Microarray probe expression measures, data normalization and statistical validation

DNA microarray technology is a high-throughput method for gaining information on gene function. Microarray technology is based on deposition/synthesis, in an ordered manner, on a solid surface, of thousands of EST sequences/genes/oligonucleotides. Due to the high number of generated datapoints, computational tools are essential in microarray data analysis and mining to grasp knowledge from experimental results. In this review, we will focus on some of the methodologies actually available to define gene expression intensity measures, microarray data normalization, and statistical validation of differential expression.     

PCR 芯片及其应用研究进展

PCR芯片实质上就是固定有与研究对象有关的许多已知基因的引物阵列并可用于PCR检测的固相载体,其制作时最关键的是目的基因的引物设计。与基于杂交的芯片技术不同,PCR芯片技术是一种高通量的,准确、灵敏的定量检测基因表达的技术,它将待测基因的引物固定于固相载体上,通过简单的、经过优化的定量PCR体系和荧光定量PCR仪,实现待检样品中已知基因的扩增,用于定量检测待检样品中已知基因的表达情况。PCR芯片由于其操作简单、结果准确、数据产出快而多等特点,已应用于疾病发病机制、药物作用机理和细菌分型等研究领域,并将在生命科学研究领域得到更为广泛的应用。     

Integration of amplified differential gene expression (ADGE) and DNA microarray

Amplified Differential Gene Expression (ADGE) provides a new concept that the ratios of differentially expressed genes are magnified before detection in order to improve both sensitivity and accuracy. This technology is now implemented with integration of DNA reassociation and PCR. The ADGE technique can be used either as a stand-alone method or in series with DNA microarray. ADGE is used in sample preprocessing and DNA microarray is used as a displaying system in the series combination. These two techniques are mutually synergistic: the quadratic magnification of ratios of differential gene expression achieved by ADGE improves the detection sensitivity and accuracy; the PCR amplification of templates enhances the signal intensity and reduces the requirement for large amounts of starting material; the high throughput for DNA microarray is maintained.     

limma powers differential expression analyses for RNA-sequencing and microarray studies

limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.     

Genome-wide analysis of lipoprotein expression in Escherichia coli MG1655

To gain insight into the cell envelope of Escherichia coli grown under aerobic and anaerobic conditions, lipoproteins were examined by using functional genomics. The mRNA expression levels of each of these genes under three growth conditions--aerobic, anaerobic, and anaerobic with nitrate--were examined by using both Affymetrix GeneChip E. coli antisense genome arrays and real-time PCR (RT-PCR). Many genes showed significant changes in expression level. The RT-PCR results were in very good agreement with the microarray data. The results of this study represent the first insights into the possible roles of unknown lipoprotein genes and broaden our understanding of the composition of the cell envelope under different environmental conditions. Additionally, these data serve as a test set for the refinement of high-throughput bioinformatic and global gene expression methods.     

Housekeeping genes in cancer: normalization of array data

Biological maintenance of cells under variable conditions should affect gene expression of only certain genes while leaving the rest unchanged. The latter, termed "housekeeping genes," by definition must reflect no change in their expression levels during cell development, treatment, or disease state anomalies. However, deviations from this rule have been observed. Using DNA microarray technology, we report here variations in expression levels of certain housekeeping genes in prostate cancer and a colorectal cancer gene therapy model system. To highlight, differential expression was observed for ribosomal protein genes in the prostate cancer cells and beta-actin in treated colorectal cells. High-throughput differential gene expression analysis via microarray technology and quantitative PCR has become a common platform for classifying variations in similar types of cancers, response to chemotherapy, identifying disease markers, etc. Therefore, normalization of the system based on housekeeping genes, such as those reported here in cancer, must be approached with caution.     

A recombineering pipeline to clone large and complex genes in Chlamydomonas

The ability to clone genes has greatly advanced cell and molecular biology research, enabling researchers to generate fluorescent protein fusions for localization and confirm genetic causation by mutant complementation. Most gene cloning is polymerase chain reaction (PCR)�or DNA synthesis-dependent, which can become costly and technically challenging as genes increase in size, particularly if they contain complex regions. This has been a long-standing challenge for the Chlamydomonas reinhardtii research community, as this alga has a high percentage of genes containing complex sequence structures. Here we overcame these challenges by developing a recombineering pipeline for the rapid parallel cloning of genes from a Chlamydomonas bacterial artificial chromosome collection. To generate fluorescent protein fusions for localization, we applied the pipeline at both batch and high-throughput scales to 203 genes related to the Chlamydomonas CO₂ concentrating mechanism (CCM), with an overall cloning success rate of 77%. Cloning success was independent of gene size and complexity, with cloned genes as large as 23 kb. Localization of a subset of CCM targets confirmed previous mass spectrometry data, identified new pyrenoid components, and enabled complementation of mutants. We provide vectors and detailed protocols to facilitate easy adoption of this technology, which we envision will open up new possibilities in algal and plant research.

