Amplification protocols introduce systematic but reproducible errors into gene expression studies

Optimizing Laser Capture Microdissection Protocol for Isolating Zone-Specific Cell Populations from Mandibular Condylar Cartilage

Mandibular condylar cartilage (MCC) is a multizonal heterogeneous fibrocartilage consisting of fibrous (FZ), proliferative (PZ), mature (MZ), and hypertrophic (HZ) zones. Gross sampling of the whole tissue may conceal some important information and compromise the validity of the molecular analysis. Laser capture microdissection (LCM) technology allows isolating zonal (homogenous) cell populations and consequently generating more accurate molecular and genetic data, but the challenges during tissue preparation and microdissection procedures are to obtain acceptable tissue section morphology that allows histological identification of the desirable cell type and to minimize RNA degradation. Therefore, our aim is to optimize an LCM protocol for isolating four homogenous zone-specific cell populations from their respective MCC zones while preserving the quality of RNA recovered. MCC and FCC (femoral condylar cartilage) specimens were harvested from 5-week-old Sprague–Dawley male rats. Formalin-fixed and frozen unfixed tissue sections were prepared and compared histologically. Additional specimens were microdissected to prepare LCM samples from FCC and each MCC zone individually. Then, to evaluate LCM-RNA integrity, 3′/m ratios of glyceraldehyde 3-phosphate dehydrogenase (GAPDH) and beta-actin (β-Actin) using quantitative reverse transcription-polymerase chain reaction (qRT-PCR) were calculated. Both fixed and unfixed tissue sections allowed reliable identification of MCC zones. The improved morphology of the frozen sections of our protocol has extended the range of cell types to be isolated. Under the empirically set LCM parameters, four homogeneous cell populations were efficiently isolated from their respective zones. The 3′/m ratio means of GAPDH and β-Actin ranged between 1.11–1.56 and 1.41–2.12, respectively. These values are in line with the reported quality control requirements. The present study shows that the optimized LCM protocol could allow isolation of four homogenous zone-specific cell populations from MCC, meanwhile preserving RNA integrity to meet the high quality requirements for subsequent molecular analyses. Thereby, accurate molecular and genetic data could be generated.

Estrogen Receptor Pathway Activity Score to Predict Clinical Response or Resistance to Neoadjuvant Endocrine Therapy in Primary Breast Cancer

Endocrine therapy is important for management of patients with estrogen receptor (ER)-positive breast cancer; however, positive ER staining does not reliably predict therapy response. We assessed the potential to improve prediction of response to endocrine treatment of a novel test that quantifies functional ER pathway activity from mRNA levels of ER pathway-specific target genes. ER pathway activity was assessed on datasets from three neoadjuvant-treated ER-positive breast cancer patient cohorts: Edinburgh: 3-month letrozole, 55 pre-/2-week/posttreatment matched samples; TEAM IIa: 3- to 6-month exemestane, 49 pre-/28 posttreatment paired samples; and NEWEST: 16-week fulvestrant, 39 pretreatment samples. ER target gene mRNA levels were measured in fresh-frozen tissue (Edinburgh, NEWEST) with Affymetrix microarrays, and in formalin-fixed paraffin-embedded samples (TEAM IIa) with qRT-PCR. Approximately one third of ER-positive patients had a functionally inactive ER pathway activity score (ERPAS), which was associated with a nonresponding status. Quantitative ERPAS decreased significantly upon therapy (P < 0.001 Edinburgh and TEAM IIa). Responders had a higher pretreatment ERPAS and a larger 2-week decrease in activity (P = 0.02 Edinburgh). Progressive disease was associated with low baseline ERPAS (P = 0.03 TEAM IIa; P = 0.02 NEWEST), which did not decrease further during treatment (P = 0.003 TEAM IIa). In contrast, the staining-based ER Allred score was not significantly associated with therapy response (P = 0.2). The ERPAS identified a subgroup of ER-positive patients with a functionally inactive ER pathway associated with primary endocrine resistance. Results confirm the potential of measuring functional ER pathway activity to improve prediction of response and resistance to endocrine therapy.

Identification of miRNA-mRNA crosstalk in CD4+ T cells during HIV-1 infection by integrating transcriptome analyses

Background

HIV-1-infected long-term nonprogressors (LTNPs) are characterized by infection with HIV-1 more than 7–10 years, but keeping high CD4⁺ T cell counts and low viral load in the absence of antiretroviral treatment, while loss of CD4⁺ T cells and high viral load were observed in the most of HIV-1-infected individuals with chronic progressors (CPs) However, the mechanisms of different clinical outcomes in HIV-1 infection needs to be further resolved.

Methods

To identify microRNAs (miRNAs) and their target genes related to distinct clinical outcomes in HIV-1 infection, we performed the integrative transcriptome analyses in two series GSE24022 and GSE6740 by GEO2R, R, TargetScan, miRDB, and Cytoscape softwares. The functional pathways of these differentially expressed miRNAs (DEMs) targeting genes were further analyzed with DAVID.

Results

We identified that 7 and 19 DEMs in CD4⁺ T cells of LTNPs and CPs, respectively, compared with uninfected controls (UCs), but only miR-630 was higher in CPs than that in LTNPs. Further, 478 and 799 differentially expressed genes (DEGs) were identified in the group of LTNPs and CPs, respectively, compared with UCs. Compared to CPs, four hundred and twenty-four DEGs were identified in LTNPs. Functional pathway analyses revealed that a close connection with miRNA-mRNA in HIV-1 infection that DEGs were involved in response to virus and immune system process, and RIG-I-like receptor signaling pathway, whose DEMs or DEGs will be novel biomarkers for prediction of clinical outcomes and therapeutic targets for HIV-1.

Conclusions

Integrative transcriptome analyses showed that distinct transcriptional profiles in CD4⁺ T cells are associated with different clinical outcomes during HIV-1 infection, and we identified a circulating miR-630 with potential to predict disease progression, which is necessary to further confirm our findings in the future.

Electronic supplementary material

The online version of this article (doi:10.1186/s12967-017-1130-y) contains supplementary material, which is available to authorized users.

Long Noncoding RNA Expression Profiling Using Arraystar LncRNA Microarrays

Arraystar LncRNA microarrays are designed for global gene expression profiling of both LncRNAs and mRNAs on the same array. The array contents feature comprehensive collections of LncRNAs and include entire sets of known coding mRNAs. Each RNA transcript is detected by a splice junction-specific probe or a unique exon sequence, such that the alternatively spliced transcript isoforms or variants are reliably and accurately detected. The highly optimized experimental protocols and efficient workflow ensure sensitive, robust, and accurate microarray data generation. Standard data analyses are provided for microarray raw data processing, data quality control, gene expression clustering and heat map visualization, differentially expressed LncRNAs and mRNAs, LncRNA subcategories, regulatory relationships of LncRNAs with the mRNAs, gene ontology, and pathway analysis. The LncRNA microarrays are powerful tools for the study of LncRNAs in biology and disease, with broad applications in gene expression profiling, gene regulatory mechanism research, LncRNA functional discovery, and biomarker development.

