Biomedical Data Science, Research & Early Development Oncology, Bayer AG, Berlin, Germany.
Université de Paris, Institut de Recherche Saint-Louis, INSERM U976, Paris, France.
Cancer Res. 2023 Feb 3;83(3):363-373. doi: 10.1158/0008-5472.CAN-22-0074.
The development of single-cell RNA sequencing (scRNA-seq) technologies has greatly contributed to deciphering the tumor microenvironment (TME). An enormous amount of independent scRNA-seq studies have been published representing a valuable resource that provides opportunities for meta-analysis studies. However, the massive amount of biological information, the marked heterogeneity and variability between studies, and the technical challenges in processing heterogeneous datasets create major bottlenecks for the full exploitation of scRNA-seq data. We have developed IMMUcan scDB (https://immucanscdb.vital-it.ch), a fully integrated scRNA-seq database exclusively dedicated to human cancer and accessible to nonspecialists. IMMUcan scDB encompasses 144 datasets on 56 different cancer types, annotated in 50 fields containing precise clinical, technological, and biological information. A data processing pipeline was developed and organized in four steps: (i) data collection; (ii) data processing (quality control and sample integration); (iii) supervised cell annotation with a cell ontology classifier of the TME; and (iv) interface to analyze TME in a cancer type-specific or global manner. This framework was used to explore datasets across tumor locations in a gene-centric (CXCL13) and cell-centric (B cells) manner as well as to conduct meta-analysis studies such as ranking immune cell types and genes correlated to malignant transformation. This integrated, freely accessible, and user-friendly resource represents an unprecedented level of detailed annotation, offering vast possibilities for downstream exploitation of human cancer scRNA-seq data for discovery and validation studies.
The IMMUcan scDB database is an accessible supportive tool to analyze and decipher tumor-associated single-cell RNA sequencing data, allowing researchers to maximally use this data to provide new insights into cancer biology.
单细胞 RNA 测序 (scRNA-seq) 技术的发展极大地促进了肿瘤微环境 (TME) 的破译。大量独立的 scRNA-seq 研究已经发表，这是一个宝贵的资源，为荟萃分析研究提供了机会。然而，大量的生物学信息、研究之间的显著异质性和可变性，以及处理异质数据集的技术挑战，为充分利用 scRNA-seq 数据带来了重大瓶颈。我们开发了 IMMUcan scDB（https://immucanscdb.vital-it.ch），这是一个完全集成的 scRNA-seq 数据库，专门用于人类癌症，非专业人士也可访问。IMMUcan scDB 包含了 56 种不同癌症类型的 144 个数据集，注释了 50 个字段，包含精确的临床、技术和生物学信息。开发了一个数据处理管道，并组织成四个步骤：(i) 数据收集；(ii) 数据处理（质量控制和样本整合）；(iii) 使用 TME 的细胞本体论分类器进行有监督的细胞注释；(iv) 以癌症类型特异性或全局方式分析 TME 的接口。该框架用于以基因为中心（CXCL13）和细胞为中心（B 细胞）的方式探索肿瘤位置的数据集，以及进行荟萃分析研究，如对免疫细胞类型和与恶性转化相关的基因进行排名。这个集成的、可自由访问的、用户友好的资源代表了前所未有的详细注释水平，为下游利用人类癌症 scRNA-seq 数据进行发现和验证研究提供了巨大的可能性。
IMMUcan scDB 数据库是一个可访问的支持工具，用于分析和破译与肿瘤相关的单细胞 RNA 测序数据，允许研究人员最大限度地利用这些数据，为癌症生物学提供新的见解。
