New molecular approaches to tissue analysis

Laser-assisted microdissection for real-time PCR sample preparation

Laser-assisted microdissection (LMD) has been developed to procure precisely the cells of interest in a tissue specimen, in a rapid and practical manner. Together with real-time PCR and RT-PCR techniques, it is now feasible to study genetic alterations, gene expression features and proteins in defined cell populations from complex normal and diseased tissues. The process that brings from sample collection to the final quantitative results is articulated in several steps, each of which requires optimal choices in order to end up with high-quality nucleic acid or protein that allows successful application of the final quantitative assays. This review will describe shortly the development of LMD technologies and the principles they are based on. Trying to highlight the advantages and disadvantages of LMD, the main problems related to specimens collection and processing, section preparation and extraction of bio-molecules from microdissected tissue samples have been analysed.

Tissue microarray for high-throughput analysis of gene expression profiles in hepatocellular carcinoma

AIM: To study the expression profiles of HBsAg, HBcAg, p21^WAF1/CIP1 (p21), Rb genes in hepatocellular carcinoma (HCC) and to investigate their roles in the hepatocar-cinogenesis.

METHODS: HCC tissue microarray containing 120-min tissues of 40 HCC cases was constructed. HBsAg, HBcAg, p21 and Rb proteins were immunohistochemically stained by streptavidin-peroxidase conjugated method (S-P). The expression loss of these genes in cancerous, para-cancerous tissues and adjacent normal liver tissues of 40 HCCs were comparatively examined.

RESULTS: The positive rate of HBsAg expression in cancerous tissues of 40 HCCs was 7.5%, which was lower than that in para-cancerous and adjacent normal liver tissues (χ² =12.774, P<0.01; χ² = 18.442, P<0.01). The positive rate of HBcAg expression in cancerous tissues of 40 HCCs was 20.0%, which was also lower than that in para-cancerous and adjacent normal liver tissues (χ² = 9.482, P<0.01; χ² = 14.645, P<0.01). p21 protein deletion rate in cancerous tissues of 40 HCCs was 27.5%, which was higher than that in para-cancerous and adjacent normal liver tissues (χ² = 7.439, P<0.01; χ² = 11.174, P<0.01). p21 protein deletion correlated remarkably with the pathological grade of HCC (χ² = 0.072, P<0.05). Rb protein deletion rate in cancerous tissues of 40 HCCs was 42.5%, which was also higher than that in para-cancerous and adjacent normal liver tissues (χ² = 10.551, P<0.01; χ² = 18.353, P<0.01). Rb protein deletion rate did not correlate remarkably with tumor size or pathological grade of HCC (χ² = 0.014, P>0.05; χ² = 0.017, P>0.05).

CONCLUSION: Expression deletion of HBsAg, HBcAg, p21 and Rb proteins in HCCs may play important roles in the carcinogenesis of HCC. Tissue microarray is an effective high-throughput technique platform for cancer research.

Application of amplified RNA and evaluation of cRNA targets for spotted-oligonucleotide microarray

Among different RNA amplification methods, T7 RNA polymerase-based in vitro transcription (IVT) that generates antisense RNA is most common in DNA microarray protocol. However, despite the fact that cRNA targets labeled during IVT are feasible for spotted-oligonucleotide microarray (spotted-oligoarray) hybridization due to complementary sequence of single-stranded oligonucleotide probe, no systemic assessment for the use of amplified cRNA targets has been reported for spotted-oligoarrays. In this investigation, we have compared the hybridization performance of amplified cRNA targets with that of cDNA targets from total RNA(T-RNA) using spotted-oligoarrays containing 18,864 genetic elements. Under the optimized hybridization conditions, we found that 86% of oligonucleotide probes were reproducibly detected by both cDNA and cRNA target protocols. In addition, cRNA targets generated by two-rounds of amplification of 10 ng T-RNA were concordant with first-round cRNA targets generated from 100 ng T-RNA by 0.858 of correlation coefficient. Taken together, we demonstrated that cRNA targets from very scant RNA amount could successfully be applied on spotted-oligoarrays, and hopefully this will facilitate the application of much smaller amount of source material based on the high-fidelity and improved target preparation of microarrays.

Fidelity and enhanced sensitivity of differential transcription profiles following linear amplification of nanogram amounts of endothelial mRNA

Although mRNA amplification is necessary for microarray analyses from limited amounts of cells and tissues, the accuracy of transcription profiles following amplification has not been well characterized. We tested the fidelity of differential gene expression following linear amplification by T7-mediated transcription in a well-established in vitro model of cytokine [tumor necrosis factor alpha (TNFalpha)]-stimulated human endothelial cells using filter arrays of 13,824 human cDNAs. Transcriptional profiles generated from amplified antisense RNA (aRNA) (from 100 ng total RNA, approximately 1 ng mRNA) were compared with profiles generated from unamplified RNA originating from the same homogeneous pool. Amplification accurately identified TNFalpha-induced differential expression in 94% of the genes detected using unamplified samples. Furthermore, an additional 1,150 genes were identified as putatively differentially expressed using amplified RNA which remained undetected using unamplified RNA. Of genes sampled from this set, 67% were validated by quantitative real-time PCR as truly differentially expressed. Thus, in addition to demonstrating fidelity in gene expression relative to unamplified samples, linear amplification results in improved sensitivity of detection and enhances the discovery potential of high-throughput screening by microarrays.

Microarray technology and gene expression analysis for the study of angiogenesis

Angiogenesis plays a major role in multiple disease processes including cancer, and new agents that modulate angiogenesis are rapidly entering clinical trials. The understanding of the biological mechanisms and downstream effects for many of these agents is poorly understood. It is therefore important that methods evolve to understand how an agent regulates angiogenesis, in order to promote a higher percentage of successful drug candidates. With the emergence of microarray technology for the evaluation of gene expression, researchers have a powerful tool for dissecting the biological mechanisms of angiogenesis. However, huge data sets and complex statistics pose a hurdle for the investigator to obtain useful and meaningful data. To eliminate problems in data analysis, proper design and planning prior to performing a microarray experiment is crucial to making valid conclusions. This review will discuss the critical factors in designing, performing and analysing microarray experiments, and the utility of various models of angiogenesis for microarray analysis.

DNA microarrays in prostate cancer

DNA microarray technology provides a means to examine large numbers of molecular changes related to a biological process in a high throughput manner. This review discusses plausible utilities of this technology in prostate cancer research, including definition of prostate cancer predisposition, global profiling of gene expression patterns associated with cancer initiation and progression, identification of new diagnostic and prognostic markers, and discovery of novel patient classification schemes. The technology, at present, has only been explored in a limited fashion in prostate cancer research. Some hurdles to be overcome are the high cost of the technology, insufficient sample size and repeated experiments, and the inadequate use of bioinformatics. With the completion of the Human Genome Project and the advance of several highly complementary technologies, such as laser capture microdissection, unbiased RNA amplification, customized functional arrays (eg, single-nucleotide polymorphism chips), and amenable bioinformatics software, this technology will become widely used by investigators in the field. The large amount of novel, unbiased hypotheses and insights generated by this technology is expected to have a significant impact on the diagnosis, treatment, and prevention of prostate cancer. Finally, this review emphasizes existing, but currently underutilized, data-mining tools, such as multivariate statistical analyses, neural networking, and machine learning techniques, to stimulate wider usage.