Mastering the Computational Challenges of Elispot Plate Evaluation

Abstract

Much has been written about Elispot and how to optimally run the assay for a wide variety of applications. But only a limited number of articles exist addressing the analysis step, the plate evaluation. Comparing that fact with the vast amount of analysis advise available for other single cell immune assay, for example, intracellular cytokine staining, the overall impression may be that Elispot evaluation is just simple enough to not require extensive elaboration and guidance. At first thought this appears reasonable because how difficult can it be counting colored spots on a white background. In addition, automated Elispot readers were already introduced more than 20 years ago (Herr et al., J Immunol Methods 203, 141–152, 1997), easing the strenuous load of manual counting and providing means to decrease the subjectivity in Elispot analysis. Just shortly thereafter however, the first report was published about the subjectivity and operator-dependency of plate evaluation even when using automated reader systems (Janetzki et al., J Immunol Methods 291, 175–183, 2004). Later, the plate evaluation was identified as a main factor causing variability in Elispot results, triggering the inclusion of recommendations on handling of artifacts and the audits of plate reading results in the Initial Elispot Harmonization guidelines (Janetzki et al., Cancer Immunol Immunother 57, 303–315, 2008; Britten et al., Cancer Immunol Immunother 57, 289–302, 2008). In follow-up, a large international study with 75 laboratories was conducted to address the current approaches taken to evaluate Elispot plates and to establish consensus guidelines for plate evaluation (Janetzki et al., Nat Protoc 10, 1098–1115, 2015). This article addresses the special challenges of plate evaluation, gives explanations for unusual observation, and provides overall recommendations on how to work through the labyrinth of available algorithms and reader settings to obtain reliable Elispot data.