Gene expression microarray experiments are expensive to conduct and guidelines for acceptable quality control at intermediate steps before and after the samples are hybridised to chips are vague. We conducted an experiment hybridising RNA from human brain to 117 U133A Affymetrix GeneChips and used these data to explore the relationship between 4 pre-chip variables and 22 post-chip outcomes and quality control measures.We found that the pre-chip variables were significantly correlated with each other but that this correlation was strongest between measures of RNA quality and cRNA yield. Post-mortem interval was negatively correlated with these variables. Four principal components, reflecting array outliers, array adjustment, hybridisation noise and RNA integrity, explain about 75% of the total post-chip measure variability. Two significant canonical correlations existed between the pre-chip and post-chip variables, derived from MAS 5.0, dChip and the Bioconductor packages affy and affyPLM. The strongest  correlated RNA integrity and yield with post chip quality control (QC) measures indexing 3'/5' RNA ratios, bias or scaling of the chip and scaling of the variability of the signal across the chip. Post-mortem interval was relatively unimportant. We also found that the RNA integrity number (RIN) could be moderately well predicted by post-chip measures B_ACTIN35, GAPDH35 and SF.We have found that the post-chip variables having the strongest association with quantities measurable before hybridisation are those reflecting RNA integrity. Other aspects of quality, such as noise measures (reflecting the execution of the assay) or measures reflecting data quality (outlier status and array adjustment variables) are not well predicted by the variables we were able to determine ahead of time. There could be other variables measurable pre-hybridisation which may be better associated with expression data quality measures. Uncovering such connections could create savings on costly microarray experiments by eliminating poor samples before hybridisation. Conducting microarray experiments using Affymetrix arrays is expensive. The quality of the starting material, for instance human post-mortem tissues, is often predetermined and samples may be scarce, leading to variable quality of the extracted RNA. We set out to explore the relationship between quality control (QC) variables used to assess samples prior to hybridisation (pre-chip) and those used to assess the quality of the hybridisation and resulting microarray data (post-chip). We sought better to define which variables were important in determining the quality of the final data and to see in turn whether any post-chip measures could predict pre-chip variables.Examination of quality in GeneChip experiments has been hampered by the relatively new technology, rapidly changing platforms (chip types) and the inability of most centres, because of expense, to run large series of samples to examine the characteristics and limitations of the technology. In addition, the output of the QC measures reflects both technical variation in the performance of the experiment and the biological variation of the samples available. Affymetrix give a series of guidelines about threshold values for quality control measures produced in the RPT file by their algorithm (GCOS or MAS 5.0) . They doDumur et al.  examinedWe examined gene expression in a large series of RNA samples extracted from post-mortem human brain : technicWe have now used these data to examine the relationship of pre-chip variables to post-chip quality control measures. Although we believe our original subjective decision to exclude samples at each step in the process from sample collection to expression analysis was justified , these dThe samples we used were derived from a series of HD and control brains from the New Zealand Neurological Foundation Human Brain Bank. The full consent of all families was obtained at the time of autopsy and the University of Auckland Human Subjects Ethics Committee approved the protocols used in these studies. For most brains RNA was isolated from three regions: caudate nucleus (CN), cerebellum (CB) and motor cortex (MC). Because there was a significant effect of brain region on some of the pre-chip (RIN and cRNA yield) and post-chip variables (SF and RawQ) (see methods for a detailed explanation of the pre- and post-chip variables) we adjusted all variables for brain region (see methods). All further analyses were carried out with the adjusted variables.The pre-chip variables included two assessments of RNA quality: a four-category subjective visual assessment of Bioanalyzer traces (SUBQUAL) carried out by two of us on all samples  and the Agilent-derived RIN, which only became available after the GeneChips had been hybridised and thus was generated retrospectively Figure . SubjectAs expected from post-mortem brain tissue, where there is little control over the events leading up to availability and preservation of tissue, PMI and RNA integrity as measured by subjective and objective assessment were variable Table . In the -18). Subjective quality and RIN also correlated significantly with yield. Figure PMI correlated moderately negatively with our subjective assessment of RNA quality , with the Agilent RIN and with cRNA yield Table . As expeWe computed quality measures using three different softwares: MAS 5.0 , dChip  and the The different algorithms generating the post-chip variables measure aspects of the same underlying hybridization process. To explore the relationships between the variables generated by the different algorithms we carried out a principal component analysis using data from U133A chips hybridised in the experiment for which measures of all variables were available N = 112). Since many of the pre- and post-chip variables were not normally distributed, we based the principal component analysis on Spearman (non-parametric) correlations. The first 4 components explained 75% of the variation  is highly correlated with variables which include IQR of relative log expression . This quantity measures the variability in the degree to which a given chip differs from a virtual median chip – that is, a chip which would have median expression for each probe set. Consistent with the property of principal components of capturing the variability in the data, those chips with highest IQR relative log expression tend to be those which are largest on PC1 Figure .The largest outliers in Figure Canonical correlation analysis was then performed in order to detect relationships between the two sets of variables: the set of 4 pre-chip and that of 22 post-chip variables. Four canonical correlations were calculated Table . These wThe first canonical correlation  indexes nearly all of the relevant relationships between the pre- and post-chip variables: it is highly significant Table . The secAlthough estimation of RIN based on post-hybridization measures is generally not of interest for a lab analyzing its own data, it is potentially useful in the context of secondary analyses done by other labs. An increasingly common example is analysis by different groups of publicly available data, for example data deposited in Gene Expression Omnibus (GEO) ,15. In tIn our data, excluding the gross outlier HC79CB, the single variables most highly correlated with RIN are B_ACTIN35, GAPDH35 and SF. B_ACTIN35 and GAPDH35 are very highly correlated with each other (ρ = 0.99), with somewhat lower correlation with SF (ρ = .55 – .65). Robust regression ,17 of RIestimated RIN = 9.8 - 1.1 * B_ACTIN35,-15), and an adjusted R2 of 0.42. Using GAPDH35 as the predictor yieldswith a residual standard error of about 0.9. Ordinary regression yields nearly identical coefficients, both of which are highly significant  and adjusted R2 of 0.36. Including SF as an additional term does not greatly improve the fit of the model. Using SF alone, we obtainwith residual standard error of about 1.1. Again, coefficients estimated by ordinary regression are very close, with highly significant coefficients  and adjusted R2 = 0.27. SF may be easier to obtain than B_ACTIN35 or GAPDH35 for users without MAS/GCOS. Figure with residual standard error of about 1.0, highly significant coefficients  or that did not generate sufficient cRNA we cannot judge their impact on our post-chip measures. There are no reports assessing the performance of the Agilent-generated RIN on GeneChip data quality to date. Using the post-chip quality control measures in our experiment, we found that samples with a RIN > 5.5 produced expression data of sufficient quality to be included in analyses. We found that longer post-mortem intervals were associated with poorer quality RNA (lower RIN or SUBQUAL) as might be expected, although the level of correlation is low, around -0.2. Tomita et al. ,18. Of tThe post-chip measures can be generated from data available in the public databases, but the pre-chip quality control measures are usually not provided. Therefore it might prove useful to predict the RIN, as an objective measure of RNA integrity, retrospectively. It is not clear how generalisable the models generated from our data will be although they indicate that there may be relationships that can provide an estimate of this information. Only examination of a large number of varied data sets will give a true indication of their general validity. However, one of these predictors might be used to obtain a 'quick and dirty' estimate of the quality of RNA from which the data is derived. More importantly, it highlights the most appropriate post-chip measures that predict RNA quality (B_ACTIN35 and GAPDH35 and to a lesser extent SF) and gives an estimate of their relationship which can be used to select chips to exclude from analysis based on their outlier status in these variables which would improve data quality, particularly in small datasets or datasets combined from several experiments.Yield of cRNA is significantly correlated with measures of RNA integrity. It is thus difficult to know if yield per se is related to PMI or whether this is simply a result of its relationship with RNA integrity. Although yield clearly reflects RNA integrity it also indexes the quality of the reactions from total RNA to cRNA applied to the chips. However, it is clear that yield and RNA integrity have different relationships with the post-chip QC factors. In our experiment where all reactions were carried out by the same person in large batches it is unlikely that there were large variations in yield due to technical factors. Our study is limited by having no systematic technical replicates, for as in most such studies this would have been too expensive. The only RNA sample that was re-hybridised to an A chip, having been re-generated from RNA, was rated as poor and failed on both occasions.It is useful to distinguish between the various facets of the catch-all term 'quality'. In chronological order: there is first the condition of the starting RNA; next is the calibre of the experimental process and resulting hybridisation; finally comes the acceptability of the resulting expression measures, including identification of outliers.array outliers, the second comprises variables assessing array adjustment, the third contains variables measuring hybridisation noise, and the fourth consists of the set of variables related to RNA integrity. Interestingly, these components correspond roughly to the three aspects mentioned above, but in the reverse order. The first and second components together give insight into the outlier status of a chip when it is considered as part of a set of chips. The component explaining most variability contains variables providing numerical assessments for outlier identification. A related but somewhat distinct aspect is given by an assessment of how far off the chip is from the others, or how the signal would need to be adjusted to make it more like the rest of the chips in the set; this is provided by variables strongly represented in the second component. The third and fourth components, respectively, reflect directly the second (hybridisation) and first (RNA integrity) areas of quality.We can think of the first four principal components as providing a grouping of post-chip measures, with each component representing a different aspect of quality  or measures reflecting data quality (outlier status and array adjustment variables) are not well predicted by the variables we were able to determine ahead of time. To the extent that random variation affects chip hybridisation, this finding is not very surprising. However, we do not rule out the possibility that there are other variables measurable pre-hybridisation which may be better associated with expression data quality measures. Uncovering such connections could create savings on costly microarray experiments by eliminating poor samples before hybridisation. We therefore encourage investigators to keep careful track of potentially relevant variables so that further studies may continue to shed light on features predictive of array quality.