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1.  Biomolecular Detection employing the Interferometric Reflectance Imaging Sensor (IRIS) 
The sensitive measurement of biomolecular interactions has use in many fields and industries such as basic biology and microbiology, environmental/agricultural/biodefense monitoring, nanobiotechnology, and more. For diagnostic applications, monitoring (detecting) the presence, absence, or abnormal expression of targeted proteomic or genomic biomarkers found in patient samples can be used to determine treatment approaches or therapy efficacy. In the research arena, information on molecular affinities and specificities are useful for fully characterizing the systems under investigation.
Many of the current systems employed to determine molecular concentrations or affinities rely on the use of labels. Examples of these systems include immunoassays such as the enzyme-linked immunosorbent assay (ELISA), polymerase chain reaction (PCR) techniques, gel electrophoresis assays, and mass spectrometry (MS). Generally, these labels are fluorescent, radiological, or colorimetric in nature and are directly or indirectly attached to the molecular target of interest. Though the use of labels is widely accepted and has some benefits, there are drawbacks which are stimulating the development of new label-free methods for measuring these interactions. These drawbacks include practical facets such as increased assay cost, reagent lifespan and usability, storage and safety concerns, wasted time and effort in labelling, and variability among the different reagents due to the labelling processes or labels themselves. On a scientific research basis, the use of these labels can also introduce difficulties such as concerns with effects on protein functionality/structure due to the presence of the attached labels and the inability to directly measure the interactions in real time.
Presented here is the use of a new label-free optical biosensor that is amenable to microarray studies, termed the Interferometric Reflectance Imaging Sensor (IRIS), for detecting proteins, DNA, antigenic material, whole pathogens (virions) and other biological material. The IRIS system has been demonstrated to have high sensitivity, precision, and reproducibility for different biomolecular interactions [1-3]. Benefits include multiplex imaging capacity, real time and endpoint measurement capabilities, and other high-throughput attributes such as reduced reagent consumption and a reduction in assay times. Additionally, the IRIS platform is simple to use, requires inexpensive equipment, and utilizes silicon-based solid phase assay components making it compatible with many contemporary surface chemistry approaches.
Here, we present the use of the IRIS system from preparation of probe arrays to incubation and measurement of target binding to analysis of the results in an endpoint format. The model system will be the capture of target antibodies which are specific for human serum albumin (HSA) on HSA-spotted substrates.
PMCID: PMC3197112  PMID: 21587155
Bioengineering; Issue 51; Interferometry; label-free; biosensing; microarray; quantification; real-time detection
2.  Basic Concepts of Microarrays and Potential Applications in Clinical Microbiology 
Clinical Microbiology Reviews  2009;22(4):611-633.
Summary: The introduction of in vitro nucleic acid amplification techniques, led by real-time PCR, into the clinical microbiology laboratory has transformed the laboratory detection of viruses and select bacterial pathogens. However, the progression of the molecular diagnostic revolution currently relies on the ability to efficiently and accurately offer multiplex detection and characterization for a variety of infectious disease pathogens. Microarray analysis has the capability to offer robust multiplex detection but has just started to enter the diagnostic microbiology laboratory. Multiple microarray platforms exist, including printed double-stranded DNA and oligonucleotide arrays, in situ-synthesized arrays, high-density bead arrays, electronic microarrays, and suspension bead arrays. One aim of this paper is to review microarray technology, highlighting technical differences between them and each platform's advantages and disadvantages. Although the use of microarrays to generate gene expression data has become routine, applications pertinent to clinical microbiology continue to rapidly expand. This review highlights uses of microarray technology that impact diagnostic microbiology, including the detection and identification of pathogens, determination of antimicrobial resistance, epidemiological strain typing, and analysis of microbial infections using host genomic expression and polymorphism profiles.
PMCID: PMC2772365  PMID: 19822891
3.  Quantitative miRNA Expression Analysis Using Fluidigm Microfluidics Dynamic Arrays 
BMC Genomics  2011;12:144.
