When analyzing scRNA-seq data with clustering algorithms, annotating the clusters with cell types is an essential step toward biological interpretation of the data. Annotations can be performed manually using known cell type marker genes. Annotations can also be automated using knowledge-driven or data-driven machine learning algorithms. Majority of cell type annotation algorithms are designed to predict cell types for individual cells in a new dataset. Since biological interpretation of scRNA-seq data is often made on cell clusters rather than individual cells, several algorithms have been developed to annotate cell clusters. In this study, we compared five cell type annotation algorithms, Azimuth, SingleR, Garnett, scCATCH, and SCSA, which cover the spectrum of knowledge-driven and data-driven approaches to annotate either individual cells or cell clusters. We applied these five algorithms to two scRNA-seq datasets of peripheral blood mononuclear cells (PBMC) samples from COVID-19 patients and healthy controls, and evaluated their annotation performance. From this comparison, we observed that methods for annotating individual cells outperformed methods for annotation cell clusters. We applied the cell-based annotation algorithm Azimuth to the two scRNA-seq datasets to examine the immune response during COVID-19 infection. Both datasets presented significant depletion of plasmacytoid dendritic cells (pDCs), where differential expression in this cell type and pathway analysis revealed strong activation of type I interferon signaling pathway in response to the infection.
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Wei, Yanjie ; Li, Min ; Skums, Pavel ; Cai, Zhipeng (Ed.)Long-time evolution has shaped a harmonious host-microbiota symbiosis consisting of intestinal microbiota in conjunction with the host immune system. Inflammatory bowel disease (IBD) is a result of the dysbiotic microbial composition together with aberrant mucosal immune responses, while the underlying mechanism is far from clear. In this report, we creatively proposed that when correlating with the host metabolism, functional microbial communities matter more than individual bacteria. Based on this assumption, we performed a systematic analysis to characterize the co-metabolism of host and gut microbiota established on a set of newly diagnosed Crohn’s disease (CD) samples and healthy controls. From the host side, we applied gene set enrichment analysis on host mucosal proteome data to identify those host pathways associated with CD. At the same time, we applied community detection analysis on the metagenomic data of mucosal microbiota to identify those microbial communities, which were assembled for a functional purpose. Then, the correlation analysis between host pathways and microbial communities was conducted. We discovered two microbial communities negatively correlated with IBD enriched host pathways. The dominant genera for these two microbial communities are known as health-benefits and could serve as a reference for designing complex beneficial microorganisms for IBD treatment. The correlated host pathways are all relevant to MHC antigen presentation pathways, which hints toward a possible mechanism of immune-microbiota cross talk underlying IBD.more » « less
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Abstract Background Crohn’s disease is a lifelong disease characterized by chronic inflammation of the gastrointestinal tract. Defining the cellular and transcriptional composition of the mucosa at different stages of disease progression is needed for personalized therapy in Crohn’s.
Methods Ileal biopsies were obtained from (1) control subjects (n = 6), (2) treatment-naïve patients (n = 7), and (3) established (n = 14) Crohn’s patients along with remission (n = 3) and refractory (n = 11) treatment groups. The biopsies processed using 10x Genomics single cell 5' yielded 139 906 cells. Gene expression count matrices of all samples were analyzed by reciprocal principal component integration, followed by clustering analysis. Manual annotations of the clusters were performed using canonical gene markers. Cell type proportions, differential expression analysis, and gene ontology enrichment were carried out for each cell type.
Results We identified 3 cellular compartments with 9 epithelial, 1 stromal, and 5 immune cell subtypes. We observed differences in the cellular composition between control, treatment-naïve, and established groups, with the significant changes in the epithelial subtypes of the treatment-naïve patients, including microfold, tuft, goblet, enterocyte,s and BEST4+ cells. Surprisingly, fewer changes in the composition of the immune compartment were observed; however, gene expression in the epithelial and immune compartment was different between Crohn’s phenotypes, indicating changes in cellular activity.
Conclusions Our study identified cellular and transcriptional signatures associated with treatment-naïve Crohn’s disease that collectively point to dysfunction of the intestinal barrier with an increase in inflammatory cellular activity. Our analysis also highlights the heterogeneity among patients within the same disease phenotype, shining a new light on personalized treatment responses and strategies.
