Integrated machine learning approaches for flow cytometric quantification of myeloid-derived suppressor cells in acute sepsis.
Integrated machine learning approaches for flow cytometric quantification of myeloid-derived suppressor cells in acute sepsis.
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DOI：
10.3389/fimmu.2022.1007016
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发表时间：
2022
影响因子：
7.3
通讯作者：
中科院分区：
文献类型：
--
作者：
高度异质性的细胞群体需要多种流式细胞术标记物用于适当的表型表征。这以指数方式增加了2D散点图分析的复杂性，并加剧了由于流量数据手动门控的变化而导致的人为错误。我们描述了一个完全基于Flowjo图形用户界面（GUI）的半自动化工作流程，该工作流程涉及逐步整合几种新的机器学习工具，用于分析脓毒症和非脓毒症危重病中的骨髓源性抑制细胞（MDSC）。流式细胞术数据的监督聚类显示与MDSC相关，但与通过手动门控获得的细胞数相比，MDSC的数量显著不同。两种量化方法都无法预测由16名重症和脓毒症患者以及5名重症和非脓毒症患者组成的队列的30天临床结局。机器学习发现，与健康对照组相比，重症和脓毒症患者的PMN-MDSC比例显著降低。这些MDSC在脓毒性和非脓毒性危重病中的比例没有差异。
Highly heterogeneous cell populations require multiple flow cytometric markers for appropriate phenotypic characterization. This exponentially increases the complexity of 2D scatter plot analyses and exacerbates human errors due to variations in manual gating of flow data. We describe a semi-automated workflow, based entirely on the Flowjo Graphical User Interface (GUI), that involves the stepwise integration of several, newly available machine learning tools for the analysis of myeloid-derived suppressor cells (MDSCs) in septic and non-septic critical illness. Supervised clustering of flow cytometric data showed correlation with, but significantly different numbers of, MDSCs as compared with the cell numbers obtained by manual gating. Neither quantification method predicted 30-day clinical outcomes in a cohort of 16 critically ill and septic patients and 5 critically ill and non-septic patients. Machine learning identified a significant decrease in the proportion of PMN-MDSC in critically ill and septic patients as compared with healthy controls. There was no difference between the proportion of these MDSCs in septic and non-septic critical illness.
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影响因子：
7.3
作者：
Nakamori Y;Park EJ;Shimaoka M
通讯作者：
Shimaoka M
影响因子：
10.1
作者：
Gabrilovich DI
通讯作者：
Gabrilovich DI
影响因子：
8.8
作者：
Vincent, JL;de Mendonca, A;Blecher, S
通讯作者：
Blecher, S
影响因子：
46.9
作者：
Dann, Emma;Henderson, Neil C.;Marioni, John C.
通讯作者：
Marioni, John C.
影响因子：
2.9
作者：
Loftus TJ;Mira JC;Ozrazgat-Baslanti T;Ghita GL;Wang Z;Stortz JA;Brumback BA;Bihorac A;Segal MS;Anton SD;Leeuwenburgh C;Mohr AM;Efron PA;Moldawer LL;Moore FA;Brakenridge SC
通讯作者：
Brakenridge SC