BREM-SC: A Bayesian Random Effects Mixture Model for Joint Clustering Single Cell Multi-omics Data
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ABSTRACT: Droplet-based single cell transcriptome sequencing (scRNA-seq) technology 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. In this study, we developed BREM-SC, a novel Bayesian Random Effects Mixture model that jointly clusters paired single cell transcriptomic and proteomic data, which will greatly facilitate researchers to jointly study transcriptome and surface proteins at the single cell level to make new biological discoveries.
Project description:Abstract: The recently developed droplet-based single cell transcriptome sequencing (scRNA-seq) technology makes it feasible to perform a population-scale scRNA-seq study, in which the transcriptome is measured for tens of thousands of single cells from multiple individuals. Despite the advances of many clustering methods, there are few tailored methods for population-scale scRNA-seq studies. Here, we develop a BAyesian Mixture Model for Single Cell sequencing (BAMM-SC) method to cluster scRNA-seq data from multiple individuals simultaneously. BAMM-SC takes raw count data as input and accounts for data heterogeneity and batch effect among multiple individuals in a unified Bayesian hierarchical model framework. Results from applications of BAMM-SC to in-house experimental scRNA-seq datasets using blood and lung cells from humans or mice demonstrate that BAMM-SC outperformed existing clustering methods with considerable improved clustering accuracy, particularly in the presence of heterogeneity among individuals. Data purpose: To evaluate the performance of BAMM-SC for clustering droplet-based scRNA-seq data in population-based study, we performed single cell RNA-seq on peripheral blood mononuclear cells (PBMC) isolated from whole blood obtained from 4 healthy donors, and on lung cells isolated from streptococcus pneumonia (SP) infected and naïve mice.
Project description:Single-cell transcriptome profiling using a 3' droplet-based platform (Chromium,10x Genomics) of CD11b+ cells isolated from the spleen of control and tumor-bearing mice, treated or not with IFN gene therapy.
Project description: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.
Project description:This is a droplet-based single cell transcriptome data set from 13 human fetal livers (6-18 PCW) and 8 skin and kidney samples (6-12 PCW). It includes 199,642 cells with a mean detected gene number of 3000.
Project description:Single-cell transcriptome profiling using a 3' droplet-based platform (Chromium,10x Genomics) of human CD45+ leukocytes isolated from leukemic HuSGM3 mice infused with CD19.28z CAR-T cells, two days after cytokine release syndrome (CRS) onset and 5 days later.
Project description:To generate a gene expression atlas of early adrenogonadal development, we performed single-cell transcriptome profiling using a high throughput droplet-based system (10X Genomics).
Project description:<p>Droplet-based single-cell RNA-seq has emerged as a powerful technique for massively parallel cellular profiling. While these approaches offer the exciting promise to deconvolute cellular heterogeneity in diseased tissues, the lack of cost-effective, reliable, and user-friendly instrumentation has hindered widespread adoption of droplet microfluidic techniques. To address this, we have developed a microfluidic control instrument that can be easily assembled from 3D printed parts and commercially available components costing approximately $575. We adapted this instrument for massively parallel scRNA-seq and deployed it in a clinical environment to perform single-cell transcriptome profiling of disaggregated synovial tissue from 5 rheumatoid arthritis patients. We sequenced 20,387 single cells from synovectomies, revealing 13 transcriptomically distinct clusters. These encompass a comprehensive and unbiased characterization of the autoimmune infiltrate, including inflammatory T and NK subsets that contribute to disease biology. Additionally, we identified fibroblast subpopulations that are demarcated via THY1 (CD90) and CD55 expression. Further experiments confirm that these represent synovial fibroblasts residing within the synovial intimal lining and subintimal lining, respectively, each under the influence of differing microenvironments. We envision that this instrument will have broad utility in basic and clinical settings, enabling low-cost and routine application of microfluidic techniques, and in particular single-cell transcriptome profiling.</p> <p>Reprinted from [Stephenson et al., Nature Communications, 2018], with permission from the Nature Publishing Group.</p>