Predicted miR-208a-3p values in plasma and urine from control and isoproterenol-treated rats and the corresponding LLOQs determined using two workflows and plotted in Fig. 6. (CSV 10 kb)

Background: We demonstrate the feasibility of creating a pair of reference samples to be used as surrogates for clinical samples measured in either a research or clinical laboratory setting. The reference sample paradigm presented and evaluated here is designed to assess the capability of a measurement process to detect true differences between two biological samples. Cell-based reference samples can be created with a biomarker signature pattern designed in silico. Clinical laboratories working in regulated applications are required to participate in proficiency testing programs; research laboratories doing discovery typically do not. These reference samples can be used in proficiency tests or as process controls that allow a laboratory to evaluate and optimize its measurement systems, monitor performance over time (process drift), assess changes in protocols, reagents, and/or personnel, maintain standard operating procedures, and most importantly, provide evidence for quality results. Results: The biomarkers of interest in this study are microRNAs (miRNAs), small non-coding RNAs involved in the regulation of gene expression. Multiple lung cancer associated cell lines were determined by reverse transcription (RT)-PCR to have sufficiently different miRNA profiles to serve as components in mixture designs as reference samples. In silico models based on the component profiles were used to predict miRNA abundance ratios between two different cell line mixtures, providing target values for profiles obtained from in vitro mixtures. Two reference sample types were tested: total RNA mixed after extraction from cell lines, and intact cells mixed prior to RNA extraction. MicroRNA profiling of a pair of samples composed of extracted RNA derived from these cell lines successfully replicated the target values. Mixtures of intact cells from these lines also approximated the target values, demonstrating potential utility as mimics for clinical specimens. Both designs demonstrated their utility as reference samples for inter-or intralaboratory testing. Conclusions: Cell-based reference samples can be created for performance assessment of a measurement process from biomolecule extraction through quantitation. Although this study focused on miRNA profiling with RT-PCR using cell lines associated with lung cancer, the paradigm demonstrated here should be extendable to genome-scale platforms and other biomolecular endpoints.

Background The potential utility of microRNA as biomarkers for early detection of cancer and other diseases is being investigated with genome-scale profiling of differentially expressed microRNA. Processes for measurement assurance are critical components of genome-scale measurements. Here, we evaluated the utility of a set of total RNA samples, designed with between-sample differences in the relative abundance of miRNAs, as process controls. Results Three pure total human RNA samples (brain, liver, and placenta) and two different mixtures of these components were evaluated as measurement assurance control samples on multiple measurement systems at multiple sites and over multiple rounds. In silico modeling of mixtures provided benchmark values for comparison with physical mixtures. Biomarker development laboratories using next-generation sequencing (NGS) or genome-scale hybridization assays participated in the study and returned data from the samples using their routine workflows. Multiplexed and single assay reverse-transcription PCR (RT-PCR) was used to confirm in silico predicted sample differences. Data visualizations and summary metrics for genome-scale miRNA profiling assessment were developed using this dataset, and a range of performance was observed. These metrics have been incorporated into an online data analysis pipeline and provide a convenient dashboard view of results from experiments following the described design. The website also serves as a repository for the accumulation of performance values providing new participants in the project an opportunity to learn what may be achievable with similar measurement processes. Conclusions The set of reference samples used in this study provides benchmark values suitable for assessing genome-scale miRNA profiling processes. Incorporation of these metrics into an online resource allows laboratories to periodically evaluate their performance and assess any changes introduced into their measurement process.

Background: Circulating microRNAs are undergoing exploratory use as safety biomarkers in drug development. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) is one common approach used to quantitate levels of microRNAs in samples that includes the use of a standard curve of calibrators fit to a regression model. Guidelines are needed for setting assay quantitation thresholds that are appropriate for this method and to biomarker pre-validation. Results: In this report, we develop two workflows for determining a lower limit of quantitation (LLOQ) for RT-qPCR assays of microRNAs in exploratory studies. One workflow is based on an error threshold calculated by a logistic model of the calibration curve data. The second workflow is based on a threshold set by the sample blank, which is the no template control for RT-qPCR. The two workflows are used to set lower thresholds of reportable microRNA levels for an example dataset in which miR-208a levels in biofluids are quantitated in a cardiac injury model. LLOQ thresholds set by either workflow are effective in filtering out microRNA values with large uncertainty estimates. Conclusions: Two workflows for LLOQ determinations are presented in this report that provide methods that are easy to implement in investigational studies of microRNA safety biomarkers and offer choices in levels of conservatism in setting lower limits of acceptable values that facilitate interpretation of results.

