Fast DNA and protein microarray tests for the diagnosis of hepatitis C virus infection on a single platform.

Hepatitis C virus (HCV) is a major cause of chronic liver disease and liver cancer, and remains a large health care burden to the world. In this study we developed a DNA microarray test to detect HCV RNA and a protein microarray to detect human anti-HCV antibodies on a single platform. A main focus of this study was to evaluate possibilities to reduce the assay time, as a short time-to-result (TTR) is a prerequisite for a point-of-care test. Significantly reducing hybridisation and washing times did not impair the assay performance. This was confirmed first using artificial targets and subsequently using clinical samples from an HCV seroconversion panel derived from a HCV-infected patient. We were able to reduce the time required for the detection of human anti-HCV antibodies to only 14 min, achieving nanomolar sensitivity. The protein microarray exhibited an analytical sensitivity comparable to that of commercial systems. Similar results were obtained with the DNA microarray using a universal probe which covered all different HCV genotypes. It was possible to reduce the assay time after PCR from 150 min to 16 min without any loss of sensitivity. Taken together, these results constitute a significant step forward in the design of rapid, microarray-based diagnostics for human infectious disease, and show that the protein microarray is currently the most favourable candidate to fill this role.

Fluorescence lifetime imaging of quantum dot labeled DNA microarrays.

Quantum dot (QD) labeling combined with fluorescence lifetime imaging microscopy is proposed as a powerful transduction technique for the detection of DNA hybridization events. Fluorescence lifetime analysis of DNA microarray spots of hybridized QD labeled target indicated a characteristic lifetime value of 18.8 ns, compared to 13.3 ns obtained for spots of free QD solution, revealing that QD labels are sensitive to the spot microenvironment. Additionally, time gated detection was shown to improve the microarray image contrast ratio by 1.8, achieving femtomolar target sensitivity. Finally, lifetime multiplexing based on Qdot525 and Alexa430 was demonstrated using a single excitation-detection readout channel.

A multiplexed protein microarray for the simultaneous serodiagnosis of human immunodeficiency virus/hepatitis C virus infection and typing of whole blood.

All donor blood samples must be tested pretransfusion to determine the donor blood type. Standard testing protocols require that assays be performed for important bloodborne pathogens such as hepatitis C, syphilis, hepatitis B, and human immunodeficiency virus. We have demonstrated proof of the concept that a protein microarray can type whole blood and detect antibody to significant pathogens simultaneously from the same donor blood sample. The data collected demonstrate the ability of the array to accurately type blood samples while also detecting the presence of antibodies against both human immunodeficiency virus and hepatitis C virus. In conclusion, we have successfully developed a platform capable of typing human whole blood samples, while at the same time testing for the presence of antibodies specific for human immunodeficiency virus/hepatitis C virus. The major benefits of this system are its amenability to expansion with additional assays, for example, rhesus typing and syphilis and/or hepatitis B virus detection, and also the adaptability of the assay to higher-throughput analysis, currently 16 individual samples per slide, but readily expandable to a 96-well format.

Evaluation of a protein microarray method for immuno-typing erythrocytes in whole blood.

All donor blood samples must be tested pre-transfusion to determine the blood type of donor erythrocytes, based on the ABO typing system. Current methods of testing are well characterised, but require a number of processing steps prior to analysis. In addition, standard testing protocols require additional assays such as hepatitis C and HIV testing be performed separately. We describe and evaluate a protein microarray platform for ABO blood typing that has the potential to be a simple reliable high throughput method, with the added capability for the integration of other important pre-transfusion tests. Sixty seven donor blood samples were incubated on microarrays printed with multiple spotted replicates of blood type antigen specific antibodies. We utilised a hold-out cross validation approach, combined with Receiver Operator Characteristic (ROC) curves to define thresholds within which a sample could be defined as being of a particular blood type. The threshold values from the ROC curve analysis demonstrated an excellent ability to accurately separate samples based on ABO blood type. The results obtained when the thresholds from the training sets were applied to test sets were also very encouraging, with misclassified samples being present in only 2 of the training sets and a mean classification error of 4.28%. When the mean thresholds were applied to the 67 donor samples, 95.5% were correctly blood typed (64 of 67 samples). We have demonstrated the ability of our protein microarray platform to successfully and accurately type human whole blood samples. We believe that this flexible platform provides a strong basis for an integrated approach for combined blood typing and pathogen testing in human whole blood.

