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Int J Biochem Cell Biol. 2015 Jun;63:66-70. doi: 10.1016/j.biocel.2015.01.020. Epub 2015 Feb 8.

Toward high-content screening of mitochondrial morphology and membrane potential in living cells.

Author information

1
Khondrion BV, Nijmegen, The Netherlands.
2
Khondrion BV, Nijmegen, The Netherlands; Department of Biochemistry, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.
3
Khondrion BV, Nijmegen, The Netherlands; Department of Pediatrics, Nijmegen Center for Mitochondria disorders, Radboud University Medical Center, Nijmegen, The Netherlands.
4
Khondrion BV, Nijmegen, The Netherlands; Department of Biochemistry, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands. Electronic address: Werner.Koopman@radboudumc.nl.

Abstract

Mitochondria are double membrane organelles involved in various key cellular processes. Governed by dedicated protein machinery, mitochondria move and continuously fuse and divide. These "mitochondrial dynamics" are bi-directionally linked to mitochondrial and cell functional state in space and time. Due to the action of the electron transport chain (ETC), the mitochondrial inner membrane displays a inside-negative membrane potential (Δψ). The latter is considered a functional readout of mitochondrial "health" and required to sustain normal mitochondrial ATP production and mitochondrial fusion. During the last decade, live-cell microscopy strategies were developed for simultaneous quantification of Δψ and mitochondrial morphology. This revealed that ETC dysfunction, changes in Δψ and aberrations in mitochondrial structure often occur in parallel, suggesting they are linked potential targets for therapeutic intervention. Here we discuss how combining high-content and high-throughput strategies can be used for analysis of genetic and/or drug-induced effects at the level of individual organelles, cells and cell populations. This article is part of a Directed Issue entitled: Energy Metabolism Disorders and Therapies.

KEYWORDS:

Data mining; Machine learning; Mitochondrial pathophysiology; Multivariate analysis; TMRM

PMID:
25668473
DOI:
10.1016/j.biocel.2015.01.020
[Indexed for MEDLINE]

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