An Automated Microfluidic Assay for Photonic Crystal Enhanced Detection and Analysis of an Antiviral Antibody Cancer Biomarker in Serum.

We report on the implementation of an automated platform for detecting the presence of an antibody biomarker for human papillomavirus-associated oropharyngeal cancer from a single droplet of serum, in which a nanostructured photonic crystal surface is used to amplify the output of a fluorescence-linked immunosorbent assay. The platform is comprised of a microfluidic cartridge with integrated photonic crystal chips that interfaces with an assay instrument that automates the introduction of reagents, wash steps, and surface drying. Upon assay completion, the cartridge interfaces with a custom laser-scanning instrument that couples light into the photonic crystal at the optimal resonance condition for fluorescence enhancement. The instrument is used to measure the fluorescence intensity values of microarray spots corresponding to the biomarkers of interest, in addition to several experimental controls that verify correct functioning of the assay protocol. In this work, we report both dose-response characterization of the system using anti-E7 antibody introduced at known concentrations into serum and characterization of a set of clinical samples from which results were compared with a conventional enzyme-linked immunosorbent assay (ELISA) performed in microplate format. The demonstrated capability represents a simple, rapid, automated, and high-sensitivity method for multiplexed detection of protein biomarkers from a low-volume test sample.

Susceptibility to quantum dot induced lung inflammation differs widely among the Collaborative Cross founder mouse strains.

Quantum dots (QDs) are engineered semiconductor nanoparticles with unique physicochemical properties that make them potentially useful in clinical, research and industrial settings. However, a growing body of evidence indicates that like other engineered nanomaterials, QDs have the potential to be respiratory hazards, especially in the context of the manufacture of QDs and products containing them, as well as exposures to consumers using these products. The overall goal of this study was to investigate the role of mouse strain in determining susceptibility to QD-induced pulmonary inflammation and toxicity. Male mice from 8 genetically diverse inbred strains (the Collaborative Cross founder strains) were exposed to CdSe-ZnS core-shell QDs stabilized with an amphiphilic polymer. QD treatment resulted in significant increases in the percentage of neutrophils and levels of cytokines present in bronchoalveolar lavage fluid (BALF) obtained from NOD/ShiLtJ and NZO/HlLtJ mice relative to their saline (Sal) treated controls. Cadmium measurements in lung tissue indicated strain-dependent differences in disposition of QDs in the lung. Total glutathione levels in lung tissue were significantly correlated with percent neutrophils in BALF as well as with lung tissue Cd levels. Our findings indicate that QD-induced acute lung inflammation is mouse strain dependent, that it is heritable, and that the choice of mouse strain is an important consideration in planning QD toxicity studies. These data also suggest that formal genetic analyses using additional strains or recombinant inbred strains from these mice could be useful for discovering potential QD-induced inflammation susceptibility loci.

Diet-induced obesity reprograms the inflammatory response of the murine lung to inhaled endotoxin.

The co-occurrence of environmental factors is common in complex human diseases and, as such, understanding the molecular responses involved is essential to determine risk and susceptibility to disease. We have investigated the key biological pathways that define susceptibility for pulmonary infection during obesity in diet-induced obese (DIO) and regular weight (RW) C57BL/6 mice exposed to inhaled lipopolysaccharide (LPS). LPS induced a strong inflammatory response in all mice as indicated by elevated cell counts of macrophages and neutrophils and levels of proinflammatory cytokines (MDC, MIP-1γ, IL-12, RANTES) in the bronchoalveolar lavage fluid. Additionally, DIO mice exhibited 50% greater macrophage cell counts, but decreased levels of the cytokines, IL-6, TARC, TNF-α, and VEGF relative to RW mice. Microarray analysis of lung tissue showed over half of the LPS-induced expression in DIO mice consisted of genes unique for obese mice, suggesting that obesity reprograms how the lung responds to subsequent insult. In particular, we found that obese animals exposed to LPS have gene signatures showing increased inflammatory and oxidative stress response and decreased antioxidant capacity compared with RW. Because signaling pathways for these responses can be common to various sources of environmentally induced lung damage, we further identified biomarkers that are indicative of specific toxicant exposure by comparing gene signatures after LPS exposure to those from a parallel study with cigarette smoke. These data show obesity may increase sensitivity to further insult and that co-occurrence of environmental stressors result in complex biosignatures that are not predicted from analysis of individual exposures.

