| Literature DB >> 31726053 |
Wanxia Li Tsai1, Laura Vian1, Valentina Giudice2, Jacqueline Kieltyka1, Christine Liu1, Victoria Fonseca1, Nathalia Gazaniga1, Shouguo Gao3, Sachiko Kajigaya3, Neal S Young3, Angélique Biancotto4, Massimo Gadina5.
Abstract
Fluorescent cell barcoding (FCB) is a multiplexing technique for high-throughput flow cytometry (FCM). Although powerful in minimizing staining variability, it remains a subjective FCM technique because of inter-operator variability and differences in data analysis. FCB was implemented by combining two-dye barcoding (DyLight 350 plus Pacific Orange) with five-color surface marker antibody and intracellular staining for phosphoprotein signaling analysis. We proposed a robust method to measure intra- and inter-assay variability of FCB in T/B cells and monocytes by combining range and ratio of variability to standard statistical analyses. Data analysis was carried out by conventional and semi-automated workflows and built with R software. Results obtained from both analyses were compared to assess feasibility and reproducibility of FCB data analysis by machine-learning methods. Our results showed efficient FCB using DyLight 350 and Pacific Orange at concentrations of 0, 15 or 30, and 250 μg/mL, and a high reproducibility of FCB in combination with surface marker and intracellular antibodies. Inter-operator variability was minimized by adding an internal control bridged across matrices used as rejection criterion if significant differences were present between runs. Computational workflows showed comparable results to conventional gating strategies. FCB can be used to study phosphoprotein signaling in T/B cells and monocytes with high reproducibility across operators, and the addition of bridge internal controls can further minimize inter-operator variability. This FCB protocol, which has high throughput analysis and low intra- and inter-assay variability, can be a powerful tool for clinical trial studies. Moreover, FCB data can be reliably analyzed using computational software. Published by Elsevier B.V.Entities:
Keywords: Computational analysis; Fluorescent cell barcoding; Phenotyping; Phosphoproteins; Variability
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Year: 2019 PMID: 31726053 PMCID: PMC6981073 DOI: 10.1016/j.jim.2019.112667
Source DB: PubMed Journal: J Immunol Methods ISSN: 0022-1759 Impact factor: 2.303