GPX-Macrophage Expression Atlas: a database for expression profiles of macrophages challenged with a variety of pro-inflammatory, anti-inflammatory, benign and pathogen insults.

BACKGROUND: Macrophages play an integral role in the host immune system, bridging innate and adaptive immunity. As such, they are finely attuned to extracellular and intracellular stimuli and respond by rapidly initiating multiple signalling cascades with diverse effector functions. The macrophage cell is therefore an experimentally and clinically amenable biological system for the mapping of biological pathways. The goal of the macrophage expression atlas is to systematically investigate the pathway biology and interaction network of macrophages challenged with a variety of insults, in particular via infection and activation with key inflammatory mediators. As an important first step towards this we present a single searchable database resource containing high-throughput macrophage gene expression studies. DESCRIPTION: The GPX Macrophage Expression Atlas (GPX-MEA) is an online resource for gene expression based studies of a range of macrophage cell types following treatment with pathogens and immune modulators. GPX-MEA follows the MIAME standard and includes an objective quality score with each experiment. It places special emphasis on rigorously capturing the experimental design and enables the searching of expression data from different microarray experiments. Studies may be queried on the basis of experimental parameters, sample information and quality assessment score. The ability to compare the expression values of individual genes across multiple experiments is provided. In addition, the database offers access to experimental annotation and analysis files and includes experiments and raw data previously unavailable to the research community. CONCLUSION: GPX-MEA is the first example of a quality scored gene expression database focussed on a macrophage cellular system that allows efficient identification of transcriptional patterns. The resource will provide novel insights into the phenotypic response of macrophages to a variety of benign, inflammatory, and pathogen insults. GPX-MEA is available through the GPX website at http://www.gti.ed.ac.uk/GPX.

Whole blood gene expression profiling of neonates with confirmed bacterial sepsis.

Neonatal infection remains a primary cause of infant morbidity and mortality worldwide and yet our understanding of how human neonates respond to infection remains incomplete. Changes in host gene expression in response to infection may occur in any part of the body, with the continuous interaction between blood and tissues allowing blood cells to act as biosensors for the changes. In this study we have used whole blood transcriptome profiling to systematically identify signatures and the pathway biology underlying the pathogenesis of neonatal infection. Blood samples were collected from neonates at the first clinical signs of suspected sepsis alongside age matched healthy control subjects. Here we report a detailed description of the study design, including clinical data collected, experimental methods used and data analysis workflows and which correspond with data in Gene Expression Omnibus (GEO) data sets (GSE25504). Our data set has allowed identification of a patient invariant 52-gene classifier that predicts bacterial infection with high accuracy and lays the foundation for advancing diagnostic, prognostic and therapeutic strategies for neonatal sepsis.

Proteolysis-inducing factor core peptide mediates dermcidin-induced proliferation of hepatic cells through multiple signalling networks.

Dermcidin is a candidate oncogene capable of increasing the number of cultured neuronal, breast cancer and prostate cancer cells and improving the survival of hepatic cells. The dermcidin gene encodes the proteolysis-inducing factor core peptide (PIF-CP) and the skin antimicrobial peptide DCD-1. The peptide responsible for inducing proliferation of cells and the mechanisms involved are unknown. In this study, we confirmed a proliferative effect of dermcidin overexpression of 20% (p<0.02) in the HuH7 human hepatic cell line. Proliferation was abrogated by prevention of PIF-CP translation or inactivation of its calcineurin-like phosphatase domain by site-directed mutagenesis. Prevention of DCD-1 translation had no effect. Treatment of cells with a 30 amino acid synthetic PIF-CP induced an analogous increase in proliferation of 14%. Microarray analysis of PIF-CP-treated cells revealed low but significant changes in 111 potential mediator genes. Pathway analysis revealed several gene networks involved in the cellular response to the peptide, one with VEGFB as a hub and two other networks converging on FOS and MYC. Quantitative PCR confirmed direct upregulation of VEGFB. These data reveal PIF-CP as the key mediator of dermcidin-induced proliferation and demonstrate induction of key oncogenic pathways.