Multiplexed cancer biomarker detection using quartz-based photonic crystal surfaces.

A photonic crystal (PC) surface is demonstrated as a high-sensitivity platform for detection of a panel of 21 cancer biomarker antigens using a sandwich enzyme-linked immunosorbent assay (ELISA) microarray format. A quartz-based PC structure fabricated by nanoimprint lithography, selected for its low autofluorescence, supports two independent optical resonances that simultaneously enable enhancement of fluorescence detection of biomarkers and label-free quantification of the density of antibody capture spots. A detection instrument is demonstrated that supports fluorescence and label-free imaging modalities, with the ability to optimize the fluorescence enhancement factor on a pixel-by-pixel basis throughout the microarray using an angle-scanning approach for the excitation laser that automatically compensates for variability in surface chemistry density and capture spot density. Measurements show that the angle-scanning illumination approach reduces the coefficient of variation of replicate assays by 20-99% compared to ordinary fluorescence microscopy, thus supporting reduction in limits of detectable biomarker concentration. Using the PC resonance, biomarkers in mixed samples were detectable at the lowest concentrations tested (2.1-41 pg/mL), resulting in a three-log range of quantitative detection.

Line-scanning detection instrument for photonic crystal enhanced fluorescence.

A laser line-scanning instrument was developed to optimize the near-field enhancement capability of a one-dimensional photonic crystal (PC) for excitation of surface-bound fluorophores. The excitation laser beam is shaped into an 8 µm × 1 mm line that is focused along the direction of the PC grating, while remaining collimated perpendicular to the grating. Such a beam configuration offers high excitation power density while simultaneously providing high resonant coupling efficiency from the laser to the PC surface. Using a panel of 21 immunofluorescence assays on the PC surface in a microarray format, the approach achieves an enhancement factor as high as 90-fold between on-resonance and off-resonance illumination. The instrument provides a capability for sensitive and inexpensive analysis of cancer biomarkers in clinical applications.

Application of photonic crystal enhanced fluorescence to cancer biomarker microarrays.

We report on the use of photonic crystal surfaces as a high-sensitivity platform for detection of a panel of cancer biomarkers in a protein microarray format. The photonic crystal surface is designed to provide an optical resonance at the excitation wavelength of cyanine-5 (Cy5), thus providing an increase in fluorescent intensity for Cy5-labeled analytes measured with a confocal microarray scanner, compared to a glass surface. The sandwich enzyme-linked immunosorbent assay (ELISA) is undertaken on a microarray platform to undertake a simultaneous, multiplex analysis of 24 antigens on a single chip. Our results show that the resonant excitation effect increases the signal-to-noise ratio by 3.8- to 6.6-fold, resulting in a decrease in detection limits of 6-89%, with the exact enhancement dependent upon the antibody-antigen interaction. Dose-response characterization of the photonic crystal antibody microarrays shows the capability to detect common cancer biomarkers in the <2 pg/mL concentration range within a mixed sample.

Protein secretion in human mammary epithelial cells following HER1 receptor activation: influence of HER2 and HER3 expression.

BACKGROUND: Protein secretion by mammary cells results in autocrine and paracrine signaling that defines cell growth, migration and the extracellular environment. Even so, we have a limited understanding of the cellular processes that regulate protein secretion. METHODS: In this study, we utilize human epithelial mammary cell (HMEC) lines that were engineered to express different levels of HER1, HER2 and HER3. Using an ELISA microarray platform, we evaluate the effects of epidermal growth factor family receptor (HER) expression on protein secretion in the HMEC lines upon initiation of HER1 receptor activation. The secreted proteins include three HER1 ligands, interleukins 1α and 18, RANTES, vascular-endothelial and platelet-derived growth factors, matrix metalloproteases 1, 2 and 9, and the extracellular portion of the HER1 and HER2 proteins. In addition, we investigate whether MAPK/Erk and PI3K/Akt signaling regulate protein secretion in these cell lines and if so, whether the involvement of HER2 or HER3 receptor alters their response to MAPK/Erk and PI3K/Akt signal pathway inhibition in terms of protein secretion. RESULTS: Differential expression of HER2 and HER3 receptors alters the secretion of a variety of growth factors, cytokines, and proteases. Some alterations in protein secretion are still observed when MAPK/Erk or PI3K/Akt signaling is inhibited. CONCLUSION: This study suggests that HER overexpression orchestrates broad changes in the tumor microenvironment by altering the secretion of a diverse variety of biologically active proteins.

