view release on metacpan or  search on metacpan

data/db_tables/protocols.xml  view on Meta::CPAN

 Depending on the experimental design, there are three useful approaches for calculating normalization
 factors. The first simply uses total measured fluorescence intensity. The assumption underlying this
 approach is that the total mass of RNA labeled with either Cy3 or Cy5 is equal. While the intensity
 for any one spot may be higher in one channel than the other, when averaged over thousands of spots
 in the array, these fluctuations should average out. Consequently, the total integrated intensity across
 all the spots in the array should be equal for both channels. Alternatively, one could add a number of
 controls in increasing but equimolar concentrations to both the labeling reactions and the sum of the
 intensities for these spots should be equal. A second approach uses linear regression analysis. For
 closely related samples, one would expect many of the genes to be expressed at nearly constant levels.
 Consequently, a scatterplot of the measured Cy5 versus Cy3 intensities should have a slope of one.
 Measured intensities for added equimolar controls should behave similarly. Under this assumption,
 one can use regression analysis techniques to calculate the slope. This is then used to rescale the data
 and adjust the slope to one. A third approach has been described by Chen et al. (2). They assume that
 some subset of housekeeping genes exists and that for these, the distribution of transcription levels
 should have some mean value ? and standard deviation ? independent of the sample. In this case, the
 ratio of measured Cy5 to Cy3 ratios for these genes can be modeled and the mean of the ratio adjusted
 to 1. Chen and collaborators describe an iterative procedure to achieve this normalization and we have
 implemented their algorithm and a variation of it that uses the entire data set, as well the total intensity
 and linear regression normalization, into a data visualization and analysis tool called TIGR
 ArrayViewer. TIGR ArrayViewer is freely available and can be obtained
 through <http://www.tigr.org/softlab/>. In any normalization approach, care must be taken in handling
 genes expressed at low levels. Statistical fluccuations in the measured levels can cause a significant
 variation in the ratios that are calculated and inefficiencies in labeling for either of the two dyes can
 cause these low intensity genes to disappear from the arrays. Typically, we only use spots in the final
 analysis where the intensities in both channels are two standard deviations above background.
 Following normalization, data are typically analyzed to identify genes that are differentially expressed.
 Most published studies have used a post-normalization cutoff of two-fold up- or down-regulation to
 define differential expression; the approach defined by Chen et al. (2) provides confidence intervals
 that can be used to identify differentially expressed genes. In order to separate genes that are truly
 differentially expressed from stochastic changes, we typically conduct three independent microarray
 assays starting from independent mRNA isolations and define differential expression based on their
 consensus.  
 Conclusion 
 The examination of gene expression using microarrays holds tremendous promise for the identification
 of candidate genes involved in a variety of processes. Indeed, the experiments that have been described
 to date have confirmed known patterns of expression and provided information on genes of unknown
 function. However, most applications have to date only allowed the identification of genes
 differentially expressed at significant levels. The true challenge, and the promise of this technique,
 will be to use it to identify genes that are consistently up- or down-regulated by 10 or 20% yet play
 significant roles in the development and progression of disease. This will require the analysis of data
 from multiple experiments and the correlation of patterns of gene expression with additional
 experimental and clinical information. Recently a variety of techniques including hierarchical
 clustering (3) and self-organizing maps (11) have been applied to the analysis of microarray
 expression data across multiple experiments. However, each of these depends on having reliable and
 reproducible data from each microarray assay. The laboratory techniques outlined here have allowed
 reproducible hybridization results such as those shown in Figure 5. Although these protocols will
 likely continue to evolve, we believe that they represent a reliable starting point for those beginning
 microarray experimentation. 
http://www.tigr.org/tdb/microarray/conciseguide.html
</protocol>
-->
<protocol xml:space="preserve"
  title="TIGR: Hybridization"
  type="hybridization">The goal in any hybridization is to obtain high
 specificity while minimizing background. We have
 developed protocols that give reproducible,
 high-quality hybridization results while maximizing
 the measured fluorescence from the array. 
 Aminosilane coated slides bind DNA with high
 efficiency. Prior to hybridization, the free amine groups
 on the slide must be blocked or inactivated, otherwise
 nonspecific binding of labeled cDNA to the slide can
 deplete the probe and produce high background.
 Although the slides can be blocked chemically, we have
 found a simple prehybridization in a solution
 containing 1% bovine serum albumin to be extremely
 effective in eliminating nonspecific binding of the probe
 to the slide. 
 Prehybridization has the additional advantage of
 washing unbound DNA from the slide prior to the
 addition of the probe. Any DNA that washes from the
 surface during hybridization competes with DNA
 bound to the slide. As the kinetics of solution
 hybridization is much more favorable than surface
 hybridization, this can dramatically decrease the
 measured fluorescence signal from the microarray. All
 prehybridization and hybridization washes are carried
 out in microscope slide staining trays (VWR Cat#
 25461-003).

 Prehybridization

 1.Prepare prehybridization buffer containing 5´SSC,
   0.1% SDS and 1% bovine serum albumin (BSA;
   Sigma Cat# A-9418).
 2.Prepare 2´ hybridization buffer containing 50%
   formamide, 10´SSC, and 0.2% SDS.
 3.Place slides to be analyzed into a Coplin jar (VWR
   Cat# 25457-200), fill with prehybridization buffer,
   and incubate at 42oC for 45 minutes.
 4.Wash the slides by dipping five times in room
   temperature MilliQ water.
 5.Dip the slides in room temperature isopropanol and
   air dry.
 Slides should be used immediately following
 prehybridization. We have found that hybridization
 efficiency decreases rapidly if the slides are allowed to
 dry for more than one hour. 

 Hybridization

 1.Combine 10ml each of purified Cy3- and
   Cy5-labeled probes, mix well and add
       COT1-DNA
       (20mg/ml)
                             1ml
                                       (LifeTechnologies; Cat#
                                               25279-011).
       Poly(A)-DNA
       (20mg/ml)
                             1ml
                                  (Pharmacia; Cat# 27-7836-01).

   to block nonspecific hybridization.
 2.Heat the probe mixture at 95oC for 3 minutes to
   denature.
 3.Centrifuge the probe in a microfuge set at maximum
   angular velocity for 1 minute.
 4.Combine the probe with an equal volume of 2´
   hybridization buffer that has been heated to 42oC.
 5.Apply the labeled probe to a prehybridized
   microarray slide and cover with a 22mm´60mm
   polyethylene hydrophobic coverslip (PGC Scientific
   Cat# 62-6504-06).