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Active changes in mitochondrial structure and organization facilitate cellular homeostasis. Because aberrant mitochondrial dynamics are implicated in a variety of human diseases, their assessment is potentially useful for diagnosis, therapy, and disease monitoring. Because current techniques for evaluating mitochondrial morphology are invasive or necessitate mitochondria-specific dyes, their clinical translation is limited. We report that mitochondrial dynamics can be monitored in vivo, within intact human skin by relying entirely on endogenous two-photon-excited fluorescence from the reduced metabolic coenzyme nicotinamide adenine dinucleotide (NADH). We established the sensitivity of this approach with in vivo, fast temporal studies of arterial occlusion-reperfusion, which revealed acute changes in the mitochondrial metabolism and dynamics of the lower human epidermal layers. In vitro hypoxic-reperfusion studies validated that the in vivo outcomes were a result of NADH fluorescence changes. To demonstrate the diagnostic potential of this approach, we evaluated healthy and cancerous human skin epithelia. Healthy tissues displayed consistent, depth-dependent morphological and mitochondrial organization patterns that varied with histological stratification and intraepithelial mitochondrial protein expression. In contrast, these consistent patterns were absent in cancerous skin lesions. We exploited these differences to successfully differentiate healthy from cancerous tissues using a predictive classification approach. Collectively, these results demonstrate that our label-free, automated, near real-time assessments of mitochondrial organization-relying solely on endogenous contrast-could be useful for accurate, noninvasive in vivo diagnosis.

This manuscript describes the development of a resources module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on implementing deep learning algorithms for biomedical image data in an interactive format that uses appropriate cloud resources for data access and analyses. Biomedical-related datasets are widely used in both research and clinical settings, but the ability for professionally trained clinicians and researchers to interpret datasets becomes difficult as the size and breadth of these datasets increases. Artificial intelligence, and specifically deep learning neural networks, have recently become an important tool in novel biomedical research. However, use is limited due to their computational requirements and confusion regarding different neural network architectures. The goal of this learning module is to introduce types of deep learning neural networks and cover practices that are commonly used in biomedical research. This module is subdivided into four submodules that cover classification, augmentation, segmentation and regression. Each complementary submodule was written on the Google Cloud Platform and contains detailed code and explanations, as well as quizzes and challenges to facilitate user training. Overall, the goal of this learning module is to enable users to identify and integrate the correct type of neural network with their data while highlighting the ease-of-use of cloud computing for implementing neural networks. This manuscript describes the development of a resource module that is part of a learning platform named ''NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [ 1 ] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses. [ABSTRACT FROM AUTHOR]

Over the last half century, the autofluorescence of the metabolic cofactors NADH (reduced nicotinamide adenine dinucleotide) and FAD (flavin adenine dinucleotide) has been quantified in a variety of cell types and disease states. With the spread of nonlinear optical microscopy techniques in biomedical research, NADH and FAD imaging has offered an attractive solution to noninvasively monitor cell and tissue status and elucidate dynamic changes in cell or tissue metabolism. Various tools and methods to measure the temporal, spectral, and spatial properties of NADH and FAD autofluorescence have been developed. Specifically, an optical redox ratio of cofactor fluorescence intensities and NADH fluorescence lifetime parameters have been used in numerous applications, but significant work remains to mature this technology for understanding dynamic changes in metabolism. This article describes the current understanding of our optical sensitivity to different metabolic pathways and highlights current challenges in the field. Recent progress in addressing these challenges and acquiring more quantitative information in faster and more metabolically relevant formats is also discussed. [ABSTRACT FROM AUTHOR]