MicroRNAs (miRNAs) represent a growing class of small non-coding RNAs that are important regulators of gene expression in both plants and animals. Studies have shown that miRNAs play a critical role in human cancer and they can influence the level of cell proliferation and apoptosis by modulating gene expression. Currently, methods for the detection and measurement of miRNA expression include small and moderate-throughput technologies, such as standard quantitative PCR and microarray based analysis. However, these methods have several limitations when used in large clinical studies where a high-throughput and highly quantitative technology needed for the efficient characterization of a large number of miRNA transcripts in clinical samples. Furthermore, archival formalin fixed, paraffin embedded (FFPE) samples are increasingly becoming the primary resource for gene expression studies because fresh frozen (FF) samples are often difficult to obtain and requires special storage conditions. In this study, we evaluated the miRNA expression levels in FFPE and FF samples as well as several lung cancer cell lines employing a high throughput qPCR-based microfluidic technology. The results were compared to standard qPCR and hybridization-based microarray platforms using the same samples.
We demonstrated highly correlated Ct values between multiplex and singleplex RT reactions in standard qPCR assays for miRNA expression using total RNA from A549 (R = 0.98; p < 0.0001) and H1299 (R = 0.95; p < 0.0001) lung cancer cell lines. The Ct values generated by the microfluidic technology (Fluidigm 48.48 dynamic array systems) resulted in a left-shift toward lower Ct values compared to those observed by ABI 7900 HT (mean difference, 3.79), suggesting that the microfluidic technology exhibited a greater sensitivity. In addition, we show that as little as 10 ng total RNA can be used to reliably detect all 48 or 96 tested miRNAs using a 96-multiplexing RT reaction in both FFPE and FF samples. Finally, we compared miRNA expression measurements in both FFPE and FF samples by qPCR using the 96.96 dynamic array and Affymetrix microarrays. Fold change comparisons for comparable genes between the two platforms indicated that the overall correlation was R = 0.60. The maximum fold change detected by the Affymetrix microarray was 3.5 compared to 13 by the 96.96 dynamic array.
The qPCR-array based microfluidic dynamic array platform can be used in conjunction with multiplexed RT reactions for miRNA gene expression profiling. We showed that this approach is highly reproducible and the results correlate closely with the existing singleplex qPCR platform at a throughput that is 5 to 20 times higher and a sample and reagent usage that was approximately 50-100 times lower than conventional assays. We established optimal conditions for using the Fluidigm microfluidic technology for rapid, cost effective, and customizable arrays for miRNA expression profiling and validation.
PMCID: PMC3062620  PMID: 21388556
4.  Assessing the Detection Capacity of Microarrays as Bio/Nanosensing Platforms 
BioMed Research International  2013;2013:310461.
Microarray is one of the most powerful detection systems with multiplexing and high throughput capability. It has significant potential as a versatile biosensing platform for environmental monitoring, pathogen detection, medical therapeutics, and drug screening to name a few. To date, however, microarray applications are still limited to preliminary screening of genome-scale transcription profiling or gene ontology analysis. Expanding the utility of microarrays as a detection tool for various biological and biomedical applications requires information about performance such as the limits of detection and quantification, which are considered as an essential information to decide the detection sensitivity of sensing devices. Here we present a calibration design that integrates detection limit theory and linear dynamic range to obtain a performance index of microarray detection platform using oligonucleotide arrays as a model system. Two different types of limits of detection and quantification are proposed by the prediction or tolerance interval for two common cyanine fluorescence dyes, Cy3 and Cy5. Besides oligonucleotide, the proposed method can be generalized to other microarray formats with various biomolecules such as complementary DNA, protein, peptide, carbohydrate, tissue, or other small biomolecules. Also, it can be easily applied to other fluorescence dyes for further dye chemistry improvement.
PMCID: PMC3845509  PMID: 24324959
5.  A High-Throughput Antibody-Based Microarray Typing Platform 
Sensors (Basel, Switzerland)  2013;13(5):5737-5748.
