Plasma MicroRNAs as sensitive and specific biomarkers of tissue injury.

BACKGROUND: MicroRNAs (miRNAs) are endogenous, small noncoding RNAs. Because of their size, abundance, tissue specificity, and relative stability in plasma, miRNAs hold promise as unique accessible biomarkers to monitor tissue injury. METHODS: We investigated the use of liver-, muscle- and brain-specific miRNAs as circulating biomarkers of tissue injury. We used a highly sensitive quantitative PCR assay to measure specific miRNAs (miR-122, miR-133a, and miR-124) in plasma samples from rats treated with liver or muscle toxicants and from a rat surgical model of stroke. RESULTS: We observed increases in plasma concentrations of miR-122, miR-133a, and miR-124 corresponding to injuries in liver, muscle, and brain, respectively. miR-122 and miR-133a illustrated specificity for liver and muscle toxicity, respectively, because they were not detectable in the plasma of animals with toxicity to the other organ. This result contrasted with the results for alanine aminotransferase (ALT) and aspartate aminotransferase, which were both increased with either organ toxicity. Furthermore, miR-122 exhibited a diagnostic sensitivity superior to that of ALT when the results were correlated to the liver histopathologic results. The miR-124 concentration increased in the plasma of rats 8 h after surgery to produce brain injury and peaked at 24 h, while the miR-122 and miR-133a concentrations remained at baseline values. CONCLUSIONS: These results demonstrate that tissue-specific miRNAs may serve as diagnostically sensitive plasma biomarkers of tissue injury.

Development of a microarray platform for FFPET profiling: application to the classification of human tumors.

BACKGROUND: mRNA profiling has become an important tool for developing and validating prognostic assays predictive of disease treatment response and outcome. 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. METHODS: 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 1 and then profiled 50 matched FFPET samples to test how well the FFPET data predicted the class labels. We also compared the sorting power of classifiers derived from FFPET sample data with classifiers derived from data from matched FF samples. RESULTS: 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 (leave-one-out cross validation) with an error rate of approximately 14% (p-value = 3.7 x 10(-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. CONCLUSION: We 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.

Multiplexed measurements of gene signatures in different analytes using the Nanostring nCounter Assay System.

BACKGROUND: We assessed NanoString's nCounter Analysis System for its ability to quantify gene expression of forty-eight genes in a single reaction with 100 ng of total RNA or an equivalent amount of tissue lysate. In the nCounter System, multiplexed gene expression target levels are directly detected, without enzymatic reactions, via two sequence-specific probes. The individual mRNA is captured with one mRNA target sequence-specific capture probe that is used in a post-hybridization affinity purification procedure. The second mRNA target specific-sequence and fluorescent-labeled colored coded probe is then used in the detection with the 3-component complex separated on a surface via an applied electric field followed by imaging. We evaluated reproducibility, accuracy, concordance with quantitative RT-PCR, linearity, dynamic range, and the ability of the system to assay different inputs (matched samples of total RNA from Flash Frozen (FF) and Formalin Fixed Paraffin Embedded Tissues (FFPET), and crude tissue lysates (CTL)). FINDINGS: The nCounter Analysis System provided data equivalent to that produced by Taqman(R)-based assays for genes expressed within the ranges of the calibration curves (above ~0.5 mRNA copies per human cell based on an assumption of 10 pg of total RNA per cell). System response was linear over more than two orders of magnitude with typical CVs of ~6% for concentrations above 1 fM (105 molecules per mL). Profiling the industry-standard MAQC data set yielded correlation coefficients of >0.83 for intensity values and >0.99 for measured ratios. Ninety percent of nCounter ratio measurements were within 1.27-1.33 fold changes of the Taqman(R) data (0.34-0.41 in log2 scale) for FF total RNA samples. CONCLUSION: The nCounter Analysis System generated robust data for multi-gene expression signatures across three different sample preparation conditions.

Improved high-throughput ultrafiltration process enables cRNA purification for gene expression profiling.

Ultrafiltration of nucleic acids has been used for a wide variety of applications, including sequence reaction purification and amplicon cleanup prior to spotting onto microarrays. Here we describe a novel process, using ultrafiltration, that purifies cRNA products for sensitive downstream applications. Initial attempts at this high-throughput purification for cRNA resulted in low sensitivity when compared against an industry standard (silica-based bind, wash, and elute purification). We modified the ultrafiltration process to include a proteinase K preincubation and a phosphate buffer wash that, when combined, increased sensitivity and signal-to-noise ratio in microarray applications. The protocol that we have developed eliminates the use of chaotropic salts (such as guanidinium thiocyanate) that are typically used in silica binding purification methods. The data demonstrate good performance for sensitive RNA applications using well-defined metrics, and thus the technique might be useful for a broader range of nucleic acid purifications.

Characterization of globin RNA interference in gene expression profiling of whole-blood samples.