® Expression Analysis Protocol, Rev. 2, March 2003, Affymetrix). Briefly, total RNA was extracted using TRIzol (Invitrogen) followed by RNeasy column cleanup (Qiagen) using the manufacturers' protocols. 10 μg total RNA from each sample was used to prepare biotinylated fragmented cRNA, with products from Affymetrix. Arrays were hybridized for 16 h in a 45°C incubator with constant rotation at 60 rpm. Chips were washed and stained on the Fluidics Station 400, and scanned using the GeneArray® 2500, according to the GeneChip® Expression Analysis Protocol, Rev. 2, March 2003 (Affymetrix). All RNA extractions and reactions were prepared using master mixes and batches of 8 and 24 respectively. Arrays were processed in batches of 16. All reactions and array hybridisations were carried out by the same person. The U133A GeneChips came from two manufacturing batches.A large set of post mortem brain samples (N = 134) that had previously been included in an analysis of gene expression using Human Genome U133A arrays (Affymetrix) were included in the current study Table  6]. The. The6]. Gene expression was quantified by robust multi-array analysis (RMA)  using thA number of pre- and post-array variables were selected for analysis on the basis of their predicted contribution to quality control assessment at various points in the procedure, from sample processing to expression data (Table (PMI) Post mortem interval was the time from death to tissue preservation, in hours.(SUBQUAL) 300 ng of total RNA was run on a 2100 Bioanalyzer (Agilent Technologies). A pre-defined four category subjective rating of RNA quality for all samples was made independently by an experienced biologist (AKH) and checked by another (RLC): in the few cases of disagreement  the sample trace was re-evaluated and a consensus was reached. In performing this assessment, attention to the following features of the total RNA trace were made: ribosomal peak definition, baseline flatness, and whether there were increased low molecular weight species. Traces were used to classify RNA quality as excellent, good, fair or poor. For subsequent statistical analysis a value of 4 was assigned to samples considered excellent, 3 to good, 2 to fair and 1 to poor total RNA integrity. This rating was used to select samples for further processing. Since a limited number of chips were available for the experiment, a minority of samples rated as 1 were not processed further,  (Table (RIN) Subsequent to the completion of the experiment, 2100 Bioanalyzer Expert software became available that enabled automatic assessment of RNA quality for mammalian eukaryotic total RNA (Agilent Technologies). The RNA integrity number (RIN) assesses RNA integrity on a scale from 0 (low integrity RNA) to 10 (high integrity RNA). The algorithm for generating a RIN number for a given RNA sample is based on the entire electrophoretic trace of the RNA sample. It uses an artificial neural network based on the determination of the most informative features that can be extracted from the traces out of 100 features identified through signal analysis. The selected features which collectively capture the most information about the integrity levels include the total RNA ratio , the height of the 18S peak, the fast area ratio  and the height of the lower marker . RINs we(YIELD) A standard amount of total RNA (10 μg) was used to carry out cDNA and subsequent cRNA reactions for each sample. The yield of adjusted cRNA  was used as a pre-chip parameter and reflects both RNA quality and the technical variation of the sample preparation.(BG) Background is a measure of the fluorescent signal on the array due to non-specific binding and autofluorescence from the array surface and the scanning wavelength (570 nm). A high background may indicate the presence of impurities such as cell debris or salts. This non-specific binding causes a low signal to noise ratio, resulting in reduced sensitivity for the detection of low expressing mRNA.(RAWQ) Raw Q assesses the pixel to pixel variation within each probe cell. Electrical noise from the scanner and sample quality can both contribute to Raw Q.(NOISE) Noise is calculated by dividing the array into 16 zones. The standard deviation of the lowest 2% of signal is calculated for each zone and then the average value for all zones is determined. Noise is used to calculate background adjusted signal values by taking a weighted average of the zone-specific noise levels.(PC_PRESENT) The number of probesets called "present" relative to the total number of probesets on the array as a percentage. A probeset is determined to be present, marginal or absent by a statistical algorithm within the MAS 5.0 software.(SF) When global scaling is performed, the overall intensity for each array is determined and is compared to a Target Intensity value in order to calculate the appropriate scaling factor. The Scaling Factor  should be comparable between arrays.ACTB and GAPDH transcripts are calculated from the chip. The 3' and 5' probesets' expression values are divided to give a ratio of 3'/5' mRNA representation. Expression values for probesets specific to the 5', middle, or 3' portion of the (DCHIP_AR_OUTLIER) The algorithm identifies outliers as a result of image contamination or saturated PM or MM signals. It cross-references one array with all the other arrays in an experiment using modelling of both perfect match (PM) and mismatch (MM) probe information at the probeset level. It flags an array as an outlier if > 5% of the probesets on that array are outliers relative to all other arrays in the experiment and recommends discarding arrays with >15% outlier probesets from analyses.(DCHIP_SING_OUTLIER) The algorithm also determines single outliers, individual probes within a probeset. These are most likely due to cross-hybridization to non-target or alternatively spliced genes.(DCHIP_PCCALL) Probesets that the dChip algorithm considers to be greater than zero, i.e. expressed, are determined as a percentage of the total number of probesets, similar to the Affymetrix %P.(MEDINT) The median intensity is determined across the array.From affyPLM (MED_NUSE) Standard error (SE) estimates for each probeset on the array are taken and adjusted so that the median standard error across all arrays is equal to 1. Arrays with elevated SE relative to other arrays are typically of lower quality. Boxplots of these values are used to compare arrays.Values are computed for each probeset by comparing the expression value on each array against the median expression value for that probeset across all arrays. The assumption is that most genes have constant expression across arrays, and thus have RLE values close to zero. Deviations from this are assessed using boxplots. A number of statistics within this analysis can be assessed as variables including: inter-quartile range of log ratios (IQR_LR), median (or bias) log ratio (B_LR), interquartile range plus absolute median of log ratios (IQRplusAbsB_LR) andthe coefficient of variation of log ratios (CV_LR) whichsummarise distributions of log ratios, or relative log expression, at the chip level. The relative log expression for each probe set is obtained by subtracting a baseline log expression from the log expression of each probe set. Due to computing limitations, we separated our experiment into two sets of chips. We used one series to fit the RMA model, and then applied the fitted model to all chips. Log ratios are computed using two different sets of chips for baseline. In LR1 the baseline is the probe set median log expression for the fitting set of chips. In LR2, the baseline is the median log expression from the group under study itself.P-value from the linear regression fit for the RNA degradation plot is also assessed as a variable (PVAL_SLOPE).(RNADEG_SLOPE) Within each probeset, probes are numbered directionally from the 5' end to the 3' end. Probe intensities are averaged by probe number, across all probesets. Outlying arrays are identified as those with a different gradient to the majority of plots within the experiment. The All analyses were performed on the combined dataset for all three brain regions  from a subset of samples (N = 112) for which data was available for all variables (U133A arrays). Each of the pre- and post- chip variables were adjusted by "brain region" in order to remove the effect of the latter, using the univariate general linear modelling technique where the "brain region" variable was fitted as a fixed effect. The residuals were considered as continuous variables and used for further analysis. The adjusted variables were assessed for normality using Skewness and Kurtosis measures in SPSS and distributions were considered non-normal if they did not fall between 0–1.Factor analysis was performed on 22 adjusted post-chip variables (see above) using SAS PROC FACTOR. The analysis is based on the Spearman correlation matrix generated using the SAS PROC CORR. Initial factors were extracted using the principal component method and rotations were then performed by the VARIMAX method. To assess relationships between the "pre-chip" and "post-chip" multidimensional variables (4 and 22 dimensions respectively), canonical correlation analysis was performed using the SAS PROC CANCOR. As above, the analysis was based on Spearman correlation matrix (SAS PROC CORR).For predicting RIN from post-chip variables, we assume a linear model. High multicollinearity between variables precludes formulation of a robust, interpretable model to predict RIN based on post-chip variables. We thus considered simple models containing one or two predictors.Robust linear regression modeling ,17 of RILaboratory work was carried out by AKH, and judgment of RNA quality by AKH and RL-C. AKH and GH collated the data. VM and DRG did the main statistical analysis and FC, AA, CK and SBD all contributed to the statistical and bioinformatic analysis of data. Study design was by AKH, JMO, CK, SJA, RLMF, RL-C and LJ. RLMF supplied the tissue and the clinical information about the samples. LJ, DRG and AKH interpreted the data and took the primary role in writing the manuscript. All authors read and commented upon the manuscript.Boxplots illustrating the summary statistics for the pre- and postchip variables from Table Click here for fileScatterplots of the pre- and post-chip variables for all chips studied showing the behaviour of the identified outlying chips in Table Click here for file


