Don S. Daly

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.

Sequence versus Structure for the Direct Detection of 16S rRNA on Planar Oligonucleotide Microarrays

ABSTRACT A two-probe proximal chaperone detection system consisting of a species-specific capture probe for the microarray and a labeled, proximal chaperone probe for detection was recently described for direct detection of intact rRNAs from environmental samples on oligonucleotide arrays. In this study, we investigated the physical spacing and nucleotide mismatch tolerance between capture and proximal chaperone detector probes that are required to achieve species-specific 16S rRNA detection for the dissimilatory metal and sulfate reducer 16S rRNAs. Microarray specificity was deduced by analyzing signal intensities across replicate microarrays with a statistical analysis-of-variance model that accommodates well-to-well and slide-to-slide variations in microarray signal intensity. Chaperone detector probes located in immediate proximity to the capture probe resulted in detectable, nonspecific binding of nontarget rRNA, presumably due to base-stacking effects. Species-specific rRNA detection was achieved by using a 22-nt capture probe and a 15-nt detector probe separated by 10 to 14 nt along the primary sequence. Chaperone detector probes with up to three mismatched nucleotides still resulted in species-specific capture of 16S rRNAs. There was no obvious relationship between position or number of mismatches and within- or between-genus hybridization specificity. From these results, we conclude that relieving secondary structure is of principal concern for the successful capture and detection of 16S rRNAs on planar surfaces but that the sequence of the capture probe is more important than relieving secondary structure for achieving specific hybridization.

Brief Communication: MASIC: A software program for fast quantitation and flexible visualization of chromatographic profiles from detected LC-MS(/MS) features

Quantitative analysis of liquid chromatography (LC)-mass spectrometry (MS) and tandem mass spectrometry (MS/MS) data is essential to many proteomics studies. We have developed MASIC(2) to accurately measure peptide abundances and LC elution times in LC-MS/MS analyses. This software program uses an efficient processing algorithm to quickly generate mass specific selected ion chromatograms from a dataset and provides an interactive browser that allows users to examine individual chromatograms with a variety of options.

Extracting and visualizing matrix-assisted laser desorption/ionization time-of-flight mass spectral fingerprints.

We have developed a method for constructing and extracting matrix-assisted laser desorption/ionization (MALDI) fingerprints. This method is fully automated and statistically based, allowing a large number of spectra to be analyzed at a time in an objective manner. This method can be used to extract the fingerprint of a particular analyte from a spectrum containing multiple analytes. Therefore, this method lends itself well to real-world applications where samples to be analyzed are likely to be impure. We illustrate this method on experimental results from a series of studies of E. coli and B. atrophaeus MALDI time-of-flight mass spectrometry (TOFMS) fingerprints.

A new approach to automated peak detection

Abstract Spectral peak detection algorithms are often difficult to automate because they either rely on somewhat arbitrary rules, or are tuned to specific spectral peak properties. One popular approach detects peaks where signal intensities exceed some threshold. This threshold is typically set arbitrarily above the noise level or manually by the user. Intensity threshold-based methods can be sensitive to baseline variations and signal intensity. Another popular peak detection approach relies on matching the spectral intensities to a reference peak shape. This approach can be very sensitive to baseline changes and deviations from the reference peak shape. Such methods can be significantly challenged by modern analytical instrumentation where the baseline tends to drift, peaks of interest may have a low signal to noise (S/N) ratio, and no well-defined reference peak shape is available. We present a new approach for spectral peak detection that is designed to be generic and easily automated. Employing a histogram-based model for spectral intensity, peaks are detected by comparing the estimated variance of observations (the x -axis of the spectrum) to the expected variance when no peak is present inside some window of interest. We compare an implementation of this approach to two existing peak detection algorithms using a series of simulated spectra.

Fingerprinting closely related xanthomonas pathovars with random nonamer oligonucleotide microarrays.

