Primary human tissues are an invaluable widely used tool for discovery of gene expression patterns which characterize disease states. Tissue processing methods remain unstandardized, leading to unanswered concerns of how to best store collected tissues and maintain reproducibility between laboratories. We subdivided uterine myometrial tissue specimens and stored split aliquots using the most common tissue processing methods  before comparing quantitative RNA expression profiles on the Affymetrix U133 human expression array. Split samples and inclusion of duplicates within each processing group allowed us to undertake a formal genome-wide analysis comparing the magnitude of result variation contributed by sample source (different patients), processing protocol (fresh vs. frozen vs. 24 or 72 hours RNALater), and random background (duplicates). The dataset was randomly permuted to define a baseline pattern of ANOVA test statistic values against which the observed results could be interpreted.14,639 of 22,283 genes were expressed in at least one sample. Patient subjects provided the greatest sources of variation in the mixed model ANOVA, with replicates and processing method the least. The magnitude of variation conferred by processing method (24 hours RNALater vs 72 hours RNALater vs. fresh vs frozen) was similar to the variability seen within replicates. Subset analysis of the test statistic according to gene functional class showed that the frequency of "outlier" ANOVA results within each functional class is overall no greater than expected by chance.Ambient storage of tissues for 24 or 72 hours in RNALater did not contribute any systematic shift in quantitative RNA expression results relative to the alternatives of fresh or frozen tissue. This nontoxic preservative enables decentralized tissue collection for expression array analysis without a requirement for specialized equipment. Many of the hopes for achieving clinical benefits of genomic medicine will hinge on the ability to develop an efficient specimen conduit from clinic to laboratory. Quantitative gene expression studies have created unprecedented tissue collection and handling challenges. In particular, the rapid degeneration of RNA, and possible perturbation of expression following excision place a high premium on prompt stabilization of tissue samples intended for expression analysis. This can be accomplished by sending a dedicated trained technologist outfitted with the necessary specialized equipment, such as liquid nitrogen, into the clinical environment. Alternatively, clinicians can be enabled to process the specimens directly in the course of patient care and send them in some stable form by unrushed and routine means for centralized processing. The latter is greatly preferred when patients are physically dispersed, and becomes essential in a multi-institutional setting.High throughput quantification of RNA expression in solid tissues has become a commonplace modality for genome-wide discovery of mechanisms of disease. Typically, groups of samples classified into comparison groups are used as a training set for expression pattern discovery, followed by validation in a fresh challenge set of annotated cases. The likelihood of success is highly dependent on the accuracy of classification within the training set, and ability to control random variables introduced during tissue processing and analytical measurement of RNA abundance. Efforts to standardize RNA quantification include sharing of information regarding probe design and use , or centFlash freezing, either by immersion in liquid nitrogen or on dry ice, is the most common means of stabilizing tissue samples intended for RNA analysis. Local access to the necessary materials and expense of cold shipping and/or storage limit these collection capabilities in most clinical settings. An additional disadvantage of frozen storage is that homogenization of frozen tissue must be accomplished rapidly to avoid the rapid RNA degeneration that occurs during thawing of a previously frozen sample.Room temperature immersion of fresh tissue samples in aqueous sulfate salt solutions (such as ammonium sulfate) at controlled pH precipitates degenerative RNAses  and otheWe studied the effects of differences between storage conditions on gene expression as measured by expression array. Duplicate uterine myometrial tissue samples from three women were processed under each of 4 fixed storage conditions – fresh, frozen, 24 hours RNA-later and 72 hours RNA-later. The 24 labeled cRNA samples Figure  were hybWe found no systematic bias in measured quantitative level of gene expression by processing method, indicating that short term storage in RNALater is a valid alternative to traditional frozen storage.3 possible ways of permuting 4 pairs of replicate samples within each subject were considered. For each of these, the F statistics were computed for each gene. To control the overall error rate, the distributions of the maximum F statistics over the genes were used. That is, for each gene, the p-value is the proportion of permutations with the maximum F statistics over all genes greater or equal to the observed value for a particular gene. A test declaring as significant any genes with p < 0.05 then guarantees that the chance of any false positives being selected is < 5%. Similar analyses were performed replacing the distribution of the maximum F statistic with the distribution of the F statistics at the 95th percentile and then at the 90th percentile. After closer examination of the 387 genes in the 5% tail, we noted that most were exhibiting expression values below 100 for all 24 samples. In fact, within a storage condition, 2 out of 3 patients exhibited null expression while the third patient showed expression values other than null but less than 100 for at least one of their replicates. Therefore, as an additional analysis, any expression values less than 100 were recoded as 100. Genes that showed expression levels of 100 across all 24 samples, and therefore lacked variability, were then removed from the analysis. This resulted in 7,853 genes for which there was at least one sample with expression level greater than 100 across all 24 samples.Of the 22,283 genes, 14,639 did not have absolutely null expression across all 24 samples. We fit the mixed model ANOVA from their log values and recorded this F statistic. The permutation distribution was used to assess the significance of F statistics calculated for each gene in the dataset. In this approach all 13,824 or (4!)Patient subjects provided the greatest sources of variation in the mixed model ANOVA, with replicates and processing method the least Figure . The magth percentile was 2.58. Corresponding p-values were 0.94, 0.55 and 0.51, respectively. The values of test statistics seen at the 95% level in a randomly permuted dataset  are evenly intermingled throughout the myometrial compartment, lending itself to physical subdivision into equivalent aliquots. This would not be possible with more complex tissues in which differing cell types are distributed asymmetrically within the specimen. Despite the equivalency of subdivided fractions that underwent varying storage treatments, it must be noted that this is a hormonally responsive tissue whose expression patterns would be expected to differ between individual women as a function of monthly changes in circulating sex hormones. We did not control for hormonal factors or indication for hysterectomy (prolapse or fibroids) but selected patients randomly. It comes as no surprise that expression differences between women, irrespective of processing method, emerged as the dominant source of inter-sample variation. This was anticipated in constructing the model, by assigning the subject source of specimens as a random variable which could be measured against the fixed processing effects. It is likely that if a larger number of women had been included in the study, the observed biologic variation attributable to subject would have been even greater. Since our goal was to compare magnitude of variation contributed by subjects to that conferred by processing method, we achieved a balanced design by having comparable degrees of freedom for those two variables.There are several critical procedural elements that must be highlighted for successful preservation of solid tissues in aqueous sulfate salt solutions such as RNALater. These reagents enter the tissue through passive diffusion, a process which follows simple physical principles. The distance between the tissue surface, which is exposed to preservative, and the innermost regions of the fragment should be minimized. We did this by cutting the tissues into 2 mm thick slices, thereby reducing the diffusion distance to 1 mm or less. Clumping of multiple fragments into a mass that excludes preservative may obviate the benefits of fine division. This can be avoided either by gentle agitation or placement in a sufficiently large container that individual pieces are likely to disperse. Results reported here are for tissues stored at room temperature (23–25°C). Storage under cooler conditions (4°C) as recommend by the manufacturer of RNALater were not directly evaluated in this experiment because it was our intent to mimic storage interval and conditions commonly encountered when sending a specimen by express courier to a centralized processing facility. Storage at temperatures substantially higher than 25°C, especially before the preservative has had an opportunity to penetrate the tissue, should be avoided.Split samples of fresh human tissue yield quantitatively similar RNA expression profiles whether processed fresh, frozen, or following 24–72 hour storage in RNALater. Formal statistical analysis shows patient source is the predominant source of variation between samples, with processing method contributing a random level of variation comparable to that seen in split duplicates (replicates). Subset analysis by functional gene category did not identify a specific class of genes which responded differently by processing method.Use of nontoxic ambient environment tissue preservatives makes it practical to engage practicing clinicians directly in decentralized sample collection for high throughput expression analysis in a central location. Tissue handling closely resembles that used by clinicians to prepare specimens for routine pathology analysis. Upon receipt in a centralized facility, the samples can either be immediately homogenized or archived at -60°C.Normal fresh uterine myometrial tissues were collected randomly from three women undergoing hysterectomy for benign uterine disease. For each hysterectomy, a single 4 to 8 gram tissue fragment was subdivided into eight aliquots composed of thin slices measuring no more than 2 mm in thickness. Replicate aliquots were immediately triaged into one of four storage conditions prior to homogenization: 1)immediate homogenization; 2)flash frozen in liquid nitrogen and storage for 48 hours at -80°C; 3)24 hour immersion in RNALater at room temperature with gentle agitation; or 4)72 hours immersion in RNALater at room temperature with gentle agitation.Tissue was solubilized in Trizol reagent , and RNA isolated according to the manufacturers instructions. In brief, the aqueous phase was resolved by addition of chloroform, and RNA precipitated from the aqueous phase by addition of isopropyl alcohol. Pelleted RNA was washed with 70% ethanol, dried, and resuspended in water. Quality of total RNA was assessed by running a non-denaturing 1% agarose tris-acetate buffer which confirmed the integrity of 18S and 28S ribosomal bands for all 24 total RNA preparations.