MatCol: a tool to measure fluorescence signal colocalisation in biological systems

Abstract Protein colocalisation is often studied using pixel intensity-based coefficients such as Pearson, Manders, Li or Costes. However, these methods cannot be used to study object-based colocalisations in biological systems. Therefore, a novel method is required to automatically identify regions...

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Autores principales:	Matloob Khushi, Christine E. Napier, Christine M. Smyth, Roger R. Reddel, Jonathan W. Arthur
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2faddc290bb648358aa38548d73e9b1a
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spelling	oai:doaj.org-article:2faddc290bb648358aa38548d73e9b1a2021-12-02T15:05:38ZMatCol: a tool to measure fluorescence signal colocalisation in biological systems10.1038/s41598-017-08786-12045-2322https://doaj.org/article/2faddc290bb648358aa38548d73e9b1a2017-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-08786-1https://doaj.org/toc/2045-2322Abstract Protein colocalisation is often studied using pixel intensity-based coefficients such as Pearson, Manders, Li or Costes. However, these methods cannot be used to study object-based colocalisations in biological systems. Therefore, a novel method is required to automatically identify regions of fluorescent signal in two channels, identify the co-located parts of these regions, and calculate the statistical significance of the colocalisation. We have developed MatCol to address these needs. MatCol can be used to visualise protein and/or DNA colocalisations and fine tune user-defined parameters for the colocalisation analysis, including the application of median or Wiener filtering to improve the signal to noise ratio. Command-line execution allows batch processing of multiple images. Users can also calculate the statistical significance of the observed object colocalisations compared to overlap by random chance using Student’s t-test. We validated MatCol in a biological setting. The colocalisations of telomeric DNA and TRF2 protein or TRF2 and PML proteins in >350 nuclei derived from three different cell lines revealed a highly significant correlation between manual and MatCol identification of colocalisations (linear regression R2 = 0.81, P < 0.0001). MatCol has the ability to replace manual colocalisation counting, and the potential to be applied to a wide range of biological areas.Matloob KhushiChristine E. NapierChristine M. SmythRoger R. ReddelJonathan W. ArthurNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-9 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Matloob Khushi Christine E. Napier Christine M. Smyth Roger R. Reddel Jonathan W. Arthur MatCol: a tool to measure fluorescence signal colocalisation in biological systems
description	Abstract Protein colocalisation is often studied using pixel intensity-based coefficients such as Pearson, Manders, Li or Costes. However, these methods cannot be used to study object-based colocalisations in biological systems. Therefore, a novel method is required to automatically identify regions of fluorescent signal in two channels, identify the co-located parts of these regions, and calculate the statistical significance of the colocalisation. We have developed MatCol to address these needs. MatCol can be used to visualise protein and/or DNA colocalisations and fine tune user-defined parameters for the colocalisation analysis, including the application of median or Wiener filtering to improve the signal to noise ratio. Command-line execution allows batch processing of multiple images. Users can also calculate the statistical significance of the observed object colocalisations compared to overlap by random chance using Student’s t-test. We validated MatCol in a biological setting. The colocalisations of telomeric DNA and TRF2 protein or TRF2 and PML proteins in >350 nuclei derived from three different cell lines revealed a highly significant correlation between manual and MatCol identification of colocalisations (linear regression R2 = 0.81, P < 0.0001). MatCol has the ability to replace manual colocalisation counting, and the potential to be applied to a wide range of biological areas.
format	article
author	Matloob Khushi Christine E. Napier Christine M. Smyth Roger R. Reddel Jonathan W. Arthur
author_facet	Matloob Khushi Christine E. Napier Christine M. Smyth Roger R. Reddel Jonathan W. Arthur
author_sort	Matloob Khushi
title	MatCol: a tool to measure fluorescence signal colocalisation in biological systems
title_short	MatCol: a tool to measure fluorescence signal colocalisation in biological systems
title_full	MatCol: a tool to measure fluorescence signal colocalisation in biological systems
title_fullStr	MatCol: a tool to measure fluorescence signal colocalisation in biological systems
title_full_unstemmed	MatCol: a tool to measure fluorescence signal colocalisation in biological systems
title_sort	matcol: a tool to measure fluorescence signal colocalisation in biological systems
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/2faddc290bb648358aa38548d73e9b1a
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MatCol: a tool to measure fluorescence signal colocalisation in biological systems

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