Wang Zhenyi, Miao Yuxin, Li Hongjun, Cheng Wenyan, Shi Minglei, Lv Gang, Zhu Yating, Zhang Junyi, Tan Tingting, Gu Jin, Zhang Michael Q, Li Jianfeng, Fang Hai, Chen Zhu, Chen Saijuan
Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Research Unit of Hematologic Malignancies Genomics and Translational Research of Chinese Academy of Medical Sciences, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
MOE Key Laboratory of Bioinformatics, Beijing National Research Center for Information Science and Technology, Bioinformatics Division, Department of Automation, Tsinghua University, Beijing 100084, China.
Genomics Proteomics Bioinformatics. 2025 May 30;23(2). doi: 10.1093/gpbjnl/qzaf002.
Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) techniques hold great value in evaluating the heterogeneity and spatial characteristics of hematopoietic cells within tissues. These two techniques are highly complementary, with scRNA-seq offering single-cell resolution and ST retaining spatial information. However, there is an urgent demand for well-organized and user-friendly toolkits capable of handling single-cell and spatial information. Here, we present HemaScope, a specialized bioinformatics toolkit featuring modular designs to analyze scRNA-seq and ST data generated from hematopoietic cells. It enables users to perform quality control, basic analysis, cell atlas construction, cellular heterogeneity exploration, and dynamical examination on scRNA-seq data. Also, it can perform spatial analysis and microenvironment analysis on ST data. Meanwhile, HemaScope takes into consideration hematopoietic cell-specific features, including lineage affiliation evaluation, cell cycle prediction, and marker gene collection. To enhance the user experience, we have deployed the toolkit in user-friendly forms: HemaScopeR (an R package), HemaScopeCloud (a web server), HemaScopeDocker (a Docker image), and HemaScopeShiny (a graphical interface). In case studies, we employed it to construct a cell atlas of human bone marrow, analyze age-related changes, and identify acute myeloid leukemia cells in mice. Moreover, we characterized the microenvironments in angioimmunoblastic T cell lymphoma and primary central nervous system lymphoma, elucidating tumor boundaries. HemaScope is freely available at https://zhenyiwangthu.github.io/HemaScope_Tutorial/.
单细胞RNA测序（scRNA-seq）和空间转录组学（ST）技术在评估组织内造血细胞的异质性和空间特征方面具有重要价值。这两种技术具有高度互补性，scRNA-seq提供单细胞分辨率，而ST保留空间信息。然而，迫切需要能够处理单细胞和空间信息的组织良好且用户友好的工具包。在此，我们展示了HemaScope，这是一个专门的生物信息学工具包，具有模块化设计，用于分析造血细胞产生的scRNA-seq和ST数据。它使用户能够对scRNA-seq数据进行质量控制、基本分析、细胞图谱构建、细胞异质性探索和动态检查。此外，它还可以对ST数据进行空间分析和微环境分析。同时，HemaScope考虑了造血细胞的特定特征，包括谱系归属评估、细胞周期预测和标记基因收集。为了提升用户体验，我们以用户友好的形式部署了该工具包：HemaScopeR（一个R包）、HemaScopeCloud（一个网络服务器）、HemaScopeDocker（一个Docker镜像）和HemaScopeShiny（一个图形界面）。在案例研究中，我们使用它构建了人类骨髓的细胞图谱，分析了与年龄相关的变化，并在小鼠中鉴定了急性髓系白血病细胞。此外，我们对血管免疫母细胞性T细胞淋巴瘤和原发性中枢神经系统淋巴瘤的微环境进行了表征，阐明了肿瘤边界。HemaScope可在https://zhenyiwangthu.github.io/HemaScope_Tutorial/免费获取。