® Expression Analysis Technical Manual) and all arrays were scanned under a low PMT (Photo Multiplier Tube) of 570 nm. Global scaling to a target value of 75 was applied to normalize all the arrays so they were comparable . The Affymetrix average-difference expression data and the P/A calls were used in the analysis. Those probe sets determined to have no detectible signal above background mismatch hybridization  were assigned a nominal value of 1 to facilitate future log transformations. Probesets having at least one tissue with detectable expression  and an average difference above either 1 or 100 were selected to define subsets of 14639 permissively or 7853 stringently expressed genes, respectively. Further analysis was performed using the natural log transformed data of these probe subsets. Data files for all specimens processed are deposited online at the Gene Expression Omnibus at the National Center for Biotechnology Information [Double-stranded cDNA was generated from 8 μg total RNA using the Superscript Choice System (Life Technologies) with T7-(dT)24 oligomer. cDNA was purified by phenol/chloroform extraction and ethanol precipitation. Biotin-labeled cRNA was prepared using the Enzo BioArray HighYield RNA Transcript labeling kit (Affymetrix). Unincorporated NTPs were removed from the biotinylated cRNA using an RNeasy kit (Qiagen). 10 μg of quality, fragmented cRNA was hybridized to each Affymetrix HG-U133A arrays containing probe sets representing approximately 22,000 genes. Array hybridization, washing was done according to the manufacturer's protocol was used, regarding storage condition as a fixed factor with four levels and subject as a random factor with three levels. The analysis of variance calculations for sums of squares in the mixed model ANOVA are identical to those for the fixed ANOVA model. Similarly, the degrees of freedom and mean squares are exactly the same. The mixed ANOVA model departs from the fixed ANOVA model only in the expected mean squares and the consequence choice of the appropriate test statistic. The mixed model also included a random storage by subject interaction. Replicate samples enabled us to estimate the replication error in the model. To test for the presence of storage main effects for each gene we divided the mean square for storage by the mean square for the interaction effect between storage and subject . The ANOtm download center [Functional annotation of probesets on the U133A chip, were downloaded from the NetaffxGM and JW conceived and designed the research plan and participated in all aspects of data collection and analysis. DF participated in data analysis and interpretation. DN and DZ performed the statistical analysis. CL and HB performed the RNA isolations, chip hybridizations, and data collation.


Over 450 transfer RNA (tRNA) genes have been annotated in the human genome. Reliable quantitation of tRNA levels in human samples using microarray methods presents a technical challenge. We have developed a microarray method to quantify tRNAs based on a fluorescent dye-labeling technique. The first-generation tRNA microarray consists of 42 probes for nuclear encoded tRNAs and 21 probes for mitochondrial encoded tRNAs. These probes cover tRNAs for all 20 amino acids and 11 isoacceptor families. Using this array, we report that the amounts of tRNA within the total cellular RNA vary widely among eight different human tissues. The brain expresses higher overall levels of nuclear encoded tRNAs than every tissue examined but one and higher levels of mitochondrial encoded tRNAs than every tissue examined. We found tissue-specific differences in the expression of individual tRNA species, and tRNAs decoding amino acids with similar chemical properties exhibited coordinated expression in distinct tissue types. Relative tRNA abundance exhibits a statistically significant correlation to the codon usage of a collection of highly expressed, tissue-specific genes in a subset of tissues or tRNA isoacceptors. Our findings demonstrate the existence of tissue-specific expression of tRNA species that strongly implicates a role for tRNA heterogeneity in regulating translation and possibly additional processes in vertebrate organisms. Transfer RNAs (tRNAs) translate the genetic code of genes into the amino acid sequence of proteins. Most amino acids have two or more codons. Every organism has multiple tRNA species reading the codons for the same amino acid (tRNA isoacceptors). In bacteria and yeast, differences in the relative abundance of tRNA isoacceptors have been found to affect the level of highly expressed proteins. This tRNA abundance–codon distribution relationship can have predictive power on the expression of genes based on their codon usages. Approximately 450 tRNA genes consisting of 49 isoacceptors and 274 different sequences have been annotated in the human genome. This work describes the first comparative analysis of tRNA expression levels in eight human tissues using microarray methods. The authors find significant, tissue-specific differences in the expression of tRNA species and coordinated expression among tRNAs decoding amino acids with similar chemical properties in distinct tissue types. Correlation of relative tRNA abundance versus the codon usage of highly expressed, tissue-specific genes can be found among a subset of tissues or tRNA isoacceptors. Differential tRNA expression in human tissues suggests that tRNA may play a unique role in regulating translation and possibly other processes in humans. These tRNA genes are scattered throughout the genome and are present on all but the Y chromosome. Twenty-two additional tRNA genes are present in human mitochondrial DNA )rial DNA ,7. Thus,To our knowledge, no systematic studies of tRNA expression among human tissues have been published. The dearth of information on tRNA expression is the result of technical and intellectual obstacles. Accurate quantitation of individual tRNA species is challenging due to the extensive secondary and tertiary structure of tRNA and numerous post-transcriptional modifications , both ofWhy should the human genome contain such a diverse array of tRNA sequences? A compelling explanation is that controlling expression of individual tRNA species enables another level of translational control for specific gene products. In bacteria and yeast, differences in the relative abundance of tRNA isoacceptors for a given amino acid clearly impact the synthesis of highly expressed proteins ,13. CodoHere we describe the comparative analysis of tRNA levels in eight human tissues and two human cell lines using a microarray method adapted from our previously developed arrays for bacterial tRNAs ,20. Our Arg isoacceptors read the six arginine codons.tRNA was quantified by taking advantage of its universally conserved 3′CCA sequence to attach a fluorescently labeled probe to tRNA present in total RNA prepared from tissues or cell lines. tRNA labeled in this manner was hybridized to DNA probes arrayed on glass slides. The brain sample was included in all hybridizations to correct for the variations in fluorescence labeling and array manufacturing. We used probes that are 70 to 80 nucleotides long, covering the length of the entire tRNA minus the conserved 3′CCA sequence. Probes at these lengths significantly increase hybridization efficiency and eliminate the sensitivity to potential variations in post-transcriptional modifications . We desiArg isoacceptors (more than ten among 70 to 75 residues) to enable design of three isoacceptor probes that separately cover tRNA genes with ACG (modified to ICG  can be found with r-values from 0.90 to 0.94 and p-values from 0.016 to 0.039 (Arg for two tissues (liver and thymus) can be found with r-values of 0.93 and 0.97 and p-values of 0.067 and 0.033, respectively  was explored in three ways. First, all data points from the same tissue are plotted. For liver and brain, a linear fit of this plot gives an relation A. Similarelation , suggestto 0.039 B. Similaectively C. SimilaWe have found that human tRNA expression varies by as much as tenfold among human tissues. Given the central role of tRNA in protein synthesis, this wide variation of tRNA abundance may reflect translational control via the availability of certain tRNAs. Since tRNA is the dominant ligand for the multitasking protein EF-1α, variations in tRNA levels may provide a mechanism to link translation with the dynamics of the cytoskeleton. Transcriptional control of tRNA genes may therefore play a role in the function of human tissues or possibly in cellular development and differentiation. tRNA abundance may also play a role in translational control of highly expressed, tissue-specific genes via their codon