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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 intra-laboratory 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. Electronic supplementary material The online version of this article (10.1186/s12896-018-0423-4) contains supplementary material, which is available to authorized users.

BackgroundThe 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.ResultsThree 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.ConclusionsThe 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 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. Electronic supplementary material The online version of this article (doi:10.1186/s12896-016-0281-x) contains supplementary material, which is available to authorized users.

Universal approaches for assessing the diagnostic performance of microarray assays are essential for the application of microarray technology to clinical and regulatory settings. Reference systems for diagnostic assays in laboratory medicine typically involve the utilization of reference samples, metrics, and reference datasets to ensure that measurements are comparable and true. For microarray performance evaluation and process improvement, reference samples can be composed of mixes of different tissue or cell line RNAs that contain tissue-selective analytes at defined target ratios. The diagnostic accuracy of detected changes in expression, measured as the area under the curve from receiver-operating characteristic plots, can provide a single commutable value for comparing assay specificity and sensitivity. Examples of applying this method for assessing overall performance are provided using public datasets generated on five commercial human whole genome microarray platforms for the MicroArray Quality Control project, a community-wide effort to address issues surrounding microarray data reliability.

A significant limitation to the analytical accuracy and precision of dual-labeled spotted cDNA microarrays is the signal error due to dye bias. Transcript-dependent dye bias may be due to gene-specific differences of incorporation of two distinctly different chemical dyes and the resultant differential hybridization efficiencies of these two chemically different targets for the same probe. Several approaches were used to assess and minimize the effects of dye bias on fluorescent hybridization signals and maximize the experimental design efficiency of a cell culture experiment. Dye bias was measured at the individual transcript level within each batch of simultaneously processed arrays by replicate dual-labeled split-control sample hybridizations and accounted for a significant component of fluorescent signal differences. This transcript-dependent dye bias alone could introduce unacceptably high numbers of both false-positive and false-negative signals. We found that within a given set of concurrently processed hybridizations, the bias is remarkably consistent and therefore measurable and correctable. The additional microarrays and reagents required for paired technical replicate dye-swap corrections commonly performed to control for dye bias could be costly to end users. Incorporating split-control microarrays within a set of concurrently processed hybridizations to specifically measure dye bias can eliminate the need for technical dye swap replicates and reduce microarray and reagent costs while maintaining experimental accuracy and technical precision. These data support a practical and more efficient experimental design to measure and mathematically correct for dye bias.

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-beta 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. Published by Elsevier Ltd.

There is a critical need for standard approaches to assess, report, and compare the technical performance of genome-scale differential gene expression experiments. 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 amongst 12 laboratories with three different measurement processes demonstrated generally consistent diagnostic power across 11 laboratories. Ratio measurement variability and bias were also comparable amongst laboratories for the same measurement process. Different biases were observed for measurement processes using different mRNA enrichment protocols., Comment: 65 pages, 6 Main Figures, 33 Supplementary Figures

The substrate specificity of the P-glycoprotein (P-170), a multidrug transporter, was studied using N-acylated daunorubicin derivatives and four MDR1 cDNA transfected cell lines. Results showed that N-acetyl-daunorubicin is a substrate, but the longer fatty acid derivatives, N-octanoyl and N-dodecanoyl daunorubicins, are not. This conclusion was reached by flow cytometric drug uptake assay, cell proliferation assays, and confocal microscopy. It was concluded that the longer fatty acid derivatives interact with plasma membranes in a way that affected P-glycoprotein function.

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. Published by Elsevier Ltd.

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.

Brief treatment of human peripheral blood lymphocytes with the potential anti-HIV compound aurintricarboxylic acid (ATA) prompts the selective release of already bound L-selectin-specific anti-Leu8 and anti-LAM1-1 antibodies from the cells. Two other anti-LAM1 antibodies, anti-LAM1-3 and anti-LAM1-5 stay antigen-bound at the same time. Interestingly, the ATA-sensitive anti-Leu8 strongly competes with the ATA-resistant anti-LAM1-3 for binding. Photobleaching fluorescence resonance energy transfer (pFRET) measurements on flow-sorted cells suggests that these two antibodies compete for the same epitope, while anti-LAM1-5-FITC and anti-Leu8-PE bind to distinct sites, although they also compete for binding. Combining the data on competition, pFRET and ATA effect, we suggest that the ATA sensitive anti-Leu8 and resistant anti-LAM1-3 bind to overlapping but non-identical epitopes. This remarkably specific effect may be exploited for designing anti-inflammatory drugs that modulate leukocyte adhesion.

Several tannins with anti-HIV activity have been described previously (Nonaka et al., J Nat Prod 53: 587-595, 1990). We have shown that the tannins chebulinic acid and punicalin were able to block the binding of HIV rgp120 to CD4. These compounds were not toxic to stimulated human peripheral blood lymphocytes at concentrations ten times above their maximal effective concentration.

The multidrug-resistant gene (MDR1) encodes an energy-dependent drug efflux pump (P-glycoprotein) for many anti-cancer drugs. We have studied the intracellular distribution of rhodamine 123 (R123), daunorubicin (DN), and doxorubicin (DOX) in cells expressing a human MDR1 gene. The distribution of these fluorescent drugs was measured by laser scanning microscopy and confocal microscopy. We devised a new method for analysis of fluorescence line scan data to determine the intracellular distribution of fluorescent probes. This method and confocal microscopy showed that R123, DN, and DOX are localized to both plasma membrane and intracellular compartments in multidrug-resistant cells. When the cells are treated with verapamil, an inhibitor of the multidrug transporter, the amount of DOX, DN, and R123 associated with the cell rises. After inhibition, the relative distribution of DOX and DN between the cell surface and intracellular structures does not change dramatically. However, R123 tends to relocalize to intracellular sites from predominantly plasma membrane sites, indicating that this dye behaves differently than the anti-cancer drugs. These results show the subcellular distributions of R123, DN, and DOX in plasma membrane, cytoplasm, and intracellular membrane systems, but do not allow definitive distinctions among existing models of how P-glycoprotein affects the distribution of drugs.

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 log2 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.