Development of a fibrinogen-specific sandwich enzyme-linked immunosorbent assay microarray assay for distinguishing between blood plasma and serum samples.

We have developed a fibrinogen-specific sandwich enzyme-linked immunosorbent assay (ELISA) microarray assay for use in qualitatively distinguishing between blood plasma and serum samples. Three capture antibodies (49D2, HPA001900, and F8512) were evaluated in conjunction with 1D6 as the detection antibody. The data show that 49D2 and (to a lesser extent) F8512 successfully identify previously unknown plasma and serum samples based on approximately a 28-fold difference in signal intensity between the sample types. This assay has utility in rapidly identifying previously archived clinical samples with incomplete annotation in a high-throughput manner prior to proteomic analyses.

Reducing heterophilic antibody interference in immunoassays using single-chain antibodies.

Sandwich enzyme-linked immunosorbent assay (ELISA) microarrays can simultaneously quantify the levels of multiple diagnostic targets in a biological sample. However, as with traditional ELISA diagnostics, endogenous antibodies in patient sera can cause interference. We demonstrate here that reducing the diagnostic capture antibody to its minimal functional unit (i.e., a single-chain antibody fragment [scFv]) is an effective strategy for reducing assay interference. Our finding illustrates a source of error introduced by the reliance on immunoglobulin-based capture reagents in sandwich immunoassays with human serum samples. We demonstrate that scFvs can be used in such assays to improve reliability by reducing heterophilic antibody interference, thereby improving biomarker analysis and validation.

An internal calibration method for protein-array studies.

Nuisance factors in a protein-array study add obfuscating variation to spot intensity measurements, diminishing the accuracy and precision of protein concentration predictions. The effects of nuisance factors may be reduced by design of experiments, and by estimating and then subtracting nuisance effects. Estimated nuisance effects also inform about the quality of the study and suggest refinements for future studies.We demonstrate a method to reduce nuisance effects by incorporating a non-interfering internal calibration in the study design and its complemental analysis of variance. We illustrate this method by applying a chip-level internal calibration in a biomarker discovery study. The variability of sample intensity estimates was reduced 16% to 92% with a median of 58%; confidence interval widths were reduced 8% to 70% with a median of 35%. Calibration diagnostics revealed processing nuisance trends potentially related to spot print order and chip location on a slide. The accuracy and precision of a protein-array study may be increased by incorporating a non-interfering internal calibration. Internal calibration modeling diagnostics improve confidence in study results and suggest process steps that may need refinement. Though developed for our protein-array studies, this internal calibration method is applicable to other targeted array-based studies.

ELISA-BASE: an integrated bioinformatics tool for analyzing and tracking ELISA microarray data.

SUMMARY: ELISA-BASE is an open source database for capturing, organizing and analyzing enzyme-linked immunosorbent assay (ELISA) microarray data. ELISA-BASE is an extension of the BioArray Software Environment (BASE) database system. AVAILABILITY: http://www.pnl.gov/statistics/ProMAT/ELISA-BASE.stm.

Predicting protein concentrations with ELISA microarray assays, monotonic splines and Monte Carlo simulation.

Making sound proteomic inferences using ELISA microarray assay requires both an accurate prediction of protein concentration and a credible estimate of its error. We present a method using monotonic spline statistical models (MS), penalized constrained least squares fitting (PCLS) and Monte Carlo simulation (MC) to predict ELISA microarray protein concentrations and estimate their prediction errors. We contrast the MSMC (monotone spline Monte Carlo) method with a LNLS (logistic nonlinear least squares) method using simulated and real ELISA microarray data sets.MSMC rendered good fits in almost all tests, including those with left and/or right clipped standard curves. MS predictions were nominally more accurate; especially at the extremes of the prediction curve. MC provided credible asymmetric prediction intervals for both MS and LN fits that were superior to LNLS propagation-of-error intervals in achieving the target statistical confidence. MSMC was more reliable when automated prediction across simultaneous assays was applied routinely with minimal user guidance.

High-throughput analysis of serum antigens using sandwich ELISAs on microarrays.