Many rapid methods have been developed for screening foods for the presence of pathogenic microorganisms. Rapid methods that have the additional ability to identify microorganisms via multiplexed immunological recognition have the potential for classification or typing of microbial contaminants thus facilitating epidemiological investigations that aim to identify outbreaks and trace back the contamination to its source. This manuscript introduces a novel, high throughput typing platform that employs microarrayed multiwell plate substrates and laser-induced fluorescence of the nucleic acid intercalating dye/stain SYBR Gold for detection of antibody-captured bacteria. The aim of this study was to use this platform for comparison of different sets of antibodies raised against the same pathogens as well as demonstrate its potential effectiveness for serotyping. To that end, two sets of antibodies raised against each of the “Big Six” non-O157 Shiga toxin-producing E. coli (STEC) as well as E. coli O157:H7 were array-printed into microtiter plates, and serial dilutions of the bacteria were added and subsequently detected. Though antibody specificity was not sufficient for the development of an STEC serotyping method, the STEC antibody sets performed reasonably well exhibiting that specificity increased at lower capture antibody concentrations or, conversely, at lower bacterial target concentrations. The favorable results indicated that with sufficiently selective and ideally concentrated sets of biorecognition elements (e.g., antibodies or aptamers), this high-throughput platform can be used to rapidly type microbial isolates derived from food samples within ca. 80 min of total assay time. It can also potentially be used to detect the pathogens from food enrichments and at least serve as a platform for testing antibodies.
PMCID: PMC3690026  PMID: 23645110
antibody; microarray; bacteria; fluorescence; microtiter plate; typing
6.  A Functional Gene Array for Detection of Bacterial Virulence Elements 
PLoS ONE  2008;3(5):e2163.
Emerging known and unknown pathogens create profound threats to public health. Platforms for rapid detection and characterization of microbial agents are critically needed to prevent and respond to disease outbreaks. Available detection technologies cannot provide broad functional information about known or novel organisms. As a step toward developing such a system, we have produced and tested a series of high-density functional gene arrays to detect elements of virulence and antibiotic resistance mechanisms. Our first generation array targets genes from Escherichia coli strains K12 and CFT073, Enterococcus faecalis and Staphylococcus aureus. We determined optimal probe design parameters for gene family detection and discrimination. When tested with organisms at varying phylogenetic distances from the four target strains, the array detected orthologs for the majority of targeted gene families present in bacteria belonging to the same taxonomic family. In combination with whole-genome amplification, the array detects femtogram concentrations of purified DNA, either spiked in to an aerosol sample background, or in combinations from one or more of the four target organisms. This is the first report of a high density NimbleGen microarray system targeting microbial antibiotic resistance and virulence mechanisms. By targeting virulence gene families as well as genes unique to specific biothreat agents, these arrays will provide important data about the pathogenic potential and drug resistance profiles of unknown organisms in environmental samples.
PMCID: PMC2367441  PMID: 18478124
7.  An FDA bioinformatics tool for microbial genomics research on molecular characterization of bacterial foodborne pathogens using microarrays 
BMC Bioinformatics  2010;11(Suppl 6):S4.
Advances in microbial genomics and bioinformatics are offering greater insights into the emergence and spread of foodborne pathogens in outbreak scenarios. The Food and Drug Administration (FDA) has developed a genomics tool, ArrayTrackTM, which provides extensive functionalities to manage, analyze, and interpret genomic data for mammalian species. ArrayTrackTM has been widely adopted by the research community and used for pharmacogenomics data review in the FDA’s Voluntary Genomics Data Submission program.
ArrayTrackTM has been extended to manage and analyze genomics data from bacterial pathogens of human, animal, and food origin. It was populated with bioinformatics data from public databases such as NCBI, Swiss-Prot, KEGG Pathway, and Gene Ontology to facilitate pathogen detection and characterization. ArrayTrackTM’s data processing and visualization tools were enhanced with analysis capabilities designed specifically for microbial genomics including flag-based hierarchical clustering analysis (HCA), flag concordance heat maps, and mixed scatter plots. These specific functionalities were evaluated on data generated from a custom Affymetrix array (FDA-ECSG) previously developed within the FDA. The FDA-ECSG array represents 32 complete genomes of Escherichia coli and Shigella. The new functions were also used to analyze microarray data focusing on antimicrobial resistance genes from Salmonella isolates in a poultry production environment using a universal antimicrobial resistance microarray developed by the United States Department of Agriculture (USDA).
