Machine Learning eJournal | 2021

Rapid Methods for the Evaluation of Fluorescent Reporters in Tissue Clearing and the Segmentation of Large Vascular Structures

 
 
 
 
 
 
 
 

Abstract


Light sheet fluorescence microscopy (LSFM) of large tissue samples does not require mechanical sectioning and allows efficient visualization of spatially complex or rare structures. Therefore, LSFM has become invaluable in developmental and biomedical research. Because sample size may limit whole mount staining, LSFM benefits from transgenic reporter organisms expressing fluorescent proteins (FPs), however, requires optical clearing and computational data visualization and analysis. The former often interferes with FPs, while the later requires massive computing resources. \n \nHere, we describe 3D-polymerized cell dispersions, a rapid and straightforward method, based on recombinant FP expression in freely selectable tester cells, to evaluate and compare fluorescence retention in different tissue-clearing protocols. For the analysis of large LSFM data, which usually require huge computing resources, we introduce a refined, interactive, hierarchical random walker approach that is capable of efficient segmentation of the vasculature in data sets even on a consumer grade PC.

Volume None
Pages None
DOI 10.2139/ssrn.3770100
Language English
Journal Machine Learning eJournal

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