流式细胞术
计算机科学
工作流程
细胞仪
聚类分析
仿形(计算机编程)
计算生物学
数据挖掘
人工智能
生物
数据库
免疫学
操作系统
作者
Staffan Holmberg-Thydén,Kirsten Grønbæk,Anne Ortved Gang,Daniel El Fassi,Sine Reker Hadrup
标识
DOI:10.1016/j.ab.2021.114210
摘要
Multicolor flow cytometry is an essential tool for studying the immune system in health and disease, allowing users to extract longitudinal multiparametric data from patient samples. The process is complicated by substantial variation in performance between each flow cytometry instrument, and analytical errors are therefore common. Here, we present an approach to overcome such limitations by applying a systematic workflow for pairing colors to markers optimized for the equipment intended to run the experiments. The workflow is exemplified by the design of four comprehensive flow cytometry panels for patients with hematological cancer. Methods for quality control, titration of antibodies, compensation, and staining of cells for obtaining optimal results are also addressed. Finally, to handle the large amounts of data generated by multicolor flow cytometry, unsupervised clustering techniques are used to identify significant subpopulations not detected by conventional sequential gating.
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