We also observed different redox states in primary mouse astrocytes and neurons, consistent with hypothesized metabolic differences. We found that glycolysis opposed the lactate dehydrogenase equilibrium to produce a reduced cytosolic NADH-NAD(+) redox state. We demonstrated its utility in several cultured and primary cell types. The engineered biosensor Peredox reports cytosolic NADH:NAD(+) ratios and can be calibrated with exogenous lactate and pyruvate. Although the initial construct reported × /, its pH sensitivity was eliminated by mutagenesis.
We constructed a fluorescent biosensor of the cytosolic NADH-NAD(+) redox state by combining a circularly permuted GFP T-Sapphire with a bacterial NADH-binding protein, Rex. By using this approach, we successfully scored images in RNA interference screens in 2 organisms for the prevalence of 15 diverse cellular morphologies, some of which were previously intractable.read more read lessĪbstract: NADH is a key metabolic cofactor whose sensitive and specific detection in the cytosol of live cells has been difficult. Finally, all of the cells in the experiment are automatically classified and each sample is scored based on the presence of cells displaying the phenotype. Next, the researcher generates a rule (i.e., classifier) to recognize cells with a phenotype of interest during a short, interactive training session using iterative feedback. First, automated cytological profiling extracts hundreds of numerical descriptors for every cell in every image. Here we present a supervised machine learning approach that uses iterative feedback to readily score multiple subtle and complex morphological phenotypes in high-throughput, image-based screens. In practical application, customizing an image-analysis algorithm or finding a sufficient number of example cells to train a machine learning algorithm can be infeasible, particularly when positive control samples are not available and the phenotype of interest is rare. Now, automated image analysis can effectively score many phenotypes. Abstract: Many biological pathways were first uncovered by identifying mutants with visible phenotypes and by scoring every sample in a screen via tedious and subjective visual inspection.
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