Colocalization by cross-correlation, a new method of colocalization suited for super-resolution microscopy
Abstract Background A common goal of scientific microscopic imaging is to determine if a spatial correlation exists between two imaged structures. This is generally accomplished by imaging fluorescently labeled structures and measuring their spatial correlation with a class of image analysis algorit...
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| Format: | Article |
| Language: | English |
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BMC
2024-02-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-024-05675-z |
| _version_ | 1797273007846064128 |
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| author | Andrew D. McCall |
| author_facet | Andrew D. McCall |
| author_sort | Andrew D. McCall |
| collection | DOAJ |
| description | Abstract Background A common goal of scientific microscopic imaging is to determine if a spatial correlation exists between two imaged structures. This is generally accomplished by imaging fluorescently labeled structures and measuring their spatial correlation with a class of image analysis algorithms known as colocalization. However, the most commonly used methods of colocalization have strict limitations, such as requiring overlap in the fluorescent markers and reporting requirements for accurate interpretation of the data, that are often not met. Due to the development of novel super-resolution techniques, which reduce the overlap of the fluorescent signals, a new colocalization method is needed that does not have such strict requirements. Results In order to overcome the limitations of other colocalization algorithms, I developed a new ImageJ/Fiji plugin, Colocalization by cross-correlation (CCC). This method uses cross-correlation over space to identify spatial correlations as a function of distance, removing the overlap requirement and providing more comprehensive results. CCC is compatible with 3D and time-lapse images, and was designed to be easy to use. CCC also generates new images that only show the correlating labeled structures from the input images, a novel feature among the cross-correlating algorithms. Conclusions CCC is a versatile, powerful, and easy to use colocalization and spatial correlation tool that is available through the Fiji update sites. Full and up to date documentation can be found at https://imagej.net/plugins/colocalization-by-cross-correlation . CCC source code is available at https://github.com/andmccall/Colocalization_by_Cross_Correlation . |
| first_indexed | 2024-03-07T14:37:28Z |
| format | Article |
| id | doaj.art-0a313f3bb261408d96e82594382cbd3a |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-03-07T14:37:28Z |
| publishDate | 2024-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-0a313f3bb261408d96e82594382cbd3a2024-03-05T20:31:55ZengBMCBMC Bioinformatics1471-21052024-02-0125112110.1186/s12859-024-05675-zColocalization by cross-correlation, a new method of colocalization suited for super-resolution microscopyAndrew D. McCall0Optical Imaging and Analysis Facility, School of Dental Medicine, University at BuffaloAbstract Background A common goal of scientific microscopic imaging is to determine if a spatial correlation exists between two imaged structures. This is generally accomplished by imaging fluorescently labeled structures and measuring their spatial correlation with a class of image analysis algorithms known as colocalization. However, the most commonly used methods of colocalization have strict limitations, such as requiring overlap in the fluorescent markers and reporting requirements for accurate interpretation of the data, that are often not met. Due to the development of novel super-resolution techniques, which reduce the overlap of the fluorescent signals, a new colocalization method is needed that does not have such strict requirements. Results In order to overcome the limitations of other colocalization algorithms, I developed a new ImageJ/Fiji plugin, Colocalization by cross-correlation (CCC). This method uses cross-correlation over space to identify spatial correlations as a function of distance, removing the overlap requirement and providing more comprehensive results. CCC is compatible with 3D and time-lapse images, and was designed to be easy to use. CCC also generates new images that only show the correlating labeled structures from the input images, a novel feature among the cross-correlating algorithms. Conclusions CCC is a versatile, powerful, and easy to use colocalization and spatial correlation tool that is available through the Fiji update sites. Full and up to date documentation can be found at https://imagej.net/plugins/colocalization-by-cross-correlation . CCC source code is available at https://github.com/andmccall/Colocalization_by_Cross_Correlation .https://doi.org/10.1186/s12859-024-05675-zColocalizationImage analysisImage cross-correlation spectroscopyCross-correlationSuper-resolution |
| spellingShingle | Andrew D. McCall Colocalization by cross-correlation, a new method of colocalization suited for super-resolution microscopy BMC Bioinformatics Colocalization Image analysis Image cross-correlation spectroscopy Cross-correlation Super-resolution |
| title | Colocalization by cross-correlation, a new method of colocalization suited for super-resolution microscopy |
| title_full | Colocalization by cross-correlation, a new method of colocalization suited for super-resolution microscopy |
| title_fullStr | Colocalization by cross-correlation, a new method of colocalization suited for super-resolution microscopy |
| title_full_unstemmed | Colocalization by cross-correlation, a new method of colocalization suited for super-resolution microscopy |
| title_short | Colocalization by cross-correlation, a new method of colocalization suited for super-resolution microscopy |
| title_sort | colocalization by cross correlation a new method of colocalization suited for super resolution microscopy |
| topic | Colocalization Image analysis Image cross-correlation spectroscopy Cross-correlation Super-resolution |
| url | https://doi.org/10.1186/s12859-024-05675-z |
| work_keys_str_mv | AT andrewdmccall colocalizationbycrosscorrelationanewmethodofcolocalizationsuitedforsuperresolutionmicroscopy |