A high-throughput system was developed to clone large, complex genes at high frequency and perform mutant complementation and protein tagging with a range of fluorophores in Chlamydomonas reinhardtii.     

Gene expression analyses of subchondral bone in early experimental osteoarthritis by microarray

Osteoarthritis (OA) is a degenerative joint disease that affects both cartilage and bone. A better understanding of the early molecular changes in subchondral bone may help elucidate the pathogenesis of OA. We used microarray technology to investigate the time course of molecular changes in the subchondral bone in the early stages of experimental osteoarthritis in a rat model. We identified 2,234 differentially expressed (DE) genes at 1 week, 1,944 at 2 weeks and 1,517 at 4 weeks post-surgery. Further analyses of the dysregulated genes indicated that the events underlying subchondral bone remodeling occurred sequentially and in a time-dependent manner at the gene expression level. Some of the identified dysregulated genes that were identified have suspected roles in bone development or remodeling; these genes include Alp, Igf1, Tgf β1, Postn, Mmp3, Tnfsf11, Acp5, Bmp5, Aspn and Ihh. The differences in the expression of these genes were confirmed by real-time PCR, and the results indicated that our microarray data accurately reflected gene expression patterns characteristic of early OA. To validate the results of our microarray analysis at the protein level, immunohistochemistry staining was used to investigate the expression of Mmp3 and Aspn protein in tissue sections. These analyses indicate that Mmp3 protein expression completely matched the results of both the microarray and real-time PCR analyses; however, Aspn protein expression was not observed to differ at any time. In summary, our study demonstrated a simple method of separation of subchondral bone sample from the knee joint of rat, which can effectively avoid bone RNA degradation. These findings also revealed the gene expression profiles of subchondral bone in the rat OA model at multiple time points post-surgery and identified important DE genes with known or suspected roles in bone development or remodeling. These genes may be novel diagnostic markers or therapeutic targets for OA.     

Bacterial artificial chromosome fingerprint arrays for the differentiation of transcriptomic differences in mycobacteria

Although microarray technology has become more widespread as a discovery tool for bacterial pathogenesis, it remains a method available only to laboratories with access to expensive equipment and costly analysis software. Mycobacterium tuberculosis, the causative agent for tuberculosis (TB), afflicts one-third of the global population, and kills between 2 and 3 million people per year. While the majority of cases of TB occur in developing areas of the world, facilities in these regions may not be able to support microarray analysis. Additionally, a major limitation of microarrays is that only genes on the array are being assayed. With acquired virulence and drug resistance in microbes, a method less dependent on a predetermined list of gene targets is advantageous. We present a method of expression analysis based on bacterial artificial chromosomes (BACs) that can be applied with standard laboratory equipment and free analysis software. This technique, bacterial artificial chromosome fingerprint arrays (BACFA), was developed and utilised to identify expression differences between intracellular strains of M. tuberculosis, one virulent (H37Rv) and one attenuated (H37Ra). Southern blots of restriction-enzyme digested BAC fragments were sequentially hybridised with strain-specific cDNA probes to generate expression profiles that were used to isolate expression differences in broth grown and intracellular bacteria. Repeat comparisons of intracellular profiles via BACFA identified genomic regions differentially expressed by the two strains. Quantitative real-time PCR was used to assess the genes located in these fragments in order to confirm or deny the differential regulation of genes. In total, we identified six genes that were differentially regulated between strains inside the host cell (pks2, aceE, Rv1571, and frdBCD). We report that BACFA is an effective technique in the expression analysis of bacteria and can be considered complementary to the high-throughput analysis offered by microarrays.     