Microarray experiments and factors which affect their reliability

Oligonucleotide microarrays belong to the basic tools of molecular biology and allow for simultaneous assessment of the expression level of thousands of genes. Analysis of microarray data is however very complex, requiring sophisticated methods to control for various factors that are inherent to the procedures used. In this article we describe the individual steps of a microarray experiment, highlighting important elements and factors that may affect the processes involved and that influence the interpretation of the results. Additionally, we describe methods that can be used to estimate the influence of these factors, and to control the way in which they affect the expression estimates. A comprehensive understanding of the experimental protocol used in a microarray experiment aids the interpretation of the obtained results. By describing known factors which affect expression estimates this article provides guidelines for appropriate quality control and pre-processing of the data, additionally applicable to other transcriptome analysis methods that utilize similar sample handling protocols.

Quantitative expression profiling in mouse spinal cord reveals changing relationships among channel and receptor mRNA levels across postnatal maturation

Neural networks ultimately arrive at functional output via interaction of the excitability of individual neurons and their synaptic interactions. We investigated the relationships between voltage-gated ion channel and neurotransmitter receptor mRNA levels in mouse spinal cord at four different postnatal time points (P5, P11, P17, and adult) and three different adult cord levels (cervical, thoracic, and lumbosacral) using quantitative reverse-transcription polymerase chain reaction (qRT-PCR). Our analysis and data visualization are novel in that we chose a focal group of voltage-gated channel subunits and transmitter receptor subunits, performed absolute quantitation of mRNA copy number for each gene from a sample, and used multiple correlation analyses and correlation matrices to detect patterns in correlated mRNA levels across all genes of interest. These correlation profiles suggest that postnatal maturation of the spinal cord includes changes among channel and receptor subunits that proceed from widespread co-regulation to more refined and distinct functional relationships.

Development of novel diagnostic and prognostic molecular markers for sporadic colon cancer

Gene expression studies are informative about changes in colon cancer, increase understanding of the biology of tumorigenesis and aid in developing diagnostic and prognostic markers. In this review, expression techniques used to examine the multistage process of colon cancer are discussed. Many genes have been found to differ in expression between normal and tumorigenic states, as early as the seemingly normal colonic crypts. The clinical usefulness of markers varies with stage, ethnicity and anatomic location of colon cancer. Thus, combinations of markers can be used to develop an approach to molecularly screen and follow the progression of this prevalent cancer.

Sources of high variance between probe signals in Affymetrix short oligonucleotide microarrays

High density oligonucleotide microarrays present a big challenge for statistical data processing methods which aim to separate changes induced by experimental factors from those caused by artifacts and measurement inaccuracies. Despite huge advances in the field of microarray probe design methods, the signal variation between probes that target a single transcript is substantially larger than their between-replicate array variability, suggesting a large influence of various probe-specific effects that introduce bias to the data. In this work we present the influence of probe-related design variations on the expression intensities of individual probes, focusing on five potential sources of high probe signal variance: the GC composition of the probe, the distance between individual probe target sites, G-quadruplex formation in the probe sequence, the occurrence of sequence motifs complementary to the oligo(dT) primer, and the specificity of unrecognized alternative splicing probeset assignment. By focusing on two high quality microarray datasets based on two distinct array designs we show the extent of variance between probes that target a specific transcript providing guidelines for the future design of microarrays and data processing methods.

Genome-wide comparison of two RNA-stabilizing reagents for transcriptional profiling of peripheral blood

Peripheral whole blood is relatively easily obtained for monitoring gene expression for biomarker discovery using transcriptomic platforms such as genome-wide microarrays. However, whole blood provides challenges caused by sensitivity for ex vivo incubation and overrepresentation of globin mRNAs. We compared the performance of 2 commercial whole blood preservation methods, TEMPUS (Applied Biosystems, Foster City, CA) and PAXgene (PreAnalytiX, Qiagen BD, Valencia, CA), using 2 RNA amplification protocols and high-density microarrays. Performance of commercial globin mRNA reduction protocol also was studied. Human peripheral blood samples collected with TEMPUS and PAXgene Blood RNA tubes were amplified with the RiboAmp OA 1 Round RNA Amplification Kit (Arcturus; Applied Biosystems) and the Affymetrix (Santa Clara, CA) small sample protocol. Affymetrix globin reduction protocol was applied for total RNA samples. Samples amplified with RiboAmp were hybridized on Illumina Sentrix HumanRef-8 Expression BeadChips (Illumina Inc, San Diego, CA) and subjected to statistical analyses. RiboAmp mRNA amplification did not notably amplify globin mRNA that is overrepresented in RNA isolated by both TEMPUS and PAXgene preservation. Enzymatic depletion of globin transcript reduced the quality of total RNA and is thus not recommendable. Microarray analysis showed acceptable correlation within and between the RNA preservation methods, but altogether 443 transcripts were differentially expressed between RNA samples preserved in TEMPUS and PAXgene tubes. We demonstrated that the 2 tested blood RNA-preservation methods combined with RiboAmp mRNA amplification may be used for microarray experiments without the need for a prior globin RNA reduction. However, because genes involved in immune cell functions and gene regulatory pathways were differentially expressed as a result of the technical bias between the preservation methods, they should not be used in the same analytic setting.

Microarray gene expression analysis of fixed archival tissue permits molecular classification and identification of potential therapeutic targets in diffuse large B-cell lymphoma

Refractory/relapsed diffuse large B-cell lymphoma (DLBCL) has a poor prognosis. Novel drugs targeting the constitutively activated NF-κB pathway characteristic of ABC-DLBCL are promising, but evaluation depends on accurate activated B cell-like (ABC)/germinal center B cell-like (GCB) molecular classification. This is traditionally performed on gene microarray expression profiles of fresh biopsies, which are not routinely collected, or by immunohistochemistry on formalin-fixed, paraffin-embedded (FFPE) tissue, which lacks reproducibility and classification accuracy. We explored the possibility of using routine archival FFPE tissue for gene microarray applications. We examined Affymetrix HG U133 Plus 2.0 gene expression profiles from paired archival FFPE and fresh-frozen tissues of 40 ABC/GCB-classified DLBCL cases to compare classification accuracy and test the potential for this approach to aid the discovery of therapeutic targets and disease classifiers in DLBCL. Unsupervised hierarchical clustering of unselected present probe sets distinguished ABC/GCB in FFPE with remarkable accuracy, and a Bayesian classifier correctly assigned 32 of 36 cases with >90% probability. Enrichment for NF-κB genes was appropriately seen in ABC-DLBCL FFPE tissues. The top discriminatory genes expressed in FFPE separated cases with high statistical significance and contained novel biology with potential therapeutic insights, warranting further investigation. These results support a growing understanding that archival FFPE tissues can be used in microarray experiments aimed at molecular classification, prognostic biomarker discovery, and molecular exploration of rare diseases.

A rapid and efficient platelet purification protocol for platelet gene expression studies

Isolation of pure platelet samples from whole blood is crucial for the study of platelet gene expression. The main obstacles to overcome in order to successfully isolate platelets from whole blood include (1) platelet activation; (2) leukocyte and red blood cell contamination, and (3) time-dependent platelet mRNA degradation. This chapter describes a rapid and highly efficient method for isolating human circulating platelets from small volumes of whole blood based on efficient inhibition of platelet activation and leukocyte removal by filtration followed by magnetic bead-depletion of residual contaminating leukocytes and red blood cells. Also described are methods for RNA extraction, cDNA synthesis, and platelet gene expression studies using both quantitative real-time PCR and microarray.