Archives of annotated formalin-fixed paraffin-embedded tissues (FFPET) are available as a potential source for retrospective studies. Methods are needed to profile these FFPET samples that are linked to clinical outcomes to generate hypotheses that could lead to classifiers for clinical applications.We developed a two-color microarray-based profiling platform by optimizing target amplification, experimental design, quality control, and microarray content and applied it to the profiling of FFPET samples. We profiled a set of 50 fresh frozen (FF) breast cancer samples and assigned class labels according to the signature and method by van 't Veer et al  and then-7). When applied to the matched FF samples, the FFPET-derived classifier was able to assign FF samples to the correct class labels with 96% accuracy. The single misclassification was attributed to poor sample quality, as measured by qPCR on total RNA, which emphasizes the need for sample quality control before profiling.When a classifier developed with matched FF samples was applied to FFPET data to assign samples to either "good" or "poor" outcome class labels, the classifier was able to assign the FFPET samples to the correct class label with an average error rate = 12% to 16%, respectively, with an Odds Ratio = 36.4 to 60.4, respectively. A classifier derived from FFPET data was able to predict the class label in FFPET samples  with an error rate of ~14% (p-value = 3.7 × 10We have optimized a platform for expression analyses and have shown that our profiling platform is able to accurately sort FFPET samples into class labels derived from FF classifiers. Furthermore, using this platform, a classifier derived from FFPET samples can reliably provide the same sorting power as a classifier derived from matched FF samples. We anticipate that these techniques could be used to generate hypotheses from archives of FFPET samples, and thus may lead to prognostic and predictive classifiers that could be used, for example, to segregate patients for clinical trial enrollment or to guide patient treatment. Standard extraction and amplification microarray protocols  total RNA, few discovery platforms have reliably been applied to formalin-fixed paraffin-embedded tissue (FFPET) samples. Some approaches that assay fewer transcripts  are promising, but do not allow for unbiased discovery of diagnostic signatures, which requires a genome-wide profiling method ,3. For et System  and NuGEt System ) and arrt System ,8) have t System -9, and it System . While st System , similarThe importance of expression-based classification of human tumors to predict treatment response or disease outcome is highlighted by recent publications ,12. van'In the clinical diagnosis of patients with cancer, it is routine to obtain a FFPET sample, but generally rare to obtain a FF sample. Consequently, the requirement of FF samples has limited expression profiling to patients treated at specialized research centers. The ability to use FFPET samples would make this technology available for virtually all cancer patients both in the context of retrospective analyses of banked samples and clinical trials seeking to identify molecular tumor characteristics associated with patient outcomes to treatment. Being able to do such analyses from FFPET samples would simplify sample biopsy collection requirements and enable retrospective studies to develop and test hypotheses for prognostic classifiers for other cancers. As additional tests are developed and as molecular profiling methods mature, health-care providers will come to rely more on such classifiers in the risk-management of disease.In this report, our primary focus was to develop sample processing and classification methods with archived FFPET samples for hypothesis generation. To this end, we optimized a microarray platform and applied it to the profiling of FFPET samples. We demonstrated that FFPET samples can be accurately assigned to class labels using a classifier developed from fresh frozen samples, and we show that a classifier derived from FFPET samples that performs well in classifying FF samples can be developed.Matched pairs of Fresh Frozen and Formalin-Fixed Paraffin-Embedded breast cancer samples were obtained from Genomics Collaborative  and Cytomyx . Colon carcinoma tissues were also from Cytomyx. RNA extraction reagents for FFPET samples were obtained from Epicentre Biotechnologies . Jurkat total RNA and amplification reagents used in this study were from Ambion . Matched FF and FFPET liver and muscle RNAs were obtained from MPI Research . The Universal Human Reference (UHR) total RNA was obtained from Stratagene . Cy-dye reagents were from GE Health sciences . Quantitative PCR reagents were purchased from Applied Biosystems . Microarrays were designed at Rosetta and manufactured by Agilent Technologies .Total RNA from FF samples was extracted by the vendor  immediately prior to shipment. For FFPET samples, the extraction protocol is adopted from MasterPure RNA Purification kit . Briefly, three 10 μm sections were subjected to paraffin solubilization with xylene. Tissue was pelleted from solution by centrifugation and residual xylene was removed by two ethanol rinses. The tissue pellets were then air dried and digested overnight in a lysis buffer with Proteinase K. Digested protein and other cellular components were removed by ammonium acetate precipitation and centrifugation followed by a DNase I treatment of the resulting supernatant. A second ammonium acetate precipitation to remove any residual protein was then performed prior to ethanol precipitation and nuclease-free water rehydration of the purified total RNA.For measuring the relative quality of total RNA from FFPET samples, primer pairs for two house-keeping genes, GAPDH and ribosomal protein L13a, were used to estimate the integrity of the total RNA . The original protocol employs oligo-dT priming for first strand cDNA synthesis, in which an oligo-dT primer incorporates a T7 RNA polymerase promoter, necessary for subsequent in vitro transcription of RNA , and co-purified with the same mass of cRNA from the UHR pool labelled with Cy5. Hybridizations were done in fluor-reversed pairs as described . LabelinC) exists in two array-based ratio profiles, if hybridization 1 consists of the ratio C versus A (A/C), and hybridization 2 consists of the ratio C versus B (B/C), re-ratioing of A/C and B/C creates a new ratio experiment B versus A (A/B).Since we are mostly interested in ratio profiles between FFPET samples, we developed methods to recover such information by re-ratioing the ratio profiles derived from each array experiment between the FFPET samples and the corresponding hybridizations with UHR as the reference. Re-ratio of the ratio profiles effectively cancels out the UHR profile while accounting for dye labelling biases and leaving only the FFPET profiles of interest. In other words, given a typical configuration in which the common reference  was initially designed to evaluate several oligonucleotide selection criteria rule sets . The cross-hybridization score reflects the smallest difference between the dG value of self binding and the largest dG value of the probe binding to any other molecular species. However, despite making the cross hybridization filters more lenient, we noticed that the cross hybridization can only be minimized since certain probes still contain relatively higher GC content compared to the majority of probes on the array in order to meet the 3' distance requirement. Selection of the genes for the HumFFPET 44 k v2.0 array was kept as close as possible to those on the current default Human 44 k v1.1 array. The human 44 k v1.1 and HumFFPET v2.0 array share 20,327 probes in common while the HumFFPET v2.0 array has 19,231 unique probes that are specifically designed for FFPET samples. Documentation for the HumFFPET 44 k v2.0 array will be available in the Gene Expression Omnibus (GEO) website in support of this publication, and the HumFFPET 44 k v2.0 array pattern will be publicly available through the Agilent eArray ordering system.Formalin-preserved samples present multiple challenges to whole-genome RNA profiling methodologies. To overcome the extensive degradation, contaminants from the formalin treatment and limited RNA mass availability, we sought to improve 1) robustness of the amplification protocol, 2) quality control assessment of FFPET samples, 3) microarray performance through probe selection and 4) experimental design by validating the use of UHR as the reference channel in two-color hybridization experiments.We started with a commercially available two-round RT-IVT protocol for the target cRNA preparation for non-FFPET samples . We used a mass imbalance of intact Jurkat RNA to assess the potential impact of degradation and chemical modifications that would vary across FFPET samples. While evaluating imbalanced FFPET samples directly was more appealing, we reasoned that the primary impact from the degradation and chemical modifications of the fixation and embedding procedures would be loss of amplifiable mRNA, which the Jurkat RNA experiment adequately approximates.To quantify the degree to which the amplification steps are susceptible to mass imbalance, we performed titrations of input mass for the first round amplification with 25 to 200 ng of Jurkat total RNA sample to model the impact of mass imbalance on microarrray data quality. Following the first round amplification using different mass inputs, a fixed amount (500 ng) of cRNA derived from each input mass titration in the first round amplification was used for the second round amplification. The resulting cRNA from the second round amplification was labelled and hybridized in fluor-reversed pair that was formed to reflect the initial first round mass imbalance between the reference input of 100 ng and the other mass inputs which were originally titrated in the first round amplification. The same experiments were done to titrate input mass for the second round amplification by holding the mass of the first round constant at 100 ng and varying the second round from 250 to 2,000 ng, with the input of 500 ng used as the baseline to form the reference for the different fluor-reversed pairs.In these mass imbalance experiments, hybridizations of fluor-reversed pairs that are formed between different mass input are still defined as 'same-vs-same' hybridizations since the exactly same mRNA-containing total RNA are used in the amplification whether the input varied in the first or the second round. If there is no amplification bias resulting from the initial mass inputs, either for the first round or the second round, then these same-vs-same hybridizations should have shown no signatures of differential expression beyond background level. Same-vs-same hybridization data are presented as a heat map in Figure The extent and nature of RNA degradation in FFPET blocks depends on FFPET preparation method, length of storage and storage conditions ,17,18. WFirst, we noted that mean log ratio of some samples displayed a dependence on the distance of the probe to the 3' end of the message. We reasoned that a non-zero slope of