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Lyophilization and Redilution

(Return to HPLC Preparation.) 

   1.Pool fractions of each Cy dye into a 50 mL conical tube. Punch holes into the cap using an 18-gauge needle. Avoid exceeding more than half the
     volume of the tube, as the samples may have a tendency to splatter during degassing or travel up the tube during lyophilization. 
   2.Freeze the samples on dry ice and transfer them to a lyophilization chamber. Wrap the chamber with foil to protect the samples from light. 
   3.Lyophilize the samples to completion (approx. 12-15 hrs for HPLC-purified samples, 6-10 hrs for Sep-Pak-purified material). HPLC-purified
     samples will have the appearance of a residue until most of the TEAA is removed. 

     Note: Steps 4 and 5 are for HPLC-purified samples only.

   4.Dilute the samples back up to 8-10 mL with 0.22 mm filtered ddH2O and lyophilize until the samples again resemble residue. This time, it may
     require up to 24 hours of lyophilization. 
   5.Repeat Step 4. Lyophilization is complete when the sample has the appearance of a film and/or specks of solid residue. Resuspend the sample in
     a small volume of 10 mM phosphate buffer, pH 7.0. Bear in mind that some of the Cy-dUTP will coat the upp er regions of the tube walls and
     may not be readily visible. Check absorbance of the sample. A560/660/A260 ratios should be around 17 for cy 3 and 25 for cy 5.

Expected overall yields: cy 3: 50-70%; cy 5: 45-65%.

Final Considerations

     Currently 150nmol of Cy-dUTP contained in a 10 mM mixture of labeled and unlabeled dNTPs is loaded onto the column listed above,
     depending on the contents and concentration of the filtrate; PCR flow-through samples tend to be cleaner and may allow for larger injection lots.
     Thus, the flow-through from PCR and RT reactions may be kept separate so that they can be processed separately. The Sep-Pak purification
     desalts the sample and is likely to remove other impurities present in the sample. HPLC purif ication can still be performed without Sep-Pak
     purification, but salt content prohibits injection of more than 50nmol. Furthermore, yields tend to be lower if the sample is not desalted prior to
     HPLC injection. 
http://cmgm.stanford.edu/pbrown/protocols/index.html
</protocol>
<!--
<protocol xml:space="preserve"
  title="TIGR: Data Collection, Normalization, and Analysis"
  type="other_protocol">Differential gene expression is assessed by scanning the hybridized arrays using a confocal laser
 scanner capable of interrogating both the Cy3- and Cy5-labeled probes and producing separate TIFF
 images for each. As is the case with arraying robots, there are a number of manufacturers that produce
 scanners capable of detecting Cy3 and Cy5 (see Table 4) and most are planning to release instruments
 capable of detecting additional dyes.
  

 Slide Scanning 

 We are currently using the ScanArray 3000 produced by GSI Lumonics. This scanner uses red and
 green Helium-Neon lasers operating at 633nm and 543nm to excite Cy5 and Cy3, respectively.
 Hybridized slides are scanned first in the Cy5 channel, and then the Cy3 channel, as Cy5 is more
 susceptible to photodegradation than Cy3. Data from each fluorescence channel is collected and stored
 as a separate 16-bit TIFF image. These images are analyzed to calculate the relative expression levels
 of each gene and to identify differentially expressed genes. The analysis process can be divided into
 two steps - image processing and data analysis. Figure 5 shows a typical hybridization image
 produced when things work well. The contrast in this image has been adjusted to allow faint spots to
 be easily visualized. Important aspects of the hybridization to note are the low level, uniform
 background and the good signal-to-noise

 Image Processing

 Image processing involves three stages. First, the spots representing the arrayed genes must be
 identified and distinguished from spurious signals that can arise due to precipitated probe or other
 hybridization artifacts or contaminants such as dust on the surface of the slide. This task is simplified
 to a certain extent because the robotic arraying systems used to construct the arrays produce a regular
 arrangement of the spotted DNA fragments. However, variable intensities and uneven slide
 backgrounds as well as some irregularities in the gridded arrays complicate the problem slightly.
 Generally, problem of grid spot location is coupled with estimation of the fluorescence background.
 For microarrays, it is important the background be calculated locally for each spot, rather than globally
 for the entire image as uneven background can often arise during the hybridization process. The second
 step in analysis of the array images is the estimation of background.
 Following spot identification and local background determination, the background-subtracted
 hybridization intensities for each spot must be calculated. There are currently two schools of thought
 regarding the calculation of intensities - the use of the median or the mean intensity for each spot. As
 array analysis generally uses ratios of measured Cy3 to Cy5 intensities to identify differentially
 expressed genes, the mean and the integrated intensities are operationally equivalent. In comparisons
 of intensities measured for normalization controls spiked into the labeling reactions, we have found
 mean intensities to give more consistent results and consequently we use these in subsequent
 calculations (V. Sharov and J. Quackenbush, in preparation).
 A number of image processing software packages are available and are listed in Table 5. We have
 developed a software package called TIGR_Spotfinder for image processing
 (&lt;http://www.tigr.org/softlab/&gt;). TIGR_Spotfinder uses a thresholding algorithm that separates spots
 from the background, allowing a grid to be laid across the spots. Having found a grid, spots are found
 within each grid element, local background is calculated, and background-subtracted, integrated
 intensities are calculated in both the Cy3 and Cy5 channels. Measured intensities are entered into the
 Molecular Analysis of Gene Expression (MAGE) database, a Sybase relational database specifically
 designed to capture gene expression data.

 Data Normalization and Analysis

 Following image processing, the data generated for the arrayed genes must be further analyzed before
 differentially expressed genes can be identified. The first step in this process is the normalization of
 the relative fluorescence intensities in each of the two scanned channels. Normalization is necessary to
 adjust for differences in labeling and detection efficiencies for the fluorescent labels and for
 differences in the quantity of starting RNA from the two samples examined in the assay. These
 problems can cause a shift in the average ratio of Cy5 to Cy3 and the intensities must be rescaled
 before an experiment can be properly analyzed. 
 The normalization strategies that can be used are based on some underlying assumptions regarding the
 data and the strategies used for each experiment should be adjusted to reflect both the system under
 study and the experimental design. The primary assumption is that for either the entire collection of
 arrayed genes or some subset of the genes such as housekeeping genes, or for some added set of
 controls, the ratio of measured expression averaged over the set should be one.
 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 &lt;http://www.tigr.org/softlab/&gt;. 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