Cardiac pressure overload hypertrophy is differentially regulated by β-adrenergic receptor subtypes

In isolated myocytes, hypertrophy induced by norepinephrine is mediated via α(1)-adrenergic receptors (ARs) and not β-ARs. However, mice with deletions of both major cardiac α(1)-ARs still develop hypertrophy in response to pressure overload. Our purpose was to better define the role of β-AR subtypes in regulating cardiac hypertrophy in vivo, important given the widespread clinical use of β-AR antagonists and the likelihood that patients treated with these agents could develop conditions of further afterload stress. Mice with deletions of β(1), β(2), or both β(1)- and β(2)-ARs were subjected to transverse aortic constriction (TAC). After 3 wk, β(1)(-/-) showed a 21% increase in heart to body weight vs. sham controls, similar to wild type, whereas β(2)(-/-) developed exaggerated (49% increase) hypertrophy. Only when both β-ARs were ablated (β(1)β(2)(-/-)) was hypertrophy totally abolished. Cardiac function was preserved in all genotypes. Several known inhibitors of cardiac hypertrophy (FK506 binding protein 5, thioredoxin interacting protein, and S100A9) were upregulated in β(1)β(2)(-/-) compared with the other genotypes, whereas transforming growth factor-β(2), a positive mediator of hypertrophy was upregulated in all genotypes except the β(1)β(2)(-/-). In contrast to recent reports suggesting that angiogenesis plays a critical role in regulating cardiac hypertrophy-induced heart failure, we found no evidence that angiogenesis or its regulators (VEGF, Hif1α, and p53) play a role in compensated cardiac hypertrophy. Pressure overload hypertrophy in vivo is dependent on a coordination of signaling through both β(1)- and β(2)-ARs, mediated through several key cardiac remodeling pathways. Angiogenesis is not a prerequisite for compensated cardiac hypertrophy.

Designing, performing, and interpreting a microarray-based gene expression study

Microarray-based assays have significantly expanded their scope and range of applications over the last 10 years, and--at least for gene expression--can be considered mainstream applications. High-throughput, microarray-based gene expression studies have proven particularly useful in the study of neurodegenerative diseases, for which they have provided key insights in understanding disease pathogenesis, regional and cellular specificity, and identification of therapeutic targets. Even though many experimental steps are currently performed in specialized core facilities, the key steps of a microarray study--experimental design, and data analysis and interpretation--are performed by the primary investigator. Knowledge of the issues related to these key steps is essential to properly perform and interpret a microarray experiment and constitutes the main focus of the present chapter. The basic analytical steps are covered, and annotated R code for the analysis of a published dataset is provided.

Accurate expression profiling of very small cell populations

BACKGROUND

Expression profiling, the measurement of all transcripts of a cell or tissue type, is currently the most comprehensive method to describe their physiological states. Given that accurate profiling methods currently available require RNA amounts found in thousands to millions of cells, many fields of biology working with specialized cell types cannot use these techniques because available cell numbers are limited. Currently available alternative methods for expression profiling from nanograms of RNA or from very small cell populations lack a broad validation of results to provide accurate information about the measured transcripts.

METHODS AND FINDINGS

We provide evidence that currently available methods for expression profiling of very small cell populations are prone to technical noise and therefore cannot be used efficiently as discovery tools. Furthermore, we present Pico Profiling, a new expression profiling method from as few as ten cells, and we show that this approach is as informative as standard techniques from thousands to millions of cells. The central component of Pico Profiling is Whole Transcriptome Amplification (WTA), which generates expression profiles that are highly comparable to those produced by others, at different times, by standard protocols or by Real-time PCR. We provide a complete workflow from RNA isolation to analysis of expression profiles.

CONCLUSIONS

Pico Profiling, as presented here, allows generating an accurate expression profile from cell populations as small as ten cells.

Selective amplification of Brucella melitensis mRNA from a mixed host-pathogen total RNA

Background

Brucellosis is a worldwide anthropozoonotic disease caused by an in vivo intracellular pathogen belonging to genus Brucella. The characterization of brucelae transcriptome's during host-pathogen interaction has been limited due to the difficulty of obtaining an adequate quantity of good quality eukaryotic RNA-free pathogen RNA for downstream applications.

Findings

Here, we describe a combined protocol to prepare RNA from intracellular B. melitensis in a quantity and quality suitable for pathogen gene expression analysis. Initially, B. melitensis total RNA was enriched from a host:pathogen mixed RNA sample by reducing the eukaryotic RNA..Then, to increase the Brucella RNA concentration and simultaneously minimize the contaminated host RNA in the mixed sample, a specific primer set designed to anneal to all B. melitensis ORF allows the selective linear amplification of sense-strand prokaryotic transcripts in a previously enriched RNA sample.

Conclusion

The novelty of the method we present here allows analysis of the gene expression profile of B. melitensis when limited amounts of pathogen RNA are present, and is potentially applicable to both in vivo and in vitro models of infection, even at early infection time points.

An optimized RNA amplification method for prokaryotic expression profiling analysis

DNA microarray technology has been extensively used for gene expression analysis of both eukaryotic and prokaryotic organisms. For eukaryotic gene expression profiling, the poly(A)-based reverse transcription of messenger RNA (mRNA) followed by T7 RNA polymerase-based in vitro transcription is generally required to produce enough RNA targets for hybridization with the microarray chips. However, the same method cannot be directly applied to prokaryotic mRNAs due to the lack of poly(A) sequences at the 3' ends. Conventional methods usually require large amounts of starting RNAs and lead to high background noise. Recently developed amplification methods enable smaller amounts of prokaryotic RNA to be used from samples with species-specific primers, oligo(dT) primers, or random primers. In this study, three target preparation methods, including the direct labeling, polyadenylation-involved oligo-dT priming, and random priming amplification (respectively referred to as DL, PAOD, and RPA hereafter) were evaluated through expression profiling of a heat shock model of Escherichia coli. The PAOD method was found to be more sensitive and more specific in differential gene expression measurements than either DL and RPA, even when the E. coli RNA was only a small proportion of the simulated eukaryotic host RNA. The results suggest that PAOD is the preferred target preparation method for prokaryotic transcriptome.

Spatiotemporal coupling of focal extracellular matrix degradation and reconstruction in the menstrual human endometrium

Coupling of focal degradation and renewal of the functional layer of menstrual endometrium is a key event of the female reproductive biology. The precise mechanisms by which the various endometrial cell populations control extracellular matrix (ECM) degradation in the functionalis while preserving the basalis and the respective contribution of basalis and functionalis in endometrium regeneration are still unclear. We therefore compared the transcriptome of stromal and glandular cells isolated by laser capture microdissection from the basalis as well as degraded and preserved areas of the functionalis in menstrual endometria. Data were validated by in situ hybridization. Expression profile of selected genes was further analyzed throughout the menstrual cycle, and their response to ovarian steroids withdrawal was studied in a mouse xenograft model. Immunohistochemistry confirmed the results at the protein level. Algorithms for sample clustering segregated biological samples according to cell type and tissue depth, indicating distinct gene expression profiles. Pairwise comparisons identified the greatest numbers of differentially expressed genes in the lysed functionalis when compared with the basalis. Strikingly, in addition to genes products associated with tissue degradation (matrix metalloproteinase and plasmin systems) and apoptosis, superficial lysed stroma was enriched in gene products associated with ECM biosynthesis (collagens and their processing enzymes). These results support the hypothesis that fragments of the functionalis participate in endometrial regeneration during late menstruation. Moreover, menstrual reflux of lysed fragments overexpressing ECM components and adhesion molecules could easily facilitate implantation of endometriotic lesions.

