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A clear distinction between oncocytoma and chromophobe renal cell carcinoma (chRCC) is critically important for clinical management of patients. But it may often be difficult to distinguish the two entities based on hematoxylin and eosin (H and E) stained sections alone. In this study, second harmonic generation (SHG) signals which are very specific to collagen were used to image collagen fibril structure. We conduct a pilot study to develop a new diagnostic method based on the analysis of collagen associated with kidney tumors using convolutional neural networks (CNNs). CNNs comprise a type of machine learning process well-suited for drawing information out of images. This study examines a CNN model's ability to differentiate between oncocytoma (benign), and chRCC (malignant) kidney tumor images acquired with second harmonic generation (SHG), which is very specific for collagen matrix. To the best of our knowledge, this is the first study that attempts to distinguish the two entities based on their collagen structure. The model developed from this study demonstrated an overall classification accuracy of 68.7% with a specificity of 66.3% and sensitivity of 74.6%. While these results reflect an ability to classify the kidney tumors better than chance, further studies will be carried out to (a) better realize the tumor classification potential of this method with a larger sample size and (b) combining SHG with two-photon excited intrinsic fluorescence signal to achieve better classification. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.
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Post-translational phosphorylation is essential to human cellular processes, but the transient, heterogeneous nature of this modification complicates its study in native systems. We developed an approach to interrogate phosphorylation and its role in protein-protein interactions on a proteome-wide scale. We genetically encoded phosphoserine in recoded E. coli and generated a peptide-based heterologous representation of the human serine phosphoproteome. We designed a single-plasmid library encoding >100,000 human phosphopeptides and confirmed the site-specific incorporation of phosphoserine in >36,000 of these peptides. We then integrated our phosphopeptide library into an approach known as Hi-P to enable proteome-level screens for serine-phosphorylation-dependent human protein interactions. Using Hi-P, we found hundreds of known and potentially new phosphoserine-dependent interactors with 14-3-3 proteins and WW domains. These phosphosites retained important binding characteristics of the native human phosphoproteome, as determined by motif analysis and pull-downs using full-length phosphoproteins. This technology can be used to interrogate user-defined phosphoproteomes in any organism, tissue, or disease of interest.
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- English (2)