usage. Determination of tRNA abundance and charging levels  for diffhttp://www.stratagene.com): brain , liver (No. 735017), vulva (No. 735067), testis (No. 735064), ovary (No. 735260), thymus (No. 540141), lymph node (No. 540021), and spleen (No. 540035). After the microarray measurements were completed, it was found that the tissue RNA samples from Stratagene underwent an LiCl precipitation step which is known to result in the loss of small RNAs [Total RNA from eight human tissues was purchased from Stratagene (http://www.ambion.com) according to manufacturer's manuals. This procedure does not include LiCl precipitation or other known steps biasing against tRNAs.The total RNA from the HeLa and HEK293 cell lines were obtained using RNAwiz , E. coli tRNATyr (No. R0258), and yeast tRNAPhe (No. R4018), were purchased from Sigma-Aldrich  and used without further purifications.The three tRNA standards, The microarray experiment consists of four steps starting from total RNA: (i) deacylation to remove remaining amino acids attached to the tRNA, (ii) selective Cy3/Cy5 labeling of tRNA, (iii) hybridization with prefabricated arrays, and (iv) data analysis.For deacylation, 0.25 μg/μl total RNA premixed with the three tRNA standards at 0.17 μM each was incubated in 100 mM Tris-HCl (pH 9.0) at 37 °C for 30 min. The solution was neutralized by the addition of an equal volume of 100 mM Na-acetate/acetic acid (pH 4.8) plus 100 mM NaCl, followed by ethanol precipitation. Deacylated total RNA was dissolved in water, and its integrity was examined using agarose gel electrophoresis.http://www.amersham.com, described in [http://www.usbweb.com) for Oligo-2. Hence, the ligation of Oligo-2 required substantially less T4 DNA ligase compared to Oligo-1.For Cy3/Cy5 labeling, tRNA in the total RNA mixture was selectively labeled with either Cy3 or Cy5 fluorophore using an enzymatic ligation method described previously ,20. The Hybridization was performed at 60 °C overnight with 1 μg each of Cy3- or Cy5-labeled total RNA mixture using Oligo-1 and 1 μg of Cy3-labeled total RNA and 2.5 μg of Cy5-labeled total RNA using Oligo-2 (because only 40% of Oligo-2 used in this work contained the Cy5 fluorophore). Multiple arrays were run using the brain reference sample labeled with either Cy3 or Cy5.Drosophila nuclear tRNA genes, 34 probes for C. elegans nuclear tRNA genes, three probes for bacterial and yeast tRNA standards, and eight probes for human tRNA hybridization controls. Nonhuman probes were used as specificity controls for hybridization of human samples. Eighteen replicates of each probe were printed on each array. The descriptions and sequences of the DNA oligonucleotide probes used for human nuclear and mitochondrial tRNA genes are provided in The microarray printing and hybridization conditions were the same as those in bacterial tRNA studies ,20. DNA http://www.axon.com) to obtain fluorescence intensities and the Cy5/Cy3 ratio per pixel at each probe spot. The averaged Cy5/Cy3 ratio per pixel at each probe spot was first normalized to an averaged value of the three tRNA standards prior to subsequent analysis. For all tissue samples, the brain total RNA was used as the reference sample at equal amounts of total RNA as determined by the UV absorbance. tRNA constitutes up to 15% of total RNA.For data analysis, arrays were scanned using GenePix 4000b scanner Click here for additional data file.Figure S2(36 KB PDF)Click here for additional data file.Table S1(10 KB PDF)Click here for additional data file.Table S2(17 KB PDF)Click here for additional data file.Table S3(8 KB PDF)Click here for additional data file.Table S4(12 KB PDF)Click here for additional data file.Table S5(429 KB XLS)Click here for additional data file.Table S6(34 KB XLS)Click here for additional data file.


Scores aimed at contributing to the optimization of exercise testing (ET) have been developed and the experience with their application in coronary artery disease (CAD) has proven to be favorable .Although there is debate on the use of scores in clinical practice, those that stand for it argue that they may decrease the rate of undiagnosed CAD, besides reducing the number of patients without disease that undergo highly expensive tests . AdditioBesides improving diagnostic and prognostic accuracy, scores remove interpretation biases and reduce variability in the decision-making process. Physicians frequently make clinical decisions based on their personal experience, instead of following a rational decision-making process, in addition to trusting more the results from more expensive tests, such as perfusion imaging or echocardiography. Using scores has shown great results, as good as or even better than the formerly mentioned tests , but theAlthough many scores adopt particular features of electrocardiographic responses in their composition, there are no references for a proposal to graduate myocardial ischemia documented in ET.Most cardiologists divide the wide spectrum of electrocardiographic alterations into just two categories , which still lack a clearer definition. Such lack of a proper categorization for ischemic response, determines an inappropriate comparison of results from large studies, thus promoting interpretation biases.To this moment, no line of research has turned primarily toward the CAD evaluation through ET, enabling a view beyond the simple dichotomy (negative/positive test), to provide more objective data about the degree of myocardial ischemia documented during the test.In this article, we propose an electrocardiographic score for myocardial ischemia graduation during the exercise testing. This is a conceptual proposition and clinical studies must be designed to validate the new score among many populations and clinical conditions. Inter-observer variations studies and comparisons with other scoring systems and diagnostic methods will help the medical community to evaluate the reliability and better understand the clinical relevance of the new exercise testing ischemic score.Diagnostic approach to CAD: The degree of ET positivity clearly influences the probability of coronary artery disease. The greater the magnitude of the ST segment depression, the greater the probability of CAD. Also, the worse the mophological type of ST segment depression, the more likely the presence of coronary insufficiency. The presence of ST segment elevation defines a high probability of severe CAD. Also, the earlier and the longer the ST segment depression, the higher the probability of severe CAD. Currently, patients that present ST segment depression of 1.0 mm of horizontal morphology, restricted to the maximal exercise, are mistakenly categorized in different studies, as in the same probabilistic level of disease as in individuals with ST segment elevation of great magnitude in the initial phase of exercise. Therapeutic management: The type, the time, and the intensity of the therapeutic management vary according to the severity of the alterations documented in ET. Frequently, such classification of severity is made empirically by most cardiologists. We are missing an objective form of classification for myocardial ischemia severity for a better basis for clinical decisions.Evaluation of therapeutics: Usually, ET are conducted to evaluate the result of different therapeutic approaches in individuals with CAD. When revascularization procedures are incomplete, graduating ischemia would be useful in monitoring clinical evolution.Risk stratification: To better establish the risk of future events, graduating alterations in ET is essential, since there is a great range of presentations of myocardial ischemia. Certainly, an individual that presents a 1.0 mm ST segment depression with horizontal morphology restricted to maximal exercise, should not be classified in the same risk level as individuals with ST segment elevation of great magnitude in the initial phase of exercise.Many studies with a prognostic approach that assessed ST segment depression as an independent risk factor, presented an interpretation bias of the results, because in clinical practice, an individual that presents a positive ET, invariably ends being invasively investigated, and consequently, being submitted to revascularization procedures. Thus, the presence of myocardial ischemia during a stress test determines a therapeutic approach that modifies the natural history of coronary artery disease.Serial and comparative analysis: A serial analysis of ET performed with the passing of time is necessary for patients who have some contraindication for myocardial revascularization. Serial monitoring of myocardial ischemia could be the basis for different adjustments of pharmacological therapeutics. A graphic demonstration of the degree of myocardial ischemia could be useful to express the patient's clinical evolution. Besides this, as clinicians are used to comparing patients with their prior condition or even patients between each other, severity gradients may have a wide range of applicability.Graduating myocardial ischemia may also minimize differences between results of ET and perfusion images. Early electrocardiographic alterations with great magnitude, may be even more valued than perfusion images, when there is homogeneous uptake of the radiotracer.Ischemic preconditioning (IPC): This is a physiological phenomenon of cardioprotection, where myocardial ischemia episodes induce a greater myocardial tolerance to a subsequent ischemic damage. This may be objectively shown by two sequential ET, where an attenuation of ischemia signs is observed in the second test [ond test . IPC indThe time for 1.0 mm of ST segment depression is considered a tolerance index. Ischemic threshold indexes correspond to heart rate and the double product at the time in which 1.0 mm ST segment depression is reached.Scientific research: To prevent great biases in the interpretation of results, an appropriate data homogenization is necessary. Graduating myocardial ischemia may reduce variability of interpretation of the results, by improving data systematization and patient classification. Scales are more systematized forms of observation, and translate a biological phenomenon in more objective