Laser microdissection-mediated isolation and in vitro transcriptional amplification of plant RNA

Protocols for laser microdissection and linear amplification of RNA from fixed, sectioned plant tissues are described. When combined with quantitative RT-PCR, microarray analysis, or RNA-sequencing, these procedures enable quantitative analyses of transcript accumulation from microscopic quantities of specific plant organs, tissues, or single cells.

An iterative data mining approach for mining overlapping coexpression patterns in noisy gene expression data

Clustering is concerned with the discovery of groupings of records in a database. Many clustering problems are defined as partitioning problems in the sense that the similar records are grouped into nonoverlapping partitions. However, the clustering of gene expression data to discover coexpressed genes may not always be meaningful if this problem is reduced into a partitioning problem. Due to the complexity of the underlying biological processes, a protein can interact with one or more other proteins belonging to different functional classes in order to perform a particular biological role. For this reason, when responding to different external stimulants, a gene that produces a particular protein can coexpress with more than one group of other genes. The gene can therefore belong to more than one group of coexpressed genes. This poses a challenge to many clustering algorithms as they are not originally developed to discover overlapping clusters in noisy gene expression data. In this paper, we propose an iterative data mining approach that consists of two phases as follows. In phase 1, a clustering algorithm is used to discover the initial, nonoverlapping partitioning of gene expression profiles in gene expression data. Then, the partition memberships of genes are redetermined iteratively in phase 2 by a pattern discovery technique so as to determine that if a gene should remain in the same partition, be moved to another partition, or be also grouped together with other genes in another partitions. The proposed approach has been tested with both artificial and real datasets. Experimental results show that it can improve the performances of existing clustering algorithms and is able to effectively discover overlapping clusters in noisy gene expression data.

Gene expression profiles associated with aging and mortality in humans

We investigated the hypothesis that gene expression profiles in cultured cell lines from adults, aged 57–97 years, contain information about the biological age and potential longevity of the donors. We studied 104 unrelated grandparents from 31 Utah CEU (Centre d’Etude du Polymorphisme Humain – Utah) families, for whom lymphoblastoid cell lines were established in the 1980s. Combining publicly available gene expression data from these cell lines, and survival data from the Utah Population Database, we tested the relationship between expression of 2151 always-expressed genes, age, and survival of the donors. Approximately 16% of 2151 expression levels were associated with donor age: 10% decreased in expression with age, and 6% increased with age. Cell division cycle 42 (CDC42) and CORO1A exhibited strong associations both with age at draw and survival after draw (multiple comparisons-adjusted Monte Carlo P-value < 0.05). In general, gene expressions that increased with age were associated with increased mortality. Gene expressions that decreased with age were generally associated with reduced mortality. A multivariate estimate of biological age modeled from expression data was dominated by CDC42 expression, and was a significant predictor of survival after blood draw. A multivariate model of survival as a function of gene expression was dominated by CORO1A expression. This model accounted for approximately 23% of the variation in survival among the CEU grandparents. Some expression levels were negligibly associated with age in this cross-sectional dataset, but strongly associated with inter-individual differences in survival. These observations may lead to new insights regarding the genetic contribution to exceptional longevity.

The removal of multiplicative, systematic bias allows integration of breast cancer gene expression datasets - improving meta-analysis and prediction of prognosis

Background

The number of gene expression studies in the public domain is rapidly increasing, representing a highly valuable resource. However, dataset-specific bias precludes meta-analysis at the raw transcript level, even when the RNA is from comparable sources and has been processed on the same microarray platform using similar protocols. Here, we demonstrate, using Affymetrix data, that much of this bias can be removed, allowing multiple datasets to be legitimately combined for meaningful meta-analyses.

Results

A series of validation datasets comparing breast cancer and normal breast cell lines (MCF7 and MCF10A) were generated to examine the variability between datasets generated using different amounts of starting RNA, alternative protocols, different generations of Affymetrix GeneChip or scanning hardware. We demonstrate that systematic, multiplicative biases are introduced at the RNA, hybridization and image-capture stages of a microarray experiment. Simple batch mean-centering was found to significantly reduce the level of inter-experimental variation, allowing raw transcript levels to be compared across datasets with confidence. By accounting for dataset-specific bias, we were able to assemble the largest gene expression dataset of primary breast tumours to-date (1107), from six previously published studies. Using this meta-dataset, we demonstrate that combining greater numbers of datasets or tumours leads to a greater overlap in differentially expressed genes and more accurate prognostic predictions. However, this is highly dependent upon the composition of the datasets and patient characteristics.

Conclusion

Multiplicative, systematic biases are introduced at many stages of microarray experiments. When these are reconciled, raw data can be directly integrated from different gene expression datasets leading to new biological findings with increased statistical power.

"Hook"-calibration of GeneChip-microarrays: chip characteristics and expression measures

Background

Microarray experiments rely on several critical steps that may introduce biases and uncertainty in downstream analyses. These steps include mRNA sample extraction, amplification and labelling, hybridization, and scanning causing chip-specific systematic variations on the raw intensity level. Also the chosen array-type and the up-to-dateness of the genomic information probed on the chip affect the quality of the expression measures. In the accompanying publication we presented theory and algorithm of the so-called hook method which aims at correcting expression data for systematic biases using a series of new chip characteristics.

Results

In this publication we summarize the essential chip characteristics provided by this method, analyze special benchmark experiments to estimate transcript related expression measures and illustrate the potency of the method to detect and to quantify the quality of a particular hybridization. It is shown that our single-chip approach provides expression measures responding linearly on changes of the transcript concentration over three orders of magnitude. In addition, the method calculates a detection call judging the relation between the signal and the detection limit of the particular measurement. The performance of the method in the context of different chip generations and probe set assignments is illustrated. The hook method characterizes the RNA-quality in terms of the 3'/5'-amplification bias and the sample-specific calling rate. We show that the proper judgement of these effects requires the disentanglement of non-specific and specific hybridization which, otherwise, can lead to misinterpretations of expression changes. The consequences of modifying probe/target interactions by either changing the labelling protocol or by substituting RNA by DNA targets are demonstrated.

Conclusion

The single-chip based hook-method provides accurate expression estimates and chip-summary characteristics using the natural metrics given by the hybridization reaction with the potency to develop new standards for microarray quality control and calibration.

3G vector-primer plasmid for constructing full-length-enriched cDNA libraries

We designed a 3G vector-primer plasmid for the generation of full-length-enriched complementary DNA (cDNA) libraries. By employing the terminal transferase activity of reverse transcriptase and the modified strand replacement method, this plasmid (assembled with a polydT end and a deoxyguanosine [dG] end) combines priming full-length cDNA strand synthesis and directional cDNA cloning. As a result, the number of steps involved in cDNA library preparation is decreased while simplifying downstream gene manipulation, sequencing, and subcloning. The 3G vector-primer plasmid method yields fully represented plasmid primed libraries that are equivalent to those made by the SMART (switching mechanism at 5' end of RNA transcript) approach.

