Dataset Information

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 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?

SUBMITTER: Khushi M

PROVIDER: S-EPMC5566543 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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

Khushi Matloob M Napier Christine E CE Smyth Christine M CM Reddel Roger R RR Arthur Jonathan W JW

Scientific reports 20170821 1

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 ...[more]

PMID: 28827650

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Project description:Background: Lipid droplets (LDs) are critical organelles associated with many physiological processes in eukaryotic cells. To visualize and study LDs, fluorescence imaging techniques including the confocal imaging as well as the emerging super-resolution imaging of stimulated emission depletion (STED), have been regarded as the most useful methods. However, directly limited by the availability of advanced LDs fluorescent probes, the performances of LDs fluorescence imaging are increasingly unsatisfied with respect to the fast research progress of LDs. Methods: We herein newly developed a superior LDs fluorescent probe named Lipi-QA as a powerful tool for LDs fluorescence imaging and biological study. Colocalization imaging of Lipi-QA and LDs fluorescent probe Ph-Red was conducted in four cell lines. The LDs staining selectivity and the photostability of Lipi-QA were also evaluated by comparing with the commercial LDs probe Nile Red. The in-situ fluorescence lifetime of Lipi-QA in LDs was determined by time-gated detection. The cytotoxicity of Lipi-QA was assessed by MTT assay. The STED saturation intensity as well as the power- and gate time-dependent resolution were tested by Leica SP8 STED super-resolution nanoscopy. The time-lapse 3D confocal imaging and time-lapse STED super-resolution imaging were then designed to study the complex physiological functions of LDs. Results: Featuring with the advantages of the super-photostability, high LDs selectivity, long fluorescence lifetime and low STED saturation intensity, the fluorescent probe Lipi-QA was capable of the long-term time-lapse three-dimensional (3D) confocal imaging to in-situ monitor LDs in 3D space and the time-lapse STED super-resolution imaging (up to 500 STED frames) to track the dynamics of LDs with nanoscale resolution (37 nm). Conclusions: Based on the state-of-the-art fluorescence imaging results, some new biological insights into LDs have been successfully provided. For instance, the long-term time-lapse 3D confocal imaging has surely answered an important and controversial question that the number of LDs would significantly decrease rather than increase upon starvation stimulation; the time-lapse STED super-resolution imaging with the highest resolution has impressively uncovered the fission process of nanoscale LDs for the first time; the starvation-induced change of LDs in size and in speed has been further revealed at nanoscale by the STED super-resolution imaging. All of these results not only highlight the utility of the newly developed fluorescent probe but also significantly promote the biological study of LDs.

Dataset Information

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

Publications

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

Similar Datasets

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