Bayesian classification of OXPHOS deficient skeletal myofibres

TL;DR

A new Bayesian method improves classification of cells with mitochondrial dysfunction, aligning better with expert assessments.

Contribution

A Bayesian hierarchical mixture model is introduced to classify OXPHOS-deficient myofibres, accounting for inter-subject variability.

Findings

The proposed Bayesian model classifies OXPHOS-deficient myofibres more accurately than existing methods.

Classifications from the model align closely with manual classifications by experts.

Natural variability between subjects is incorporated into the model for improved accuracy.

Abstract

Mitochondria are organelles in most human cells which release the energy required for cells to function. Oxidative phosphorylation (OXPHOS) is a key biochemical process within mitochondria required for energy production and requires a range of proteins and protein complexes. Mitochondria contain multiple copies of their own genome (mtDNA), which codes for some of the proteins and ribonucleic acids required for mitochondrial function and assembly. Pathology arises from genetic defects in mtDNA and can reduce cellular abundance of OXPHOS proteins, affecting mitochondrial function. Due to the continuous turn-over of mtDNA, pathology is random and neighbouring cells can possess different OXPHOS protein abundance. Estimating the proportion of cells where OXPHOS protein abundance is too low to maintain normal function is critical to understanding disease severity and predicting disease progression. Currently, one method to classify single cells as being OXPHOS deficient is prevalent in the literature. The method compares a patient’s OXPHOS protein abundance to that of a small number of healthy control subjects. If the patient’s cell displays an abundance which differs from the abundance of the controls then it is deemed deficient. However, due to the natural variation between subjects and the low number of control subjects typically available, this method is inflexible and often results in a large proportion of patient cells being misclassified. These misclassifications have significant consequences for the clinical interpretation of these data. We propose a single-cell classification method using a Bayesian hierarchical mixture model, which allows for inter-subject OXPHOS protein abundance variation. The model accurately classifies an example dataset of OXPHOS protein abundances in skeletal muscle fibres (myofibres). When comparing the proposed and existing model classifications to manual classifications performed by experts, the proposed model results in estimates of the proportion of deficient myofibres that are consistent with expert manual classifications.

Mitochondria are responsible for energy production within cells and mitochondrial diseases correspond to a group of rare genetic disorders which can affect their function. However, it is common for each cell to not be affected in the same way, and neighbouring cells are often observed with varying levels of mitochondrial function. Estimating the proportion of affected cells in a sample is important for measuring disease progression and severity. One way to measure mitochondrial function is through the abundance of proteins related to their function. Using this data we have developed a method to classify single cells with a mitochondrial defect which allows for the natural variability between subjects. We show that our approach produces classifications in higher agreement with those performed by experts when compared to an existing classification method.

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