In this work, we show that deep learning and SLIM can be combined to quickly deliver superior results in tissue screening applications. This concept of combining QPI label-free data with AI with the purpose of extracting molecular specificity has been recently introduced by our laboratory as phase imaging with computational specificity (PICS) [Nat. Comm., in press]. Training on ten thousand SLIM images of piglet brain tissue with the 71-layer transfer learning model Xception, we created a two-parameter classification to differentiate the gestational size: either appropriate for gestational age (AGA) or small for gestational age (SGA), and diet: either an experimental regimen high in hydrolyzed fats or a control diet, with an accuracy of 80% and 81%, respectively, and a four-parameter classification (diet and size) with 62% accuracy. These results are significant, as it would otherwise be impossible for a trained histopathologist to distinguish such discrepancies.
Although both neurons and oligodendrocytes have been well studied individually, very little is known about how they interact with each other. New methods are needed to further study the intricacies of this interplay in terms of cellular and molecular dynamics. Spatial Light Interference Microscopy (SLIM) is a quantitative phase imaging technique that generates phase maps related to the dry mass content of the sample. In this work, we study the ability of SLIM to quantify myelination at the axonal level. We imaged a series of cocultures comprising hippocampal neurons and oligodendrocytes, of varying densities, using SLIM, and evaluated dry mass formation and growth of myelin.
Deficient myelination in the internal capsule of the brain is associated with neurodevelopmental delays, particularly in high-risk infants such as those born small for gestational age (SGA). New methods are needed to further study this condition and assess how it relates to early life nourishment. MRI technology has been effective at measuring brain growth and composition but lacks myelin specificity and is low resolution. The development of new quantitative approaches that are rapid and precise may complement MRI results with insight into the pathology of deficient myelination and efficacy of nutritional interventions. Color Spatial Light Interference Microscopy (cSLIM) uses a brightfield objective and RGB camera to generate phase map images in conjunction with a regular brightfield image. Using paraffin embedded brain tissue sections, stained myelin was segmented from a brightfield image and, with a binary mask, those portions were quantitatively analyzed in the corresponding phase maps. This technique was therefore sensitive to subtle variations in myelin density. The results of this study indicate a positive correlation between an experimental diet, rich in critical nutrients such as iron, and dry mass levels of myelin in the internal capsules of both appropriate (AGA) and SGA piglets. In summary, neonatal dietary treatments affect the degree of myelination in certain regions of the brain, irrespective of gestational size, and may therefore impact cognitive health.
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