this plot indicates a bias in the data quality , and that the 3' slope of mean log ratio could be used as a key quality metric for microarray hybridization. In fact, the 3' slope metric is analogous to the RNA Degradation metric  on AffymThen, we developed a qPCR-based assay to measure the relative abundance of transcripts in the total RNA and the amplified cRNA to quantify the relative quality of FFPET-derived total RNA as a way to relate the RNA quality to the subsequent microarray hybridization quality. Then, as we had done with FF samples, we started by selecting a set of FFPET samples that had been previously profiled and that cover a range of microarray data quality, and then assayed several housekeeping transcripts by qPCR. Figure In summary, the hybridization data of a sample of ideal quality should show no correlation between the Ct count of the total RNA or the amplified cRNA and the detected expression pattern, either positive or negative. Since the measured Ct count from the total RNA and amplified cRNA correlate with the quality of microarray hybridization Figure , the qPCStandard microarrays with probes approximately 500 nucleotides or greater from the 3' end have been found to be ill-suited for FFPET profiling. Therefore, we designed an array with probe content more suited to nature of FFPET samples and less susceptible to cross-hybridization by optimizing probe distance to the 3' end of the transcript, the probe base composition and the OSD cross-hybridization filter. While we had preliminary success at obtaining gene expression data from FFPET samples on a 44 K array (Human 44 K) designed for FF samples, the raw intensity of signals measured on the array drops off more precipitously for FFPET samples than FF samples as the probes' distance from the 3' end is increased. This is due to the extensive degradation of total RNA and the use of 3' poly-A priming amplification approach. One way to overcome the reduced intensity of hybridizations with cRNA from the 3' poly-A priming method is to optimize the probe content and composition on the array to reflect the proximity of amplified cRNA sequence of each transcript to its 3' end from degraded FFPET samples. To this end, we designed an array specifically suited to profiling FFPET samples with probe sequences within the first 400 nucleotides of the 3' end of each transcript , a set of matched FF and FFPET liver and muscle samples were used to measure the sensitivity and specificity of the HumFFPET 44 k 2.0 array in comparison with our standard Human 44 k array (see methods for details). To compare the overall performance of the FFPET and standard array designs, we used a ROC curve analysis  to show 4 k 2.0, Using the HumFFPET 44 k 2.0 array with a set of FF and FFPET samples, we found that the measured expression ratio correlation between FF and FFPET samples is also improved on the HumFFPET 44 k 2.0 array (r = 0.80) over the Human 44 k array  versus a FFPET-based self-reference pool. UHR RNA is composed of total RNA from 10 human cell lines and has been used widely as a reference in gene-profiling experiments, microarray platform evaluations, and cross-platform comparisons . Using UBefore applying the two-color microarray platform for classification, we validated the optimized platform measuring the correlation of gene expression profiles from the matched fresh frozen and FFPET tumor samples. As shown in Figure A number of papers have recently been published utilizing microarray technology to identify prognostic and diagnostic biomarkers as a tool to predict treatment response or disease outcome ,23. SuchTo answer the first question, we chose a well established biomarker, the breast cancer prognostic signature identified by van 't Veer et al. . In the Using the prognostic scores from 50 FF samples, we assigned 25 "good prognosis" patients and 25 "poor prognosis" patients according to their signature patterns. Figure -7), with errors mostly coming from poor quality samples flagged by qPCR. If poor quality samples as indicated by qPCR are excluded, the error rate will be 10% or less, mostly caused by samples originally very close to the threshold. More importantly, the classifier derived from the FFPET samples is confirmed by the FF sample data  process to select "prognostic" genes based only on FFPET profiling data. The FFPET-derived classifier prediction accuracy was then evaluated against the FF samples. As shown in Figure Enabling personalized medicine in the near future will rely to a large extent on extracting data from well-annotated archived samples for which the three-to-five year outcome of the subject is known. We consider this approach as a bridging strategy for patient stratification and enrollment, during which time the hypotheses are tested and confirmed. Part of this strategy includes integrating FDA guidelines relating to retrospective sample and data mining; key factors in this regard are (1) storage conditions; (2) samples are representative of intended use; (3) samples meet inclusion/exclusion criteria; and (4) performance is comparable to that expected from a prospective study . The FDAWe demonstrated the ability to derive gene expression-based classifiers from FFPET data that sort patient samples into class labels that recapitulates the sorting of FF samples. The method involved the development of an optimized microarray platform with two-round RT-IVT amplification using 100 ng of total RNA input for the target preparation, a quantitative PCR method for assessing the relative quality of FFPET samples and a custom microarray with content and probe features specifically optimized for FFPET profiling. We found this microarray platform reliably and reproducibly measured differential gene expression of FFPET samples with a good correlation to corresponding FF samples. FFPET samples were correctly assigned to class labels developed from FF-derived classifiers. Although we cannot directly attribute the success of the classifiers to the optimizations performed on the profiling platform, further study with FFPET samples also showed our platform is of sufficient quality to enable hypothesis generation that could be validated with FF or FFPET samples in controlled, well-designed clinical trials.The authors declare that they have no competing interests.SD, RS, JG carried RNA extraction optimization, amplification, and hybridization. SD, SL, HD, TF, GT, BF and MM contributed to the design of the studies. MZ and MM drafted the manuscript. AK and JC designed the FFPET array. HD and YW developed the classifier and led the interpretation of the data. All authors read and approved the final manuscript.Two-round amplification workflow. Diagram of the Two-round amplification work flow used for amplification of total RNAClick here for fileFF to FFPET correlation. The correlation between the FF and FFPET samples is increased significantly on the HumFFPET 44 k array 2.0 when compared to the Human 44 k v1.1 array.Click here for file


The quantitative polymerase chain reaction (qPCR) is a widely utilized method for gene-expression analysis. However, insufficient material often compromises large-scale gene-expression studies. The aim of this study is to evaluate an RNA pre-amplification method to produce micrograms of cDNA as input for qPCR.The linear isothermal Ribo-SPIA pre-amplification method  was first evaluated by measuring the expression of 20 genes in RNA samples from six neuroblastoma cell lines and of 194 genes in two commercially available reference RNA samples before and after pre-amplification, and subsequently applied on a large panel of 738 RNA samples extracted from neuroblastoma tumours. All RNA samples were evaluated for RNA integrity and purity. Starting from 5 to 50 nanograms of total RNA the sample pre-amplification method was applied, generating approximately 5 microgams of cDNA, sufficient to measure more than 1000 target genes. The results obtained from this study show a constant yield of pre-amplified cDNA independent of the amount of input RNA; preservation of differential gene-expression after pre-amplification without introduction of substantial bias; no co-amplification of contaminating genomic DNA; no necessity to purify the pre-amplified material; and finally the importance of good RNA quality to enable pre-amplification.Application of this unbiased and easy to use sample pre-amplification technology offers great advantage to generate sufficient material for diagnostic and prognostic work-up and enables large-scale qPCR gene-expression studies using limited amounts of sample material. Amongst the various methods available to measure gene-expression, the reverse transcription quantitative polymerase chain reaction (RT-qPCR) is the most rapid, sensitive, and reproducible method -5. HowevTherefore, it seems that a method capable of pre-amplifying nanogram quantities of RNA is essential, to ensure that sufficient material is available for high-throughput gene-expression profiling. Various pre-amplification methods have been proposed including as well PCR-based ,7 as linThis paper extensively evaluates the linear isothermal Ribo-SPIA pre-amplification method for qPCR ,18. The Total RNA was extracted from 6 neuroblastoma cell lines and 738 fresh frozen neuroblastoma tumour biopsies according to three methods in collaborating laboratories. Two commercial RNA samples were mixed  from Stratagene and Human Brain Reference RNA (HBRR) from Ambion) to generate the four MAQC  reference samples .In order to assess the RNA purity and integrity, we performed a SPUD assay for the detection of enzymatic inhibitors  and a caStarting from 5, 15, or 50 ng of total RNA, the WT-Ovation RNA Pre-amplification method (NuGEN) was used according to the manufacturer's instructions, generating approximately 5 μg of cDNA ,18.In parallel the same RNA extracted from the neuroblastoma cell lines and the MAQC samples were used for conventional cDNA synthesis using the iScript cDNA Synthesis Kit according to the manufacturer's instructions (Bio-Rad).A qPCR assay was designed for each gene [Additional files See [Additional file In order to assess the influence of the amount of input RNA on the yield of pre-amplified cDNA we measured the expression of ten reference genes after pre-amplification starting from 5, 15 or 50 ng as input RNA from three cultured neuroblastoma cells and UHRR. Figure MYCN single copy (MNS) and three MYCN amplified (MNA) neuroblastoma cell lines, we first measured the expression of 10 known differentially expressed genes  before ed genes  before aed genes . QualityNEUROD1; RTPrimerDB ID 8113 [NEUROD1 could be observed in the pre-amplified cell lines spiked with DNA as resulting DNA concentration after a 200× dilution of the pre-amplified product is lower than 0.5 pg/μl, which is below the detection level for qPCR. Moreover, the Cq-value of NEUROD1 was equal in the HGDNA that had undergone the above described pre-amplification procedure and in the HGDNA used as positive control. These results indicate that DNA is not co-amplified (data not shown).In order to determine if residual DNA in the RNA extract is co-amplified and consequently might confound the results, we pre-amplified pure human genomic DNA (HGDNA) and two RNA samples from neuroblastoma cell lines verified for absence of DNA and subsequently spiked with 1% and 10% HGDNA (2 ng DNA per 20 ng RNA input for pre-amplification) (Roche). We next performed qPCR with a DNA-specific primer pair ( ID 8113 ) and useTo determine if purification of the pre-amplification product is required we performed a SPUD