and quantified information.Countless situations of clinical practice require graduating myocardial ischemia documented in ET. We may enumerate the following situations:    Scores to assess CAD may be classified didactically, as pre-test, post-test, simplified, multivariate, diagnostic or prognostic. Diagnostic scores are structured, essentially aiming at disease probability estimation. When only clinical variables are considered, this is deemed as a pre-test analysis; by including ET parameters, this is deemed a post-test score. A diagnostic score may be prognostic when severe CAD estimation is made . Prognostic scores are structured to assess risk, mainly of cardiovascular death.There are scores based on multivariate equations that possess complex and difficult calculations. Simplified scores derived from those equations, which as a table, allow for a quick estimation of coronary artery disease through the simple addition of points. Computerized systems perform the calculations of multivariate predictive equations, which may by data weighing and logistic regression, make a diagnosis as accurate as more expensive and sophisticated tests. In spite of forecast equations being possibly intimidating for clinicians, there are programs that automatize such calculations.Although there is a great abundance of alternatives and proposals of scores in ET, with many of them considering aspects of electrocardiographic response in their composition, a few are scores specifically aimed at electrocardiographic aspects. Within the scores that consider exclusive aspects of electrocardiographic response, those of Atenas  and KoidThis is a system that depends on identifying electrocardiographic variables that may be clearly defined and graduated according to a scale of values, the points of which represent a graduation of myocardial ischemia that is documented in ET.Within the great range of electrocardiographic alterations, this scale classifies the different patterns into 3 fundamental aspects: magnitude, morphology and moment of ST segment deviations. Each aspect is hierarchically classified into 5 types and graduated from 0 to 4 points -3, resulUpsloping ST segment depression: The rapid and slow upsloping patterns are classified within this item. The point of reference for measuring ST segment depression in the upsloping type is the Y point, at 80 ms from the J point (J80).ST segment convex depression: The presence of ST segment convexity characterizes this morphological pattern. It should be measured at the Y point, at 80 ms from the J point (J80).ST segment horizontal depression: It should be measured at the Y point, at 80 ms from the J point (J80).ST segment downward depression: It should be measured at the J point. ST segment elevation: It should be measured at the Y point, at 40 ms from the J point (J40).The electrocardiographic interpretation should be systematic and comprehensive, weighing globally the findings for a proper correlation establishment with morphological types defined by the scale of ischemia. There are 4 morphological patterns of depression and one morphological type of ST segment elevation :    UpsNo ST segment deviations. The point of reference (J or Y) is in the PQ baseline or keeps the same magnitude of the baseline situation.ST segment shift of small magnitude. ST segment depression or elevation inferior to 1.0 mm.ST segment shift from 1 to 1.5 mm. ST segment depression or elevation between 1.0 mm and 1.5 mm.ST segment shift from 1.6 to 2.0 mm. ST segment elevation or depression superior to 1.5 mm and inferior or equal to 2.0 mm.ST segment shift superior to 2.0 mm. ST segment depression or elevation of great magnitude.The measurement should be conducted according to a morphological pattern of ST segment deviations. The lead with the greatest alteration is adopted for scoring. To make up the score, the magnitude of deviations is classified into 5 categories:    Transitory peak. When ST segment shift occurs exclusively in the phase of exercise, appearing after 10 MET, which corresponds to stress test that is altered after the third stage of Bruce's protocol. The total resolution of ST segment elevation should occur before the first minute of recovery.Peak and/or recovery. It corresponds to tests where ST segment deviations appear after 10 MET or after the third stage of Bruce's protocol, with a total resolution of ST segment shift after the first minute of recovery. ST segment deviations that occurred exclusively in recovery should also be considered in this pattern.Early with rapid reversion. When ST segment shift occurs between 5 and 10 MET, with total resolution of ST segment shift that occurs before the third minute of recovery.Early with slow reversion. When ST segment shift occurs between 5 and 10 MET, with total resolution of ST segment shift that occurs after the third minute of recovery.Very early. When ST segment shift occurs with up to 5 MET, corresponding to alteration that occurs in the first stage of Bruce's protocol.To score this component, 5 patterns are considered that are exemplified in To validate ST segment alterations that characterize myocardial ischemia, the following should be used: morphological data, referential baseline stability, tracing quality, definition and number of leads involved, type and calibration of the equipment and recording system.ET are considered to be satisfactory when they present recordings in a representative amount, such that visualization would enable a diagnostic conclusion. ET are considered to be unsatisfactory when their reading is hampered by the presence of interference, artifacts or wide variations of the baseline. To validate interpretation, there should be at least three beats with a stable baseline. Electrocardiographic alterations determined by breathing alterations, movements and variations of the baseline, all should be excluded of the morphological analysis.Using only 3 simultaneous leads substantially hinders the sensitivity to detect myocardial ischemia, and using bipolar precordial leads increases sensitivity but decreases specificity due to a greater amplification of the electrocardiographic signal. Inappropriate equipment may produce artifact alterations, magnify or mask alterations by problems of acquisition, processing or even inappropriate graphic printing of electrocardiograms.Leads involved in morphological alterations. Specific leads and the number of leads should be pondered in global interpretation. When morphological alterations affect isolated leads, without comprising a region; or affect a not so relevant lead (e.g. aVR), such alterations should not be validated to define ischemia. Leads with a simultaneous recording should be compared to assess the equivalence of alterations.The measurement of any ST segment shift is made from the PQ baseline and not from the PR line. ST segment depressions with downsloping morphology are measured at the J point, and horizontal, upsloping and convex patterns are measured at the J80 point. ST segment elevations should be measured at J40 point.The slow upsloping pattern is considered abnormal; however, it was excluded as positivity criterion, since it determines a high rate of false positives. The convex pattern is not so acknowledged in international literature, and it determines a high rate of false positives as well; it normalizes rapidly in recovery, before the first minute. Classically, the horizontal and downsloping patterns are the ones that define the internationally accepted positivity criteria, and the downsloping pattern indicates a more severe ischemia .It is worthwhile to highlight that positivity criteria are the same for both genders, since the electrocardiographic manifestation of ischemia is not different between men and women. What changes is the CAD probability. The probabilistic analysis of coronary disease involves not only gender, but also age, symptoms and other numberless risk factors. These numberless variables may be organized and classified through scores, indexes or diagnostic and/or prognostic scales.ECG morphological alterations in rest should be weighed in interpreting electrocardiographic response. In the case of baseline ST depression, ST depression line is adopted as a reference to measure the possible additional shift during exercise. In the cases where baseline ECG presents great morphological alterations, with depressions greater than 1.0 mm, there is limitation of morphological analysis to define myocardial ischemia. Morphological interpretation of ECG to define ischemia should also be considered limited in the following situations: use of digitalis, left ventricular enlargement, intraventricular conduction disorder, ventricular pre-excitation and long QT.Q wave increases physiologically during exercise. When there is doubtful ST segment depression, observing Q wave reduction should be applied to validate the alteration.The presence of pathological Q wave in baseline ECG should be considered to interpret the possible ST segment elevation in stress test. Once ST segment elevation occurs, with magnitude criterion, in the presence of electrically inactive area, dyskinesia should be investigated. To make up the score for ischemia, ST segment elevations with areas with pathological Q waves should not be considered.In the case of early repolarization pattern in baseline ECG, the measurement of possible ST segment depression is considered from PQ baseline.When baseline ECG presents ST segment elevation in electrically inactive area, the reference line for measurement is considered to be the level of elevation to interpret possible additional elevations. As a general rule, ST segment elevations are measured at 0.40 sec from the J point.In the presence of baseline RBBB, the presence of ST segment depressions is only validated in the V5 and V6, D1, aVL, D2, D3 and aVF leads.LBBB hinders morphological analysis to define myocardial ischemia. Morphometric analysis of ST segment deviations (elevation or depression) does not allow characterizing myocardial ischemia in most cases.ST segment morphological alterations that occur after transitory episodes of branch blocks or tachyarrhythmias, are considered to be a memory effect (electronic