High-dimensional flow cytometry data: goldmine or fool's gold?

IF 2.5 4区 生物学 Q3 BIOCHEMICAL RESEARCH METHODS
Paula Niewold
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Abstract

Since its inception in the 1970s flow cytometry has been a valuable tool to study the characteristics of cells. Technical advancements lead to a rapid increase in the number of lasers and detectors, enabling assessment of an expanding number of parameters. As every flow cytometry user has experienced the frustration of not being able to fit all markers of interest into their panel, the prevailing mindset seemingly became the bigger the better. So, when spectral flow cytometers became commercially available, their ability to combine spectrally similar fluorophores, measure cellular autofluorescence and utilize the full light spectrum more effectively was received with enthusiasm. These attributes make spectral cytometry particularly applicable for limited or precious (clinical) material.

The recent paper from Dott et al. describes the development of a standardized protocol for sample handling, staining and acquisition, for the application of two large spectral panels in a cohort study using the ID7000™ Spectral Cell Analyzer (Sony Biotechnology).1 The authors specifically address the repeatability and reproducibility of staining over time, which is relevant in the context of cohort studies. By combining a 34- and 35-color spectral panel, the authors were able to quantify and identify 182 cell phenotypes in whole blood samples.

Our expanding immunological knowledge of rare and unique subsets through deep profiling has necessitated the increasing number of cellular parameters required in a single panel for accurate cell identification. However, a new set of challenges arise with increasing panel size. Although the principle of panel design and analysis remain similar, assessing data quality, defining cell populations and translating phenotypic changes into biological insight become increasingly difficult. While many papers have addressed one of these challenges, Dott et al. touch upon all three of these concerns.1

Paula Niewold: Conceptualization; writing – original draft.

高维流式细胞仪数据:金矿还是傻子的黄金?
流式细胞仪自 20 世纪 70 年代问世以来,一直是研究细胞特征的重要工具。技术的进步使激光器和检测器的数量迅速增加,从而使评估参数的数量不断扩大。每一位流式细胞仪用户都曾经历过无法将所有感兴趣的标记物都纳入检测范围的苦恼,因此普遍的想法似乎是检测范围越大越好。因此,当光谱流式细胞仪投入商用时,其结合光谱相似的荧光团、测量细胞自发荧光和更有效地利用全光谱的能力受到了热烈欢迎。Dott 等人最近发表的论文介绍了在一项队列研究中使用 ID7000™ 光谱细胞分析仪(索尼生物技术公司)1 应用两个大型光谱面板时,样本处理、染色和采集标准化方案的开发情况。通过结合 34 色和 35 色光谱面板,作者能够量化和鉴定全血样本中的 182 种细胞表型。通过深度剖析,我们对罕见和独特亚群的免疫学知识不断扩大,这就要求在单个面板中提供越来越多的细胞参数,以准确鉴定细胞。然而,随着样本量的增加,一系列新的挑战也随之而来。虽然细胞图谱设计和分析的原理仍然相似,但评估数据质量、定义细胞群以及将表型变化转化为生物学洞察力却变得越来越困难。虽然许多论文都论述了其中的一个难题,但 Dott 等人的研究却触及了所有这三个问题1:构思;写作--原稿。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Cytometry Part A
Cytometry Part A 生物-生化研究方法
CiteScore
8.10
自引率
13.50%
发文量
183
审稿时长
4-8 weeks
期刊介绍: Cytometry Part A, the journal of quantitative single-cell analysis, features original research reports and reviews of innovative scientific studies employing quantitative single-cell measurement, separation, manipulation, and modeling techniques, as well as original articles on mechanisms of molecular and cellular functions obtained by cytometry techniques. The journal welcomes submissions from multiple research fields that fully embrace the study of the cytome: Biomedical Instrumentation Engineering Biophotonics Bioinformatics Cell Biology Computational Biology Data Science Immunology Parasitology Microbiology Neuroscience Cancer Stem Cells Tissue Regeneration.
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