Biomedical subjects
Publications and source records attributed to Ramesh Ramakrishnan.
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DNA microarrays enable users to obtain information on differences in transcript abundance on a massively parallel scale. Recently, however, data analyses have revealed potential pitfalls related to image acquisition, variability and misclassifications in replicate measurements, cross-hybridization and sensitivity limitations. We have generated a series of analytical tools to address the manufacturing, detection and data analysis components of a microarray experiment. Together, we have used these tools to optimize performance in an expression profiling study. We demonstrate three significant advantages of the Motorola CodeLink platform: sensitivity of one copy per cell, coefficients of variation of 10% in the hybridization signals across slides and across target preparations, and specificity in distinguishing highly homologous sequences. Slides where oligonucleotide probes are spotted in 6-fold redundancy were used to demonstrate the effect of replication on data quality. Lastly, the differential expression ratios obtained with the CodeLink expression platform were validated against those obtained with quantitative reverse transcription-PCR assays for 54 genes.
DNA microarrays have been used in applications ranging from the assignment of gene function to analytical uses in prognostics. However, the detection sensitivity, cross hybridization, and reproducibility of these arrays can affect experimental design and data interpretation. Moreover, several technologies are available for fabrication of oligonucleotide microarrays. We review these technologies and performance attributes and, with data sets generated from human brain RNA, present statistical tools and methods to analyze data quality and to mine and visualize the data. Our data show high reproducibility and should allow an investigator to discern biological and regional variability from differential expression. Although we have used brain RNA as a model system to illustrate some of these points, the oligonucleotide arrays and methods employed in this study can be used with cell lines, tissue sections, blood, and other fluids. To further demonstrate this point, we provide data generated from total RNA sample sizes of 200 ng.
DNA microarrays are powerful tools to detect changes in transcript abundance in multiple samples in parallel. However, detection of differential transcript levels requires a reproducible sample (target) preparation method in addition to a high-performance microarray. Therefore, we optimized a target-preparation method that converts the poly(A)(+) RNA fraction of total RNA into complementary DNA, then generates biotin-labeled complementary RNA from the cDNA. We measured the efficiency of incorporation of biotin-containing nucleotides by an enzymatic digestion, followed by resolution via analytical high-performance liquid chromatography (HPLC). When the target was hybridized to a sensitive and reproducible microarray platform, low coefficients of variation in both hybridization intensities and differential expression ratios across target preparations were observed. Nearly identical hybridization intensities and expression ratios are observed regardless of whether poly(A)(+)-enriched RNA or total RNA is used as the starting material. We show the ability to discern biological and production variability through the use of different lots of commercial samples as visualized by hierarchical clustering. Automation of the target-preparation procedure shows equivalence to the manual procedure, reproducible yields of target, and low variability as measured by hybridization to microarrays. Most importantly, RNA mixing experiments show a linear and quantitative amplification in probe hybridization signals for >6000 genes across the entire signal range.
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Explore the source record for details and available documents.