Ask Eivind Heggernes, Tschan-Plessl Astrid, Hoel Hanna Julie, Kolstad Arne, Holte Harald, Malmberg Karl-Johan
Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.
The Precision Immunotherapy Alliance, University of Oslo, Oslo, Norway.
Patterns (N Y). 2024 May 13;5(7):100989. doi: 10.1016/j.patter.2024.100989. eCollection 2024 Jul 12.
Flow cytometry is a powerful technology for high-throughput protein quantification at the single-cell level. Technical advances have substantially increased data complexity, but novel bioinformatical tools often show limitations in statistical testing, data sharing, cross-experiment comparability, or clinical data integration. We developed MetaGate as a platform for interactive statistical analysis and visualization of manually gated high-dimensional cytometry data with integration of metadata. MetaGate provides a data reduction algorithm based on a combinatorial gating system that produces a small, portable, and standardized data file. This is subsequently used to produce figures and statistical analyses through a fast web-based user interface. We demonstrate the utility of MetaGate through a comprehensive mass cytometry analysis of peripheral blood immune cells from 28 patients with diffuse large B cell lymphoma along with 17 healthy controls. Through MetaGate analysis, our study identifies key immune cell population changes associated with disease progression.
流式细胞术是一种在单细胞水平上进行高通量蛋白质定量的强大技术。技术进步显著增加了数据的复杂性，但新型生物信息学工具在统计测试、数据共享、跨实验可比性或临床数据整合方面往往存在局限性。我们开发了MetaGate作为一个平台，用于对手动门控的高维细胞术数据进行交互式统计分析和可视化，并整合元数据。MetaGate提供了一种基于组合门控系统的数据简化算法，该算法生成一个小型、便携且标准化的数据文件。随后，通过基于网络的快速用户界面，利用该文件生成图表和统计分析。我们通过对28例弥漫性大B细胞淋巴瘤患者以及17名健康对照者的外周血免疫细胞进行全面的质谱流式细胞术分析，展示了MetaGate的实用性。通过MetaGate分析，我们的研究确定了与疾病进展相关的关键免疫细胞群体变化。