Technical variables in high-throughput miRNA expression profiling: much work remains to be done

MicroRNA (miRNA) gene expression profiling has provided important insights into plant and animal biology. However, there has not been ample published work about pitfalls associated with technical parameters in miRNA gene expression profiling. One source of pertinent information about technical variables in gene expression profiling is the separate and more well-established literature regarding mRNA expression profiling. However, many aspects of miRNA biochemistry are unique. For example, the cellular processing and compartmentation of miRNAs, the differential stability of specific miRNAs, and aspects of global miRNA expression regulation require specific consideration. Additional possible sources of systematic bias in miRNA expression studies include the differential impact of pre-analytical variables, substrate specificity of nucleic acid processing enzymes used in labeling and amplification, and issues regarding new miRNA discovery and annotation. We conclude that greater focus on technical parameters is required to bolster the validity, reliability, and cultural credibility of miRNA gene expression profiling studies.

Exon level integration of proteomics and microarray data

Background

Previous studies comparing quantitative proteomics and microarray data have generally found poor correspondence between the two. We hypothesised that this might in part be because the different assays were targeting different parts of the expressed genome and might therefore be subjected to confounding effects from processes such as alternative splicing.

Results

Using a genome database as a platform for integration, we combined quantitative protein mass spectrometry with Affymetrix Exon array data at the level of individual exons. We found significantly higher degrees of correlation than have been previously observed (r = 0.808). The study was performed using cell lines in equilibrium in order to reduce a major potential source of biological variation, thus allowing the analysis to focus on the data integration methods in order to establish their performance.

Conclusion

We conclude that part of the variation observed when integrating microarray and proteomics data may occur as a consequence both of the data analysis and of the high granularity to which studies have until recently been limited. The approach opens up the possibility for the first time of considering combined microarray and proteomics datasets at the level of individual exons and isoforms, important given the high proportion of alternative splicing observed in the human genome.

Statistical methods in cardiac gene expression profiling: from image to function

By providing genome-scale information on gene expression, microarray technology has gained popularity in diverse areas including clinical medicine. However, the analysis and interpretation of microarray data are often complicated. This chapter describes various strategies for microarray data analysis. The analysis starts with the scanned image of a microarray. The image information is processed and summarized to numerical values that represent the abundance of transcripts. Technical variability and systematic biases can be minimized with the proper procedures of background correction and normalization. Considerable numbers of genes are not expressed or not detected by microarray technology. Those genes can be filtered out before further statistical comparison to reduce the dimensionality of the problem. The next step in analysis involves statistical comparison, cluster analysis, and visualization. Genes from the same cluster are considered to be coexpressed and/or coregulated. Also, we can group coexpressed genes into categories by their biological function and cellular location. By combining prior knowledge and statistical results, we can make an inference based on the gene expression profiles.

A global view of Staphylococcus aureus whole genome expression upon internalization in human epithelial cells

Background

Staphylococcus aureus, a leading cause of chronic or acute infections, is traditionally considered an extracellular pathogen despite repeated reports of S. aureus internalization by a variety of non-myeloid cells in vitro. This property potentially contributes to bacterial persistence, protection from antibiotics and evasion of immune defenses. Mechanisms contributing to internalization have been partly elucidated, but bacterial processes triggered intracellularly are largely unknown.

Results

We have developed an in vitro model using human lung epithelial cells that shows intracellular bacterial persistence for up to 2 weeks. Using an original approach we successfully collected and amplified low amounts of bacterial RNA recovered from infected eukaryotic cells. Transcriptomic analysis using an oligoarray covering the whole S. aureus genome was performed at two post-internalization times and compared to gene expression of non-internalized bacteria. No signs of cellular death were observed after prolonged internalization of Staphylococcus aureus 6850 in epithelial cells. Following internalization, extensive alterations of bacterial gene expression were observed. Whereas major metabolic pathways including cell division, nutrient transport and regulatory processes were drastically down-regulated, numerous genes involved in iron scavenging and virulence were up-regulated. This initial adaptation was followed by a transcriptional increase in several metabolic functions. However, expression of several toxin genes known to affect host cell integrity appeared strictly limited.

Conclusion

These molecular insights correlated with phenotypic observations and demonstrated that S. aureus modulates gene expression at early times post infection to promote survival. Staphylococcus aureus appears adapted to intracellular survival in non-phagocytic cells.

Comparison of target labeling methods for use with Affymetrix GeneChips

Background

Several different commercial one-cycle labeling kits are available for preparation of the target for use with the Affymetrix GeneChip platform. However, there have been no evaluations of these different kits to determine if comparable results were generated. We report on the cRNA target synthesis, labeling efficiency and hybridization results using the One-Cycle Target Labeling Assay™ (Affymetrix), the BioArray RNA Amplification and Labeling System™ (Enzo Life Sciences), and the Superscript RNA Amplification System (Invitrogen Life Technologies).

Results

The only notable difference between kits was in the yield of cRNA target synthesized during in vitro transcription, where the BioArray assay had to be repeated several times in order to have sufficient target. However, each kit resulted in comparable signal and detection calls when hybridized to the Affymetrix GeneChip.

Conclusion

These 3 one-cycle labeling kits produce comparable hybridization results. This provides users with several kit options and flexibility when using the Affymetrix system.

Evaluation of global RNA amplification and its use for high-throughput transcript analysis of laser-microdissected endosperm

Laser microdissection (LM) provides a useful method for isolating specific cells or tissues from biological samples. Here, we adapted microdissection protocols to allow high-resolution transcript analysis of different tissues from developing Arabidopsis seed. Sufficient RNA ( approximately 50 ng) was extracted from endosperm tissue for RT-PCR. However, to obtain enough RNA for microarray analyses, it was necessary to amplify the RNA. PCR- and IVT-based amplification methods were investigated and several important technical aspects of amplification were identified (such as target truncation and alterations in signal intensity). We found that when starting from only 50 ng of RNA, amplification methods based on PCR and IVT produced sufficient product for reliable microarray hybridizations, with two-round IVT giving the best results. Microarray analyses, using endosperm-derived RNA amplified by two-round IVT, reproducibly identified endosperm enriched marker genes. Thus, when combined with RNA-amplification protocols, LM is a robust and reliable technique for high-throughput tissue-specific gene expression analysis.

Microarray technology offers a novel tool for the diagnosis and identification of therapeutic targets for male infertility

Male infertility is now a major reproductive health problem because of an increasing number of environmental pollutants and chemicals, which eventually result in gene mutations. Genetic alterations caused by environmental factors account for a significant percentage of male infertility. Microarray technology is a powerful tool capable of measuring simultaneously the expression of thousands of genes expressed in a single sample. Eventually, advances in genetic technology will allow for the diagnosis of patients with male infertility due to congenital reasons or environmental factors. Since its introduction in 1994, microarray technology has made significant advances in the identification and characterization of novel or known genes possibly correlated with male infertility in mice, as well as in humans. This provides a rational basis for the application of microarray to establishing molecular signatures for the diagnosis and gene therapy targets of male infertility. In this review, the differential gene expression patterns characterized by microarray in germ and somatic cells at different steps of development or in response to stimuli, as well as a number of novel or known genes identified to be associated with male infertility in mice and humans, are addressed. Moreover, issues pertaining to measurement reproducibility are highlighted for the application of microarray data to male infertility.

