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2D-DIGE

Application of 2D-DIGE in Cancer Proteomics Toward Personalized Medicine... [Pg.135]

Advanced 2D-PAGE,2D-DIGE, and its Application for Biomarker Development... [Pg.140]

Fig. 2. Basic protocoi of 2D-DiGE for two protein sampies.The different protein samples are labeled with different fluorescent dyes (Cy3 and Cy5), mixed together and separated on the same 2D gel. After gel electrophoresis, the gels are scanned with laser at the appropriate wavelength for Cy3 and Cy5. A single gel can generate two 2D images, so that gel-to-gel variations are canceled out. We can compare as many protein samples as the number of available fluorescent dyes. Fig. 2. Basic protocoi of 2D-DiGE for two protein sampies.The different protein samples are labeled with different fluorescent dyes (Cy3 and Cy5), mixed together and separated on the same 2D gel. After gel electrophoresis, the gels are scanned with laser at the appropriate wavelength for Cy3 and Cy5. A single gel can generate two 2D images, so that gel-to-gel variations are canceled out. We can compare as many protein samples as the number of available fluorescent dyes.
D-DIGE Facilitates High-Throughput Gel-Based Proteomics Applications... [Pg.143]

The problems inherent to 2D-PAGE that are solved with the use of 2D-DIGE are summarized in Table 2. [Pg.144]

Data-Mining for Cancer Proteomics Using 2D-DIGE Data and Clinicopathological Information... [Pg.145]

Practical Biomarker Discovery Using 2D-DIGE Data... [Pg.147]

For these reasons, our research group includes basic researchers including a bioinformatics specialist, clinicians, pathologists, and industry partners, in a way that best-optimizes the use of 2D-DIGE related methods for biomarker development studies (Fig. 4). [Pg.150]

Fig. 4. Application of bioinformatics tools to 2D-DIGE data analysis. Proteome data consisting of the normalized spot intensity values are exported from the image analysis software and their correlation with clinicopathological data examined. Using informatics tools including clustering algorithms and machine-learning methods, a novel cancer classification based on proteome data is established, and key proteomic features and proteins corresponding to biomarker candidates are identified. Fig. 4. Application of bioinformatics tools to 2D-DIGE data analysis. Proteome data consisting of the normalized spot intensity values are exported from the image analysis software and their correlation with clinicopathological data examined. Using informatics tools including clustering algorithms and machine-learning methods, a novel cancer classification based on proteome data is established, and key proteomic features and proteins corresponding to biomarker candidates are identified.

See other pages where 2D-DIGE is mentioned: [Pg.373]    [Pg.135]    [Pg.138]    [Pg.140]    [Pg.141]    [Pg.141]    [Pg.142]    [Pg.143]    [Pg.143]    [Pg.143]    [Pg.143]    [Pg.144]    [Pg.145]    [Pg.145]    [Pg.145]    [Pg.145]    [Pg.146]    [Pg.146]    [Pg.147]    [Pg.148]    [Pg.148]    [Pg.149]    [Pg.149]    [Pg.149]    [Pg.149]    [Pg.151]    [Pg.151]    [Pg.151]    [Pg.154]   
See also in sourсe #XX -- [ Pg.6 ]

See also in sourсe #XX -- [ Pg.2 , Pg.11 , Pg.111 , Pg.112 , Pg.113 ]




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