assay as described in [additional file In a last step of the evaluation of the necessity of pre-amplification clean-up, we measured the expression of ten reference genes in ten samples before and after pre-amplification. Comparison of the cumulative distribution plots of the ddCq-values obtained on purified and non-purified pre-amplified product showed that the plots almost completely overlap, providing further evidence that purification is not required [Additional file In order to assess the RNA quality of 738 neuroblastoma tumour samples, we performed a capillary gel electrophoresis analysis to establish an RQI. All samples were pre-amplified and qPCR was performed to measure the expression of two low abundant universally expressed reference genes (SDHA and HPRT1) [Additional file An import limitation of gene-expression analysis in the current diagnostic workflow is the fact that often minimal amounts of biomaterial are procured. As such, in many cases only a few nanograms of total RNA are available. In order to measure a large number of genes on this limited material and to maximize the number of samples through collaborative studies, a robust sample pre-amplification method is required. In this study we evaluated the linear isothermal Ribo-SPIA pre-amplification method for qPCR-based gene-expression analysis in cancer cell lines and commercially available reference samples, optimized the pre-amplification workflow, and used the method in a large clinical sample set.First, we could clearly demonstrate that differential expression is preserved after pre-amplification and that no substantial bias is introduced. The fold-changes between pre-amplified samples were compared to those observed between non-amplified samples in the largest set to date , revealing an accurate preservation of relative transcriptome composition despite the pre-amplification process. This is in accordance with previously reported findings on smaller datasets using qPCR ,26. HoweWe also assessed the need of DNase treatment before and of purification after pre-amplification. The results obtained show that neither of these procedures is required. This is an important finding, especially in large-scale gene-expression studies, as both techniques are time-consuming and add a substantial cost to the experiments. Furthermore, DNase treatment may lead to a loss of material and of mRNA integrity due to the exposure of the RNA samples to a high temperature during heat inactivation required for many commercial DNases.SDHA and HPRT1). As expected, pre-amplification of highly degraded samples turned out to be unsuccessful. In addition, there was a negative correlation between the Cq-values of the reference genes and the RQI. A possible explanation for the imperfect negative correlation is the use of random primers in the RNA pre-amplification process, resulting in successful pre-amplification of partially compromised RNA samples.Monitoring RNA quality and using intact RNA is of critical importance to obtain reliable gene-expression data and to ensure reproducibility of the results ,28. In tAs the tumour sample size is often very limited, the applied RNA pre-amplification procedure offers the possibility to perform large multicenter studies. This enabled us to establish and validate a robust prognostic multigene-expression signature in the largest neuroblastoma study cohort till now . MoreoveAn additional advantage of the evaluated pre-amplification method is its potential usefulness to generate a sufficient nucleic acids concentration for use in ultra high-throughput qPCR systems. These systems operate with very low volumes and have the potential disadvantage of compromised detection sensitivity as only limited volumes of nucleic acids can be added. As the concentration of the pre-amplified material is very high, this technique may offer a solution and should be evaluated in future studies.In conclusion, the results obtained from this study indicate that differential gene-expression is preserved after sample pre-amplification using the linear isothermal Ribo-SPIA pre-amplification method, that DNA is not co-amplified, that a pre-amplification clean-up step is not required, and that the pre-amplification product is free of enzymatic inhibitors. Application of this unbiased and straightforward pre-amplification technology offers a great advantage in terms of accessibility of material for diagnostic and prognostic work-up and enables large-scale qPCR gene-expression studies.MYCN amplified; MNS: MYCN single copy; RNA: Ribonucleic acid; RQI: RNA quality index (determined by microfluidic capillary electrophoresis as a measure for RNA integrity); RT-qPCR: reverse transcription quantitative polymerase chain reaction; UHRR: Universal Human Reference RNA.Cq: quantification cycle; dCq: difference in quantification cycle or delta-Cq ; ddCq: difference in dCq or delta-delta-Cq  and interpretation of the data. KDP participated in the data analysis. FPT and SLF participated in the design and validation of the primers. ADP, FSP and JVS coordinated the study. All authors contributed to the revision of the manuscript for important intellectual content. All authors read and approved the final version of the manuscript.RDML file 1. Primer sequences of the MYCN and MYCN regulated genes and raw data from the expression analyses on the 6 neuroblastoma cell lines.Click here for fileRDML file 2. Primer sequences of the MAQC target genes and raw data from the expression analyses on the 4 MAQC reference samples.Click here for fileSupplemental Material and Methods. Details on sample preparation, gene-expression analysis, formulas and raw data availability.Click here for fileSupplemental Figure S1. Frequency distribution (left-axis) and cumulative frequency (right-axis) of the difference in quantification cycle value (dCq) induced by sample pre-amplification (x-axis) for 194 genes measured in the 4 MAQC reference samples. There is a clear sequence-specific pre-amplification bias, meaning that some sequences or parts of transcripts pre-amplify better than others.Click here for fileSupplemental Figure S2. SPUD assay for the detection of enzymatic inhibitors in purified pre-amplified samples (P) and in non-purified pre-amplified samples (NP) with negative control (NC) and positives controls with known inhibitor (PC). Difference in Cq or delta-Cq (dCq) (NP or P vs. NC) < 1 indicates absence of enzymatic inhibitors.Click here for fileSupplemental Figure S3. PCR efficiencies estimated with two different single curve efficiency algorithms, PCR Miner (red) and LinReg (blue). Efficiencies of purified  and non-purified  pre-amplified samples for each gene are comparable indicating that non-purified pre-amplified samples do not contain inhibitors and amplify with the same PCR efficiency.Click here for fileSupplemental Figure S4. Cumulative distribution plot of the delta-delta Cq (ddCq) before and after pre-amplification for 10 reference genes and 10 samples without purification of the pre-amplified product (black) and with purification of the pre-amplified product (grey). Each dot represents a ddCq-value between 2 samples before and after pre-amplification. Purification is not required for the preservation of differential expression.Click here for fileRDML file 3. Primer sequences of HPRT1 and SDHA and raw data from the expression analyses on 738 neuroblastoma tumour samples.Click here for file


Gene expression profiling of small numbers of cells requires high-fidelity amplification of sub-nanogram amounts of RNA. Several methods for RNA amplification are available; however, there has been little consideration of the accuracy of these methods when working with very low-input quantities of RNA as is often required with rare clinical samples. Starting with 250 picograms-3.3 nanograms of total RNA, we compared two linear amplification methods 1) modified T7 and 2) Arcturus RiboAmp HS and a logarithmic amplification, 3) Balanced PCR. Microarray data from each amplification method were validated against quantitative real-time PCR (QPCR) for 37 genes.For high intensity spots, mean Pearson correlations were quite acceptable for both total RNA and low-input quantities amplified with each of the 3 methods. Microarray filtering and data processing has an important effect on the correlation coefficient results generated by each method. Arrays derived from total RNA had higher Pearson's correlations than did arrays derived from amplified RNA when considering the entire unprocessed dataset, however, when considering a gene set of high signal intensity, the amplified arrays had superior correlation coefficients than did the total RNA arrays.Gene expression arrays can be obtained with sub-nanogram input of total RNA. High intensity spots showed better correlation on array-array analysis than did unfiltered data, however, QPCR validated the accuracy of gene expression array profiling from low-input quantities of RNA with all 3 amplification techniques. RNA amplification and expression analysis at the sub-nanogram input level is both feasible and accurate if data processing is used to focus attention to high intensity genes for microarrays or if QPCR is used as a gold standard for validation. Expression array analysis has provided valuable new insights into the biology and pathophysiology of many cancers -4. HowevFeasibility and reproducibility has been established for linear amplification from nanogram to low microgram input quantities of total RNA to yield high fidelity gene amplification products suitable for gene expression microarray analysis -16. In aTo test our hypothesis, serial dilutions of stock RNA was employed for a comparison of 3 amplification techniques with assessment by technical replicates of microarrays and with subsequent validation by QPCR of both total and amplified RNA.Total RNA was prepared from the BT474 cell line and Stratagene Universal Human Reference RNA (StratRef)  using the Arcturus PicoPure RNA isolation kit  as per manufacturer's instructions. StratRef is composed of total RNA from 10 human cell lines and is designed to be used as a reference for microarray gene-profiling experiments. Serial dilutions of StratRef and BT474 RNA served as the substrate for all amplification reactions to minimize sources of variability.24NN  in the presence of 10 μg of random hexamer (Amersham Pharmacia) and oligo d(T)in vitro transcription (IVT) based on a modified T7 amplification[in vitro transcription were performed[Total RNA from the BT474 breast cancer cell line and StratRef was linearly amplified through two rounds of ification. Reverseification, howeverification. Second performed. Samplesin vitro transcription according to the manufacturer's published instructions . Their protocol specified that a minimum input of 100–500 picograms of total RNA are required for successful amplification with this kit designed specifically for low-input total RNA samples, which is equivalent to 10–50 cells. Per the manufacturer's specifications, 200 ng of Poly dIdC nucleic acid carrier was added to each reaction. IVT reaction time was 6.5 hours.Total RNA from the BT474 breast cancer cell line and StratRef , was linearly amplified through two rounds of Total RNA from the BT474 breast cancer cell line and StratRef , was reverse transcribed using separate oligo dTT7 primers, pooled and exponentially amplified in the same PCR tube. BrieflyTo 20 μl of purified-ligated DNA, we used the Advantage 2 PCR Polymerase system as per manufacturer's instructions , dNTP mix 10 mM ea.), 1 μl of 10 μM common primer P1  than the linear