Zhang 2021 PLOS ONE
|Zhang X, Yuan T, Keijer J, de Boer VCJ (2021) OCRbayes: a Bayesian hierarchical modeling framework for Seahorse extracellular flux oxygen consumption rate data analysis. PLOS ONE 16:e0253926.|
Abstract: Background: Mitochondrial dysfunction is involved in many complex diseases. Efficient and accurate evaluation of mitochondrial functionality is crucial for understanding pathology as well as facilitating novel therapeutic developments. As a popular platform, Seahorse extracellular flux (XF) analyzer is widely used for measuring mitochondrial oxygen consumption rate (OCR) in living cells. A hidden feature of Seahorse XF OCR data is that it has a complex data structure, caused by nesting and crossing between measurement cycles, wells and plates. Surprisingly, statistical analysis of Seahorse XF data has not received sufficient attention, and current methods completely ignore the complex data structure, impairing the robustness of statistical inference.
Results: To rigorously incorporate the complex structure into data analysis, here we developed a Bayesian hierarchical modeling framework, OCRbayes, and demonstrated its applicability based on analysis of published data sets.
Conclusions: We showed that OCRbayes can analyze Seahorse XF OCR experimental data derived from either single or multiple plates. Moreover, OCRbayes has potential to be used for diagnosing patients with mitochondrial diseases.
• Bioblast editor: Gnaiger E
Selected quotes and comments
- In addition to cell number difference, technical, procedural or instrumental noise can also contribute to between well variation.
- Due to batch effects such as plating, culturing or environmental differences between time and laboratories, OCR measurements will differ between plates.
- We processed the original data by removing wells in which single or more OCR measurements were missing.
- If the FDR was below 0.05, we considered that the difference between patient and control cell line was statistically significant.
- Comment: Even statistically-oriented publications ignore the statistical paradigm on 'significance' (Amrhein et al 2019).
- .. we observed considerable variation between the replicate wells as well as measurement cycles in these Seahorse assays.
- .. technical noise including 1) between measurement cycle variation, 2) between well variation and 3) between plate variation.
- .. cell physiology should not substantially change within a phase.
- Comment: This assumption ignores the possible time effect on O2 flow in a given respiratory state.
- "A typical Seahorse assay includes three measurement cycles for each phase."
- "From our perspective, the Seahorese OCR data include three levels, including 1) measurement cycle, 2) well and 3) plate."
- 'Measurement cycle' is a section of the experiment.
- 'Phase' is a respiratory state.
- "For every interval, multiple measurement cycles are performed.
- 'Interval' is a respiratory state.
- Gnaiger E (2021) Bioenergetic cluster analysis – mitochondrial respiratory control in human fibroblasts. MitoFit Preprints 2021.8. https://doi.org/10.26124/mitofit:2021-0008
Labels: MiParea: Respiration, Instruments;methods, mtDNA;mt-genetics, nDNA;cell genetics
Coupling state: LEAK, ROUTINE, ET Pathway: ROX
MitoFit 2021 BCA