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- W2583929979 abstract "In the last several years, the development of myriad localization-based fluorescence microscopy techniques has enabled the study of cellular structures at spatial resolutions up to an order of magnitude better than previous optical techniques allowed. These localization microscopy techniques work by acquiring the positions of single fluorophore molecules with diverse methods of varying complexity, but they all share the requirement that in order to create quality images, the cellular structures being studied they must be labeled at quite high densities. Such dense labeling is not always experimentally achievable nor desirable, most especially for live cell imaging studies. However, it is still possible to learn much about the underlying structures in more sparsely labeled samples using correlative analysis. In this study, we use an information theory framework to represent the process of building a localization microscopy image and develop a “completeness index” for the resulting images that aids in image quality assessment. We develop a test with human observers to identify the baseline range of completeness index values required for identification of features commonly found in fluorescence images of cellular structures. We then use simulated and real localization microscopy datasets to determine how to maximize the information gained from correlation analysis of datasets with low completeness and determine guidelines to avoid introducing data processing artifacts into the data. We also confirm the application of bootstrapping methods to estimate the uncertainty of the measurements taken from correlations with these sparsely labeled datasets. Finally, we apply the analysis an example of TIRF-based localization imaging; the distribution of actin-modifying proteins around the site of HIV-entry into a T-cell." @default.
- W2583929979 created "2017-02-10" @default.
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- W2583929979 date "2017-02-01" @default.
- W2583929979 modified "2023-10-18" @default.
- W2583929979 title "Localization Microscopy Theory and Practice for Data Analysis on Sparsely Labeled Samples" @default.
- W2583929979 doi "https://doi.org/10.1016/j.bpj.2016.11.779" @default.
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