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SMITE (single molecule imaging toolbox extraordinaire), a MATLAB package by David Schodt; Michael J. Wester; Mohamadreza Fazel; Hanieh Mazloom-Farsibaf; Sandeep Pallikkuth; Sajjad A. Khan; Marjolein B.M. Meddens; Farzin Farzam; William K. Kanagy; Derek A. Rinaldi; Elton D. Jhamba; Keith A. Lidke is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is SMITE (single molecule imaging toolbox extraordinaire), a MATLAB package about?

Quantitatively describing spatial patterns in biological substrates is a difficult task that requires careful consideration of labeling methods, imaging modality, and the motivating hypothesis. This problem is commonly addressed using intensity correlation paradigms which correlate the brightness between fluorescence images of co-labeled biomolecules as a measure of their colocalization. These approaches ultimately characterize the superposition of fluorescence signals and thus are limited to binary assessment of spatial relationships at the scale of the image resolution. We've previously demonstrated an image analysis framework, which leverages nearest neighbor (NN) distribution functions (F, G, and L) for richer, more informative quantification of the spatial distribution of immunofluorescently imaged biomolecules on all spatial scales captured within the image. Briefly, biomolecule signals and relevant biological structures are segmented, biomolecules are localized relative to landmarks, which are either structures or other biomolecules, by measuring NN distances between biomolecule and landmark voxels, and finally their global spatial association is assessed by comparing the re

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Author

David Schodt; Michael J. Wester; Mohamadreza Fazel; Hanieh Mazloom-Farsibaf; Sandeep Pallikkuth; Sajjad A. Khan; Marjolein B.M. Meddens; Farzin Farzam; William K. Kanagy; Derek A. Rinaldi; Elton D. Jhamba; Keith A. Lidke

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)