,5,6)
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