memory), and are not considered to define myocardial ischemia.Altered beats that follow early ventricular or supraventricular beats, are not considered for morphological analysis.Alterations that may be correlated to myocardial ischemia in other contexts. E.g.: T wave pseudo-normalization and inversion; they are not considered in the composition of ischemia score. Of note, late inversions of T wave in the recovery phase are frequently mistaken by ST segment depressions with downward morphology.QRS complex of great magnitude (very broad R wave), which determines greater magnitudes of ST segment deviations. A small ST alteration may be magnified when the R wave is large. When there is low QRS voltage, with R wave amplitude inferior to 11 mm, the rate of false negatives is greater . Thus, sHypertensive patients, with baseline ECG alterations compatible to LVE, have a positive predictive value of impaired test. Anyway, a normal ST in LVE carriers has a high negative predictive value for coronary artery disease. While there is LVE, ST segment alterations should not be considered for the ischemia score.Digitalis and other antiarrhythmic drugs substantially affect ventricular repolarization, limiting morphological analysis to define myocardial ischemia and invalidating the use of the score for ischemia. It is essential to weigh the result of the test when these specific medications are used, or even when there are still residues of therapeutic influence.After episodes of tachyarrhythmias, whether supraventricular or ventricular, ventricular repolarization alterations may be observed that resemble myocardial ischemia. Thus, morphological alterations that occur immediately after sustained tachyarrhythmia episodes are not considered for ischemia score composition.Atrioventricular and sinoatrial blocks are not directly correlated to myocardial ischemia, even when transitory during exercise. They should not be considered for ischemia score composition.The analysis of electrocardiographic response should be made by the visual method instead of the computerized method, which is not considered to validate the interpretation due to the high degree of contamination by artifacts.Prolonged cool down  may obscure possible early ST segment alterations.Accelerated atrioventricular conduction favor Ta wave expression, which manifests as ST segment depression with a pattern similar to the slow rising one. Such depression is more emphasized in D2, D3 and aVF, and should be considered as an electrocardiographic response deviant from normal .As the morphological interpretation of the electrocardiogram is subjective, and there is a large amount of data that are manipulated during the process of analysis, there has been an increasing need to systematize and organize the process of data interpretation.Over the last decades, cardiologists are focused on expensive and sophisticated diagnostic methods, in the belief that they can provide a better diagnostic and/or prognostic accuracy. Collecting scientific data, it is still possible to verify that conventional ET can, most of the times, be superior to more recent tests when scores are used .There are many bases or scores to classify CAD based on the duration of signs or symptoms or in anatomical location and in the extension of the atherosclerotic plaque . Also inA score, besides systematizing and organizing the interpretation of the data from the ET, may be a useful quantifying instrument for alterations. The proposed scale for ischemia may be understood as an optimized score, where there are legitimized entries of data classified by applications in different specific populations.Scales are useful to characterize and quantify, i.e. translate better the clinical phenomenon into objective and quantified information. The graduation system for ischemia that we propose, categorizes the response in patterns based on three predominant aspects that, when added, result in a scale or score. This system enables a description of the extension of myocardial ischemia, and even in a carrier of defined CAD, may involve a given number of correlative objectives, namely: help the physician to plan the treatment; provide a better prognostic definition; help to assess the results of the treatment; facilitate the exchange of information between the treatment centers; contribute with the continuing research about myocardial ischemia.It is important to reach a greater harmony in the record of data that result from ET, and promote a more precise frame for the extension of ischemia, to correlate it better with the most diverse patterns of coronary anatomy involvement, facilitating the exchange of information between different centers as well.Scientific research is the classical territory to use scales, thus guaranteeing that the information collected about specific alterations is organized in patterns and feasible of comparison in a reliable, consistent and reproducible fashion.The graduation system proposed primarily works with classification of myocardial ischemia documented by electrocardiogram during ET. The score has three components: magnitude, morphology and moment of alterations. All these aspects are easily evaluated, thus allowing a good integration of information between clinicians and specialists. Although the different parameters of this scale are already evaluated as a routine, and were previously studied , there iThis system will enable structuring more objective and optimized criteria, permitting a better discrimination and classification of patterns of the different electrocardiographic expressions of myocardial ischemia, one of the most representative paradigms of the multifaceted disease.Myocardial ischemia is an expression of CAD and it affects the electric activity of the heart, which represents the documentary proof of ischemic disease. To be able to document myocardial ischemia, there is a series of factors that should be considered: presence of critical coronary obstruction, number of coronary arteries with critical lesions, presence of collateral circulation, location of coronary obstruction, extension of ischemia, insufficient increase of myocardial demand of oxygen, anemia, concomitant valve diseases, superimposed coronary spasm, hydroelectrolytic disorders, number of electrocardiographic leads, position of electrodes, limitations in acquisition and processing of cardiac electric signal, baseline electrocardiogram alterations, quality of electrocardiographic recording, QRS complex width, therapeutic influence, etc.The result of the ST does not confirm the presence or absence of CAD, and it should be correlated with other pertinent data for a more coherent probabilistic analysis . As any Tests considered negative are not a synonym of normal tests, since they include true negatives and false negatives. Besides this, even true negatives include situations of normal coronary arteries and coronary arteries with non-critical lesions. Therefore, positive tests are not synonyms of abnormal tests, since they contemplate true-positive and false-positive results. And, even true positives include situations of critical coronary artery disease and angiographically normal coronary arteries (microcirculatory disease).For a system to be widely accepted, it needs to be practical, easy to remember and interpret, both by the clinician and the specialist. Anyway, seeking simplicity cannot determine analysis errors and regard alterations in an absolute way. The score for ischemia we propose, focuses on the essential aspects of the ET results, with a representative and consistent character, and in an easily understandable fashion. Besides this, even physiological manifestations are considered: when the score is zero, it means that there was no documentation of myocardial ischemia.The scale does not include angina pectoris that manifests during the test, because we consider that symptoms are described in a very subjective way for qualification and quantification, with wide inter-observer interpretation variations. Other clinical factors were not considered, because clinical information is not commonly available, or it is incomplete or uncertain.As concomitance of highly severe hemodynamic alterations is very frequent with more severe ST segment deviations, indexes such as inotropic deficit and chronotropic incompetence were not considered for the score.We do not oppose detailed analysis of clinical, hemodynamic and functional capacity variables, and integrating data as a whole. We just think that the magnitude of electrocardiographically documented myocardial ischemia surmounts other clinical and hemodynamic parameters besides displaying a greater reproducibility as well.The scale cannot be applied in situations where there are limitations of morphological analysis to define myocardial ischemia;It is necessary for the cardiologist to be initially trained for a routine use of this score of ischemia, taking into consideration the homogenization of possible conceptual differences;There may be small imprecision in measuring the magnitude of ST segment deviations;As the morphological interpretation of the electrocardiogram is subjective, some ventricular repolarization patterns, even deviations from normal, may be confused in the score of morphological aspects. Less objective items tend to yield differences in scores, which may result in inter- and intra-observer variations.Critical aspects to use the score include:    In fact, it is to be expected that there should be imperfections in the proposed system, but this is a proposal that better clarifies and classifies the different aspects of myocardial ischemia. With a routine use of ischemia score, different observers may elicit the different aspects of the scale with a high degree of consistency, determining a low probability of inappropriate categorization, and reducing inter-observer discrepancies. Training on the application of scales in clinical assays, may favor a better systematization of data.In practice, this ischemia score may prevent badly structured and disorganized clinical cases from resulting in ambiguities and misunderstandings, positioning the patient in a better defined prognostic and/or diagnostic category.