amplifications , an aliquot of RNA from the same tube of StratRef RNA and an aliquot of the same preparation of BT474 RNA as was used to minimize input variability. Reverse transcription of this RNA was performed before shipping the cDNA on dry ice for the subsequent Balanced PCR reactions. Coupling, array hybridization and analysis were all performed in the Haqq lab, as were all other techniques described.The molecular weight profile and integrity of each amplified RNA/DNA species was evaluated using the Agilent Bioanalyzer 2100  with a RNA 6000 Pico Lab Chip.1.25 μg of amplified RNAs (aRNAs) produced with Modified T7 and Arcturus RiboAmp HS were converted to amino-allyl modified cDNA and coupled to N-hydroxysuccinimidyl esters of Cy3 or Cy5 . The BalThe 20,862 cDNAs used in these studies were from Research Genetics , now Invitrogen . On the basis of Unigene build 166, these clones represent 19,740 independent loci. Hybridization, washing, scanning and primary data analysis was performed as previously described,24. A toGene expression was analyzed with Cluster using thcDNA was made from total RNA for both BT474 and StratRef, in 100-μL reactions using M-MLV reverse transcriptase and random hexamers incubated at 25°C for 10 min then 48°C for 30 min. Expression of each gene was analyzed using the 5' nuclease assay with theThe mean expression ratios for each of the 3 amplification techniques and for the total RNA method were calculated for each technique. Analysis A is defined as the 37 QPCR genes, Analysis B, the high intensity dataset, is defined as all genes that had a 635 Median Intensity OR a 532 Median Intensity > 1500. Analysis C, the unfiltered dataset, is defined as all of the genes on the microarray that had data present for 90% of the arrays. Sensitivity, specificity and percentage correct were calculated for each method in Analysis A using QPCR results as the gold standard. All pairs of correlation coefficients for Analysis B and C were used to perform intramethod and intermethod comparisons of mean correlation coefficients for all methods using expression results from microarrays of unamplified RNA as the gold standard.Figure The lower limits of total RNA required for each method were defined as the lowest RNA input amount where amplification reactions consistently yielded sufficient product to permit analysis on cDNA microarrays and confirmatory functional assays. These were 500 pg for modified T7, 250 pg for Arcturus RiboAmp HS, and 500 pg for balanced PCR Table . All 3 tThe size of the amplified products ranged from 100–4400 bases for all attempted amplifications. For the modified T7 method, after two rounds of amplification the product measured a mean of 3400 bases on the Agilent Bioanalyzer PicoChip. For Arcturus RiboAmp HS, after two rounds of linear amplification the product measured a mean of 3372 bases on the Agilent Bioanalyzer PicoChip. The Balanced PCR products were evaluated on a 1% Agarose gel and measured over 3500 bases (data not shown). Amplification reactions that did not yield the requisite 1.25 ug of RNA were considered unsuccessful; all successful reactions were validated with the Agilent Bioanalyzer and none required study exclusion on that basis.The Pearson's correlations and standard deviations for intra-method and inter-method comparisons for the high intensity genes are shown in Table Table QPCR of amplified RNA was correlated with the QPCR of total RNA for all methods with R2 of 0.87 for modified T7, R2 0.86 for Arcturus RiboAmp HS and R2 0.75 for balanced PCR.When dealing with clinical samples, microarray results are often validated with QPCR; therefore techniques that demonstrate a low false expression result (FER) by this type of analysis are very desirable. We defined a FER as number of discordant values divided by number of genes analyzed for expression ratio by microarray compared to QPCR using unamplified total RNA. Both array and QPCR measurements were normalized to levels of β-glucuronidase. As shown in Table When we compared QPCR of amplified to total RNA for each method , the FER was 0% (0/21 genes) for Arcturus, 4.5% (1/22 genes) for modified T7 and 15% (3/19 genes) for Balanced PCR.Not surprisingly, the unfiltered arrays showed poorer overall correlation even between replicate arrays than did the analysis filtered for high intensity spots. Even the unamplified arrays had correlation coefficients somewhat lower than expected when array data was unfiltered, indicating that the filtration process removes the effect of background and nonspecific hybridization on the cDNA arrays. The filtration process did not change the individual expression ratios but rather focused the analysis on genes in which the hybridization gave a particularly strong signal, a strategy that seems logical when attempting to work with very rare clinical specimens to ensure a lower false call rate. Other groups have reported analysis of total RNA arrays with input as high as 20–40 ug per microarray, which may have improved the results for our total RNA arrays That beiRNA amplification technologies serve translational clinical research well. Linear amplification already has enabled examination of gene expression in clinical core needle biopsies, surgicaWhile each method was able to provide data in the sub-nanogram range, certain methods are advantageous over others in terms of lower limit of RNA that can reliably be amplified, cost per reaction and the number of days required for processing of samples. The Arcturus RiboAmp HS method was more reliable at generating expression arrays at the threshold of below a nanogram of total RNA. For modified T7 and balanced PCR, a nanogram of total RNA will ensure reliability. The Arcturus amplification procedure was able to produce very substantial amounts of nucleic acids from just 250 pg of total RNA and therefore should be considered more reliable than the other two methods at lower input thresholds. Caretti et al. performed a comparison of amplifications of a colon biopsy subjected to laser capture microdissection with purification of an estimated 1 nanogram of RNA per specimen; they compared the two cycle Arcturus OA and the one cycle Nugen amplification and found that the Arcturus method showed the lowest variance and highest correlation. HoweverShearstone et al performed a comparison of their laboratory's novel IVT amplification, termed BIIB, to Arturus Ribo Amp HS, Nugen Ovation, Affymetrix One and Affymetrix Two Cycle. Their mOur paper describes a platform independent measure, the FER, useful in comparing amplification methods based on QPCR versus microarray. FER was calculated based on QPCR of total RNA rather than amplified RNA, since there was not sufficient amplified product available for performing QPCR on all 37 primer probe pairs. In addition, QPCR of amplified RNA is biased towards recapitulating the results of the microarray experiment due to truncation of the RNA products.Each of the three amplification techniques yielded fairly consistent expression results within the constraints of each technique's input threshold of total RNA, both on microarray analysis and when compared to QPCR. Based on these excellent correlations, it is feasible to reproducibly perform high fidelity amplifications by a variety of techniques when starting with sub-nanogram input quantities of total RNA. However, when attempting expression arrays analysis from less than 500 pg input, linear amplification with Arcturus RiboAmp HS was more successful than the other methods studied.Below 1 ng, the modified T7 method could not reproducibly amplify such that insufficient RNA was typically generated for even a single microarray hybridization. Only by performing multiple attempts at amplification were we able to achieve successful amplified product with this technique. While we were successful in hybridizing 3 arrays with this method at 500 pg we do not recommend this method below 1 ng of input total RNA as several operators quite experienced with this method could not repeat these results. One drawback of this technique is the greater length of time involved (3 days) compared to other amplification reactions (2 days) and the relative complexity of the protocol.Arcturus RiboAmp HS was able to provide expression array data at a lower input concentration than any of the other tested methods and we were able to use smaller amounts than the manufacturer's recommended minimum sample input of 500 pg total RNA. Below 250 pg, this method typically failed to amplify in our hands . This likely represents a theoretical limit of 25 cells total RNA content , unless specialized tissues that bear more RNA such as oocytes are examBalanced PCR is a promising technique for the amplification of low-input quantities of RNA. It maintains a high degree of accuracy with an input as low as 667 pg of RNA (FER 10.8–13.5%). While theoretical concern exists regarding the accuracy of logarithmic amplification methods, this method overcomes the potential problem by stopping the PCR reaction before the logarithmic phase of the PCR curve. This method had the lowest cost per reaction and also required the least amount of technician time compared to the other methods. In addition, it has been recently demonstrated that the same balanced-PCR protocol used for cDNA amplification may also be used for the unbiased amplification of whole genomic DNA followed by array-CGH analysis. HoweverThese results have certain important limitations to consider. First, hybridizations were carried out sequentially over a period of several months rather than all at the same time. It is recognized that arrays that are hybridized together under identical conditions are more similar to each other than arrays hybridized on separate occasions. Additionally, no dye swap experiments were performed. The rationale for this is that since a standardized universal reference (StratRef) was employed, it has been demonstrated that this mitigates the effect of potential experimental bias introduced by separate hybridization reactions and even permits the comparisons of array data between members of the research community. Use of Each laboratory will have to weigh their decision on which amplification technique is most suitable based on factors including amount of starting input total RNA, cost per reaction, technician time, and experience/comfort level with the techniques. Laboratories that routinely work with samples in excess of 1 ng starting material should focus on cost-savings as each of the methods tested proved to be reliable above this threshold. Balanced PCR could be further optimized to include amino-allyl-dUTP incorporation in the PCR reaction. This would facilitate indirect Cy dye labeling, which would reduce the labeling cost for this method.It is important to ascertain the linearity of a chosen method at the low input range before going on to work with precious clinical specimens. Each of the 3 tested methods performed surprisingly accurately when amplifying from low inputs of total RNA based on microarray analysis validated with QPCR of 37 genes.We have demonstrated that it is feasible to reliably and accurately perform expression profiling from sub-nanogram quantities of total RNA. These methods will likely enable exciting new directions for molecular analysis of samples previously considered to be of insufficient quantity of total RNA for expression profiling. The data processing and filtration of microarray results is of fundamental importance when attempting analysis of amplification reactions from sub-nanogram input amounts of total RNA.(QPCR): quantitative real-time PCR; (SMART PCR): switching mechanism of 5'end of RNA template PCR; (StratRef): Stratagene Universal Human Reference RNA.JEL developed the study design, carried out the preparation of RNA, the total RNA array hybridizations, the Arcturus and modified T7 RNA amplifications and hybridizations, quantification and assessment of transcript integrity, coordinated efforts with the laboratory of GMM, participated in data analysis, drafted the manuscript and performed critical revisions. MJM participated in data analysis and critical revision of the manuscript. JS performed the QPCR and provided the BT474 cell line. GMM and GW performed the balanced PCR reactions. LJE participated in study design and conception. JWP participated in study design and conception. CMH and his laboratory provided the modified T7 protocol, conducted gene expression array quality control and assisted with data analysis. CMH participated in study design.Table S2. QPCR genes.Click here for fileFigure S1: Dynamic Range of QPCR Probes for BT474 Versus StratRef. The delta delta CT of our QPCR probes covered a dynamic range of negative 21 to positive 9 and were selected without bias towards any of the amplification techniques.Click here for fileTable S1-8. SAM Analysis.Click here for file


