High-fidelity mRNA amplification for gene profiling

The completion of the Human Genome Project has made possible the comprehensive analysis of gene expression, and cDNA microarrays are now being employed for expression analysis in cancer cell lines or excised surgical specimens. However, broader application of cDNA microarrays is limited by the amount of RNA required: 50-200 microg of total RNA (T-RNA) and 2-5 microg poly(A) RNA. To broaden the use of cDNA microarrays, some methods aiming at intensifying fluorescence signal have resulted in modest improvement. Methods devoted to amplifying starting poly(A) RNA or cDNA show promise, in that detection can be increased by orders of magnitude. However, despite the common use of these amplification procedures, no systematic assessment of their limits and biases has been documented. We devised a procedure that optimizes amplification of low-abundance RNA samples by combining antisense RNA (aRNA) amplification with a template-switching effect (Clonetech, Palo Alto, CA). The fidelity of aRNA amplified from 1:10,000 to 1:100,000 of commonly used input RNA was comparable to expression profiles observed with conventional poly(A) RNA- or T-RNA-based arrays.     

Advantages of mRNA amplification for microarray analysis

Expanding applications of cDNA microarrays such as fine needle aspiration biopsy and laser capture microdissection necessitate the ability to perform arrays with minute starting amounts of RNA. While methods for amplifying RNA have been advocated, the fidelity of array results using amplified material has not been fully validated. Here we demonstrate preserved fidelity in arrays using one or two rounds of mRNA amplification, validated by downstream real-time quantitative PCR. In addition, the quality of the array data was superior to that obtained using total RNA. Based on these results, we recommend routine mRNA amplification for all cDNA microarray-based analysis of gene expression.     

Optimized procedures for microarray analysis of histological specimens processed by laser capture microdissection

Analysis of cell-specific gene expression patterns using microarrays can reveal genes that are differentially expressed in diseased and normal tissue, as well as identify genes associated with specialized cellular functions. However, the cellular heterogeneity of the tissues precludes the resolution of expression profiles of specific cell types. While laser capture microdissection (LCM) can be used to obtain purified cell populations, the limited quantity of RNA isolated makes it necessary to perform an RNA amplification step prior to microarray analysis. The linearity and reproducibility of two RNA amplification protocols--the Baugh protocol (Baugh et al., 2001, Nucleic Acids Res 29:E29) and an in-house protocol have been assessed by conducting microarray analyses. Cy3-labeled total RNA from the colorectal cell line Colo-205 was compared to Cy5-labeled Colo-205 amplified RNA (aRNA) generated with each of the two protocols, using a human 10K cDNA array. The correlation of the gene intensities between amplified and total RNA measured in the two channels of each microarray was 0.72 and 0.61 for the Baugh protocol and the in-house protocol, respectively. The two protocols were further evaluated using aRNA obtained from normal colonic crypt cross-sections isolated via LCM. In both cases a microarray profile representative of colonic mucosa was obtained; statistically, the Baugh protocol was superior. Furthermore, a substantial overlap between highly expressed genes in the Colo-205 cells and colonic crypts underscores the reliability of the microarray analysis of LCM-derived material. Taken together, these results demonstrate that LCM-derived tissue from histological specimens can generate abundant amounts of high-quality aRNA for subsequent microarray analysis.     

Comparison of gene expression measurements from cDNA and 60-mer oligonucleotide microarrays

As the data generated by microarray technology continue to amass, it is necessary to compare and combine gene expression data from different platforms. To evaluate the performance of cDNA and long oligonucleotide (60-mer) arrays, we generated gene expression profiles for two cancer cell lines and compared the data between the two platforms. All 6182 unique genes represented on both platforms were included in the analysis. A limited correlation (r = 0.4708) was obtained and the difference in measurement of low-expression genes was considered to contribute to the limited correlation. Further restriction of the data set to differentially expressed genes detected in cDNA microarrays (1205 genes) and oligonucleotide arrays (1325 genes) showed modest correlations of 0.7076 and 0.6441 between the two platforms. Quantitative real-time PCR measurements of a set of 10 genes showed better correlation with oligonucleotide arrays. Our results demonstrate that there is substantial variation in the data generated from cDNA and 60-mer oligonucleotide arrays. Although general agreement was observed in measurements of differentially expressed genes, we suggest that data from different platforms could not be directly amassed.