BACKGROUND: Blood-based biomarker discovery with gene expression profiling has been hampered by interference from endogenous, highly abundant alpha- and beta-globin transcripts. We describe a means to quantify the interference of globin transcripts on profiling and the effectiveness of globin transcript mitigation by (a) defining and characterizing globin interference, (b) reproducing globin interference with synthetic transcripts, and (c) using ROC curves to measure sensitivity and specificity for a protocol for removing alpha- and beta-globin transcripts. METHODS: We collected blood at 2 sites and extracted total RNA in PreAnalytiX PAXgene tubes. As a reference for characterizing interference, we supplemented aliquots of total RNA with synthesized globin transcripts and total RNA from human brain. Selected aliquots were processed with Ambion GLOBINclear to remove globin transcripts. All aliquots were labeled and hybridized to Agilent DNA microarrays by means of pooling schemes designed to quantify the mitigation of globin interference and to titrate gene expression signatures. Quantitative reverse transcription-PCR data were generated for comparison with microarray results. RESULTS: Our supplementation and pooling strategy for comparing the microarray data among samples demonstrated that mitigation could reduce an interference signature of >1000 genes to approximately 200. Analysis of samples of endogenous globin transcripts supplemented with brain RNA indicated that results obtained with the GLOBINclear treatment approach those of peripheral blood mononuclear cell preparations. CONCLUSION: We confirmed that both the absolute concentrations of globin transcripts and differences in transcript concentrations within a sample set are factors that cause globin interference (Genes Immun 2005;6:588-95). The methods and transcripts we have developed may be useful for quantitatively characterizing globin mRNA interference and its mitigation.

Reagent preparation and storage for amplification of microarray hybridization targets with a fully automated system.

The advent of automated systems for gene expression profiling has accentuated the need for the development of convenient and cost-effective methods for reagent preparation. We have developed a method for the preparation and storage of pre-aliquoted cocktail plates that contain all reagents required for amplification of nucleic acid by reverse transcription and in vitro transcription reactions. Plates can be stored at -80 degrees C for at least 1 month and kept in a hotel at 4 degrees C for at least 24 h prior to use. Microarray data quality generated from these pre-aliquoted reagent plates is not statistically different between cRNA amplified with stored cocktails and cRNA amplified with freshly prepared cocktails. Deployment of pre-aliquoted, stored cocktail plates in a fully automated system not only increases the throughput of amplifying cRNA targets from thousands of RNA samples, but could also considerably reduce reagent costs and potentially improve process robustness.

Use of a mixed tissue RNA design for performance assessments on multiple microarray formats.

The comparability and reliability of data generated using microarray technology would be enhanced by use of a common set of standards that allow accuracy, reproducibility and dynamic range assessments on multiple formats. We designed and tested a complex biological reagent for performance measurements on three commercial oligonucleotide array formats that differ in probe design and signal measurement methodology. The reagent is a set of two mixtures with different proportions of RNA for each of four rat tissues (brain, liver, kidney and testes). The design provides four known ratio measurements of >200 reference probes, which were chosen for their tissue-selectivity, dynamic range coverage and alignment to the same exemplar transcript sequence across all three platforms. The data generated from testing three biological replicates of the reagent at eight laboratories on three array formats provides a benchmark set for both laboratory and data processing performance assessments. Close agreement with target ratios adjusted for sample complexity was achieved on all platforms and low variance was observed among platforms, replicates and sites. The mixed tissue design produces a reagent with known gene expression changes within a complex sample and can serve as a paradigm for performance standards for microarrays that target other species.

A microarray platform comparison for neuroscience applications.

To address the need for high sensitivity in gene expression profiling of small neural tissue samples ( approximately 100 ng total RNA), we compared a novel RT-PCR-IVT protocol using fluor-reverse pairs on inkjet oligonucleotide microarrays and an RT-IVT protocol using 33P labeling on nylon cDNA arrays. The comparison protocol was designed to evaluate these systems for sensitivity, specificity, reproducibility, and linearity. We developed parameters, thresholds, and testing conditions that could be used to differentiate various systems that spanned detection chemistry and instrumentation; probe number and selection criteria; and sample processing protocols. We concluded that the inkjet system had better performance in sensitivity, specificity, and reproducibility than the nylon system, and similar performance in linearity. Between these two platforms, the data indicates that the inkjet system would perform better for the transcriptional profiling of 100 ng total RNA samples for neuroscience studies.

Effects of atmospheric ozone on microarray data quality.

A data anomaly was observed that affected the uniformity and reproducibility of fluorescent signal across DNA microarrays. Results from experimental sets designed to identify potential causes (from microarray production to array scanning) indicated that the anomaly was linked to a batch process; further work allowed us to localize the effect to the posthybridization array stringency washes. Ozone levels were monitored and highly correlated with the batch effect. Controlled exposures of microarrays to ozone confirmed this factor as the root cause, and we present data that show susceptibility of a class of cyanine dyes (e.g., Cy5, Alexa 647) to ozone levels as low as 5-10 ppb for periods as short as 10-30 s. Other cyanine dyes (e.g., Cy3, Alexa 555) were not significantly affected until higher ozone levels (> 100 ppb). To address this environmental effect, laboratory ozone levels should be kept below 2 ppb (e.g., with filters in HVAC) to achieve high quality microarray data.