Comparison of amplification methods for transcriptomic analyses of low abundance prokaryotic RNA sources

Microarrays have established as instrumental for bacterial detection, identification, and genotyping as well as for transcriptomic studies. For gene expression analyses using limited numbers of bacteria (derived from in vivo or ex vivo origin, for example), RNA amplification is often required prior to labeling and hybridization onto microarrays. Evaluation of the fidelity of the amplification methods is crucial for the robustness and reproducibility of microarray results. We report here the first utilization of random primers and the highly processive Phi29 phage polymerase to amplify material for transcription profiling analyses. We compared two commercial amplification methods (GenomiPhi and MessageAmp kits) with direct reverse-transcription as the reference method, focusing on the robustness of mRNA quantification using either microarrays or quantitative RT-PCR. Both amplification methods using either poly-A tailing followed by in vitro transcription, or direct strand displacement polymerase, showed appreciable linearity. Strand displacement technique was particularly affordable compared to in vitro transcription-based (IVT) amplification methods and consisted in a single tube reaction leading to high amplification yields. Real-time measurements using low-, medium-, and highly expressed genes revealed that this simple method provided linear amplification with equivalent results in terms of relative messenger abundance as those obtained by conventional direct reverse-transcription.

Microarray analysis after RNA amplification can detect pronounced differences in gene expression using limma

BACKGROUND

RNA amplification is necessary for profiling gene expression from small tissue samples. Previous studies have shown that the T7 based amplification techniques are reproducible but may distort the true abundance of targets. However, the consequences of such distortions on the ability to detect biological variation in expression have not been explored sufficiently to define the true extent of usability and limitations of such amplification techniques.

RESULTS

We show that expression ratios are occasionally distorted by amplification using the Affymetrix small sample protocol version 2 due to a disproportional shift in intensity across biological samples. This occurs when a shift in one sample cannot be reflected in the other sample because the intensity would lie outside the dynamic range of the scanner. Interestingly, such distortions most commonly result in smaller ratios with the consequence of reducing the statistical significance of the ratios. This becomes more critical for less pronounced ratios where the evidence for differential expression is not strong. Indeed, statistical analysis by limma suggests that up to 87% of the genes with the largest and therefore most significant ratios (p < 10e(-20)) in the unamplified group have a p-value below 10e(-20) in the amplified group. On the other hand, only 69% of the more moderate ratios (10e(-20) < p < 10e(-10)) in the unamplified group have a p-value below 10e(-10) in the amplified group. Our analysis also suggests that, overall, limma shows better overlap of genes found to be significant in the amplified and unamplified groups than the Z-scores statistics.

CONCLUSION

We conclude that microarray analysis of amplified samples performs best at detecting differences in gene expression, when these are large and when limma statistics are used.

Single-cell expression profiling of human epidermal stem and transit-amplifying cells: Lrig1 is a regulator of stem cell quiescence

Considerable progress has been made in characterizing epidermal stem cells by microarray analysis of FACS-selected populations. One limitation of this approach is that the gene expression profiles represent the average of the cell population, potentially masking cellular heterogeneity of functional significance. To overcome this problem, we have performed single-cell expression profiling. We have generated cDNA libraries from single human epidermal cells, designated as stem or transit-amplifying cells on the basis of Delta1 and melanoma-associated chondroitin sulfate proteoglycan expression. Of the 14 putative stem cell markers identified, we selected one, the EGF receptor antagonist leucine-rich repeats and immunoglobulin-like domains 1 (Lrig1), for further study. Lrig1 was expressed in groups of basal cells in human interfollicular epidermis previously identified as enriched for stem cells. Overexpression of Lrig1 decreased keratinocyte proliferation but did not affect the proportion of stem and transit-amplifying cells, as judged by clonal growth characteristics. Down-regulation of Lrig1 using siRNA increased cell-surface EGF receptor levels, enhanced activation of downstream pathways, and stimulated proliferation. Lrig1 acted in part by negatively regulating the Myc promoter. We propose that Lrig1 maintains epidermal stem cells in a quiescent nondividing state, and that Lrig1 down-regulation triggers proliferation.

Amplification of low quantity bacterial RNA for microarray studies: time-course analysis of Leptospirillum ferrooxidans under nitrogen-fixing conditions

We have developed a method for the amplification of low quantity total bacterial RNA for DNA microarrays analysis. Current methods are based on the linear amplification by the in vitro transcription from the T7 promoter, similar to that used for eukaryotic mRNA amplification. For the incorporation of T7 promoter, the prokaryotic RNA must be enzymatically modified for the incorporation of a polyA tail at the 3' end to emulate the eukaryotic mRNA. The method we describe and validate herein avoids this step by the direct and random incorporation of the T7 promoter. From 500 ng of total bacterial RNA, we obtained 130-150 microg of antisense RNA, such products being good substrate for fluorescent labelling and DNA microarray analysis. The method was validated with bacterial samples from which it is very difficult to obtain sufficient amounts and quality of total RNA for global gene expression analysis. This is critical for low cell density growing microorganisms, environmental samples, or many extremophiles where the composition of the cultural media severely affects the RNA yield, like in the case of the acidophile and iron oxidizer Gram-negative bacterium Leptospirillum ferrooxidans. We further validated our amplification method in parallel experiments with non-amplified RNA by following the expression of the L. ferrooxidans nif regulon along the time-course of growth.

An alternative method to amplify RNA without loss of signal conservation for expression analysis with a proteinase DNA microarray in the ArrayTube format

Background

Recent developments in DNA microarray technology led to a variety of open and closed devices and systems including high and low density microarrays for high-throughput screening applications as well as microarrays of lower density for specific diagnostic purposes. Beside predefined microarrays for specific applications manufacturers offer the production of custom-designed microarrays adapted to customers' wishes.

Array based assays demand complex procedures including several steps for sample preparation (RNA extraction, amplification and sample labelling), hybridization and detection, thus leading to a high variability between several approaches and resulting in the necessity of extensive standardization and normalization procedures.

Results

In the present work a custom designed human proteinase DNA microarray of lower density in ArrayTube^® format was established. This highly economic open platform only requires standard laboratory equipment and allows the study of the molecular regulation of cell behaviour by proteinases. We established a procedure for sample preparation and hybridization and verified the array based gene expression profile by quantitative real-time PCR (QRT-PCR). Moreover, we compared the results with the well established Affymetrix microarray. By application of standard labelling procedures with e.g. Klenow fragment exo^-, single primer amplification (SPA) or In Vitro Transcription (IVT) we noticed a loss of signal conservation for some genes. To overcome this problem we developed a protocol in accordance with the SPA protocol, in which we included target specific primers designed individually for each spotted oligomer. Here we present a complete array based assay in which only the specific transcripts of interest are amplified in parallel and in a linear manner. The array represents a proof of principle which can be adapted to other species as well.

Conclusion

As the designed protocol for amplifying mRNA starts from as little as 100 ng total RNA, it presents an alternative method for detecting even low expressed genes by microarray experiments in a highly reproducible and sensitive manner. Preservation of signal integrity is demonstrated out by QRT-PCR measurements. The little amounts of total RNA necessary for the analyses make this method applicable for investigations with limited material as in clinical samples from, for example, organ or tumour biopsies. Those are arguments in favour of the high potential of our assay compared to established procedures for amplification within the field of diagnostic expression profiling. Nevertheless, the screening character of microarray data must be mentioned, and independent methods should verify the results.

Scanning microarrays at multiple intensities enhances discovery of differentially expressed genes

MOTIVATION

Scanning parameters are often overlooked when optimizing microarray experiments. A scanning approach that extends the dynamic data range by acquiring multiple scans of different intensities has been developed.