Microarray technology provides a powerful tool for defining gene expression profiles of airway epithelium that lend insight into the pathogenesis of human airway disorders. The focus of this study was to establish rigorous quality control parameters to ensure that microarray assessment of the airway epithelium is not confounded by experimental artifact. Samples  of trachea, large and small airway epithelium were collected by fiberoptic bronchoscopy of 144 individuals and hybridized to Affymetrix microarrays. The pre- and post-chip quality control (QC) criteria established, included: (1) RNA quality, assessed by RNA Integrity Number (RIN) ≥ 7.0; (2) cRNA transcript integrity, assessed by signal intensity ratio of GAPDH 3' to 5' probe sets ≤ 3.0; and (3) the multi-chip normalization scaling factor ≤ 10.0.Of the 223 samples, all three criteria were assessed in 191; of these 184 (96.3%) passed all three criteria. For the remaining 32 samples, the RIN was not available, and only the other two criteria were used; of these 29 (90.6%) passed these two criteria. Correlation coefficients for pairwise comparisons of expression levels for 100 maintenance genes in which at least one array failed the QC criteria (average Pearson r = 0.90 ± 0.04) were significantly lower (p < 0.0001) than correlation coefficients for pairwise comparisons between arrays that passed the QC criteria (average Pearson r = 0.97 ± 0.01). Inter-array variability was significantly decreased (p < 0.0001) among samples passing the QC criteria compared with samples failing the QC criteria.Based on the aberrant maintenance gene data generated from samples failing the established QC criteria, we propose that the QC criteria outlined in this study can accurately distinguish high quality from low quality data, and can be used to delete poor quality microarray samples before proceeding to higher-order biological analyses and interpretation. The assessment of gene expression of the human transcriptome using microarray technology is a powerful tool for identifying genes and gene expression patterns involved in mechanisms of normal organ function and the pathogenesis of disease -3. MicroWhile it is easy to obtain the cells, the output from microarray data critically depends on the quality of the RNA and the cRNA derivatives hybridized to the microarray -27. AlthSome of the results of these studies have been previously reported in the form of an abstract .6 cells were recovered from trachea, large airway and small airway in all five pulmonary phenotypic groups and cell counts were not dependent upon phenotype of the subject or site of bronchial brushing (p > 0.05 by ANOVA). From all locations, an average of 99 to 100% of all cells recovered were epithelial with less than 1% contamination by non-epithelial cells. The cell differentials varied depending on location as previously described [A total of 223 samples of airway epithelium were obtained by bronchial brushing from three different locations  from 144 subjects with 5 different pulmonary phenotypes . For the GAPDH 3'/5' signal intensity ratio and scaling factor criteria, all 223 samples were included.Of the 223 samples, all three criteria were assessed in 191; of these 184 (96.3%) passed all three criteria. For the remaining 32 samples, the RIN was not available, and only the other two criteria were used; of these 29 (90.6%) passed these two criteria. Only 10 (4.5%) failed at least one QC criterion, and were therefore considered to have failed QC. The overall breakdown of samples failing QC was: 2 large airway samples  and 8 small airway samples . The greatest source of failure was the scaling factor criterion, which contributed to 70% of the overall failures. All of the 10 samples failing the QC criteria failed the RIN and/or scaling factor criterion, indicating that these metrics may be the most sensitive to technical variance, and therefore are central to assessing overall array quality. While 7 samples failed by one criterion each, 1 sample failed by both the RIN and GAPDH 3'/5' ratio criteria, and 2 samples failed by both the RIN and scaling factor criteria, suggesting that the quality control parameters exert correlated effects on array performance.The RNA quality was examined by the Bioanalyzer-generated RIN score in 191 samples for which there was data available (see above). Based on published data ,31-33, s2 = 0.92; p < 0.0001), application of addition cutoff criteria beyond GAPDH was considered redundant. In the context of airway epithelium, although GAPDH is not an ideal "housekeeping" gene as its expression may vary under different conditions, this does not interfere with its use in assessing cRNA quality [As a metric for the efficiency of transcription and amplification of antisense cRNA from the cDNA derivative of the starting RNA material, the signal intensities for the probe sets for GAPDH residing at the 5' end and within the 600 nucleotides most proximal to the priming site at the 3' end of the transcript were compared. For all samples hybridized to microarrays, 3' to 5' probe set intensities for the GAPDH gene were extracted to compute the 3'/5' signal intensity ratio. Based on published data ,23,34-36 quality .The scaling factor was used as an overall index of the microarray hybridization, washing, and scanning process. Scaling factor values for all 223 samples computed at a target intensity value of 500 were examined. The criterion of scaling factor values ≤ 10.0 was established . Correlation coefficient values indicated that samples passing QC criteria were highly correlated with other samples passing QC criteria (average Pearson r = 0.97) while samples failing QC criteria showed lower correlations with all other samples . By contrast, across the "fail" data set, the median coefficient of variation for the 100 genes was 35.7% .Of the 24 samples passing QC criteria that were used for the correlation matrix analysis, 10 samples matched in airway location with the 10 samples failing QC criteria were selected to assess coefficient of variation of each of the 100 maintenance genes. Expression levels for the 100 maintenance genes showed significantly greater variability among the 10 samples failing QC criteria ("fail" data set) than among the 10 samples passing QC criteria , was as2<0.05 for all genes) with age (average 45 ± 8.8) across the 144 individuals from whom airway epithelium was derived. None of the genes showed strong correlation (r2<0.15) with smoking history (average pack-yr 30 ± 18). Correlation analysis of expression levels with pulmonary function parameters showed no relationship .To examine potential causes of the variation in maintenance gene expression levels unrelated to the QC criteria, differences among the subjects were assessed. The 223 airway epithelial samples acquired for this study were derived from 144 individuals, as it was possible for a single individual to undergo bronchial brushing at one or more of the three target sites: trachea, large airway, and small airway. By independent linear regression, there was no correlation of gene expression level for the 100 maintenance genes  was used to compared samples that passed QC to those that failed. For this analysis, an independent set of microarray data that failed QC was available from a technician training program in the Weill Cornell Medical College Department of Genetic Medicine. From this training program, 11 microarrays that failed QC were available from small airway epithelium samples collected from individuals with COPD . The data from these 11 samples was compared to microarray data from n = 11 samples  from the small airway epithelium of individuals with COPD that passed all QC criteria  of trachea, large airway and small airway were obtained from healthy subjects and from subjects with lung disease, including smokers and non-smokers, to assess quality control criteria for microarray analysis. Using Affymetrix Human Genome U133 Plus 2.0 arrays, a tripartite QC cutoff was established consisting of: (1) RNA quality, assessed by RNA Integrity Number (RIN) ≥ 7.0 using Agilent 2100 Bioanalyzer software; (2) cRNA transcript integrity, assessed by signal intensity ratio ≤ 3.0 of GAPDH 3' to 5' probe sets; and (3) the multi-chip normalization scaling factor ≤ 10.0. Of the 223 samples, 10 failed one or more of the QC criteria in a way that did not depend on phenotype of the subject or location of sampling. By using the QC cutoff criteria, the inter-array variability, as assessed by the coefficient of variation in the expression levels for 100 maintenance genes, decreased significantly. These QC criteria should be applicable to minimize experimental variation in gene expression microarray experiments.vs formalin-fixed paraffin-embedded (FFPE) pelleted human bone marrow stromal cells, despite all RNA samples having equivalent and comparable 28s/18s ratios as visualized by computerized gel electrophoresis, more than twice as many genes were identified as expressed in snap frozen cells than in formalin-fixed paraffin-embedded cells, reflecting possible RNA quality effects in play that were not captured by quantitative assessment of the rRNA subunit peak heights [We have previously utilized the 28s/18s rRNA peak ratio, as calculated by electropherogram, to verify quality of RNA samples prior to microarray hybridization . However heights .Since the implementation of the Agilent Bioanalyzer RIN software, we have relied on the RIN as the primary indicator of RNA integrity, based on published data showing that the RIN accounts for numerous properties of the RNA degradation process to provide an unambiguous and comprehensive index of the overall quality of the starting material ,41,43,44in vitro transcription reactions and hybridization has the potential to save substantial costs in wasted reagents and technical time.Illustrating the predictive power of the RIN as a pre-chip criterion, linear regression modeling and ordinary least squares linear regression have shown that the scaling factor and GAPDH 3'/5' signal intensity ratio are negatively correlated with the RIN value . InteresPublished recommendations for an acceptable range of scaling factors computed at the same target intensity value vary in numerical fold cutoffs, or alternately, suggest all values