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Publication Detail

Title: Automated fluorescence intensity and gradient analysis enables detection of rare fluorescent mutant cells deep within the tissue of RaDR mice.

Authors: Wadduwage, Dushan N; Kay, Jennifer; Singh, Vijay Raj; Kiraly, Orsolya; Sukup-Jackson, Michelle R; Rajapakse, Jagath; Engelward, Bevin P; So, Peter T C

Published In Sci Rep, (2018 08 14)

Abstract: Homologous recombination (HR) events are key drivers of cancer-promoting mutations, and the ability to visualize these events in situ provides important information regarding mutant cell type, location, and clonal expansion. We have previously created the Rosa26 Direct Repeat (RaDR) mouse model wherein HR at an integrated substrate gives rise to a fluorescent cell. To fully leverage this in situ approach, we need better ways to quantify rare fluorescent cells deep within tissues. Here, we present a robust, automated event quantification algorithm that uses image intensity and gradient features to detect fluorescent cells in deep tissue specimens. To analyze the performance of our algorithm, we simulate fluorescence behavior in tissue using Monte Carlo methods. Importantly, this approach reduces the potential for bias in manual counting and enables quantification of samples with highly dense HR events. Using this approach, we measured the relative frequency of HR within a chromosome and between chromosomes and found that HR within a chromosome is more frequent, which is consistent with the close proximity of sister chromatids. Our approach is both objective and highly rapid, providing a powerful tool, not only to researchers interested in HR, but also to many other researchers who are similarly using fluorescence as a marker for understanding mammalian biology in tissues.

PubMed ID: 30108260 Exiting the NIEHS site

MeSH Terms: Animals; Carcinogenesis/genetics; Chromatids/genetics; Chromatids/metabolism; Chromosomes, Mammalian/genetics*; Chromosomes, Mammalian/metabolism; Computer Simulation; Fluorescence; Genes, Reporter/genetics; Homologous Recombination; Image Processing, Computer-Assisted/methods*; Luminescent Proteins/chemistry; Luminescent Proteins/genetics; Mice; Mice, Transgenic; Models, Genetic*; Molecular Imaging/methods*; Monte Carlo Method; Mutation; Neoplasms/diagnostic imaging; Neoplasms/genetics; Neoplasms/pathology; Optical Imaging/methods*; Pancreas/diagnostic imaging; Pancreas/pathology; Repetitive Sequences, Nucleic Acid/genetics; Support Vector Machine

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