RESULTS

Data from each of three scan intensities (low, medium, high) were analyzed separately using multiple scan and linear regression approaches to identify and compare the sets of genes that exhibit statistically significant differential expression. In the multiple scan approach only one-third of the differentially expressed genes were shared among the three intensities, and each scan intensity identified unique sets of differentially expressed genes. The set of differentially expressed genes from any one scan amounted to < 70% of the total number of genes identified in at least one scan. The average signal intensity of genes that exhibited statistically significant changes in expression was highest for the low-intensity scan and lowest for the high-intensity scan, suggesting that low-intensity scans may be best for detecting expression differences in high-signal genes, while high-intensity scans may be best for detecting expression differences in low-signal genes. Comparison of the differentially expressed genes identified in the multiple scan and linear regression approaches revealed that the multiple scan approach effectively identifies a subset of statistically significant genes that linear regression approach is unable to identify. Quantitative RT-PCR (qRT-PCR) tests demonstrated that statistically significant differences identified at all three scan intensities can be verified.

AVAILABILITY

The data presented can be viewed at http://www.ncbi.nlm.nih.gov/geo/ under GEO accession no. GSE3017.

Systematic comparison of the T7-IVT and SMART-based RNA preamplification techniques for DNA microarray experiments

BACKGROUND

Small biological samples obtained from biopsies or laser microdissection often do not yield sufficient RNA for successful microarray hybridization; therefore, RNA amplification is performed before microarray experiments. We compared 2 commonly used techniques for RNA amplification.

METHODS

We compared 2 commercially available methods, Arcturus RiboAmp for in vitro transcription (IVT) and Clontech BD SMART for PCR, to preamplify 50 ng of total RNA isolated from mouse livers and kidneys. Amplification factors of 3 sequences were determined by real-time PCR. Differential expression profiles were compared within and between techniques as well as with unamplified samples with 10K 50mer oligomer-spotted microarrays (MWG Biotech). The microarray results were validated on the transcript and protein levels by comparison with public expression databases.

RESULTS

Amplification factors for specific sequences were lower after 2 rounds of IVT than after 12 cycles of SMART. Furthermore, IVT showed a clear decrease in amplification with increasing distance of the amplified sequences from the polyA tail, indicating generation of smaller products. In the microarray experiments, reproducibility of the duplicates was highest after SMART. In addition, SMART-processed samples showed higher correlation when compared with unamplified samples as well as with expression databases.

CONCLUSIONS

Whenever 1 round of T7-IVT does not yield sufficient product for microarray hybridization, which is usually the case when <200 ng of total RNA is used as starting material, we suggest the use of SMART PCR for preamplification.

Detection of differentially expressed glycogenes in trabecular meshwork of eyes with primary open-angle glaucoma

PURPOSE

To identify differentially expressed glycogenes in trabecular meshwork (TM) of eyes with primary open-angle glaucoma (POAG).

METHODS

Total RNA was isolated from TM of cadaveric eyes derived from donors with diagnosed glaucomas of different etiologies and from normal control subjects. RNA was amplified and hybridized to the GLYCOv2 oligonucleotide microarray that contains probes for carbohydrate-binding proteins, glycosyltransferases, and other genes involved in the regulation of glycosylation. Statistical analysis was used to identify differentially expressed genes between normal and POAG samples.

RESULTS

This study revealed that POAG TM and normal TM have distinct gene expression profiles. Of the 2001 genes on the array, 19 genes showed differential expression of greater than 1.4-fold in POAG. Mimecan and activinA, which have been shown to be upregulated in models of glaucoma, were both found to be elevated in POAG TM. Many genes were identified for the first time to be differentially regulated in POAG. Among the upregulated genes were: (1) cell adhesion molecules including platelet endothelial cell adhesion molecule-1 and P-selectin, both of which are targets of NFkappaB, which has been shown to be activated in glaucomatous TM; (2) lumican, a core protein of keratan sulfate proteoglycans; and (3) the receptor for IL6, a cytokine that has been shown to be upregulated in TM in response to elevated intraocular pressure. Among the downregulated genes were chondroitin-4-O-sulfotransferase involved in the synthesis of chondroitin sulfate chains and the receptor for PDGFbeta, a growth factor that has been shown to stimulate both TM cell proliferation and phagocytic activity. Results for several genes were confirmed by RTq-PCR.

CONCLUSIONS

Microarray technology was used to show, for the first time, that POAG TM has a distinct glycogene expression profile. Differentially expressed glycogenes identified in this study have not been previously investigated for their role in the pathogenesis of POAG and thus are novel factors for further study of the mechanism of the disease and for their possible use as diagnostic markers.

Comparative evaluation of linear and exponential amplification techniques for expression profiling at the single-cell level

Comparison of the performance of three methods for amplifying single-cell amounts of RNA for use in expression profiling shows that PCT amplification is more reliable than linear amplification.

Background

Single-cell microarray expression profiling requires 10⁸-10⁹-fold amplification of the picogram amounts of total RNA typically found in eukaryotic cells. Several methods for RNA amplification are in general use, but little consideration has been given to the comparative analysis of those methods in terms of the overall validity of the data generated when amplifying from single-cell amounts of RNA, rather than their empirical performance in single studies.

Results

We tested the performance of three methods for amplifying single-cell amounts of RNA under ideal conditions: T7-based in vitro transcription; switching mechanism at 5' end of RNA template (SMART) PCR amplification; and global PCR amplification. All methods introduced amplification-dependent noise when mRNA was amplified 10⁸-fold, compared with data from unamplified cDNA. PCR-amplified cDNA demonstrated the smallest number of differences between two parallel replicate samples and the best correlation between independent amplifications from the same cell type, with SMART outperforming global PCR amplification. SMART had the highest true-positive rate and the lowest false-positive rate when comparing expression between two different cell types, but had the lowest absolute discovery rate of all three methods. Direct comparison of the performance of SMART and global PCR amplification on single-cell amounts of total RNA and on single neural stem cells confirmed these findings.

Conclusion

Under the conditions tested, PCR amplification was more reliable than linear amplification for detecting true expression differences between samples. SMART amplification had a higher true-positive rate than global amplification, but at the expense of a considerably lower absolute discovery rate and a systematic compression of observed expression ratios.

Optimization and validation of small quantity RNA profiling for identifying TNF responses in cultured human vascular endothelial cells

INTRODUCTION

Affymetrix oligonucleotide microarrays are widely used in basic and applied research (Lander, E.S., (1999). Array of hope. Nature Genetics 21, 3-4; Lockhart, D.J. & Winzeler, E.A. (2000) Genomics, gene expression and DNA arrays. Nature 405, 827-836.) The need for a significant amount of starting RNA has limited its use in applications where the amount of RNA is limiting, such as with Laser Captured Microdissection (LCM), small biopsies, or peripheral blood in rodent models. To overcome this limitation, various RNA amplification and labeling methods have been described, however, further optimization and validation of these methods are needed.

METHODS

Here we reported using the Arcturus technology to optimize amplification and labeling of small amounts of RNA for Affymetrix microarray studies. We assessed the technical feasibility and variation introduced by differences in starting RNA quantity and differences in technical performance by microarray hybridization.

RESULTS

We demonstrated that the current approach is reliable to amplify as little as 40 ng total RNA, and it is suitable for Affymetrix studies yielding satisfactory quantitative chip performance. We also showed that differences in labeling methods contribute more to variation than the differences in starting RNA quantity per se. As a model, we studied the well-documented TNF-induced inflammatory responses in cultured human vascular endothelial cells. We were able to recapitulate the TNF-induced responses using small RNA sample profiling.