RNA mixture Cq values. (CSV 18Â kb)

Many biomaterial scaffolds have been advanced to provide synthetic cell niches for tissue engineering and drug screening applications; however, current methods for comparing scaffold niches focus on cell functional outcomes or attempt to normalize materials properties between different scaffold formats. We demonstrate a three-dimensional (3D) cellular morphotyping strategy for comparing biomaterial scaffold cell niches between different biomaterial scaffold formats. Primary human bone marrow stromal cells (hBMSCs) were cultured on 8 different biomaterial scaffolds, including fibrous scaffolds, hydrogels, and porous sponges, in 10 treatment groups to compare a variety of biomaterial scaffolds and cell morphologies. A bioinformatics approach was used to determine the 3D cellular morphotype for each treatment group by using 82 shape metrics to analyze approximately 1000 cells. We found that hBMSCs cultured on planar substrates yielded planar cell morphotypes, while those cultured in 3D scaffolds had elongated or equiaxial cellular morphotypes with greater height. Multivariate analysis was effective at distinguishing mean shapes of cells in flat substrates from cells in scaffolds, as was the metric L-1-depth (the cell height along its shortest axis after aligning cells with a characteristic ellipsoid). The 3D cellular morphotyping technique enables direct comparison of cellular microenvironments between widely different types of scaffolds and design of scaffolds based on cell structure-function relationships.

Spike-in RNAs are valuable controls for a variety of gene expression measurements. The External RNA Controls Consortium developed test sets that were used in a number of published reports. Here we provide an authoritative table that summarizes, updates, and corrects errors in the test version that ultimately resulted in the certified Standard Reference Material 2374. We have noted existence of anti-sense RNA controls in the material, corrected sub-pool memberships, and commented on control RNAs that displayed inconsistent behavior.

Extracellular microRNAs (miRNAs) represent a promising new source of toxicity biomarkers that are sensitive indicators of site of tissue injury. In order to establish reliable approaches for use in biomarker validation studies, the HESI technical committee on genomics initiated a multi-site study to assess sources of variance associated with quantitating levels of cardiac injury induced miRNAs in biofluids using RT-qPCR. Samples were generated at a central site using a model of acute cardiac injury induced in male Wistar rats by 0.5 mg/kg isoproterenol. Biofluid samples were sent to 11 sites for measurement of 3 cardiac enriched miRNAs (miR-1-3p, miR-208a-3p, and miR-499-5p) and 1 miRNA abundant in blood (miR-16-5p) or urine (miR-192-5p) by absolute quantification using calibration curves of synthetic miRNAs. The samples included serum and plasma prepared from blood collected at 4 h, urine collected from 6 to 24 h, and plasma prepared from blood collected at 24 h post subcutaneous injection. A 3 parameter logistic model was utilized to fit the calibration curve data and estimate levels of miRNAs in biofluid samples by inverse prediction. Most sites observed increased circulating levels of miR-1-3p and miR-208a-3p at 4 and 24 h after isoproterenol treatment, with no difference seen between serum and plasma. The biological differences in miRNA levels and sample type dominated as sources of variance, along with outlying performance by a few sites. The standard protocol established in this study was successfully implemented across multiple sites and provides a benchmark method for further improvements in quantitative assays for circulating miRNAs.

