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Assessment of inter-operator variability in peripheral monocyte subset gating strategy using flow cytometry in patients with suspected acute stroke.

Abstract

BACKGROUND: Innovative tools to reliably identify patients with acute stroke are needed. Peripheral monocyte subsets, i.e. 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 24h 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) & 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 on 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.

Authors: Heng E, Neuwirth M, Mas F, Contant G, Mazighi M, Feriel J, Montpellier B, Brumpt C, Jourdi G, Curis E, Siguret V,
Journal: Cytometry A;2023Nov16. doi:10.1002/cyto.a.24810
Year: 2023
PubMed: PMID: 37971361 (Go to PubMed)