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Lookup NU author(s): Jordan Childs, Dr Amy VincentORCiD, Dr Conor LawlessORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2025 Childs et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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.
Author(s): Childs J, Gomes TB, Vincent AE, Golightly A, Lawless C
Publication type: Article
Publication status: Published
Journal: PLoS Computational Biology
Year: 2025
Volume: 21
Issue: 2
Online publication date: 19/02/2025
Acceptance date: 07/01/2025
Date deposited: 10/03/2025
ISSN (print): 1553-734X
ISSN (electronic): 1553-7358
Publisher: Public Library of Science
URL: https://doi.org/10.1371/journal.pcbi.1012770
DOI: 10.1371/journal.pcbi.1012770
Data Access Statement: All code required to replicate the results can be found at https://github.com/jordanbchilds/oxphosDeficientClassification & https://github.com/jordanbchilds/analysis2Dmito.
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