Background: Highly multiplexed assays for quantitation of RNA transcripts are being used in many areas of biology and medicine. Using data generated by these transcriptomic assays requires measurement assurance with appropriate controls. Methods to prototype and evaluate multiple RNA controls were developed as part of the External RNA Controls Consortium (ERCC) assessment process. These approaches included a modified Latin square design to provide a broad dynamic range of relative abundance with known differences between four complex pools of ERCC RNA transcripts spiked into a human liver total RNA background.Results: ERCC pools were analyzed on four different microarray platforms: Agilent 1- and 2-color, Illumina bead, and NIAID lab-made spotted microarrays; and two different second-generation sequencing platforms: the Life Technologies 5500xl and the Illumina HiSeq 2500. Individual ERCC controls were assessed for reproducible performance in signal response to concentration among the platforms. Most demonstrated linear behavior if they were not located near one of the extremes of the dynamic range. Performance issues with any individual ERCC transcript could be attributed to detection limitations, platform-specific target probe issues, or potential mixing errors. Collectively, these pools of spike-in RNA controls were evaluated for suitability as surrogates for endogenous transcripts to interrogate the performance of the RNA measurement process of each platform. The controls were useful for establishing the dynamic range of the assay, as well as delineating the useable region of that range where differential expression measurements, expressed as ratios, would be expected to be accurate.Conclusions: The modified Latin square design presented here uses a composite testing scheme for the evaluation of multiple performance characteristics: linear performance of individual controls, signal response within dynamic range pools of controls, and ratio detection between pairs of dynamic range pools. This compact design provides an economical sample format for the evaluation of multiple external RNA controls within a single experiment per platform. These results indicate that well-designed pools of RNA controls, spiked into samples, provide measurement assurance for endogenous gene expression studies.

Measurement of RNA can be used to study and monitor a range of infectious and non-communicable diseases, with profiling of multiple gene expression mRNA transcripts being increasingly applied to cancer stratification and prognosis. An international comparison study (Consultative Committee for Amount of Substance (CCQM)-P103.1) was performed in order to evaluate the comparability of measurements of RNA copy number ratio for multiple gene targets between two samples. Six exogenous synthetic targets comprising of External RNA Control Consortium (ERCC) standards were measured alongside transcripts for three endogenous gene targets present in the background of human cell line RNA. The study was carried out under the auspices of the Nucleic Acids (formerly Bioanalysis) Working Group of the CCQM. It was coordinated by LGC (United Kingdom) with the support of National Institute of Standards and Technology (USA) and results were submitted from thirteen National Metrology Institutes and Designated Institutes. The majority of laboratories performed RNA measurements using RT-qPCR, with datasets also being submitted by two laboratories based on reverse transcription digital polymerase chain reaction and one laboratory using a next-generation sequencing method. In RT-qPCR analysis, the RNA copy number ratios between the two samples were quantified using either a standard curve or a relative quantification approach. In general, good agreement was observed between the reported results of ERCC RNA copy number ratio measurements. Measurements of the RNA copy number ratios for endogenous genes between the two samples were also consistent between the majority of laboratories. Some differences in the reported values and confidence intervals ('measurement uncertainties') were noted which may be attributable to choice of measurement method or quantification approach. This highlights the need for standardised practices for the calculation of fold change ratios and uncertainties in the area of gene expression profiling.

Background Genome-scale “-omics” measurements are challenging to benchmark due to the enormous variety of unique biological molecules involved. Mixtures of previously-characterized samples can be used to benchmark repeatability and reproducibility using component proportions as truth for the measurement. We describe and evaluate experiments characterizing the performance of RNA-sequencing (RNA-Seq) measurements, and discuss cases where mixtures can serve as effective process controls. Results We apply a linear model to total RNA mixture samples in RNA-seq experiments. This model provides a context for performance benchmarking. The parameters of the model fit to experimental results can be evaluated to assess bias and variability of the measurement of a mixture. A linear model describes the behavior of mixture expression measures and provides a context for performance benchmarking. Residuals from fitting the model to experimental data can be used as a metric for evaluating the effect that an individual step in an experimental process has on the linear response function and precision of the underlying measurement while identifying signals affected by interference from other sources. Effective benchmarking requires well-defined mixtures, which for RNA-Seq requires knowledge of the post-enrichment ‘target RNA’ content of the individual total RNA components. We demonstrate and evaluate an experimental method suitable for use in genome-scale process control and lay out a method utilizing spike-in controls to determine enriched RNA content of total RNA in samples. Conclusions Genome-scale process controls can be derived from mixtures. These controls relate prior knowledge of individual components to a complex mixture, allowing assessment of measurement performance. The target RNA fraction accounts for differential selection of RNA out of variable total RNA samples. Spike-in controls can be utilized to measure this relationship between target RNA content and input total RNA. Our mixture analysis method also enables estimation of the proportions of an unknown mixture, even when component-specific markers are not previously known, whenever pure components are measured alongside the mixture.