Peptide-tags for enhanced DNA microarray performance.

DNA microarrays are powerful tools for gene expression analysis and genotyping studies in research and diagnostic applications. A high sensitivity and short time-to-result are prerequisites for their practical application in the clinic. The hybridization efficiency of DNA microarrays depends on the probe density and the probe orientation and thus their accessibility for target molecules. In order to find an optimal probe immobilization procedure a set of different oligonucleotide modifications was tested on epoxy silane functionalized glass slides. It was found that histidine-tagged oligonucleotides resulted in the highest amount of bound probe and by far the best hybridization efficiencies. The detection limit obtained with histidine-tagged probes was up to two orders of magnitude lower compared to commonly used probe modifications. In order to further investigate the binding mechanism of histidine-tags towards functionalized glass substrates a set of different peptide-tags with and without free terminal amino-groups and with different amino acid compositions was tested. The results indicate an impact of the terminal amino group on the covalent surface binding and of aromatic amino acid residues on the enhanced hybridisation efficiency.

Multi-factorial analysis of class prediction error: estimating optimal number of biomarkers for various classification rules.

Machine learning and statistical model based classifiers have increasingly been used with more complex and high dimensional biological data obtained from high-throughput technologies. Understanding the impact of various factors associated with large and complex microarray datasets on the predictive performance of classifiers is computationally intensive, under investigated, yet vital in determining the optimal number of biomarkers for various classification purposes aimed towards improved detection, diagnosis, and therapeutic monitoring of diseases. We investigate the impact of microarray based data characteristics on the predictive performance for various classification rules using simulation studies. Our investigation using Random Forest, Support Vector Machines, Linear Discriminant Analysis and k-Nearest Neighbour shows that the predictive performance of classifiers is strongly influenced by training set size, biological and technical variability, replication, fold change and correlation between biomarkers. Optimal number of biomarkers for a classification problem should therefore be estimated taking account of the impact of all these factors. A database of average generalization errors is built for various combinations of these factors. The database of generalization errors can be used for estimating the optimal number of biomarkers for given levels of predictive accuracy as a function of these factors. Examples show that curves from actual biological data resemble that of simulated data with corresponding levels of data characteristics. An R package optBiomarker implementing the method is freely available for academic use from the Comprehensive R Archive Network (http://www.cran.r-project.org/web/packages/optBiomarker/).

Cell interaction microarray for blood phenotyping.

Microarrays promise great advances in areas of diagnostic testing where there is a need to perform multiple assays in parallel. In the short term, protein microarrays have a greater potential to impact diagnostics than DNA arrays due to their potential for direct sample measurements. Here, we report an antibody microarray technique for selectively recognizing glycan and peptide motifs on the surface of red blood cells. We present results demonstrating the optimization and efficacy of the microarray approach as a highly sensitive and specific microscale multiplex assay for blood typing. We also show that our microarray can be used to screen red blood cell surface antigens using whole blood in a label-free detection mode. Finally, our results indicate this method has potential for broader applications in biochip medicine.

Improved silicon nitride surfaces for next-generation microarrays.

This work reports how the use of a standard integrated circuit (IC) fabrication process can improve the potential of silicon nitride layers as substrates for microarray technology. It has been shown that chemical mechanical polishing (CMP) substantially improves the fluorescent intensity of positive control gene and test gene microarray spots on both low-pressure chemical vapor deposition (LPCVD) and plasma-enhanced chemical vapor deposition (PECVD) silicon nitride films, while maintaining a low fluorescent background. This results in the improved discrimination of low expressing genes. The results for the PECVD silicon nitride, which has been previously reported as unsuitable for microarray spotting, are particularly significant for future devices that hope to incorporate microelectronic control and analysis circuitry, due to the film's use as a final passivating layer.