Enzyme-linked immunosorbent assay (ELISA) microarrays promise to be a powerful tool for the detection and validation of disease biomarkers. ELISA microarrays are capable of simultaneous detection of many proteins using a small sample volume. Although there are many potential pitfalls to the use of ELISA microarrays, these can be avoided by careful planning of experiments. In this chapter we describe a high-throughput protocol for processing ELISA microarrays that will result in reliable and reproducible data.

Immobilization strategies for single-chain antibody microarrays.

Sandwich ELISA microarrays have great potential for validating disease biomarkers. Each ELISA relies on robust-affinity reagents that retain activity when immobilized on a solid surface or when labeled for detection. Single-chain antibodies (scFv) are affinity reagents that have greater potential for high-throughput production than traditional IgG. Unfortunately, scFv are typically less active than IgG following immobilization on a solid surface and not always suitable for use in sandwich ELISAs. We therefore investigated different immobilization strategies and scFv constructs to determine a more robust strategy for using scFv as ELISA reagents. Two promising strategies emerged from these studies: (i) the precapture of epitope-tagged scFv using an antiepitope antibody and (ii) the direct printing of a thioredoxin (TRX)/scFv fusion protein on glass slides. Both strategies improved the stability of immobilized scFv and increased the sensitivity of the scFv ELISA microarray assays, although the antiepitope precapture method introduced a risk of reagent transfer. Using the direct printing method, we show that scFv against prostate-specific antigen (PSA) are highly specific when tested against 21 different IgG-based assays. In addition, the scFv microarray PSA assay gave comparable quantitative results (R(2) = 0.95) to a commercial 96-well ELISA when tested using human serum samples. In addition, we find that TRX-scFv fusions against epidermal growth factor and toxin X have good LOD. Overall, these results suggest that minor modifications of the scFv construct are sufficient to produce reagents that are suitable for use in multiplex assay systems.

Surface chemistries for antibody microarrays.

Enzyme-linked immunosorbent assay (ELISA) microarrays promise to be a powerful tool for the detection of disease biomarkers. The original technology for printing ELISA microarray chips and capturing antibodies on slides was derived from the DNA microarray field. However, due to the need to maintain antibody structure and function when immobilized, surface chemistries used for DNA microarrays are not always appropriate for ELISA microarrays. In order to identify better surface chemistries for antibody capture, a number of commercial companies and academic research groups have developed new slide types that could improve antibody function in microarray applications. In this review we compare and contrast the commercially available slide chemistries, as well as highlight some promising recent advances in the field.

ELISA microarray technology as a high-throughput system for cancer biomarker validation.

A large gap currently exists between the ability to discover potential biomarkers and the ability to assess the real value of these proteins for cancer screening. One major challenge in biomarker validation is the inherent variability in biomarker levels. This variability stems from the diversity across the human population and the considerable molecular heterogeneity between individual tumors, even those that originate from a single tissue. An additional challenge with cancer screening is that most cancers are rare in the general population, meaning that assay specificity must be very high. Otherwise, the number of false positives will be much greater than the number of true positives. Due to these challenges associated with biomarker validation, it is necessary to analyze thousands of samples in order to obtain a clear idea of the utility of a screening assay. Enzyme-linked immunosorbent assay (ELISA) microarray technology can simultaneously quantify levels of multiple proteins and, thus, has the potential to accelerate validation of protein biomarkers for clinical use. This review will discuss current ELISA microarray technology and potential advances that could help to achieve the reproducibility and throughput that are required to evaluate cancer biomarkers.

Evaluating concentration estimation errors in ELISA microarray experiments.

