An active, collaborative approach to learning skills in flow cytometry

Advances in science education research have the potential to improve the way students learn to perform scientific interpretations and understand science concepts. We developed active, collaborative activities to teach skills in manipulating flow cytometry data using FlowJo software. Undergraduate students were given compensated clinical flow cytometry listmode output (FCS) files and asked to design a gating strategy to diagnose patients with different hematological malignancies on the basis of their immunophenotype. A separate cohort of research trainees was given uncompensated data files on which they performed their own compensation, calculated the antibody staining index, designed a sequential gating strategy, and quantified rare immune cell subsets. Student engagement, confidence, and perceptions of flow cytometry were assessed using a survey. Competency against the learning outcomes was assessed by asking students to undertake tasks that required understanding of flow cytometry dot plot data and gating sequences. The active, collaborative approach allowed students to achieve learning outcomes not previously possible with traditional teaching formats, for example, having students design their own gating strategy, without forgoing essential outcomes such as the interpretation of dot plots. In undergraduate students, favorable perceptions of flow cytometry as a field and as a potential career choice were correlated with student confidence but not the ability to perform flow cytometry data analysis. We demonstrate that this new pedagogical approach to teaching flow cytometry is beneficial for student understanding and interpretation of complex concepts. It should be considered as a useful new method for incorporating complex data analysis tasks such as flow cytometry into curricula.

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