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Spectral imaging toolbox: segmentation, hyperstack reconstruction, and batch processing of spectral images for the determination of cell and model membrane lipid order

Spectral imaging toolbox: segmentation, hyperstack reconstruction, and batch processing of spectral images for the determination of cell and model membrane lipid order
Spectral imaging toolbox: segmentation, hyperstack reconstruction, and batch processing of spectral images for the determination of cell and model membrane lipid order
Background: Spectral imaging with polarity-sensitive fluorescent probes enables the quantification of cell and model membrane physical properties, including local hydration, fluidity, and lateral lipid packing, usually characterized by the generalized polarization (GP) parameter. With the development of commercial microscopes equipped with spectral detectors, spectral imaging has become a convenient and powerful technique for measuring GP and other membrane properties. The existing tools for spectral image processing, however, are insufficient for processing the large data sets afforded by this technological advancement, and are unsuitable for processing images acquired with rapidly internal-ized fluorescent probes.

Results: Here we present a MATLAB spectral imaging toolbox with the aim of overcoming these limitations. In addition to 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 rapidly internal-ized. Furthermore, handling for hyperstacks, 3D reconstruction and batch processing facilitates analysis of data sets generated by time series, z-stack, and area scan microscope operations. Finally, the object size distribution is deter-mined, which can provide insight into the mechanisms underlying changes in membrane properties and is desirable for e.g. studies involving model membranes and surfactant coated particles. Analysis is demonstrated for cell membranes, cell-derived vesicles, model membranes, and microbubbles with environmentally-sensitive probes Laurdan, carboxyl-modified Laurdan (C-Laurdan), Di-4-ANEPPDHQ, and Di-4-AN(F)EPPTEA (FE), for quantification of 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 spectral imaging datasets with a reliable method for membranes segmentation and no ability in programming required.
1471-2105
254-261
Aron, Miles
4d9c7843-bbe5-4a5d-975f-ea58a09fc621
Browning, Richard J.
32d183de-738d-49d7-8e74-af7b0a18e73f
Carugo, Dario
0a4be6cd-e309-4ed8-a620-20256ce01179
Sezgin, Erdinc
7b0b5507-2421-46f8-8ef0-04a7bc510348
Bernardino de la Serna, Jorge
d568eb8d-3a54-4fa0-bbf0-7429300aa31f
Eggeling, Christian
8003f376-30f6-49b4-bb50-bab8545b5ffd
Stride, Eleanor
c0143e95-81fa-47c8-b9bc-5b4fc319bba6
Aron, Miles
4d9c7843-bbe5-4a5d-975f-ea58a09fc621
Browning, Richard J.
32d183de-738d-49d7-8e74-af7b0a18e73f
Carugo, Dario
0a4be6cd-e309-4ed8-a620-20256ce01179
Sezgin, Erdinc
7b0b5507-2421-46f8-8ef0-04a7bc510348
Bernardino de la Serna, Jorge
d568eb8d-3a54-4fa0-bbf0-7429300aa31f
Eggeling, Christian
8003f376-30f6-49b4-bb50-bab8545b5ffd
Stride, Eleanor
c0143e95-81fa-47c8-b9bc-5b4fc319bba6

Aron, Miles, Browning, Richard J., Carugo, Dario, Sezgin, Erdinc, Bernardino de la Serna, Jorge, Eggeling, Christian and Stride, Eleanor (2017) Spectral imaging toolbox: segmentation, hyperstack reconstruction, and batch processing of spectral images for the determination of cell and model membrane lipid order BMC Bioinformatics, 18, pp. 254-261. (doi:10.1186/s12859-017-1656-2).

Record type: Article

Abstract

Background: Spectral imaging with polarity-sensitive fluorescent probes enables the quantification of cell and model membrane physical properties, including local hydration, fluidity, and lateral lipid packing, usually characterized by the generalized polarization (GP) parameter. With the development of commercial microscopes equipped with spectral detectors, spectral imaging has become a convenient and powerful technique for measuring GP and other membrane properties. The existing tools for spectral image processing, however, are insufficient for processing the large data sets afforded by this technological advancement, and are unsuitable for processing images acquired with rapidly internal-ized fluorescent probes.

Results: Here we present a MATLAB spectral imaging toolbox with the aim of overcoming these limitations. In addition to 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 rapidly internal-ized. Furthermore, handling for hyperstacks, 3D reconstruction and batch processing facilitates analysis of data sets generated by time series, z-stack, and area scan microscope operations. Finally, the object size distribution is deter-mined, which can provide insight into the mechanisms underlying changes in membrane properties and is desirable for e.g. studies involving model membranes and surfactant coated particles. Analysis is demonstrated for cell membranes, cell-derived vesicles, model membranes, and microbubbles with environmentally-sensitive probes Laurdan, carboxyl-modified Laurdan (C-Laurdan), Di-4-ANEPPDHQ, and Di-4-AN(F)EPPTEA (FE), for quantification of 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 spectral imaging datasets with a reliable method for membranes segmentation and no ability in programming required.

Text BINF-D-16-00849_R1 - Accepted Manuscript
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Text M Aron - BMC Bioinformatics - Version of Record
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More information

Accepted/In Press date: 24 April 2017
e-pub ahead of print date: 12 May 2017
Organisations: Bioengineering Group

Identifiers

Local EPrints ID: 407957
URI: http://eprints.soton.ac.uk/id/eprint/407957
ISSN: 1471-2105
PURE UUID: 0af5a63e-fc6a-47a6-88b2-3232e00cc316

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Date deposited: 05 May 2017 01:05
Last modified: 31 Oct 2017 17:32

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Contributors

Author: Miles Aron
Author: Richard J. Browning
Author: Dario Carugo
Author: Erdinc Sezgin
Author: Jorge Bernardino de la Serna
Author: Christian Eggeling
Author: Eleanor Stride

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