{"title":"Assessment of inter-operator variability in peripheral monocyte subset gating strategy using flow cytometry in patients with suspected acute stroke","authors":"Evelyne Heng, Marie Neuwirth, Floriane Mas, Geneviève Contant, Mikaël Mazighi, Joffrey Feriel, Bertrand Montpellier, Caren Brumpt, Georges Jourdi, Emmanuel Curis, Virginie Siguret","doi":"10.1002/cyto.a.24810","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Innovative tools to reliably identify patients with acute stroke are needed. Peripheral monocyte subsets, that is, classical-Mon1, intermediate-Mon2, and non-classical-Mon3, with their activation marker expression analyzed using flow-cytometry (FCM) could be interesting cell biomarker candidates.</p>\n </section>\n \n <section>\n \n <h3> Aim</h3>\n \n <p>To assess the inter-operator variability in a new peripheral monocyte subset gating strategy using FCM in patients with suspected acute stroke.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>In BOOST-study (“Biomarkers-algOrithm-for-strOke-diagnoSis-and Treatment-resistance-prediction,” NCT04726839), patients ≥18 years with symptoms suggesting acute stroke within the last 24 h were included. Blood was collected upon admission to emergency unit. FCM analysis was performed using the FACS-CANTO-II® flow-cytometer and Flow-Jo™-software. Analyzed markers were CD45/CD91/CD14/CD16 (monocyte backbone) and CD62L/CD11b/HLA-DR/CD86/CCR2/ICAM-1/CX3CR1/TF (activation markers). Inter-operator agreement (starting from raw-data files) was quantified by the measure distribution and, for each patient, the coefficient of variation (CV).</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Three operators analyzed 20 patient blood samples. Median inter-operator CVs were below the pre-specified tolerance limits (10% [for Mon1 counts], 20% [Mon2, Mon3 counts], 15% [activation marker median-fluorescence-intensities]). We observed a slight, but systematic, inter-operator effect. Overall, absolute inter-operator differences in fractions of monocyte subsets were <0.03.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>Our gating strategy allowed monocyte subset gating with an acceptable inter-operator variability. Although low, the inter-operator effect should be considered in monocyte data analysis of BOOST-patients.</p>\n </section>\n </div>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"105 3","pages":"171-180"},"PeriodicalIF":2.5000,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24810","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cytometry Part A","FirstCategoryId":"99","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/cyto.a.24810","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
Background
Innovative tools to reliably identify patients with acute stroke are needed. Peripheral monocyte subsets, that is, classical-Mon1, intermediate-Mon2, and non-classical-Mon3, with their activation marker expression analyzed using flow-cytometry (FCM) could be interesting cell biomarker candidates.
Aim
To assess the inter-operator variability in a new peripheral monocyte subset gating strategy using FCM in patients with suspected acute stroke.
Methods
In BOOST-study (“Biomarkers-algOrithm-for-strOke-diagnoSis-and Treatment-resistance-prediction,” NCT04726839), patients ≥18 years with symptoms suggesting acute stroke within the last 24 h were included. Blood was collected upon admission to emergency unit. FCM analysis was performed using the FACS-CANTO-II® flow-cytometer and Flow-Jo™-software. Analyzed markers were CD45/CD91/CD14/CD16 (monocyte backbone) and CD62L/CD11b/HLA-DR/CD86/CCR2/ICAM-1/CX3CR1/TF (activation markers). Inter-operator agreement (starting from raw-data files) was quantified by the measure distribution and, for each patient, the coefficient of variation (CV).
Results
Three operators analyzed 20 patient blood samples. Median inter-operator CVs were below the pre-specified tolerance limits (10% [for Mon1 counts], 20% [Mon2, Mon3 counts], 15% [activation marker median-fluorescence-intensities]). We observed a slight, but systematic, inter-operator effect. Overall, absolute inter-operator differences in fractions of monocyte subsets were <0.03.
Conclusion
Our gating strategy allowed monocyte subset gating with an acceptable inter-operator variability. Although low, the inter-operator effect should be considered in monocyte data analysis of BOOST-patients.
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.