BACKGROUND: Conventional data analysis of flow cytometry-based basophil activation testing requires repetitive, labor-intensive analysis that hampers efforts to standardize testing for clinical applications. Using an open-source platform, we developed and implemented a programmatic approach to the analysis of the basophil activation test (BAT) by flow cytometry. METHODS: Using the BÜHLMANN FlowCAST® assay, peripheral blood from peanut allergic patients undergoing oral immunotherapy was incubated with peanut allergens (Arah1, Arah2, Arah6, whole peanut extract) and stained with fluorescent antibodies to CCR3 and CD63 for the development of a data-driven programmatic analysis using Bioconductor and R. Basophil identification using clustering and classification was validated using manually gated comparisons in an experimental subset. Reproducibility of CD63 upregulation set on unstimulated or anti-FcERI stimulated basophils was compared. RESULTS: BAT analysis of 294 experiments was successful in 91.5% using the above approach, with a total of 7,166 individual basophil activation tests from 269 experiments. We estimate this represents a net saving of 1340 min of labor by a skilled operator. Medium-based gating correlated to respective manual gating more closely than anti-FcERI based gating (R = 0.96 vs. R = 0.84, P < 0.001). Only 2% of the basophil activation results were significantly different from manual gating. Quality measures of the experiments and other measures of basophil activation were also provided by the analysis. CONCLUSIONS: We present a novel data-driven flow cytometric platform for the analysis of clinical basophil activation testing, providing a high throughput objective approach to basophil activation analysis.
BACKGROUND: Conventional data analysis of flow cytometry-based basophil activation testing requires repetitive, labor-intensive analysis that hampers efforts to standardize testing for clinical applications. Using an open-source platform, we developed and implemented a programmatic approach to the analysis of the basophil activation test (BAT) by flow cytometry. METHODS: Using the BÜHLMANN FlowCAST® assay, peripheral blood from peanutallergicpatients undergoing oral immunotherapy was incubated with peanut allergens (Arah1, Arah2, Arah6, whole peanut extract) and stained with fluorescent antibodies to CCR3 and CD63 for the development of a data-driven programmatic analysis using Bioconductor and R. Basophil identification using clustering and classification was validated using manually gated comparisons in an experimental subset. Reproducibility of CD63 upregulation set on unstimulated or anti-FcERI stimulated basophils was compared. RESULTS: BAT analysis of 294 experiments was successful in 91.5% using the above approach, with a total of 7,166 individual basophil activation tests from 269 experiments. We estimate this represents a net saving of 1340 min of labor by a skilled operator. Medium-based gating correlated to respective manual gating more closely than anti-FcERI based gating (R = 0.96 vs. R = 0.84, P < 0.001). Only 2% of the basophil activation results were significantly different from manual gating. Quality measures of the experiments and other measures of basophil activation were also provided by the analysis. CONCLUSIONS: We present a novel data-driven flow cytometric platform for the analysis of clinical basophil activation testing, providing a high throughput objective approach to basophil activation analysis.
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