AIMC Topic: Microscopy, Fluorescence

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Fifty high-content light sheet fluorescence microscopy datasets of Tribolium castaneum embryogenesis.

Scientific data
The red flour beetle (Tribolium castaneum) is a key model organism in developmental biology, genetics, and agricultural research. To address the limited availability of high-quality microscopy data documenting its embryonic morphogenesis, we assemble...

Volumetric localization microscopy with deep learning.

Nature communications
Super-resolution microscopy, particularly localization-based methods, necessitates careful balancing of optical complexity, computational demands, and user accessibility. Conventional strategies typically adopt either deterministic or learning-based ...

PixlMap: A generalisable pixel classifier for cellular phenotyping in multiplex immunofluorescence images.

PloS one
Multiplexed methods for the detection of protein expression generate extremely data-rich images of intact tissue sections. These images are invaluable for the quantification and analysis of complex biology and biomarker development. However, their in...

In-depth 3D exploration of autosomal dominant polycystic kidney disease through light sheet fluorescence microscopy.

Scientific reports
Autosomal Dominant Polycystic Kidney Disease (ADPKD) is the most prevalent genetic kidney disorder. Animal preclinical studies are one of the main tools to study this disease, often through either 2D histology imaging for high-resolution analysis or ...

Deep learning detection of dynamic exocytosis events in fluorescence TIRF microscopy.

PLoS computational biology
Segmentation and detection of biological objects in fluorescence microscopy is of paramount importance in cell imaging. Deep learning approaches have recently shown promise to advance, automatize and accelerate analysis. However, most of the interest...

Self-driving microscopy detects the onset of protein aggregation and enables intelligent Brillouin imaging.

Nature communications
The process of protein aggregation, central to neurodegenerative diseases like Huntington's, is challenging to study due to its unpredictable nature and relatively rapid kinetics. Understanding its biomechanics is crucial for unraveling its role in d...

Highly adaptable deep-learning platform for automated detection and analysis of vesicle exocytosis.

Nature communications
Activity recognition in live-cell imaging is labor-intensive and requires significant human effort. Existing automated analysis tools are largely limited in versatility. We present the Intelligent Vesicle Exocytosis Analysis (IVEA) platform, an Image...

Analysis of the carotenoid cycle during microbial growth by combining fluorescence imaging and deep learning.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Detection and real-time tracking of changes in the relative carotenoid content during microbial growth can be extremely challenging. Additionally, analyzing its content cycling time can also be highly difficult. In this study, Raman tweezers were emp...

A modular fluorescent camera unit for wound imaging.

Communications biology
Advanced imaging tools are revolutionizing the diagnosis, treatment, and monitoring of medical conditions, offering unprecedented insights into live cell behavior and biophysical markers. We introduce a modular, hand-held fluorescent microscope featu...

CellSeg3D, Self-supervised 3D cell segmentation for fluorescence microscopy.

eLife
Understanding the complex three-dimensional structure of cells is crucial across many disciplines in biology and especially in neuroscience. Here, we introduce a set of models including a 3D transformer (SwinUNetR) and a novel 3D self-supervised lear...