Options available for profiling small samples: a review of sample amplification technology when combined with microarray profiling

The possibility of performing microarray analysis on limited material has been demonstrated in a number of publications. In this review we approach the technical aspects of mRNA amplification and several important implicit consequences, for both linear and exponential procedures. Amplification efficiencies clearly allow profiling of extremely small samples. The conservation of transcript abundance is the most important issue regarding the use of sample amplification in combination with microarray analysis, and this aspect has generally been found to be acceptable, although demonstrated to decrease in highly diluted samples. The fact that variability and discrepancies in microarray profiles increase with minute sample sizes has been clearly documented, but for many studies this does appear to have affected the biological conclusions. We suggest that this is due to the data analysis approach applied, and the consequence is the chance of presenting misleading results. We discuss the issue of amplification sensitivity limits in the light of reports on fidelity, published data from reviewed articles and data analysis approaches. These are important considerations to be reflected in the design of future studies and when evaluating biological conclusions from published microarray studies based on extremely low input RNA quantities.

Manual exfoliation plus immunomagnetic bead separation as an initial step toward translational research

CONTEXT

The development of biotechnologic platforms capable of high throughput analysis has ushered in a promising new era of translational medicine. However, most studies to date are based on in vitro cell lines or substitute models for human disease. Although these model systems have proven insightful, it is readily becoming apparent that human clinical tissue must be studied in order to fully understand all the nuances of human disease. Studies that are based on human tissue, however, are limited by qualitative and quantitative issues, factors often precluding their use in high throughput studies.

OBJECTIVE

To develop a simple and rapid tissue procurement protocol for use in obtaining a homogeneous epithelial cell population from clinical tissue and the recovery of nucleic acids and proteins of high quality and quantity. Also, to determine if the technique preserves tissue, thereby allowing morphologic correlation with molecular findings.

DESIGN

Performance of manual exfoliation to procure cells from clinical resection specimens and use of immunomagnetic beads embedded with the antibody ber-Ep4 for the positive enrichment of a homogeneous epithelial cell population. Nucleic acids and proteins are then separated using a phenol plus guanidine thiocyante solution. Nucleic acids and proteins are quantitated and qualitatively analyzed using standard laboratory techniques.

RESULTS

Nucleic acids and proteins of high quality and quantity were recovered following manual exfoliation and immunomagnetic bead separation. Tissue architecture was not destroyed, thus permitting histologic and molecular correlation.

CONCLUSIONS

A simple and reproducible protocol is presented that may enable the molecular profiling of clinically resected tissue. Although the technique is currently limited to certain tissue and tumor types, further research will broaden its overall application.

Laser microdissection of plant tissue: what you see is what you get

Laser microdissection (LM) utilizes a cutting or harvesting laser to isolate specific cells from histological sections; the process is guided by microscopy. This provides a means of removing selected cells from complex tissues, based only on their identification by microscopic appearance, location, or staining properties (e.g., immunohistochemistry, reporter gene expression, etc.). Cells isolated by LM can be a source of cell-specific DNA, RNA, protein or metabolites for subsequent evaluation of DNA modifications, transcript/protein/metabolite profiling, or other cell-specific properties that would be averaged with those of neighboring cell types during analysis of undissected complex tissues. Plants are particularly amenable to the application of LM; the highly regular tissue organization and stable cell walls of plants facilitate the visual identification of most cell types even in unstained tissue sections. Plant cells isolated by LM have been the starting point for a variety of genomic and metabolite studies of specific cell types.

Optimized blood cell profiling method for genomic biomarker discovery using high-density microarray

High-quality biomarkers for disease progression, drug efficacy and toxicity liability are essential for improving the efficiency of drug discovery and development. The identification of drug-activity biomarkers is often limited by access to and the quantity of target tissue. Peripheral blood has increasingly become an attractive alternative to tissue samples from organs as source for biomarker discovery, especially during early clinical studies. However, given the heterogeneous blood cell population, possible artifacts from ex vivo activations, and technical difficulties associated with overall performance of the assay, it is challenging to profile peripheral blood cells directly for biomarker discovery. In the present study, Applied BioSystems' blood collection system was evaluated for its ability to isolate RNA suitable for use on the Affymetrix microarray platform. Blood was collected in a TEMPUS tube and RNA extracted using an ABI-6100 semi-automated workstation. Using human and rat whole blood samples, it was demonstrated that the RNA isolated using this approach was stable, of high quality and was suitable for Affymetrix microarray applications. The microarray data were statistically analysed and compared with other blood protocols. Minimal haemoglobin interference with RNA labelling efficiency and chip hybridization was found using the TEMPUS tube and extraction method. The RNA quality, stability and ease of handling requirement make the TEMPUS tube protocol an attractive approach for expression profiling of whole blood to support target and biomarker discovery.

cRNA target preparation for microarrays: Comparison of gene expression profiles generated with different amplification procedures

Microarray technology has become a standard tool for generation of gene expression profiles to explore human disease processes. Being able to start from minute amounts of RNA extends the fields of application to core needle biopsies, laser capture microdissected cells, and flow-sorted cells. Several RNA amplification methods have been developed, but no extensive comparability and concordance studies of gene expression profiles are available. Different amplification methods may produce differences in gene expression patterns. Therefore, we compared profiles processed by a standard microarray protocol with three different types of RNA amplification: (i) two rounds of linear target amplification, (ii) random amplification, and (iii) amplification based on a template switching mechanism. The latter two methods accomplish target amplification in a nonlinear way using PCR technology. Starting from as little as 50 ng of total RNA, the yield of labeled cRNA was sufficient for hybridization to Affymetrix HG-U133A GeneChip array using the respective methods. Replicate experiments were highly reproducible for each method. In comparison with the standard protocol, all three approaches are less sensitive and introduced a minor but clearly detectable bias of the detection call. In conclusion, the three amplification protocols used are applicable for GeneChip analysis of small tissue samples.

Transcriptome profiling of individual larvae of two different developmental modes in the poecilogonous polychaete Streblospio benedicti (Spionidae)

Understanding the range of biochemical and physiological phenotypes in a cohort of embryos or larvae is crucial to understanding the lifespan, dispersal potential, and recruitment success of the early life history stages of a species. In this study, a novel kinetic assay has been employed to profile the transcriptome pool complexity in individual larvae of both planktotrophic and lecithotrophic developmental modes in the poecilogonous polycheate Streblospio benedicti. Using a nano-scale synthesis strategy, the mRNA pool in a single embryo or larva can be amplified into cDNA for quantitative characterization in a high-throughput, kinetic reannealing assay in a 96-well, microtiterplate format. This assay generates transcript-pool complexity estimates at 1 degrees C temperature increments for each sample producing 3,360 quantitative measurements per 96-well plate. Measuring transcriptome complexity on 8 individual planktotrophic and 8 individual lecithotrophic larvae (with 4 duplicate assays for each individual) reveals a more complex gene expression profile in planktotrophic larvae and a lower level of interindividual variation in expression patterns in lecithotrophic larvae. Although differences in these gene expression patterns are more likely due to physiological differences between feeding and non-feeding larval types in these late-stage individuals, this is one of the first assessments of inter-individual variation in gene expression patterns in marine invertebrate larvae and indicates a large potential for developmental variability.