Quantitative analysis of super-resolved structures using ASAP.

Published in: Nature methods (2019)

Super-resolution microscopy allows imaging of cellular structures with high throughput and detail. However, the efficient and quantitative analysis of images generated is challenging with existing tools. Here, we develop ASAP (automated structures analysis program) to enable rapid and automated detection, classification and quantification of super-resolved structures. We validate ASAP on ground truth data and demonstrate its broad applicability by analyzing images of nucleoporins, TORC1 complexes, endocytic vesicles and Bax pores.

Keyphrases

high resolution
deep learning
high throughput
machine learning
convolutional neural network
optical coherence tomography
loop mediated isothermal amplification
single cell
mass spectrometry
label free
oxidative stress
single molecule
big data
induced apoptosis