Team:Berkeley/Project

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<b>MiCodes:</b> Enabling library screens with microscopy by connecting genotypes to observable phenotypes
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Many applications in synthetic biology demand precise control over subcellular localization, cell morphology, motility, and other such phenotypes that are only observable via microscopy. At present, engineering these properties is challenging due in large part to the inherent throughput limitation imposed by microscopy. We have developed a strategy that enables high-throughput library screening with microscopy by coupling a unique fluorescence signature with each genotype present in a library. These MiCodes (microscopy barcodes) are generated by targeting combinations of fluorophores to several organelles within yeast, and they eliminate the need to isolate and observe clonal populations separately. MiCodes can potentially scale to library sizes of 10^6 or more, and their analysis can be largely automated using existing image processing software. As a proof of principle, we applied MiCodes to the problem of finding unique pairs of protein-protein interaction parts.
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Microscopy is a versatile and widely used imaging technology that allows us to observe various phenotypes that are not easily obtainable using other techniques. For example, microscopy can show us how cells move around in their environment, how they are shaped and form networks with other cells, and how proteins and other metabolites can be spatially localized within cells. Such phenomena require direct visualization, which best achieved through microscopy.  </div>
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MiCodes - enabling library screens with microscopy by connecting genotypes to observable phenotypes
 
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Many applications in synthetic biology demand precise control over subcellular localization, cell morphology, motility, and other such phenotypes that are only observable via microscopy. At present, engineering these properties is challenging due in large part to the inherent throughput limitation imposed by microscopy. We have developed a strategy that enables high-throughput library screening with microscopy by coupling a unique fluorescence signature with each genotype present in a library. These MiCodes (microscopy barcodes) are generated by targeting combinations of fluorophores to several organelles within yeast, and they eliminate the need to isolate and observe clonal populations separately. MiCodes can potentially scale to library sizes of 10^6 or more, and their analysis can be largely automated using existing image processing software. As a proof of principle, we applied MiCodes to the problem of finding unique pairs of protein-protein interaction parts.
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When we engineer systems in synthetic biology using cellular features such as motility, morphology and subcellular localization, we usually like to do so using libraries.  
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This is due to our current limitation of guessing which parameters to choose to optimize a given system. By using libraries, we can examine a large parameter space and find trends present in our system and even optimal parameters to use for our design. It is difficult to screen libraries of certain cellular phenomena in high throughput using current technologies, so we need to look at other possible methods.
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== The Micodes Library ==
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Intro here... link to more specifications [[Team:Berkeley/Project/Micodes | here ]]
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By localizing fluorescent proteins to specific organelles, each cell can be given a "microscopic barcode", or <b>MiCode</b>. Below you can see a single sample MiCode. Each member of a library will get a unique MiCode, distinguishing it from the rest of the library and tying the MiCode phenotype to a specific genotype.
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<p align="center">This example MiCode has four targeted organelles: nucleus-red, vacuolar membrane-red, plasma membrane-blue, actin-green.
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== Application: Leucine Zippers ==
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Intro here... link to more specifications [[Team:Berkeley/Project/LeucineZippers | here ]]
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To do this, we needed to decide which organelles to target and how to target them. We also had to optimize our choice of promoters so that one fluorescent protein signal was not too strong or too weak in comparison to the rest.
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Thanks for reading, etc... for a summary of what we've accomplished, check out our [[Team:Berkeley/Results | results]] page.
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Latest revision as of 23:10, 21 October 2012

header
iGEM Berkeley iGEMBerkeley iGEMBerkeley

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MiCodes: Enabling library screens with microscopy by connecting genotypes to observable phenotypes

Many applications in synthetic biology demand precise control over subcellular localization, cell morphology, motility, and other such phenotypes that are only observable via microscopy. At present, engineering these properties is challenging due in large part to the inherent throughput limitation imposed by microscopy. We have developed a strategy that enables high-throughput library screening with microscopy by coupling a unique fluorescence signature with each genotype present in a library. These MiCodes (microscopy barcodes) are generated by targeting combinations of fluorophores to several organelles within yeast, and they eliminate the need to isolate and observe clonal populations separately. MiCodes can potentially scale to library sizes of 10^6 or more, and their analysis can be largely automated using existing image processing software. As a proof of principle, we applied MiCodes to the problem of finding unique pairs of protein-protein interaction parts.


Microscopy is a versatile and widely used imaging technology that allows us to observe various phenotypes that are not easily obtainable using other techniques. For example, microscopy can show us how cells move around in their environment, how they are shaped and form networks with other cells, and how proteins and other metabolites can be spatially localized within cells. Such phenomena require direct visualization, which best achieved through microscopy.


When we engineer systems in synthetic biology using cellular features such as motility, morphology and subcellular localization, we usually like to do so using libraries. This is due to our current limitation of guessing which parameters to choose to optimize a given system. By using libraries, we can examine a large parameter space and find trends present in our system and even optimal parameters to use for our design. It is difficult to screen libraries of certain cellular phenomena in high throughput using current technologies, so we need to look at other possible methods.


By localizing fluorescent proteins to specific organelles, each cell can be given a "microscopic barcode", or MiCode. Below you can see a single sample MiCode. Each member of a library will get a unique MiCode, distinguishing it from the rest of the library and tying the MiCode phenotype to a specific genotype.

This example MiCode has four targeted organelles: nucleus-red, vacuolar membrane-red, plasma membrane-blue, actin-green.



To do this, we needed to decide which organelles to target and how to target them. We also had to optimize our choice of promoters so that one fluorescent protein signal was not too strong or too weak in comparison to the rest.





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