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cDNA-Basic Concept

Module by: Ewa Paszek

Summary: This course is a short series of lectures on Statistical Bioinformatics. Topics covered are listed in the Table of Contents. The notes were prepared by Ewa Paszek, Lukasz Wita and Marek Kimmel. The development of this course has been supported by NSF 0203396 grant.

cDNA-Basic Concept

Recently, several types of the DNA microarrays were introduced. Applications of microarrays range from the study of gene expression in yeast( Lashkari et al., 1997) under different environmental stress conditions to the comparison of gene expression profiles for tumors from cancer patients (Golub et al., 1999). The first approach is to use the chemically synthesized form of DNA called COMPLEMENTARY DNA (cDNA), which contains only coding part of the sequence, complementary to its corresponding mRNA transcript. Microarrays have a form of microscope slides containing hundreds to thousands of immobilized DNA samples that are hybridized in a manner very similar to the Northern (Alwine et al., 1977)and Southern blot (Southern, 1975). The main function of a microarray is to detect the level of mRNA transcript of genes of interest. The plates are incubated in the solution containing genetic material under consideration. The mRNA transcripts floating in the solution would hybridize to their complementary cDNA, previously placed on the microarray chip. Since the cDNA on the chip is fluorescently labeled, every spot will emit a light in the ultraviolet environment, intensity of which depends on the amount of hybridized mRNA (Schena et al., 1995). The differentiation of the cDNA’s ultraviolet dye allows the comparison of the gene expression under different experimental conditions (case- control studies). The preparation of the microarray for case-control study is schematically depicted on Figure 1. Initial data obtained from DNA microarrays are in the form of scanned images. Coding the gene expression by means of colors can be helpful for building d genetic maps and graphical data processing. Expression gene map is presented in the form of a table; the rows of which corresponds to the consecutive genes and columns represent different samples, for example under multiple experimental conditions or for different patients. More informations available at: Bioconductors, follow link to training .

Figure 1: Overview of Procedures for Preparing and Analyzing Microarrays of Complementary DNA (cDNA). As shown in Panel A, reference RNA and tumor RNA is labeled by reverse transcription with different fluorescent dyes (green for the reference cells and red for the tumor cells) and hybridized to a cDNA microarray containing robotically printed cDNA clones. As shown in Panel B, the slides are scanned with a confocal laser-scanning microscope, and color images are generated for each hybridization with RNA from the tumor and reference cells. Genes up-regulated in the tumors appear red, whereas those with decreased expression appear green. Genes with similar levels of expression in the two samples appear yellow. Genes of interest are selected on the basis of the differences in the level of expression by known tumor classes (e.g., BRCA1-mutation–positive and BRCA2-mutation–positive). Statistical analysis determines whether these differences in the gene-expression profiles are greater than would be expected by chance. As shown in Panel C, the differences in the patterns of gene expression between tumor classes can be portrayed in the form of a color-coded plot, and the relations between tumors can be portrayed in the form of a multidimensional-scaling plot. Tumors with similar gene-expression profiles cluster close to one another in the multidimensional-scaling plot.
The spotted array technology.
The spotted array technology. (spot.gif)

References

  1. Alwine, J.C., Kemp, D.J., Stark, G.R. (1977). Method for detection of specific RNAs in agarose gels by transfer to diazobenzyloxymethyl-paper and hybridization with DNA probes. Proc. Natl. Acad. Sci. U. S. A., 74, 5350–5354.
  2. Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J.P., Coller, H., Loh, M.L., Downing, J.R., Caligiuri, M., Abloomfield, C.D., Lander, E.S. (1999). Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science, 286, 531–537.
  3. Lashkari, D.A., DeRisi, J.L., McCusker, J.H., Namath, A.F., Gentile, C., Hwang, S.Y., Brown, P.O., Davis, R.W. (1997). Yeast microarrays for genome wide parallel genetic and gene expression analysis. Proc. Natl. Acad. Sci. U. S. A., 94, 13057–13062.
  4. Schena, M., Shalon, D., Davis, R.W., Brown, P.O. (1995). Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science, 270,
  5. Southern, E.M. (1975). ) Detection of specific sequences among DNA fragments separated by gel electrophoresis. J. Mol. Biol., 98, 503–517.

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