DISCO: a database of Deeply Integrated human Single-Cell Omics data

Abstract

The ability to study cellular heterogeneity at single cell resolution is making single-cell sequencing increasingly popular. However, there is no publicly available resource that offers an integrated cell atlas with harmonized metadata that users can integrate new data with. Here, we present DISCO (https://www.immunesinglecell.org/), a database of Deeply Integrated Single-Cell Omics data. The current release of DISCO integrates more than 18 million cells from 4593 samples, covering 107 tissues/cell lines/organoids, 158 diseases, and 20 platforms. We standardized the associated metadata with a controlled vocabulary and ontology system. To allow large scale integration of single-cell data, we developed FastIntegration, a fast and high-capacity version of Seurat Integration. We also developed CELLiD, an atlas guided automatic cell type identification tool. Employing these two tools on the assembled data, we constructed one global atlas and 27 sub-atlases for different tissues, diseases, and cell types. DISCO provides three online tools, namely Online FastIntegration, Online CELLiD, and CellMapper, for users to integrate, annotate, and project uploaded single-cell RNA-seq data onto a selected atlas. Collectively, DISCO is a versatile platform for users to explore published single-cell data and efficiently perform integrated analysis with their own data.

An integrated cell atlas of the human lung in health and disease

“…Unpublished data was generated as follows: Barbry_unpubl: Barbry_unpubl: Nasal and tracheobronchial samples were collected from IPF patients after obtention of their informed consent, following a protocol approved by CHU Nice, registered at clinicaltrials under reference NCT04529993. The libraries were prepared as described in Deprez et al 13 , and yielded an average of 61,000±11,000 cells per sample, with a viability above 95%. The single cell suspension was used to generate single cell libraries following the V3.1 protocol for 3' chemistry from 10x Genomics (CG000204).…”

“…The power of large-scale single-cell cross-dataset analysis has been demonstrated in several recent studies [9][10][11][12][13][14][15] . We previously performed the first single-cell meta-analysis of the respiratory system, aggregating expression data of three genes important for SARS-CoV-2 cell entry from 31 datasets, spanning over 200 individuals 11 .…”

“…Especially when large numbers of individuals, rather than cells, are aggregated, it becomes possible to answer population-level questions using single-cell data. However, currently available integrated atlases are limited in the number of genes 11 , human samples 12,14 , datasets 14 , or cell types 9,12 , and include only non-harmonized or limited cell type annotations [9][10][11][12][13] and subject metadata [9][10][11][12][13] (e.g. age, BMI, smoking status).…”

Screening the Potential Biomarkers of COVID-19-Related Thrombosis Through Bioinformatics Analysis

“…HPA RNA-seq data are displayed in different colors based on cell type, including B cells, T cells, natural killer (NK) cells, monocytes, granulocytes, dendritic cells and total peripheral blood mononuclear cells (PBMCs). The DISCO ( Li et al, 2022 ) database was used annotate the single-cell sequencing data of blood cells from patients with COVID-19. The expression level of each hub gene in each cell is shown.…”

“…Then, the Human Protein Atlas ( Uhlen et al, 2019 ) (HPA) ( https://www.proteinatlas.org/ ) was used to explore the expression profile of each hub gene in human immune cells. Subsequently, the Deeply Integrated human Single-Cell Omics (DISCO) database ( Li et al, 2022 ) ( https://www.immunesinglecell.org/ ) was used to clarify the expression level of the hub genes in all blood cells of COVID-19 patients. Finally, the “clusterProfiler” ( Yu et al, 2012 ) package of R software was used for the enrichment analysis of the hub genes, and the “ggplot2” package of R software was used to visually present the results of the enrichment analysis.…”