CeDR Atlas: a knowledgebase of cellular drug response

Abstract

Drug response to many diseases varies dramatically due to the complex genomics and functional features and contexts. Cellular diversity of human tissues, especially tumors, is one of the major contributing factors to the different drug response in different samples. With the accumulation of single-cell RNA sequencing (scRNA-seq) data, it is now possible to study the drug response to different treatments at the single cell resolution. Here, we present CeDR Atlas (available at https://ngdc.cncb.ac.cn/cedr), a knowledgebase reporting computational inference of cellular drug response for hundreds of cell types from various tissues. We took advantage of the high-throughput profiling of drug-induced gene expression available through the Connectivity Map resource (CMap) as well as hundreds of scRNA-seq data covering cells from a wide variety of organs/tissues, diseases, and conditions. Currently, CeDR maintains the results for more than 582 single cell data objects for human, mouse and cell lines, including about 140 phenotypes and 1250 tissue-cell combination types. All the results can be explored and searched by keywords for drugs, cell types, tissues, diseases, and signature genes. Overall, CeDR fine maps drug response at cellular resolution and sheds lights on the design of combinatorial treatments, drug resistance and even drug side effects.

Figure 1.

Schematic overview of CeDR. (A) Data curation and collection from different sources. (B) Data quality control and preprocessing. (C) Illustration of the algorithm to infer cellular drug response.

Figure 2.

Screenshots of web pages for CeDR. (A) The web images in the home page allow users to switch sources and tissues with detailed data summary information and phenotypes. (B) Quick search functions in the home page. (C) The browse page allows users to browse and search all the project, tissues, phenotypes and other detailed information. (D) Summary of the selected dataset and cell type-drug association network. (E) Detailed results for each cell type with associated drugs and corresponding signature genes.

Figure 3.

Drug response at the single cell Level: Pancreas as an example. (A) Tissue subpopulations maintain diverse response to different drugs. (B) UMap visualization for major cell types across different datasets. (C) Cell type-drug associated network. The circular node represents cell types and the hexagon denotes associated drugs. Different interactions refer to treatments or side effects and the shape of the edge denotes the corresponding phenotypes. Here, only the top 10 drugs with corresponding cell types were shown in (C). (D) Functional enrichment analysis for signature genes referring to acinar-dapsone association. (E) Matrix plot of signature genes in pancreas cancer scRNA-seq dataset referring to buetulin-macrophage association.