BACKGROUND: Enzyme-linked immunosorbent assay (ELISA) is a standard immunoassay to estimate a protein's concentration in a sample. Deploying ELISA in a microarray format permits simultaneous estimation of the concentrations of numerous proteins in a small sample. These estimates, however, are uncertain due to processing error and biological variability. Evaluating estimation error is critical to interpreting biological significance and improving the ELISA microarray process. Estimation error evaluation must be automated to realize a reliable high-throughput ELISA microarray system. In this paper, we present a statistical method based on propagation of error to evaluate concentration estimation errors in the ELISA microarray process. Although propagation of error is central to this method and the focus of this paper, it is most effective only when comparable data are available. Therefore, we briefly discuss the roles of experimental design, data screening, normalization, and statistical diagnostics when evaluating ELISA microarray concentration estimation errors. RESULTS: We use an ELISA microarray investigation of breast cancer biomarkers to illustrate the evaluation of concentration estimation errors. The illustration begins with a description of the design and resulting data, followed by a brief discussion of data screening and normalization. In our illustration, we fit a standard curve to the screened and normalized data, review the modeling diagnostics, and apply propagation of error. We summarize the results with a simple, three-panel diagnostic visualization featuring a scatterplot of the standard data with logistic standard curve and 95% confidence intervals, an annotated histogram of sample measurements, and a plot of the 95% concentration coefficient of variation, or relative error, as a function of concentration. CONCLUSIONS: This statistical method should be of value in the rapid evaluation and quality control of high-throughput ELISA microarray analyses. Applying propagation of error to a variety of ELISA microarray concentration estimation models is straightforward. Displaying the results in the three-panel layout succinctly summarizes both the standard and sample data while providing an informative critique of applicability of the fitted model, the uncertainty in concentration estimates, and the quality of both the experiment and the ELISA microarray process.

Interactions of phospholipase D and cytochrome P450 protein stability.

Previous studies have suggested a relationship between cytochrome P450 (P450) 3A (CYP3A) conformation and the phospholipid composition of the associated membrane. In this study, we utilized a novel microsomal incubation system that mimics many of the characteristics of CYP3A degradation pathway that have been observed in vivo and in cultured cells to study the effects of phospholipid composition on protein stability. We found that addition of phosphatidylcholine-specific phospholipase D (PLD) stabilized CYP3A in this system, but that phosphatidylinositol-specific phospholipase C (PLC) was without effect. Addition of phosphatidic acid also stabilized CYP3A protein in the microsomes. The use of 1,10-phenanthroline (phenanthroline), an inhibitor of PLD activity, decreased CYP3A stability in incubated microsomes. Similarly, 6-h treatment of primary cultures of rat hepatocytes with phenanthroline resulted in nearly complete loss of CYP3A protein. Treatment of rats with nicardipine or dimethylsulfoxide (DMSO), which have been shown to affect CYP3A stability, altered the phospholipid composition of hepatic microsomes. It did not appear, though, that the changes in phospholipid composition that resulted from these in vivo treatments accounted for the change in CYP3A stability observed in hepatic microsomes from these animals.

A rational approach for discovering and validating cancer markers in very small samples using mass spectrometry and ELISA microarrays.

Identifying useful markers of cancer can be problematic due to limited amounts of sample. Some samples such as nipple aspirate fluid (NAF) or early-stage tumors are inherently small. Other samples such as serum are collected in larger volumes but archives of these samples are very valuable and only small amounts of each sample may be available for a single study. Also, given the diverse nature of cancer and the inherent variability in individual protein levels, it seems likely that the best approach to screen for cancer will be to determine the profile of a battery of proteins. As a result, a major challenge in identifying protein markers of disease is the ability to screen many proteins using very small amounts of sample. In this review, we outline some technological advances in proteomics that greatly advance this capability. Specifically, we propose a strategy for identifying markers of breast cancer in NAF that utilizes mass spectrometry (MS) to simultaneously screen hundreds or thousands of proteins in each sample. The best potential markers identified by the MS analysis can then be extensively characterized using an ELISA microarray assay. Because the microarray analysis is quantitative and large numbers of samples can be efficiently analyzed, this approach offers the ability to rapidly assess a battery of selected proteins in a manner that is directly relevant to traditional clinical assays.

A protein microarray ELISA for screening biological fluids.

Protein microarrays permit the simultaneous measurement of many proteins in a small sample volume and therefore provide an attractive approach for the quantitative measurement of proteins in biological fluids, including serum. This chapter describes a microarray enzyme-linked immunosorbent assay (ELISA). Capture antibodies are immobilized onto a glass surface; the covalently attached antibodies bind a specific antigen from a sample overlaying the array. A second, biotinylated antibody that recognizes the same antigen as the first antibody, but at a different epitope, is then used for detection. Detection is based on an enzymatic signal-enhancement method known as tyramide signal amplification (TSA). By coupling a microarray-ELISA format with the signal amplification of tyramide deposition, the assay sensitivity is as low as sub-pg/mL.