Abstract The biomarkers of interest in this study are microRNA (miRNA), small non-coding RNAs involved in the regulation of gene expression. Currently, over 1000 human miRNAs have been identified. Several laboratories within the Early Detection Research Network are currently profiling miRNA as potential biomarkers. The reference sample paradigm presented and evaluated here is designed to provide measurement assurance in the earliest phases of a cancer biomarker development pipeline. Three commercially available samples of human total RNA (brain, liver, and placenta) were selected for detectable differences in miRNA expression based upon previously reported miRNA microarray experiments. For each round of this study, replicate samples of each tissue were aliquoted, blinded, and distributed to participating laboratories. The samples were then profiled using the standard workflows of each laboratory. This was repeated over multiple rounds of measurement. Raw data were processed in pair-wise comparisons between tissue types. Log2 transformed ratios were calculated for each detectable miRNA. For each of these miRNA ratios, the results among laboratories were compared to the consensus value to provide a measure of concordance and help identify laboratories that might need to make adjustments to their process. Results demonstrated repeatability within labs and reproducibility among labs with respect to direction and magnitude of differential miRNA expression between tissues for a set of miRNAs of interest (MOI). For the first round, 79% of reported results for all log2 ratio combinations and MOI were in agreement with the consensus values. Subsequent rounds demonstrated increased concordance, with 91 and 100% agreement, respectively. The data from the nine “neat” tissue total RNA samples were also used for in silico modeling to predict the values for two complex mixtures based on three components. The two mixture designs provide a pair of reference samples with designed-in reciprocal ratios of 2:1 for brain and placenta, as well as a shared 1:1 liver component. For two brain-selective and two placenta-selective MOI, 91% of reported values were in close agreement with the target values. Overall, measurement results were harmonious for a set of miRNAs of interest measured by all participants, both within rounds and between rounds. The laboratories were also able to demonstrate the ability to resolve the 2-fold differences created with complex mixtures. These results suggest that endogenous miRNAs present within total RNA may be suitable as QA/QC process controls. Citation Format: P. Scott Pine, Lynn R. Sorbara, Sudhir Srivastava, Marc Salit. miRNA reference samples for interlaboratory study. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3967. doi:10.1158/1538-7445.AM2015-3967

9 10 Abstract: 11 12 Background: 13 14 Genome-scale "-omics" measurements are challenging to benchmark due to the enormous variety 15 of unique biological molecules involved. Mixtures of previously-characterized samples can be 16 used to benchmark repeatability and reproducibility using component proportions as truth for the 17 measurement. We describe and evaluate experiments characterizing the performance of RNA-

External RNA spike-in control ratio abundance mixtures enable assessment of technical performance of a gene expression experiment and comparison of performance between gene expression experiments. We've developed an R package, the “erccdashboard”, to analyze ratio abundance mixtures of the External RNA Control Consortium (ERCC) controls for the purpose of gene expression experiment method validation. For RNA-Seq, analysis of sequence reads with existing QC metrics provides an assessment of sequencing and mapped read quality, the analysis of ERCC control ratio mixtures is intended to provide QC metrics that are relevant for gene expression analysis methods including transcript quantification and differential expression testing. The erccdashboard package addresses this critical analysis need with QC metrics for gene expression experiments including dynamic range, biases and variability in ratio measurements, diagnostic performance (discrimination of true positive and true negative controls), and empirical determination of the limit of detection of ratios. Using the erccdashboard package quantitative differences between these ratio measurement performance metrics were shown for experiments within the same laboratory, between different laboratories, and for different gene expression measurement technologies (sequencing platforms and microarrays). The ERCC control ratio measurements also provide a quantification of biases that may be addressed with normalization approaches, such as differences in mRNA fraction of total RNA for a pair of gene expression experiment samples and batch effects. The erccdashboard R software package is freely available and may be adopted as part of the QC process for any gene expression measurement analysis pipeline.

Differences in gene expression of human bone marrow stromal cells (hBMSCs) during culture in three-dimensional (3D) nanofiber scaffolds or on two-dimensional (2D) films were investigated via pathway analysis of microarray mRNA expression profiles. Previous work has shown that hBMSC culture in nanofiber scaffolds can induce osteogenic differentiation in the absence of osteogenic supplements (OS). Analysis using ontology databases revealed that nanofibers and OS regulated similar pathways and that both were enriched for TGF-β and cell-adhesion/ECM-receptor pathways. The most notable difference between the two was that nanofibers had stronger enrichment for cell-adhesion/ECM-receptor pathways. Comparison of nanofibers scaffolds with flat films yielded stronger differences in gene expression than comparison of nanofibers made from different polymers, suggesting that substrate structure had stronger effects on cell function than substrate polymer composition. These results demonstrate that physical (nanofibers) and biochemical (OS) signals regulate similar ontological pathways, suggesting that these cues use similar molecular mechanisms to control hBMSC differentiation.

