@article{Aron:2017:10.1186/s12859-017-1656-2,
author = {Aron, M and Browning, R and Carugo, D and Sezgin, E and Bernardino, de la Serna J and Eggeling, C and Stride, E},
doi = {10.1186/s12859-017-1656-2},
journal = {BMC Bioinformatics},
title = {Spectral imaging toolbox: segmentation, hyperstack reconstruction, and batch processing of spectral images for the determination of cell and model membrane lipid order},
url = {http://dx.doi.org/10.1186/s12859-017-1656-2},
volume = {18},
year = {2017}
}

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TY  - JOUR
AB  - Background: Spectral imaging with polarity-sensitive fluorescent probes enables the quantification of cell and modelmembrane physical properties, including local hydration, fluidity, and lateral lipid packing, usually characterized by thegeneralized polarization (GP) parameter. With the development of commercial microscopes equipped with spectraldetectors, spectral imaging has become a convenient and powerful technique for measuring GP and other membraneproperties. The existing tools for spectral image processing, however, are insufficient for processing the large data setsafforded by this technological advancement, and are unsuitable for processing images acquired with rapidlyinternalized fluorescent probes.Results: Here we present a MATLAB spectral imaging toolbox with the aim of overcoming these limitations. In additionto common operations, such as the calculation of distributions of GP values, generation of pseudo-colored GP maps,and spectral analysis, a key highlight of this tool is reliable membrane segmentation for probes that are rapidlyinternalized. Furthermore, handling for hyperstacks, 3D reconstruction and batch processing facilitates analysis of datasets generated by time series, z-stack, and area scan microscope operations. Finally, the object size distribution isdetermined, which can provide insight into the mechanisms underlying changes in membrane properties and isdesirable for e.g. studies involving model membranes and surfactant coated particles. Analysis is demonstratedfor cell membranes, cell-derived vesicles, model membranes, and microbubbles with environmentally-sensitiveprobes Laurdan, carboxyl-modified Laurdan (C-Laurdan), Di-4-ANEPPDHQ, and Di-4-AN(F)EPPTEA (FE), for quantificationof the local lateral density of lipids or lipid packing.Conclusions: The Spectral Imaging Toolbox is a powerful tool for the segmentation and processing of large spectralimaging datasets with a reliable method for membrane segmentation and no ability in programmin
AU  - Aron,M
AU  - Browning,R
AU  - Carugo,D
AU  - Sezgin,E
AU  - Bernardino,de la Serna J
AU  - Eggeling,C
AU  - Stride,E
DO  - 10.1186/s12859-017-1656-2
PY  - 2017///
SN  - 1471-2105
TI  - Spectral imaging toolbox: segmentation, hyperstack reconstruction, and batch processing of spectral images for the determination of cell and model membrane lipid order
T2  - BMC Bioinformatics
UR  - http://dx.doi.org/10.1186/s12859-017-1656-2
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000402093900001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/69262
VL  - 18
ER  - 

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