Assessing RNA quality is essential for gene expression analysis, as the inclusion of degraded samples may influence the interpretation of expression levels in relation to biological and/or clinical parameters. RNA quality can be analyzed by agarose gel electrophoresis, UV spectrophotometer, or microcapillary electrophoresis traces, and can furthermore be evaluated using different methods. No generally accepted recommendations exist for which technique or evaluation method is the best choice. The aim of the present study was to use microcapillary electrophoresis traces from the Bioanalyzer to compare three methods for evaluating RNA quality in 24 fresh frozen invasive breast cancer tissues: 1) Manual method = subjective evaluation of the electropherogram, 2) Ratio Method = the ratio between the 28S and 18S peaks, and 3) RNA integrity number (RIN) method = objective evaluation of the electropherogram. The results were also related to gene expression profiling analyses using 27K oligonucleotide microarrays, unsupervised hierarchical clustering analysis and ontological mapping.vs. Ratio showed concordance (good vs. degraded RNA) in 20/24, Manual vs. RIN in 23/24, and Ratio vs. RIN in 21/24 samples. All three methods were concordant in 20/24 samples. The comparison between RNA quality and gene expression analysis showed that pieces from the same tumor and with good RNA quality clustered together in most cases, whereas those with poor quality often clustered apart. The number of samples clustering in an unexpected manner was lower for the Manual (n = 1) and RIN methods (n = 2) as compared to the Ratio method (n = 5).Comparing the methods pair-wise, Manual i.e. when the RNA became degraded. Ontological mapping using GoMiner  revealed deoxyribonuclease activity, collagen, regulation of cell adhesion, cytosolic ribosome, and NADH dehydrogenase activity, to be the five categories most affected by RNA quality.Assigning the data into two groups, RIN ≥ 6 or RIN < 6, all but one of the top ten differentially expressed genes showed decreased expression in the latter group; The results indicate that the Manual and RIN methods are superior to the Ratio method for evaluating RNA quality in fresh frozen breast cancer tissues. The objective measurement when using the RIN method is an advantage. Furthermore, the inclusion of samples with degraded RNA may profoundly affect gene expression levels. In breast cancer, for example, microarrays have been suggested to be useful for predicting clinical outcome and for tailoring treatment strategies for individual patients -3. This e tumors ,4,5.e.g. the Agilent 2100 Bioanalyzer [Microarrays were first described by Schena and co-workers in 1995 . The difanalyzer . The Bioanalyzer ,9. FurthIn the present study we have focused on 1) different ways of evaluating the quality of RNA, 2) how the quality of RNA influences microarray-based gene expression analyses, and 3) which type of gene categories that are affected by decreased RNA quality.The results indicate that the Manual and RIN methods are superior to the Ratio method for evaluating RNA quality in fresh frozen breast cancer tissues. The objectively obtained measurement of the RIN method is, in addition, clearly an advantage. Furthermore, the inclusion of samples with degraded RNA can profoundly influence gene expression profiles, and hence clustering of samples as well as absolute expression levels of individual genes.We analyzed the RNA quality using three different methods; Manual, Ratio and RIN, respectively (see Methods). Visual inspection of the Bioanalyzer electropherograms showed that of the six samples included, the majority were degraded at room temperature, but after different lengths of time [see Additional file Similar results were obtained using the Ratio method. According to the Ratio method however, Sample 3 was considered good at 10 minutes Fig. , whereasThe electropherograms from one of the samples (Sample 3), showed an unexpected appearance over time Fig. . It was vs. Ratio showed concordance in 20/24, Manual vs. RIN in 23/24, and Ratio vs. RIN in 21/24 samples. All three methods showed concordant results in 20 of the 24 samples.In summary, pair-wise comparisons of the methods revealed that Manual Our hypothesis was that if the RNA quality of the sample was good for all four time periods, the corresponding gene expression profiles should be similar and the samples should consequently cluster together. Conversely, upon RNA degradation, changes in gene expression profiles would cause the sample replicates to cluster apart. Using unsupervised hierarchical clustering to assess which samples clustered together, we noted that the samples clustered into two separate groups, one including most of the good samples (including those partly degraded) and one including most of the degraded samples, irrespective of evaluation method Fig. .i.e. samples considered to be of good RNA quality clustering with degraded samples or vice versa) was five   to find Gene Ontology (GO) mapping using GoMiner , . In 20/24 (83%) samples, all three methods came to the same result (good or degraded RNA). The Manual and RIN methods were concordant in 23/24 (96%) samples, whereas the Ratio method showed discordant results with the other two methods in four and three samples, respectively. In some of the discordant samples, the discrepancy could be explained by values near the cut-off. The results indicate that the Manual and RIN methods are more similar to one another than the Ratio method is to either. This finding is in line with the evaluation principles. While both the Manual and RIN methods take the whole electropherogram into consideration, manually or objectively, the Ratio method relies only on the ratio between the 28S and 18S peaks. Furthermore, the ratio calculation is based on area measurements and is heavily dependent on the definitions of the start and end of the peaks. In addition, small peaks make this measurement even more uncertain, which is often the case with partly degraded samples. Therefore, the ribosomal ratio may not be sufficient to evaluate RNA degradation efficiently in all instances. Copois and co-workers, using colorectal cancer, liver metastases, and normal colon, compared the ratio method with the computer-based RIN and Degradometer methods, as well as with an in-house "RNA Quality Scale" method, and came to the conclusion that the 28S/18S ratio resulted in misleading categorization . To addrImbeaud and co-workers obtained similar results in their study including both cell lines and different normal tissues, demonstrating ambiguity with the Ratio method . When rii.e. samples considered to be of good RNA quality clustered with degraded samples or vice versa. These results indicate that the Manual and RIN methods are more concordant and superior to the Ratio method for evaluating RNA quality in fresh frozen breast cancer samples. An advantage with the RIN method in comparison with the Manual method is that it yields an objective measurement, whereas the subjective interpretation of the Manual method, especially for the partly degraded group, may show both intra- and inter-individual variation. In order to validate the cut-off of 6 for the RIN method, we also tested 5 or 7 as cut-offs. The number of samples clustering in an unexpected way was thereby increased to three and seven, respectively. The use of 6 as a cut-off was also strengthened when the RIN values were compared to the Pearson correlation coefficients of the association between the gene expression of the samples for the different time points (2–3 minutes to 50 minutes) and the gene expression after 50 seconds.In concordance with the above-mentioned studies, the results of our investigation demonstrate that the gene expression profiles change considerably upon RNA degradation. We hypothesized that if the RNA quality in different samples from the same breast tumor was good, the corresponding gene expression profiles should be similar, and the samples should consequently cluster together. In contrast, when RNA is degraded, changes in gene expression profiles would cause the samples to cluster apart. Our findings indicate that the results of the RNA quality evaluation using the Manual and RIN methods were more concordant with the results of the clustering analyses than when using the Ratio method. While only one  and two (RIN) sample replicates clustered apart, five samples clustered in an unexpected way when the Ratio method was used, One sample showed an unexpected appearance over time, as the RNA quality appeared superior after extended exposure to room temperature compared to shorter time periods when it was deemed degraded Fig. . This suOf the top ten most differentially expressed genes, all but one showed decreased gene expression levels in the RIN < 6 compared to the RIN ≥ 6 group Fig. , suggeste.g. differences in tissue composition. Some samples may be rich in fatty tissue, whereas others may be rich in epithelial cancer cells. Furthermore, the amount of connective tissue may also influence the amount and quality of extracted RNA. Another explanation for the differences between samples may be that the time period from surgical excision until the sample is placed at -80°C varies and that they are collected from several pathological departments, with different routines. The tissue composition and suboptimal sample collection procedures may also explain the relatively low ratio values obtained in breast cancer, in comparison with other tissue materials. In a recent publication from our group [Our results demonstrate that RNA was degraded at room temperature, but the RNA in the six samples showed variable sensitivity. This variation may be explained by different sensitivity to room temperature due to ur group , we had ur group ,18.From the electropherograms it was, furthermore, demonstrated that RNA degradation is a gradual process. Not all RNA follows the same pattern during degradation; however, the larger ribosome is typically degraded first, resulting in a decrease and broadening of this peak. Consequently, as degradation proceeds, there is a decrease in the 28S to 18S ribosomal ratio and an increase in the baseline signal between the two ribosomal peaks.The results indicate that the Manual and RIN methods are superior to the Ratio method for evaluating RNA quality in fresh frozen breast cancer tissues. The RIN method gives an objective measure of RNA quality, while the Manual method may be subject to inter-, as well as intra-observer variation. In addition, the inclusion of samples with degraded RNA may affect the outcome of the study, as the levels of gene expression are highly dependent upon RNA integrity. Based on our experience, we recommend RIN values ≥ 6 to be used for fresh frozen breast cancer tissue.Frozen samples from six patients were retrieved from the tissue bank (-80°C) owned by the South