Apollo-NADP(+): a spectrally tunable family of genetically encoded sensors for NADP(+).

NADPH-dependent antioxidant pathways have a critical role in scavenging hydrogen peroxide (H2O2) produced by oxidative phosphorylation. Inadequate scavenging results in H2O2 accumulation and can cause disease. To measure NADPH/NADP(+) redox states, we explored genetically encoded sensors based on steady-state fluorescence anisotropy due to FRET (fluorescence resonance energy transfer) between homologous fluorescent proteins (homoFRET); we refer to these sensors as Apollo sensors. We created an Apollo sensor for NADP(+) (Apollo-NADP(+)) that exploits NADP(+)-dependent homodimerization of enzymatically inactive glucose-6-phosphate dehydrogenase (G6PD). This sensor is reversible, responsive to glucose-stimulated metabolism and spectrally tunable for compatibility with many other sensors. We used Apollo-NADP(+) to study beta cells responding to oxidative stress and demonstrated that NADPH is significantly depleted before H2O2 accumulation by imaging a Cerulean-tagged version of Apollo-NADP(+) with the H2O2 sensor HyPer.

Apollo-NADP+ reveals in vivo adaptation of NADPH/NADP+ metabolism in electrically activated pancreatic ß cells.

Several genetically encoded sensors have been developed to study live cell NADPH/NADP+ dynamics, but their use has been predominantly in vitro. Here, we developed an in vivo assay using the Apollo-NADP+ sensor and microfluidic devices to measure endogenous NADPH/NADP+ dynamics in the pancreatic ß cells of live zebrafish embryos. Flux through the pentose phosphate pathway, the main source of NADPH in many cell types, has been reported to be low in ß cells. Thus, it is unclear how these cells compensate to meet NADPH demands. Using our assay, we show that pyruvate cycling is the main source of NADP+ reduction in ß cells, with contributions from folate cycling after acute electrical activation. INS1E ß cells also showed a stress-induced increase in folate cycling and further suggested that this cycling requires both increased glycolytic intermediates and cytosolic NAD+. Overall, we show in vivo application of the Apollo-NADP+ sensor and reveal that ß cells are capable of adapting NADPH/NADP+ redox during stress.

Leveraging multimodal microscopy to optimize deep learning models for cell segmentation.

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 using 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 candidate cell labeling strategies.