Abstract
One of the most challenging objective for clinical cytometry in prospective multicenter immunomonitoring trials is to compare frequencies, absolute numbers of leukocyte populations and further the mean fluorescence intensities of cell markers, especially when the data are generated from different instruments. Here, we describe an innovative standardization workflow to compare all data to carry out any large-scale, prospective multicentric flow cytometry analysis whatever the duration, the number or type of instruments required for the realization of such projects.
Highlights
One of the most challenging objective for clinical cytometry in prospective multicenter immunomonitoring trials is to compare frequencies, absolute numbers of leukocyte populations and further the mean fluorescence intensities of cell markers, especially when the data are generated from different instruments
One of the challenges not yet fully resolved for clinical cytometry[1] is to compare, in prospective multicenter immunomonitoring trials, the frequencies and absolute values of various leukocyte populations, especially if the data are generated from different instruments and from different companies
The objective was to compare the distribution of leukocyte populations and the mean fluorescence intensities (MFIs) of the markers of the studied populations in order to establish a new classification of autoimmune diseases in relation to all the "omic" collection of data
Summary
The effective harmonization of the instruments through this procedure (Fig. 1, step 1) enables the realization of the large-scale multicentric phenotypic analyzes and assumes the stability of the 11 instruments throughout the duration of the study. Executed on the LMD files of the blood test sample stained with DuraClone Panel 1, the script allowed MFIs of the membrane markers corresponding to the MFIs obtained without PMT modification, with coefficients of variation of less than 5% (Supplementary Figure 1d and Supplementary Table 3). This R script makes it possible to standardize the results generated by the machine (Fig. 1, step 2) and ensures the stability of its data throughout the duration of the project. Values at the beginning (December 2014) and at the end of the inclusions (December 2018) have been compared and found statistically reproducible (not shown)
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