OCR-Stats: Robust estimation and statistical testing of mitochondrial respiration activities using Seahorse XF Analyzer

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Abstract Accurate quantification of cellular and mitochondrial bioenergetic activity is of great interest in medicine and biology. Mitochondrial stress tests performed with Seahorse Bioscience XF Analyzers allow estimating different bioenergetic measures by monitoring oxygen consumption rates (OCR) of living cells in multi-well plates. However, studies of statistical best practices for determining OCR measurements and comparisons have been lacking so far. Therefore, we performed mitochondrial stress tests in 126 96-well plates involving 203 fibroblast cell lines to understand how OCR behaves across different biosamples, wells, and plates. We show that the noise of OCR is multiplicative, that outlier data points can concern individual measurements or all measurements of a well, and that the inter-plate variation is greater than intra-plate variation. Based on these insights, we developed a novel statistical method, OCR-Stats, that: i) robustly estimates OCR levels modeling multiplicative noise and automatically identifying outlier data points and outlier wells; and ii) performs statistical testing between samples, taking into account the different magnitudes of the between- and within-plates variations. This led to a significant reduction of the coefficient of variation across plates of basal respiration by 36% and of maximal respiration by 32%. Moreover, using positive and negative controls, we show that our statistical test outperforms existing methods, which either suffer from an excess of false positives (within-plates methods), or of false negatives (between-plates methods). Altogether, the aim of this study is to propose statistical good practices to support experimentalists in designing, analyzing, testing and reporting results of mitochondrial stress tests using this high throughput platform.

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