Bayesian classification of OXPHOS deficient skeletal myofibres

Area of Science:

• Cellular biology
• Genetics
• Biochemistry

Background:

• Mitochondria are vital organelles responsible for cellular energy production through oxidative phosphorylation (OXPHOS).
• Mitochondrial DNA (mtDNA) mutations can impair OXPHOS protein levels, leading to cellular dysfunction and disease.
• Assessing OXPHOS protein abundance in single cells is crucial for understanding disease severity and progression.

Purpose of the Study:

• To develop and validate a novel single-cell classification method for identifying cells with deficient oxidative phosphorylation (OXPHOS) protein abundance.
• To address the limitations of current methods that misclassify cells due to inter-subject variability and small control groups.
• To improve the accuracy of disease severity assessment and progression prediction in mitochondrial disorders.

Main Methods:

• A Bayesian hierarchical mixture model was developed to analyze single-cell OXPHOS protein abundance data.
• The proposed model accounts for natural variations in protein abundance between individuals.
• The model's performance was evaluated using a dataset of skeletal muscle fibers (myofibers) and compared against existing methods and expert classifications.

Main Results:

• The proposed Bayesian model accurately classified OXPHOS protein abundance in skeletal muscle fibers.
• The model demonstrated improved classification consistency compared to the existing method, especially when considering inter-subject variability.
• Estimates of deficient myofibers from the proposed model aligned well with expert manual classifications.

Conclusions:

• The developed Bayesian hierarchical mixture model offers a more robust and accurate approach for classifying single cells based on OXPHOS protein abundance.
• This improved classification has significant implications for the clinical interpretation of mitochondrial disease data.
• The method provides more reliable estimates of disease burden, aiding in better disease management and prognosis.