Giorgia Milli
Improving recall of In situ sequencing by self-learned features and classical image analysis techniques.
Rel. Elisa Ficarra. Politecnico di Torino, Master of science program in Biomedical Engineering, 2018
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Abstract
Image-based sequencing method to decode mRNA fragments directly in fixed tissue samples allows to carry out the gene expression profile preserving morphological and spatial information of cells and tissues. This approach called in situ sequencing makes it possible to directly visualize, at sub-cellular resolution, where in a tissue sample a given gene is active, to quantify its expression, and to distinguish among many different cell types at the same time. Such information are fundamental to gain a better understanding about tissue and disease development (such as cancer) and cells interplay. Since each gene is composed by a specific sequence of bases, the search is addressed to targeted sequences which must be decoded over multiple staining and imaging cycles, and retrieved by processing multichannel fluorescent biological images of the analyzed samples.
However, signal density, high signal to noise ratio, and microscopes' resolution limits make decoding challenging
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