Spectral imaging toolbox: segmentation, hyperstack reconstruction, and batch processing of spectral images for the determination of cell and model membrane lipid order
<h4>Background</h4> <p>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)...
| Main Authors: | , , , , , , |
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| Format: | Journal article |
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BioMed Central
2017
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| _version_ | 1797089065868197888 |
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| author | Aron, M Browning, R Carugo, D Sezgin, E Bernardino de la Serna, J Eggeling, C Stride, E |
| author_facet | Aron, M Browning, R Carugo, D Sezgin, E Bernardino de la Serna, J Eggeling, C Stride, E |
| author_sort | Aron, M |
| collection | OXFORD |
| description | <h4>Background</h4> <p>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, spec-tral 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 internalized fluorescent probes. </p> <h4>Results</h4> <p>Here we present a MATLAB spectral imaging toolbox with the aim of overcoming these limitations. In addition to com-mon 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 internalized. Furthermore, handling for hyperstacks, 3D reconstruction and batch processing facilitates analysis of data sets generat-ed by time series, z-stack, and area scan microscope operations. Finally, the object size distribution is determined, 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.</p> <h4>Conclusions</h4> <p>The Spectral Imaging Toolbox is a powerful tool for the segmentation and processing of large spectral imaging datasets with a reliable method for membrane segmentation and no ability in programming required. For reviewers the software is available at: https://ora.ox.ac.uk/objects/uuid:4375842f-3598-418d-8aa3-9b31f5023401. It will be further available on the MATLAB file exchange following acceptance for publication.</p> |
| first_indexed | 2024-03-07T02:59:08Z |
| format | Journal article |
| id | oxford-uuid:b056f39b-1c52-4f96-9d06-ffe6983eb751 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T02:59:08Z |
| publishDate | 2017 |
| publisher | BioMed Central |
| record_format | dspace |
| spelling | oxford-uuid:b056f39b-1c52-4f96-9d06-ffe6983eb7512022-03-27T03:55:50ZSpectral imaging toolbox: segmentation, hyperstack reconstruction, and batch processing of spectral images for the determination of cell and model membrane lipid orderJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:b056f39b-1c52-4f96-9d06-ffe6983eb751Symplectic Elements at OxfordBioMed Central2017Aron, MBrowning, RCarugo, DSezgin, EBernardino de la Serna, JEggeling, CStride, E <h4>Background</h4> <p>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, spec-tral 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 internalized fluorescent probes. </p> <h4>Results</h4> <p>Here we present a MATLAB spectral imaging toolbox with the aim of overcoming these limitations. In addition to com-mon 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 internalized. Furthermore, handling for hyperstacks, 3D reconstruction and batch processing facilitates analysis of data sets generat-ed by time series, z-stack, and area scan microscope operations. Finally, the object size distribution is determined, 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.</p> <h4>Conclusions</h4> <p>The Spectral Imaging Toolbox is a powerful tool for the segmentation and processing of large spectral imaging datasets with a reliable method for membrane segmentation and no ability in programming required. For reviewers the software is available at: https://ora.ox.ac.uk/objects/uuid:4375842f-3598-418d-8aa3-9b31f5023401. It will be further available on the MATLAB file exchange following acceptance for publication.</p> |
| spellingShingle | Aron, M Browning, R Carugo, D Sezgin, E Bernardino de la Serna, J Eggeling, C Stride, E Spectral imaging toolbox: segmentation, hyperstack reconstruction, and batch processing of spectral images for the determination of cell and model membrane lipid order |
| title | Spectral imaging toolbox: segmentation, hyperstack reconstruction, and batch processing of spectral images for the determination of cell and model membrane lipid order |
| title_full | Spectral imaging toolbox: segmentation, hyperstack reconstruction, and batch processing of spectral images for the determination of cell and model membrane lipid order |
| title_fullStr | Spectral imaging toolbox: segmentation, hyperstack reconstruction, and batch processing of spectral images for the determination of cell and model membrane lipid order |
| title_full_unstemmed | Spectral imaging toolbox: segmentation, hyperstack reconstruction, and batch processing of spectral images for the determination of cell and model membrane lipid order |
| title_short | Spectral imaging toolbox: segmentation, hyperstack reconstruction, and batch processing of spectral images for the determination of cell and model membrane lipid order |
| title_sort | spectral imaging toolbox segmentation hyperstack reconstruction and batch processing of spectral images for the determination of cell and model membrane lipid order |
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