William W Lau, Calvin A Johnson, Sara Lioi, Joseph A Mindell
{"title":"Three-Dimensional Spot Detection in Ratiometric Fluorescence Imaging For Measurement of Subcellular Organelles.","authors":"William W Lau, Calvin A Johnson, Sara Lioi, Joseph A Mindell","doi":"10.1145/2506583.2512387","DOIUrl":null,"url":null,"abstract":"<p><p>Lysosomes are subcellular organelles playing a vital role in the endocytosis process of the cell. Lysosomal acidity is an important factor in assuring proper functioning of the enzymes within the organelle, and can be assessed by labeling the lysosomes with pH-sensitive fluorescence probes. To enhance our understanding of the acidification mechanisms, the goal of this work is to develop a method that can accurately detect and characterize the acidity of each lysosome captured in ratiometric fluorescence images. We present an algorithm that utilizes the <i>h</i>-dome transformation and reconciles spots detected independently from two wavelength channels. We evaluated our algorithm using simulated images for which the exact locations were known. The <i>h</i>-dome algorithm achieved an <i>f</i>-score as high as 0.890. We also computed the fluorescence ratios from lysosomes in live HeLa cell images with known lysosomal pHs. Using leave-one-out cross-validation, we demonstrated that the new algorithm was able to achieve much better pH prediction accuracy than the conventional method.</p>","PeriodicalId":90404,"journal":{"name":"2013 ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics : ACM - BCB 2013 : Washington, D.C., U.S.A., September 22 - 25, 2013. ACM Conference on Bioinformatics, Computational Biology and Biomedical Informa...","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/2506583.2512387","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics : ACM - BCB 2013 : Washington, D.C., U.S.A., September 22 - 25, 2013. ACM Conference on Bioinformatics, Computational Biology and Biomedical Informa...","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2506583.2512387","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Lysosomes are subcellular organelles playing a vital role in the endocytosis process of the cell. Lysosomal acidity is an important factor in assuring proper functioning of the enzymes within the organelle, and can be assessed by labeling the lysosomes with pH-sensitive fluorescence probes. To enhance our understanding of the acidification mechanisms, the goal of this work is to develop a method that can accurately detect and characterize the acidity of each lysosome captured in ratiometric fluorescence images. We present an algorithm that utilizes the h-dome transformation and reconciles spots detected independently from two wavelength channels. We evaluated our algorithm using simulated images for which the exact locations were known. The h-dome algorithm achieved an f-score as high as 0.890. We also computed the fluorescence ratios from lysosomes in live HeLa cell images with known lysosomal pHs. Using leave-one-out cross-validation, we demonstrated that the new algorithm was able to achieve much better pH prediction accuracy than the conventional method.