High-Throughput Image Analysis of Lipid-Droplet-Bound Mitochondria

Nathanael Miller, Dane Wolf, Nour Alsabeeh, Kiana Mahdaviani, Mayuko Segawa, Marc Liesa, Orian S. Shirihai

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

4 Scopus citations

Abstract

Changes to mitochondrial architecture are associated with various adaptive and pathogenic processes. However, quantification of changes to mitochondrial structures is limited by the yet unmet challenge of defining the borders of each individual mitochondrion within an image. Here, we describe a novel method for segmenting primary brown adipocyte (BA) mitochondria images. We describe a granular approach to quantifying subcellular structures, particularly mitochondria in close proximity to lipid droplets: peridroplet mitochondria. In addition, we lay out a novel machine-learning-based mitochondrial segmentation method that eliminates the bias of manual mitochondrial segmentation and improves object recognition compared to conventional thresholding analyses. By applying these methods, we discovered a significant difference between cytosolic and peridroplet BA mitochondrial H2O2 production and validated the machine-learning algorithm in BA via norepinephrine-induced mitochondrial fragmentation and comparing manual analyses to the automated analysis. This approach provides a high-throughput analysis protocol to quantify ratiometric probes in subpopulations of mitochondria in adipocytes.

Original languageEnglish
Title of host publicationMethods in Molecular Biology
PublisherHumana Press Inc.
Pages285-303
Number of pages19
DOIs
StatePublished - 1 Jan 2021
Externally publishedYes

Publication series

NameMethods in Molecular Biology
Volume2276
ISSN (Print)1064-3745
ISSN (Electronic)1940-6029

Keywords

  • Brown adipocyte morphology
  • Image analysis
  • Machine learning
  • Mitochondria

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics

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