Swedish Breast Cancer Group. In order to obtain RNA of different quality, four equally sized pieces (by weight) from each invasive breast cancer sample were placed at room temperature for four different lengths of time: 50 seconds, 2–3 minutes, 10 minutes, and 30 minutes, after which the samples were placed in liquid nitrogen.The ethical committee at Lund University approved this project.i.e. the ratio between the ribosomal subunits, 28S/18S.The samples were pulverized with a Micro-dismembrator II , and RNA was extracted using Trizol reagent , and purified with Qiagen RNeasy Midi columns . The RNA concentration was determined using a Nanodrop Spectrophotometer . The RNA quality was assessed using an Agilent 2100 Bioanalyzer  together with the reagents in the RNA 6000 Nano LabChip kit. All samples were within the kit capacity (5–500 ng/μl). The Agilent 2100 Bioanalyzer generates an electropherogram and a gel-like image and displays results such as sample RNA concentration and the so called ribosomal ratio, e.g. Fig. e.g. Fig. e.g. Fig. i.e. to proceed to the hybridization step. However, methods that rely on visual inspection are subjective and have a tendency to vary over time. A more objective way to evaluate the quality of RNA may be to use a certain threshold for the 28S/18S ratio as a cut-off (Ratio method). From previous studies, we have established a threshold for the Bioanalyzer ratio at ≥ 0.65 (data not shown). A more recent approach is to use the RNA Integrity Number (RIN) method, which is a standardization of RNA quality control [e.g. type of organism, type of tissue, type of microarray platform, RNA extraction procedure, etc.) the validation procedure needs to be repeated. There are, thus, no established cut-off values and each laboratory needs to establish their own.The electropherogram can be evaluated in three ways. With visual inspection,  the quality of RNA is considered good if the electropherogram shows two distinct peaks, one for 28S and one for 18S, and a flat baseline . The median values for the partly degraded and degraded were 6 (range: 3–7) and 4 (range: 2–6), respectively. Based on these results we considered values greater or equal to 6 to represent good RNA. This cut-off was therefore also used in the present study.® dCTP , and 5 μg of reference RNA , consisting of a pool of ten different tumor cell lines, was labeled with Cy5® dCTP , according to the manufacturer's instructions using the reagents in the ChipShot™ labeling system kit .Five micrograms of tumor RNA was labeled with Cy3Arrays were produced by the Swegene DNA Microarray Resource Centre, Department of Oncology at Lund University, Sweden, using a set of 26,819 70 base-pair human oligonucleotide probes , which were obtained from Operon Biotechnologies, Inc. . The probes represent 16,641 gene symbols.2 and pre-treated using the Pronto!™ Plus System 6 , according to the manufacturer's instructions. Arrays were scanned at two wavelengths using an Agilent G2505A DNA microarray scanner , with 10 μm resolution. Gene Pix Pro 4.0 software , was used for image analysis. Gene names were linked to the spots and spots with poor quality were manually excluded. Raw-data are available at Gene Expression Omnibus [Prior to hybridization, slides were UV-cross linked at 800 mJ/cm Omnibus .Background correction of Cy3 and Cy5 intensities was calculated, using the median feature and the median local background intensities provided in the data matrix. Within arrays, intensity ratios for individual features were calculated as background corrected intensity of tumor sample divided by background corrected intensity of reference sample. The data matrix was uploaded to BASE , where tSpots with intensities lower than zero, and spots that were flagged bad or not found were excluded. Reporters that were not present in 100% of the arrays were filtered out, and the data was normalized using Lowess , resultiOntological mapping using the publicly available software GoMiner  was perfi.e. a low correlation coefficient and a RIN value above the cut-off or vice versa (Fig. In order to validate the RIN cut-off value, the RIN values were compared to the Pearson correlation coefficients Fig. . In BASErsa Fig. . Both saMF conceived of the study. CS contributed to the development of methodology and executed the experiments. CS and JE analyzed and interpreted the data. CS, MF and IH took active part in writing the manuscript. All authors read and approved the final manuscript.Bioanalyzer electropherograms. Bioanalyzer electropherograms for the six samples at different time points.Click here for fileGO categories. Gene ontology analysis of the 7,672 differentially expressed genes using GoMiner, with a p-value ≤ 0.05 and with ≥ 3 changed genes in each category.Click here for file


Microarray gene expression (MAGE) signatures allow insights into the transcriptional processes of leukemias and may evolve as a molecular diagnostic test. Introduction of MAGE into clinical practice of leukemia diagnosis will require comprehensive assessment of variation due to the methodologies. Here we systematically assessed the impact of three different total RNA isolation procedures on variation in expression data: method A: lysis of mononuclear cells, followed by lysate homogenization and RNA extraction; method B: organic solvent based RNA isolation, and method C: organic solvent based RNA isolation followed by purification.We analyzed 27 pediatric acute leukemias representing nine distinct subtypes and show that method A yields better RNA quality, was associated with more differentially expressed genes between leukemia subtypes, demonstrated the lowest degree of variation between experiments, was more reproducible, and was characterized with a higher precision in technical replicates. Unsupervised and supervised analyses grouped leukemias according to lineage and clinical features in all three methods, thus underlining the robustness of MAGE to identify leukemia specific signatures.The signatures in the different subtypes of leukemias, regardless of the different extraction methods used, account for the biggest source of variation in the data. Lysis of mononuclear cells, followed by lysate homogenization and RNA extraction represents the optimum method for robust gene expression data and is thus recommended for obtaining robust classification results in microarray studies in acute leukemias. Microarrays have been demonstrated to be a powerful technology capable of successfully identifying novel taxonomies for various types of cancers -5 and geHere we present a comparative study of the microarray data using three different RNA isolation and purification techniques . We have performed standardized experiments with total RNA extracted from pediatric acute leukemia patients to investigate whether different extraction protocols (see methods) result in comparable gene expression data from the same sample source Figure . MoreoveBacillus subtilis control transcripts from the Poly-A control kit  are greater or equal to 1, and the intensity ratio of the 3' probe set to the 5' probe set for the housekeeping gene GAPD is less than 3.0. Four samples showed a higher 3'/5' GAPD ratio  but had otherwise acceptable quality parameters.In this study we first monitored data quality parameters. All gene expression profiles passed the quality filter and met our criteria for inclusion into further data analyses [see Additional File GAPD ratios. Preparations of total RNA by TRIzol (method B) yield slightly higher amount of cRNA, generate a lower image background as measured by Q value, but have a higher 3'/5' GAPD ratio. When the total RNA was prepared by TRIzol followed by RNeasy purification (method C) the cRNA yield was high, the background low, with the 3'/5' GAPD ratio being a little bit higher than for preparations of total RNA by QIAshredder homogenization followed by RNeasy purification. All three preparation methods generated an acceptable range of present calls on the whole genome microarray.As illustrated in Figure Simpleaffy" Bioconductor analysis package [Total RNA quality can also be indirectly assessed by a so-called RNA degradation plot analysis as implemented in the " package . The samTo assess the comparability of global gene expression data between samples isolated with different preparation methods it is useful to examine the overall signal distribution of all probe sets as density curve for each microarray experiment. Outlier experiments would be detected by their different behavior of the density curves. As shown in Figure We next investigated the consistency of gene expression measurements of leukemia samples when using different total RNA extraction methods by performing an unsupervised hierarchical clustering analysis. Expression data have been normalized using the PQN algorithm . 2821 geA supervised analysis was performed to assess the potential impact of the use of different total RNA extraction methods on a leukemia classification approach. An all-pairwise t-test analysis identified differentially expressed genes that would distinguish between the 9 classes of pediatric leukemias that are represented in our dataset. A gene set of 1089 differentially expressed probe sets was then examined by three-dimensional PCA. As shown in Figure As shown in Figure We next investigated the percentage of overlapping genes that are found to be differentially expressed between the three methods used when analyzing the various leukemia subclasses in a supervised way. The percentage of overlapping genes is another suitable parameter to address the impact of the use of different total RNA extraction methods on a leukemia classification approach. Figure Additionally, to further illustrate the assay performance, a statistical power analysis for the RNA preparation methods A, B, and C is performed based on the Bioconductor package "ssize". The power analysis is used, for statistical comparison of identical leukemia samples, to assess the precision of technical replicates obtained from different RNA preparation methods. The data sets generated based on the preparations of total RNA following the methods A and B have greater average statistical power than the microarray data set based on method C [see Additional File In summary, these analyses indicate that preparation of total RNA by QIAshredder homogenization followed by RNeasy purification is a robust sample preparation method for microarray experiments that outperforms other procedures for isolation of total RNA.2), box plots, scatter plots, and coefficient of variation (CV) assessments. These analyses included all 54675 probe sets represented on the HG-U133 Plus 2.0 microarray.As three patients had been analyzed with three technical replicates Figure  we there2 range from 0.985 to 0.989 for preparations of total RNA by QIAshredder homogenization followed by RNeasy purification (method A), 0.976 to 0.987 