ABSTRACT Current bacterial DNA-typing methods are typically based on gel-based fingerprinting methods. As such, they access a limited complement of genetic information and many independent restriction enzymes or probes are required to achieve statistical rigor and confidence in the resulting pattern of DNA fragments. Furthermore, statistical comparison of gel-based fingerprints is complex and nonstandardized. To overcome these limitations of gel-based microbial DNA fingerprinting, we developed a prototype, 47-probe microarray consisting of randomly selected nonamer oligonucleotides. Custom image analysis algorithms and statistical tools were developed to automatically extract fingerprint profiles from microarray images. The prototype array and new image analysis algorithms were used to analyze 14 closely related Xanthomonas pathovars. Of the 47 probes on the prototype array, 10 had diagnostic value (based on a chi-squared test) and were used to construct statistically robust microarray fingerprints. Analysis of the microarray fingerprints showed clear differences between the 14 test organisms, including the separation of X. oryzae strains 43836 and 49072, which could not be resolved by traditional gel electrophoresis of REP-PCR amplification products. The proof-of-application study described here represents an important first step to high-resolution bacterial DNA fingerprinting with microarrays. The universal nature of the nonamer fingerprinting microarray and data analysis methods developed here also forms a basis for method standardization and application to the forensic identification of other closely related bacteria.

Mixed-effects statistical model for comparative LC-MS proteomics studies.

Comparing a proteins concentrations across two or more treatments is the focus of many proteomics studies. A frequent source of measurements for these comparisons is a mass spectrometry (MS) analysis of a proteins peptide ions separated by liquid chromatography (LC) following its enzymatic digestion. Alas, LC-MS identification and quantification of equimolar peptides can vary significantly due to their unequal digestion, separation, and ionization. This unequal measurability of peptides, the largest source of LC-MS nuisance variation, stymies confident comparison of a proteins concentration across treatments. Our objective is to introduce a mixed-effects statistical model for comparative LC-MS proteomics studies. We describe LC-MS peptide abundance with a linear model featuring pivotal terms that account for unequal peptide LC-MS measurability. We advance fitting this model to an often incomplete LC-MS data set with REstricted Maximum Likelihood (REML) estimation, producing estimates of model goodness-of-fit, treatment effects, standard errors, confidence intervals, and protein relative concentrations. We illustrate the model with an experiment featuring a known dilution series of a filamentous ascomycete fungus Trichoderma reesei protein mixture. For 781 of the 1546 T. reesei proteins with sufficient data coverage, the fitted mixed-effects models capably described the LC-MS measurements. The LC-MS measurability terms effectively accounted for this major source of uncertainty. Ninety percent of the relative concentration estimates were within 0.5-fold of the true relative concentrations. Akin to the common ratio method, this model also produced biased estimates, albeit less biased. Bias decreased significantly, both absolutely and relative to the ratio method, as the number of observed peptides per protein increased. Mixed-effects statistical modeling offers a flexible, well-established methodology for comparative proteomics studies integrating common experimental designs with LC-MS sample processing plans. It favorably accounts for the unequal LC-MS measurability of peptides and produces informative quantitative comparisons of a proteins concentration across treatments with objective measures of uncertainties.

Evaluating concentration estimation errors in ELISA microarray experiments.

BackgroundEnzyme-linked immunosorbent assay (ELISA) is a standard immunoassay to estimate a proteins 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.ResultsWe 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.ConclusionsThis 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.

ProMAT: protein microarray analysis tool

SUMMARY ProMAT is a software tool for statistically analyzing data from enzyme-linked immunosorbent assay microarray experiments. The software estimates standard curves, sample protein concentrations and their uncertainties for multiple assays. ProMAT generates a set of comprehensive figures for assessing results and diagnosing process quality. The tool is available for Windows or Mac, and is distributed as open-source Java and R code. AVAILABILITY ProMAT is available at http://www.pnl.gov/statistics/ProMAT. ProMAT requires Java version 1.5.0 and R version 1.9.1 (or more recent versions). ProMAT requires either Windows XP or Mac OS 10.4 or newer versions.

Automated Microarray Image Analysis Toolbox for MATLAB

UNLABELLED The Automated Microarray Image Analysis (AMIA) Toolbox for MATLAB is a flexible, open-source, microarray image analysis tool that allows the user to customize analyses of microarray image sets. This tool provides several methods to identify and quantify spot statistics, as well as extensive diagnostic statistics and images to evaluate data quality and array processing. The open, modular nature of AMIA provides access to implementation details and encourages modification and extension of AMIAs capabilities. AVAILABILITY The AMIA Toolbox is freely available at http://www.pnl.gov/statistics/amia. The AMIA Toolbox requires MATLAB 6.5 (R13) (MathWorks, Inc. Natick, MA), as well as the Statistics Toolbox 4.1 and Image Processing Toolbox 4.1 for MATLAB or more recent versions. CONTACT amanda.white@pnl.gov