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Lipid Droplets

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Automated System for Small-Population Single-Particle Processing Enabled by Exclusive Liquid Repellency.

SLAS technology
Exclusive liquid repellency (ELR) describes an extreme wettability phenomenon in which a liquid phase droplet is completely repelled from a solid phase when exposed to a secondary immiscible liquid phase. Earlier, we developed a multi-liquid-phase op...

Evaluating adipocyte differentiation of bone marrow-derived mesenchymal stem cells by a deep learning method for automatic lipid droplet counting.

Computers in biology and medicine
Stem cells are a group of competent cells capable of self-renewal and differentiating into osteogenic, chondrogenic, and adipogenic lineages. These cells provide the possibility of successfully treating patients. During differentiation into adipose t...

Label-Free Tomographic Imaging of Lipid Droplets in Foam Cells for Machine-Learning-Assisted Therapeutic Evaluation of Targeted Nanodrugs.

ACS nano
Lipid droplet (LD) accumulation, a key feature of foam cells, constitutes an attractive target for therapeutic intervention in atherosclerosis. However, despite advances in cellular imaging techniques, current noninvasive and quantitative methods hav...

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

Methods in molecular biology (Clifton, N.J.)
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 mitoc...

Deep learning classification of lipid droplets in quantitative phase images.

PloS one
We report the application of supervised machine learning to the automated classification of lipid droplets in label-free, quantitative-phase images. By comparing various machine learning methods commonly used in biomedical imaging and remote sensing,...

Label-free multiplexed microtomography of endogenous subcellular dynamics using generalizable deep learning.

Nature cell biology
Simultaneous imaging of various facets of intact biological systems across multiple spatiotemporal scales is a long-standing goal in biology and medicine, for which progress is hindered by limits of conventional imaging modalities. Here we propose us...

Automated quantification of vacuole fusion and lipophagy in from fluorescence and cryo-soft X-ray microscopy data using deep learning.

Autophagy
During starvation in the yeast vacuolar vesicles fuse and lipid droplets (LDs) can become internalized into the vacuole in an autophagic process named lipophagy. There is a lack of tools to quantitatively assess starvation-induced vacuole fusion and...

Recent advances in label-free imaging and quantification techniques for the study of lipid droplets in cells.

Current opinion in cell biology
Lipid droplets (LDs), once considered mere storage depots for lipids, have gained recognition for their intricate roles in cellular processes, including metabolism, membrane trafficking, and disease states like obesity and cancer. This review explore...

Deep-learning based flat-fielding quantitative phase contrast microscopy.

Optics express
Quantitative phase contrast microscopy (QPCM) can realize high-quality imaging of sub-organelles inside live cells without fluorescence labeling, yet it requires at least three phase-shifted intensity images. Herein, we combine a novel convolutional ...

Artificial intelligence-enabled lipid droplets quantification: Comparative analysis of NIS-elements Segment.ai and ZeroCostDL4Mic StarDist networks.

Methods (San Diego, Calif.)
Lipid droplets (LDs) are dynamic organelles that are present in almost all cell types, with a particularly high prevalence in adipocytes. The phenotype of LDs in these cells reflects their maturity, metabolic activity and function. Although LDs quant...