There is a critical need for standard approaches to assess, report and compare the technical performance of genome-scale differential gene expression experiments. Here we assess technical performance with a proposed standard 'dashboard' of metrics derived from analysis of external spike-in RNA control ratio mixtures. These control ratio mixtures with defined abundance ratios enable assessment of diagnostic performance of differentially expressed transcript lists, limit of detection of ratio (LODR) estimates and expression ratio variability and measurement bias. The performance metrics suite is applicable to analysis of a typical experiment, and here we also apply these metrics to evaluate technical performance among laboratories. An interlaboratory study using identical samples shared among 12 laboratories with three different measurement processes demonstrates generally consistent diagnostic power across 11 laboratories. Ratio measurement variability and bias are also comparable among laboratories for the same measurement process. We observe different biases for measurement processes using different mRNA-enrichment protocols.

Background Molecular biomarkers that are based on mRNA transcripts are being developed for the diagnosis and treatment of a number of diseases. DNA microarrays are one of the primary technologies being used to develop classifiers from gene expression data for clinically relevant outcomes. Microarray assays are highly multiplexed measures of comparative gene expression but have a limited dynamic range of measurement and show compression in fold change detection. To increase the clinical utility of microarrays, assay controls are needed that benchmark performance using metrics that are relevant to the analysis of genomic data generated with biological samples. Results Ratiometric controls were prepared from commercial sources of high quality RNA from human tissues with distinctly different expression profiles and mixed in defined ratios. The samples were processed using six different target labeling protocols and replicate datasets were generated on high density gene expression microarrays. The area under the curve from receiver operating characteristic plots was calculated to measure diagnostic performance. The reliable region of the dynamic range was derived from log 2 ratio deviation plots made for each dataset. Small but statistically significant differences in diagnostic performance were observed between standardized assays available from the array manufacturer and alternative methods for target generation. Assay performance using the reliable range of comparative measurement as a metric was improved by adjusting sample hybridization conditions for one commercial kit. Conclusions Process improvement in microarray assay performance was demonstrated using samples prepared from commercially available materials and two metrics - diagnostic performance and the reliable range of measurement. These methods have advantages over approaches that use a limited set of external controls or correlations to reference sets, because they provide benchmark values that can be used by clinical laboratories to help optimize protocol conditions and laboratory proficiency with microarray assays.

Stem cell response to a library of scaffolds with varied 3D structures was investigated. Microarray screening revealed that each type of scaffold structure induced a unique gene expression signature in primary human bone marrow stromal cells (hBMSCs). Hierarchical cluster analysis showed that treatments sorted by scaffold structure and not by polymer chemistry suggesting that scaffold structure was more influential than scaffold composition. Further, the effects of scaffold structure on hBMSC function were mediated by cell shape. Of all the scaffolds tested, only scaffolds with a nanofibrous morphology were able to drive the hBMSCs down an osteogenic lineage in the absence of osteogenic supplements. Nanofiber scaffolds forced the hBMSCs to assume an elongated, highly branched morphology. This same morphology was seen in osteogenic controls where hBMSCs were cultured on flat polymer films in the presence of osteogenic supplements (OS). In contrast, hBMSCs cultured on flat polymer films in the absence of OS assumed a more rounded and less-branched morphology. These results indicate that cells are more sensitive to scaffold structure than previously appreciated and suggest that scaffold efficacy can be optimized by tailoring the scaffold structure to force cells into morphologies that direct them to differentiate down the desired lineage.

This chapter reviews several techniques which combine the use of laser microbeams with antibodies to study molecular and cellular biology. An overview of the basic properties of lasers and their integration with microscopes and computers is provided. Biophysical applications, such as fluorescence recovery after photobleaching to measure molecular mobility and fluorescence resonance energy transfer to measure molecular distances, as well as ablative applications for the selective inactivation of proteins or the selective killing of cells are described. Other techniques, such as optical trapping, that do not rely on the interaction of the laser with the targeting antibody, are also discussed.