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==MiCodes==
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Current library screening methods typically discard all information regarding a majority of phenotypes that fail to meet a given threshold. Often, information from phenotypes beyond this threshold is of value—to demonstrate that the library has been fully explored, or to validate models correlating genotype and phenotype. To characterize the entire spectrum of phenotypes with a high-throughput method, we propose to apply microscopy to library screening. This requires a phenotype observable by microscopy connected to a construct genotype. MiCodes (microscopy barcodes) provide a unique phenotypic tag for each library member, which we generated by targeting combinations of fluorophores to several organelles within yeast. MiCodes can potentially scale to library sizes of 1,000,000 or more, using an automatable method to measure many cells in parallel. MiCodes act as a consistent, high throughput technology that connects genetic information to a visible phenotype for each member of the library.
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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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To learn more, visit the <a href="https://2012.igem.org/Team:Berkeley/Project">Project subpage</a> or jump straight to the section that interests you via the boxes below.
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Please visit our [[Team:Berkeley/Project|Project page]] for more details!
 
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<a href="https://2012.igem.org/Team:Berkeley/Project/Localization">
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<p style="text-align:center; color:#cccccc;">Localization tags, promoter optimization, and promoter characterization.</p> </div>
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<a href="https://2012.igem.org/Team:Berkeley/Project/Construction">
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<p style="text-align:center; color:#FFFFFF;">MiCode construction using Golden Gate Assembly.</p></div>
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<a href="https://2012.igem.org/Team:Berkeley/Project/Automation">
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<p style="text-align:center; color:#cccccc;">Automation techniques for image acquisition and cell identification with Matlab and Cell Profiler.</p></div>
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<a href="https://2012.igem.org/Team:Berkeley/Project/Zippers">
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<p style="text-align:center; color:#FFFFFF;">Leucine Zippers affinity assay.</p></div>
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<p>The UC Berkeley iGEM team would like to thank Agilent and the Synthetic Biology Institute for their financial support, Synberc for their administrative support, and IDT for discounted oligos. </p>
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<a href="http://synbio.berkeley.edu/">
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Latest revision as of 06:09, 22 October 2012

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iGEM Berkeley iGEMBerkeley iGEMBerkeley

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.

To learn more, visit the Project subpage or jump straight to the section that interests you via the boxes below.


Localization tags, promoter optimization, and promoter characterization.

MiCode construction using Golden Gate Assembly.

Automation techniques for image acquisition and cell identification with Matlab and Cell Profiler.

Leucine Zippers affinity assay.


The UC Berkeley iGEM team would like to thank Agilent and the Synthetic Biology Institute for their financial support, Synberc for their administrative support, and IDT for discounted oligos.

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