Cameron, W. D. | Search bioRxiv

bioRxiv2022

Targeting Apollo-NADP+ to image NADPH generation in pancreatic beta-cell organelles

NADPH/NADP+ redox state supports numerous reactions related to cell growth and survival; yet the full impact is difficult to appreciate due to organelle compartmentalization of NADPH and NADP+. To study glucose-stimulated NADPH production in pancreatic beta-cell organelles, we targeted the Apollo-NADP+ sensor by first selecting the most pH stable version of the single colour sensor. We subsequently targeted mTurquoise2-Apollo-NADP+ to various organelles and confirmed activity in the cytoplasm, mitochondrial matrix, nucleus, and peroxisome. Finally, we measured the glucose- and glutamine-stimulated NADPH responses by single and dual colour imaging of the targeted sensors. Overall, we developed multiple organelle-targeted Apollo-NADP+ sensors to reveal the prominent role of beta-cell mitochondria in determining NADPH production in the cytoplasm, nucleus, and peroxisome.

cell biology↗

bioRxiv2020

Cell segmentation using deep learning: comparing label and label-free approaches using hyper-labeled image stacks

Deep learning provides an opportunity to automatically segment and extract cellular features from high-throughput microscopy images. Many labeling strategies have been developed for this purpose, ranging from the use of fluorescent markers to label-free approaches. However, differences in the channels available to each respective training dataset make it difficult to directly compare the effectiveness of these strategies across studies. Here we explore training models using subimage stacks composed of channels sampled from larger, hyper-labeled, image stacks. This allows us to directly compare a variety of labeling strategies and training approaches on identical cells. This approach revealed that fluorescence-based strategies generally provide higher segmentation accuracies but were less accurate than label-free models when labeling was inconsistent. The relative strengths of label and label-free techniques could be combined through the use of merging fluorescence channels and out-of-focus brightfield images. Beyond comparing labeling strategies, using subimage stacks for training was also found to provide a method of simulating a wide range of labeling conditions, increasing the ability of the final model to accommodate a greater range of experimental setups.

bioengineering↗