within 2 standard deviations from the mean in either direction ,34,35. Hin vivo tissue from which sample RNA is limiting and alternate technical procedures are utilized, the scaling factor metric can be useful to assess the impact of technical artifact, and the quality of the expression data. For example, in an analysis of small sample RNAs from rat liver, significantly increased scaling factor values indicated that the amplification technique used contributed to technical variability in the form of a substantial decrease in the percent of transcripts detected on the array [In gene expression profiling studies of samples obtained from biopsies, cell sorting, or laser capture microdissection, yields of cellular RNA are often small quantities  and require specialized amplification methods to generate sufficient biotinylated cRNA for array hybridization ,54. In the array . In conthe array .Despite large amounts of published lung gene expression data, there is often little attention focused on microarray quality control, with the consequent risk of skewing the data by including poor quality arrays in the analysis ,57-63. Fin vitro transcription and amplification reagent used, and the date of array hybridization. These factors may contribute to batch effects, where the overall intensity of a batch of microarrays more closely resembles the batch than the rest of the group of arrays [One methodology for testing data integrity is that of unsupervised hierarchical sample clustering based on Spearman correlations-based distance metric ,65. The f arrays ,66. WhilAnother strategy often used for differentiating high quality from low quality microarray data is based on outlier status of any given sample in an experiment. Software packages such as dChip  and Probe Profiler can identify intensity outliers of a sample in a group of microarrays, and take into account such features of the array hybridization such as brightness, saturation, dynamic range, and background -67. The The current study provides an efficient and simple approach for quality assessment of gene expression microarray data. It emphasizes good experimental execution and discarding unsatisfactory microarrays rather than salvaging data through complex statistical analyses of array data of variable quality. We provide a standardized tripartite criteria specifically addressing starting RNA quality, integrity of the cRNA transcript, and hybridization efficiency. Each parameter has been assigned a threshold value, outside of which samples are readily identifiable as being low quality and can be eliminated or re-hybridized before proceeding to analysis. All measures are available through Agilent Bioanalyzer software and the Affymetrix GCOS report automatically generated after array washing and scanning. Although the Agilent Bioanalyzer and Affymetrix platforms are widely used, analogous criteria may be applied for alternate methodologies. For example, assessment of the relative signal for probes representing the 3' and 5' ends of any mRNA could be included as QC for any microarray platform.In the context of data sharing via public repositories, the criteria presented in this study has the benefit of including two parameters that are guaranteed to be available for any Affymetrix data deposited in GEO. The initial processing by GCOS of CEL files produces a Quality Report containing the 3'/5' GAPDH signal intensity ratio and a multi-chip normalization scaling factor for the array. The GCOS software is available for free download from Affymetrix and can be applied by all investigators. In this way, two of the three QC criteria discussed in this paper provide a consistent quality control approach to not only current data, but also to previously published, archived data. Even though the RIN criterion as applied here requires specialized equipment and software, the RIN can be indirectly predicted from the 3'/5' ratio which is extracted from the CEL files deposited in GEO .In the context that minimizing undesirable technical variation allows for more accurate analysis of gene expression and increased power for significance testing, we propose that the simple method described here, consisting of a universally available set of three criteria, can ensure that microarray data reflects biological differences as opposed to experimental variability.nd-3rd order bronchi) and small airway (10th-12th order bronchi) in five phenotypic groups  and flicking five to ten times. An aliquot of 0.5 ml was used for differential cell count and the remainder (4.5 ml) was centrifuged at 6,000 rpm for 10 minutes within less than 60 minutes from the time of bronchial brushing. Pelleted airway epithelial cells were lysed with the TRIzol reagent , and after chloroform extraction the RNA was purified directly from the aqueous phase using the RNeasy MinElute RNA isolation kit . For each sample, 1 μl of RNA was used for quantification of yield by NanoDrop ND-1000 spectrophotometer  and quality assessment by Agilent 2100 Bioanalyzer software. The samples were stored in RNA Secure  at -80°C until time of biotin-labeled cRNA preparation.Fiberoptic bronchoscopy was performed to obtain pure populations of tracheal, large and small airway epithelium by using methods previously described ,29,73. Bin vitro transcription reaction and the Genechip Sample Cleanup Module was used for cleanup of the biotin-labeled cRNA . Final yield of biotin-labeled cRNA was confirmed by NanoDrop spectrophotometric analysis. For each sample, 10 μg of biotin-labeled cRNA was fragmented, and hybridized to the Human Genome U133 Plus 2.0 array  according to Affymetrix protocols, processed by the Affymetrix GeneChip Fluidics Station 450 and scanned with an Affymetrix GeneChip Scanner 3000 7G , as previously described [th percentile of all measurements on that array. All microarray data has been deposited at the Gene Expression Omnibus (GEO) site .Double stranded cDNA was synthesized from 1.0 to 2.0 μg of total RNA using the GeneChip One-Cycle cDNA Synthesis Kit, followed by cleanup of the double stranded product with the GeneChip Sample Cleanup Module. The GeneChip IVT Labeling Kit was used for the 16 hr The selection of the three QC criteria was targeted towards addressing quality control in the three integral stages of the microarray process: (1) extraction of the starting RNA material; (2) synthesis of cDNA and antisense biotin-labeled cRNA target; and (3) the array hybridization efficiency.An RNA Integrity Number (RIN) for each RNA sample in this study was generated by an Agilent Bioanalyzer algorithm that uses a Bayesian approach to train and select a prediction model incorporating features extracted from an electropherogram including pre-region, 5S-region, fast-region, 18S-fragment, inter-region, 28S-fraction, precursor-region, and post-region ,44. RIN in vitro transcription of cRNA can result in under representation of the 5' moiety of the transcript [Per Affymetrix guidelines, the ratio of the 3' to 5' signal intensity values can be used as a method of quality control for the array data ,35,36,75anscript ,52. In aanscript ,34,35. TAccording to Affymetrix microarray guidelines, comparable scaling factors between arrays in a given experiment are critical to minimizing differences in overall signal intensities, thereby allowing for more reliable detection of biologically relevant changes ,52. Basea priori knowledge that they exhibit relatively low signal variation over different sample types and are consistently called Present in a large number of different tissues and cell lines . In the present study, for notational convenience, we use the term "gene" in place of "probe set", as each one of the 100 probe sets represents a different gene.Samples that failed any one of the three criteria described were considered to have failed the QC criteria, while those samples that passed all three criteria were considered to have passed the QC criteria. To confirm the validity of this quality assessment strategy, expression levels were determined for a set of 100 constitutively expressed maintenance genes and differences in gene expression profile for these genes were compared between the samples failing the quality control criteria and the samples passing the QC criteria. The set of control genes was selected by Affymetrix using the To examine potential causes for variation in QC criterion values between samples, the effects of differences in phenotype or biologic origin of the sample were assessed by ANOVA.U test.In regard to the maintenance genes we used Pearson's correlation to assess correlation in expression levels for the 100 maintenance genes among the 10 samples  that failed the QC criteria and 24 randomly selected samples that passed QC criteria  . The ide® Genomics Suite software (version 6.8 Copyright© 2008) for 11 COPD subjects who failed chip quality control and 11 COPD subjects who passed . Affymetrix HG-U133 Plus 2.0 CEL files were imported into Partek using the Robust Multi-chip Average (RMA) method. All 54,675 log2-transformed small airway gene expression data were mapped to principal components to preserve the variation of this data, projected in 3 dimensions, and plotted. In order to identify the specific probe sets that were differentially expressed between the two groups, microarray data were processed using the MAS5 algorithm (Affymetrix Microarray Suite Version 5 software), which takes into account the perfect match and mismatch probes. MAS5-processed data were normalized using GeneSpring by setting measurements <0.01 to 0.01 and by normalizing per chip to the median expression value on that array and, per gene to the median expression value for each gene across all arrays. Genes that were significantly differentially expressed between the two groups were selected according to the following criteria: (1) P call of "Present" in 20% of samples; (2) magnitude of fold change in average expression value for pass QC vs fail QC of >1.5; and (3) p < 0.01 using a t test with a Benjamini-Hochberg correction to limit the false positive rate [To compare gene expression profiles in samples that passed QC to those that failed QC, principal components analysis was carried out using Partekive rate .TR conducted the microarray experiments, performed the analysis and helped draft the manuscript. TO and NH participated in study design and statistical analysis, supervised the data collection and helped draft the manuscript. WW assisted with data analysis and study design BGH coordinated the collection of all of the clinical samples used in the microarray analysis MA, DD, and MT processed samples and conducted microarray experiments RGC conceived of the study, and assisted in study design and helped to draft the manuscript.All authors have read and approved the final version of the manuscript. Demographics of the Study Population and Biologic Samples for the Comparison of Fail QC or Pass QC Data. All subjects from both groups were smokers with COPD. The "passed" and "failed" groups are comprised of samples that passed or failed, respectively, the QC criteria. Data is presented as mean ± standard deviation.Click here for fileSignificant Genes in the Small Airways Epithelium of Smokers with COPD Between Chips that Failed QC and Chips that Passed QC. Shown are the 888 probe sets that are differentially expressed (using criteria of a fold change greater than 1.5 and a p value, with Benjamini-Hochberg correction, less than 0.01, in n = 11 pass QC samples and n = 11 fail QC samples, all from the small airway epithelium of individuals with COPD.Click here for file