Colocalization Benchmark Source

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The Colocalization Benchmark Source
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DescriptionFree source of benchmark images to validate colocalization in fluorescence microscopy studies
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Authors Vadim Zinchuk, Yong Wu, Olga Grossenbacher-Zinchuk
Release date2012
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Data format Lossless Tagged Image File Format (TIFF)
Website www.colocalization-benchmark.com
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License Free
Version1.1.1

The Colocalization Benchmark Source (CBS) is a free collection of downloadable images to test and validate the degree of colocalization of markers in any fluorescence microscopy studies. Colocalization is a visual phenomenon when two molecules of interest are associated with the same structures in the cells and potentially share common functional characteristics. [1] [2] [3]

In fluorescence microscopy, colocalization refers to observation of the spatial overlap between two different fluorescent labels, each having a separate emission wavelength, to see if the different "targets" are located in the same area of the cell or very near to one another. The definition can be split into two different phenomena, co-occurrence, which refers to the presence of two fluorophores in the same pixel, and correlation, a much more significant statistical relationship between the fluorophores indicative of a biological interaction. This technique is important to many cell biological and physiological studies during the demonstration of a relationship between pairs of bio-molecules.

Contents

CBS provides researchers with reference tools to verify the results of quantitative colocalization measurements. [4] It serves as a specialised bioimage informatics database of computer-simulated images with exactly known (pre-defined) values of colocalization. They were created using image simulation algorithm. These benchmark images can be downloaded as sets as well as separately. By calculating and comparing the values of coefficients on their images versus benchmark images, researchers can validate the results of quantitative colocalization studies. The use of CBS images was described in a number of studies. [5] [6] [7] [8]

In natural and social sciences, and sometimes in other fields, quantitative research is the systematic empirical investigation of observable phenomena via statistical, mathematical, or computational techniques. The objective of quantitative research is to develop and employ mathematical models, theories, and hypotheses pertaining to phenomena. The process of measurement is central to quantitative research because it provides the fundamental connection between empirical observation and mathematical expression of quantitative relationships.

Bioimage informatics is a subfield of bioinformatics and computational biology. It focuses on the use of computational techniques to analyze bioimages, especially cellular and molecular images, at large scale and high throughput. The goal is to obtain useful knowledge out of complicated and heterogeneous image and related metadata.

Database organized collection of data

A database is an organized collection of data, generally stored and accessed electronically from a computer system. Where databases are more complex they are often developed using formal design and modeling techniques.

Examples

Researchers can submit examples of custom images when the benchmark images were used to validate colocalization on them. Submitted images are then posted on the site of CBS together with description of their properties and the values of coefficients, such as Pearson's correlation coefficient (Rr) , overlap coefficient (R), and others. The template for submitting information about custom images can be downloaded from CBS site.

See also

Biological database database of biological information

Biological databases are libraries of life sciences information, collected from scientific experiments, published literature, high-throughput experiment technology, and computational analysis. They contain information from research areas including genomics, proteomics, metabolomics, microarray gene expression, and phylogenetics. Information contained in biological databases includes gene function, structure, localization, clinical effects of mutations as well as similarities of biological sequences and structures.

Related Research Articles

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Fluorescence microscope

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STED microscopy

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Fluorescence cross-correlation spectroscopy

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Nestor J. Zaluzec Microscopist

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References

  1. Bolte S & Cordelieres FP (2006). "A guided tour into subcellular colocalization analysis in light microscopy." J Microsc 224:213–232.
  2. Comeau JW et al. (2006). "A guide to accurate fluorescence microscopy colocalization measurements." Biophys J 91:4611– 4622.
  3. Zinchuk V & Grossenbacher-Zinchuk O (2009). "Recent advances in quantitative colocalization analysis: focus on neuroscience." Prog Histochem Cytochem 44:125-172.
  4. Manders E et al. (1993). "Measurement of colocalization of objects in dual-color confocal images." J Microsc Oxford 169:375–382.
  5. Wu Y et al. (2010). "Quantitative determination of spatial protein-protein correlations in fluorescence confocal microscopy." Biophys J 98:493-504.
  6. Zinchuk V et al. (2011). "Quantifying spatial correlations of fluorescent markers using enhanced background reduction with protein proximity index and correlation coefficient estimations." Nat Protoc 6:1554-1567.
  7. Zinchuk V & Grossenbacher-Zinchuk O (2011). "Quantitative colocalization analysis of confocal fluorescence microscopy images." Curr Protoc Cell Biol Unit 4.19. Archived 2009-11-28 at the Wayback Machine
  8. Zinchuk V et al. (2013). "Bridging the gap between qualitative and quantitative colocalization results in fluorescence microscopy studies." Sci Rep 3:1365 doi:10.1038/srep01365.