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https://hdl.handle.net/10356/103329
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. |
Keywords: | DRNTU::Engineering::Computer science and engineering RaDR Fuorescence |
Issue Date: | 2018 | Source: | Wadduwage, D. N., Kay, J., Singh, V. R., Kiraly, O., Sukup-Jackson, M. R., Rajapakse, J., ... So, P. T. C. (2018). Automated fluorescence intensity and gradient analysis enables detection of rare fluorescent mutant cells deep within the tissue of RaDR mice. Scientific Reports, 8(1), 12108-. doi:10.1038/s41598-018-30557-9 | Series/Report no.: | Scientific Reports | 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. | URI: | https://hdl.handle.net/10356/103329 http://hdl.handle.net/10220/47301 |
DOI: | 10.1038/s41598-018-30557-9 | Schools: | School of Computer Science and Engineering | Rights: | © 2018 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Te images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. | Fulltext Permission: | open | Fulltext Availability: | With Fulltext |
Appears in Collections: | SCSE Journal Articles |
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Automated fluorescence intensity and gradient analysis enables detection of rare fluorescent mutant cells deep within the tissue of RaDR mice.pdf | 3.98 MB | Adobe PDF | ![]() View/Open |
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