for TRIzol isolation (method B), and 0.967 to 0.988 for TRIzol followed by RNeasy purification (method C). Between the three different sample preparation methods the mean value of R2 is 0.952 and standard deviation is 0.005 for method A versus method B, 0.976 mean value and 0.005 standard deviation for method A versus method C, and 0.965 mean value and 0.011 standard deviation for method B versus method C, respectively.As shown in Figure Analysis of coefficient of variation is a useful way for assessment of reproducibility and precision of the gene expression profiles generated from three different total RNA sources. The box plots demonstrate the variability in gene expression measurements within the three technical replicates using different sample preparation methods [see Additional File Recent investigations successfully applied gene expression microarrays to classify known tumor types and also various hematological malignancies ,25,28-34P = 5,308e-12), the isolation of total RNA using QIAshredder homogenization followed by RNeasy purification (method A) resulted in a better quality of starting material as demonstrated by the A260/280 ratio of cRNA , by very reproducible low 3'/5' GAPD ratios, and by consistently lower scaling factors . This was then further examined by a so-called RNA degradation plot analysis as implemented in the Simpleaffy Bioconductor analysis package [After a first analysis of the quality of our microarray data, we could assert that since in all cases the quality parameters met our criteria, each of the three preparation methods is able to generate acceptable gene expression profiles of pediatric leukemias. We found that samples representing different leukemia subclasses and extracted using different RNA preparation methods are characterized by a high comparability of gene expression data thus demonstrating that sample preparation procedures do not impair the overall probe set signal intensity distribution. Importantly, even though yielding lower amounts of cRNA if compared to TRIzol (method B) and TRIzol followed by RNeasy (method C) protocols  differentially expressed genes between the nine distinct leukemia categories that we studied here, all samples are clearly separated by leukemia lineages and without being influenced by the total RNA isolation method. Furthermore, AML with normal karyotype is separated from the two patient samples with AML with t(11q23)/MLL demonstrating an intra-lineage distinction within the AML group. The same separation can be observed in the B lineage ALL group where samples with the chromosomal aberrations t, t, t, or t are split into distinct groups. As such, this is also an independent confirmation of the clustering organizations as presented in recent gene expression profiling studies of acute lymphoblastic leukemias ,30-33,36The first conclusion we draw from this study is that underlying biological characteristics of the pediatric acute leukemia classes are quite significant and largely exceed the variations between different total RNA sample preparation protocols. Having shown that at a chosen false discovery rate of 0.01% method A is producing a higher number of differentially expressed genes as compared to method B and method C, we would propose that lysis of the mononuclear cells, followed by lysate homogenization (QIAshredder) and total RNA purification (Qiagen) is the more robust total RNA isolation procedure for gene expression experiments using microarray technology. The importance of this new data is further strengthened by the analysis of the technical replicates. In fact, the gene expression data obtained with method A show the lowest degree of variation and are more reproducible, as compared to the alternative methods we tested for the isolation of total RNA. Finally, all these evidences, combined with the standardized microarray analysis protocol that we followed for this study let us conclude that the initial homogenization of the leukemia cell lysate followed by total RNA purification using spin columns is currently the optimal protocol available with respect to the robustness of gene expression data and that this method is practical for a routine laboratory use. Here we limited our microarray study to pediatric leukemia, but certainly these statements could also be applied to similar cohorts of adult leukemias.E2A-PBX1), t(MLL-AF4), t(BCR-ABL) t(TEL-AML1), t(AML1-ETO), t(PML-RARA), inv(16)(CBFB-MYH11), and t were screened following the BIOMED-1 concert action protocol [MLL rearrangement  and #26 is t) and AML patients with normal karyotype or other abnormalities (n = 2). The ALL group included Pro-B-ALL t (n = 1), Pro-B-ALL/c-ALL with t (n = 2), T-ALL (n = 5), c-ALL with t (n = 3), Pre-B-ALL with t (n = 1), B lineage ALL with hyperdiploid karyotype (n = 3), and B lineage ALL negative for the screened recurrent translocations and with a DNA index value equal to 1.0 (n = 8). The percentage of blast cells ranged between 70% and 98%.Between December 2005 and March 2006 samples from twenty-seven acute pediatric leukemia patients were analyzed at the time of diagnosis. All patients received a laboratory diagnosis based on white blood cell count, cytomorphology, cytochemistry, multiparameter immunophenotyping, cytogenetics, fluorescence in situ hybridization (FISH), and molecular genetics (PCR). Chromosome aberrations t1;19). Subsequently, total RNA was extracted from aliquots of 5 × 106 cells and 10 × 106 cells following two distinct total RNA purification method A and method B, respectively . Total RNA obtained from method B was either used for the subsequent microarray analysis without further purification (method B), or was additionally purified following method C . Microarray analysis was performed on each sample and each preparation method (Affymetrix HG-U133 Plus 2.0). Thus, for 24 patient samples a total of 72 microarrays were analyzed , resulting in additional 27 gene expression profiles on Affymetrix HG-U133 Plus 2.0 microarrays  and electropherogram .Mononuclear cells were processed immediately after or within 24 hours after the biopsy was obtained. Appearance and fluidity of the samples were monitored before starting with RNA isolation. Total RNA was isolated using three different methods. Method A: lysis of the mononuclear cells, followed by lysate homogenization using a biopolymer shredding system in a microcentrifuge spin-column format , followed by total RNA purification using selective binding columns . The cell lysate homogenization phase reduces viscosity caused by high-molecular-weight cellular components and cell debris. Method B: TRIzol RNA isolation . Method C: TRIzol RNA isolation (Invitrogen) followed by a purification step . The RNA purification step previously mentioned combines the selective binding properties of a silica-based membrane with the speed of microspin technology. This system allows only RNA longer than 200 bases to bind to the silica membrane, providing an enriching for mRNA since nucleotides shorter than 200 nucleotides are selectively excluded. In all three methods we followed the protocols provided by the manufacturers. After extraction, total RNA was stored at -80°C until used for microarray analyses. RNA quality was assessed on the Agilent Bioanalyzer 2100 using the Agilent RNA 6000 Nano Assay kit . RNA concentration was determined using the NanoDrop ND-1000 spectrophotometer . The overall total RNA quality was assessed by A24 – T7 primer  and the Poly-A control transcripts . The generated cDNA was purified using the GeneChip Sample Cleanup Module (Affymetrix). Then, labeled cRNA was generated using the Microarray RNA target synthesis kit (Roche Applied Science) and an in vitro transcription labeling nucleotide mixture (Affymetrix). The generated cRNA was purified using the GeneChip Sample Cleanup Module (Affymetrix) and quantified using the NanoDrop ND-1000 spectrophotometer. In each preparation an amount of 11.0 μg cRNA were fragmented with 5× Fragmentation Buffer (Affymetrix) in a final reaction volume of 25 μl. The incubation steps during cDNA synthesis, in vitro transcription reaction, and target fragmentation were performed using the Hybex Microarray Incubation System  and Eppendorf ThermoStat plus instruments . Hybridization, washing, staining and scanning protocols, respectively, were performed on Affymetrix GeneChip instruments  as recommended by the manufacturer.From each RNA preparation 2.0 μg of total RNA were converted into double-stranded cDNA by reverse transcription using a cDNA Synthesis System kit including an oligo(dT)260/A280 ratio included: (i) background noise , (ii) percentage of present called probe sets, (iii) scaling factor, (iv) information about exogenous Bacillus subtilis control transcripts from the Affymetrix Poly-A control kit , and (v) the ratio of intensities of 3' probes to 5' probes for a housekeeping gene (GAPD).Microarray image files (.cel data) were generated using default Affymetrix microarray analysis parameters GCOS 1.2 software). Subsequently, intensity signals were calculated based on the non-central trimmed mean of Perfect Match intensities with Quantile Normalization . For eac softwareThe data pre-processing included the summarization to generate probe set level signals for each microarray experiment and was performed using the PS or PQN algorithms as described elsewhere . To analThis study is part of the MILE Study (Microarray Innovations In LEukemia) program, an ongoing collaborative effort headed by the European Leukemia Network (ELN) and sponsored by Roche Molecular Systems, Inc., addressing gene expression signatures in acute and chronic leukemias. This study further supports the AmpliChip Leukemia Test program, a gene expression microarray test for the subclassification of leukemia. Roche Molecular Systems, Inc. has business relationships with Qiagen and is currently validating Qiagen products for the AmpliChip Leukemia Test.MCDO performed the microarray experiments and wrote the paper, LT contributed to perform the experiments, AZ, RL, and WML analyzed the microarray data, GB recorded clinical data, GK supervised the study and writing of the manuscript, and AK provided the original concept of the study, and contributed to writing the paper.Supplementary Data. This file contains supplementary figures with additional comments explaining details of analysis, results, and interpretation.Click here for fileThis Excel file contains further details about each total RNA isolation method, including cRNA quality and quantity values as well as microarray quality and quantity values for each experiment.Click here for fileThis Excel file contains details about each total RNA isolation method and leukemia classification details for each CEL file. All microarray raw data (*.cel files) are available online through the Gene Expression Omnibus database with the series accession number GSE7757.Click here for file