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Mitochondria

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PINK1 dominated mitochondria associated genes signature predicts abdominal aortic aneurysm with metabolic syndrome.

Biochimica et biophysica acta. Molecular basis of disease
Abdominal aortic aneurysm (AAA) is typically asymptomatic but a devastating cardiovascular disorder, with overall mortality exceeding 80 % once it ruptures. Some patients with AAA may also have comorbid metabolic syndrome (MS), suggesting a potential...

DeepContact: High-throughput quantification of membrane contact sites based on electron microscopy imaging.

The Journal of cell biology
Membrane contact site (MCS)-mediated organelle interactions play essential roles in the cell. Quantitative analysis of MCSs reveals vital clues for cellular responses under various physiological and pathological conditions. However, an efficient tool...

Fear memory-associated synaptic and mitochondrial changes revealed by deep learning-based processing of electron microscopy data.

Cell reports
Serial section electron microscopy (ssEM) can provide comprehensive 3D ultrastructural information of the brain with exceptional computational cost. Targeted reconstruction of subcellular structures from ssEM datasets is less computationally demandin...

Deep neural network automated segmentation of cellular structures in volume electron microscopy.

The Journal of cell biology
Volume electron microscopy is an important imaging modality in contemporary cell biology. Identification of intracellular structures is a laborious process limiting the effective use of this potentially powerful tool. We resolved this bottleneck with...

Instance segmentation of mitochondria in electron microscopy images with a generalist deep learning model trained on a diverse dataset.

Cell systems
Mitochondria are extremely pleomorphic organelles. Automatically annotating each one accurately and precisely in any 2D or volume electron microscopy (EM) image is an unsolved computational challenge. Current deep learning-based approaches train mode...

Evaluation of Image Classification for Quantifying Mitochondrial Morphology Using Deep Learning.

Endocrine, metabolic & immune disorders drug targets
BACKGROUND: Mitochondrial morphology reversibly changes between fission and fusion. As these changes (mitochondrial dynamics) reflect the cellular condition, they are one of the simplest indicators of cell state and predictors of cell fate. However, ...

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning.

Journal of visualized experiments : JoVE
The quantitative analysis of subcellular organelles such as mitochondria in cell fluorescence microscopy images is a demanding task because of the inherent challenges in the segmentation of these small and morphologically diverse structures. In this ...

Predicting the Mitochondrial Toxicity of Small Molecules: Insights from Mechanistic Assays and Cell Painting Data.

Chemical research in toxicology
Mitochondrial toxicity is a significant concern in the drug discovery process, as compounds that disrupt the function of these organelles can lead to serious side effects, including liver injury and cardiotoxicity. Different in vitro assays exist to ...

An interactive deep learning-based approach reveals mitochondrial cristae topologies.

PLoS biology
The convolution of membranes called cristae is a critical structural and functional feature of mitochondria. Crista structure is highly diverse between different cell types, reflecting their role in metabolic adaptation. However, their precise three-...

Plantorganelle Hunter is an effective deep-learning-based method for plant organelle phenotyping in electron microscopy.

Nature plants
Accurate delineation of plant cell organelles from electron microscope images is essential for understanding subcellular behaviour and function. Here we develop a deep-learning pipeline, called the organelle segmentation network (OrgSegNet), for pixe...