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Alliance"],"accepted":{"date-parts":[[2026,2,27]]},"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:p>Mitochondrial respirometry, the measurement of oxygen consumption rate (OCR) by the electron transport chain (ETC), is a cornerstone of mitochondrial biology and the gold standard for measurements of mitochondrial function. However, existing respirometry methodologies are poorly suited for large-scale studies and high-throughput applications, ultimately limiting the applicability of these methods. This limitation necessitates new methodologies, which are more easily scaled as mitochondrial studies become more complex and diverse. In this study, we detail a respirometry approach we have developed for high-throughput applications including optimized plate layouts, volume-based sample normalization, robust control selection, and automated data processing and quality control. Furthermore, we validate these methodologies across a respirometry study running 703 human brain samples, totaling more than 10,000 data points, which underwent our automated data processing and quality control techniques. Our workflow streamlines assay preparation, execution, and analysis to make respirometry scalable, while reducing operator burden and preserving data integrity. 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