MitoDecode: quantitative phenotypic decoding of mitochondrial stress states from fluorescence images
The quantitative measurement of mitochondrial stress from fluorescence images of cells remains a challenging task. Existing methods rely on mitochondrial segmentation, manually predefined features, and discrete phenotype severity classes, which are prone to error and bias. Here, we present MitoDecode, a deep learning network that automatically assesses type and severity of mitochondrial stress…
MitoDecode is a novel deep learning network that efficiently quantifies mitochondrial stress from fluorescence microscopy images of cells. Traditionally, measuring mitochondrial stress has been difficult due to methods relying on mitochondrial segmentation, manual feature selection, and discrete stress severity levels, which are vulnerable to errors and biases.
MitoDecode overcomes these issues by eliminating the need for segmentation, being parameter-free, and applicable to both traditional and super-resolution imaging techniques.
Upon processing a fluorescence image of a cell, MitoDecode delivers a phenotypic probability profile for each individual cell. This profile indicates both the type and degree of stress experienced by the cell, enabling comparisons between different stressors. This detailed readout also helps uncover underlying mechanisms and detect subtle variations in stress responses that might otherwise go unnoticed.
MitoDecode's adaptability is demonstrated across various cell types, imaging modalities, and stress conditions. It can reliably identify mitochondrial stress phenotypes, even when stressors affect non-mitochondrial pathways. The researchers validate MitoDecode's accuracy by comparing its results to biochemical assays and showcase its potential utility in drug treatment phenotyping. Ultimately, MitoDecode could prove valuable in early-stage disease detection, drug discovery, and generating mechanistic leads.
Written by urgent.news from bioRxiv's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