3′-End Sequencing for Expression Quantification (3SEQ) from Archival Tumor Samples

Gene expression microarrays are the most widely used technique for genome-wide expression profiling. However, microarrays do not perform well on formalin fixed paraffin embedded tissue (FFPET). Consequently, microarrays cannot be effectively utilized to perform gene expression profiling on the vast majority of archival tumor samples. To address this limitation of gene expression microarrays, we designed a novel procedure (3′-end sequencing for expression quantification (3SEQ)) for gene expression profiling from FFPET using next-generation sequencing. We performed gene expression profiling by 3SEQ and microarray on both frozen tissue and FFPET from two soft tissue tumors (desmoid type fibromatosis (DTF) and solitary fibrous tumor (SFT)) (total n = 23 samples, which were each profiled by at least one of the four platform-tissue preparation combinations). Analysis of 3SEQ data revealed many genes differentially expressed between the tumor types (FDR<0.01) on both the frozen tissue (∼9.6K genes) and FFPET (∼8.1K genes). Analysis of microarray data from frozen tissue revealed fewer differentially expressed genes (∼4.64K), and analysis of microarray data on FFPET revealed very few (69) differentially expressed genes. Functional gene set analysis of 3SEQ data from both frozen tissue and FFPET identified biological pathways known to be important in DTF and SFT pathogenesis and suggested several additional candidate oncogenic pathways in these tumors. These findings demonstrate that 3SEQ is an effective technique for gene expression profiling from archival tumor samples and may facilitate significant advances in translational cancer research.     

Highly parallel genome-wide expression analysis of single mammalian cells

Background

We have developed a high-throughput amplification method for generating robust gene expression profiles using single cell or low RNA inputs.

Methodology/Principal Findings

The method uses tagged priming and template-switching, resulting in the incorporation of universal PCR priming sites at both ends of the synthesized cDNA for global PCR amplification. Coupled with a whole-genome gene expression microarray platform, we routinely obtain expression correlation values of R²∼0.76–0.80 between individual cells and R²∼0.69 between 50 pg total RNA replicates. Expression profiles generated from single cells or 50 pg total RNA correlate well with that generated with higher input (1 ng total RNA) (R²∼0.80). Also, the assay is sufficiently sensitive to detect, in a single cell, approximately 63% of the number of genes detected with 1 ng input, with approximately 97% of the genes detected in the single-cell input also detected in the higher input.

Conclusions/Significance

In summary, our method facilitates whole-genome gene expression profiling in contexts where starting material is extremely limiting, particularly in areas such as the study of progenitor cells in early development and tumor stem cell biology.     

乳酸菌基因芯片应用研究进展

基因芯片技术是上世纪90年代兴起的一种对成百上千甚至上万个基因同时进行检测的新技术,具有高通量、并行化的特点,广泛应用于基因表达谱测定、基因功能预测、基因突变检测和多态性分析等方面。多种乳酸菌基因组全序列以及其大量EST、16S rDNA、16S-23S基因间区和功能基因序列测定的完成,有力地推动了基因芯片技术在乳酸菌研究中的应用。介绍了基因芯片的基本原理及乳酸菌基因芯片在基因表达、种属鉴定等研究中的应用进展,以期更好地利用和开发乳酸菌基因芯片。     

Bacterial artificial chromosome fingerprint arrays for the differentiation of transcriptomic differences in mycobacteria

Although microarray technology has become more widespread as a discovery tool for bacterial pathogenesis, it remains a method available only to laboratories with access to expensive equipment and costly analysis software. Mycobacterium tuberculosis, the causative agent for tuberculosis (TB), afflicts one-third of the global population, and kills between 2 and 3 million people per year. While the majority of cases of TB occur in developing areas of the world, facilities in these regions may not be able to support microarray analysis. Additionally, a major limitation of microarrays is that only genes on the array are being assayed. With acquired virulence and drug resistance in microbes, a method less dependent on a predetermined list of gene targets is advantageous. We present a method of expression analysis based on bacterial artificial chromosomes (BACs) that can be applied with standard laboratory equipment and free analysis software. This technique, bacterial artificial chromosome fingerprint arrays (BACFA), was developed and utilised to identify expression differences between intracellular strains of M. tuberculosis, one virulent (H37Rv) and one attenuated (H37Ra). Southern blots of restriction-enzyme digested BAC fragments were sequentially hybridised with strain-specific cDNA probes to generate expression profiles that were used to isolate expression differences in broth grown and intracellular bacteria. Repeat comparisons of intracellular profiles via BACFA identified genomic regions differentially expressed by the two strains. Quantitative real-time PCR was used to assess the genes located in these fragments in order to confirm or deny the differential regulation of genes. In total, we identified six genes that were differentially regulated between strains inside the host cell (pks2, aceE, Rv1571, and frdBCD). We report that BACFA is an effective technique in the expression analysis of bacteria and can be considered complementary to the high-throughput analysis offered by microarrays.