The application of ArrayTrackTM to different microarray platforms demonstrates its utility in microbial genomics research, and thus will improve the capabilities of the FDA to rapidly identify foodborne bacteria and their genetic traits (e.g., antimicrobial resistance, virulence, etc.) during outbreak investigations. ArrayTrackTM is free to use and available to public, private, and academic researchers at
PMCID: PMC3026378  PMID: 20946615
8.  SNP arrays: comparing diagnostic yields for four platforms in children with developmental delay 
BMC Medical Genomics  2014;7:70.
Molecular karyotyping is now the first-tier genetic test for patients affected with unexplained intellectual disability (ID) and/or multiple congenital anomalies (MCA), since it identifies a pathogenic copy number variation (CNV) in 10-14% of them. High-resolution microarrays combining molecular karyotyping and single nucleotide polymorphism (SNP) genotyping were recently introduced to the market. In addition to identifying CNVs, these platforms detect loss of heterozygosity (LOH), which can indicate the presence of a homozygous mutation or uniparental disomy. Since these abnormalities can be associated with ID and/or MCA, their detection is of particular interest for patients whose phenotype remains unexplained. However, the diagnostic yield obtained with these platforms is not confirmed, and the real clinical value of LOH detection has not been established.
We selected 21 children affected with ID, with or without congenital malformations, for whom standard genetic analyses failed to provide a diagnosis. We performed high-resolution SNP array analysis with four platforms (Affymetrix Genome-Wide Human SNP Array 6.0, Affymetrix Cytogenetics Whole-Genome 2.7 M array, Illumina HumanOmni1-Quad BeadChip, and Illumina HumanCytoSNP-12 DNA Analysis BeadChip) on whole-blood samples obtained from children and their parents to detect pathogenic CNVs and LOHs, and compared the results with those obtained on a moderate resolution array-based comparative genomic hybridization platform (NimbleGen CGX-12 Cytogenetics Array), already used in the clinical setting.
We identified a total of four pathogenic CNVs in three patients, and all arrays successfully detected them. With the SNP arrays, we also identified a LOH containing a gene associated with a recessive disorder consistent with the patient’s phenotype (i.e., an informative LOH) in four children (including two siblings). A homozygous mutation within the informative LOH was found in three of these patients. Therefore, we were able to increase the diagnostic yield from 14.3% to 28.6% as a result of the information provided by LOHs.
This study shows the clinical usefulness of SNP arrays in children with ID, since they successfully detect pathogenic CNVs, identify informative LOHs that can lead to the diagnosis of a recessive disorder. It also highlights some challenges associated with the use of SNP arrays in a clinical laboratory.
Electronic supplementary material
The online version of this article (doi:10.1186/s12920-014-0070-0) contains supplementary material, which is available to authorized users.
PMCID: PMC4299176  PMID: 25539807
Comparative genomic hybridization (CGH); Congenital abnormalities; Consanguinity; DNA copy number variation (CNV); Intellectual disability; Loss of heterozygosity (LOH); Microarray analysis; Single nucleotide polymorphism (SNP); Uniparental disomy (UPD)
9.  Metagenomic Assay for Identification of Microbial Pathogens in Tumor Tissues 
mBio  2014;5(5):e01714-14.
Screening for thousands of viruses and other pathogenic microorganisms, including bacteria, fungi, and parasites, in human tumor tissues will provide a better understanding of the contributory role of the microbiome in the predisposition for, causes of, and therapeutic responses to the associated cancer. Metagenomic assays designed to perform these tasks will have to include rapid and economical processing of large numbers of samples, supported by straightforward data analysis pipeline and flexible sample preparation options for multiple input tissue types from individual patients, mammals, or environmental samples. To meet these requirements, the PathoChip platform was developed by targeting viral, prokaryotic, and eukaryotic genomes with multiple DNA probes in a microarray format that can be combined with a variety of upstream sample preparation protocols and downstream data analysis. PathoChip screening of DNA plus RNA from formalin-fixed, paraffin-embedded tumor tissues demonstrated the utility of this platform, and the detection of oncogenic viruses was validated using independent PCR and deep sequencing methods. These studies demonstrate the use of the PathoChip technology combined with PCR and deep sequencing as a valuable strategy for detecting the presence of pathogens in human cancers and other diseases.
This work describes the design and testing of a PathoChip array containing probes with the ability to detect all known publicly available virus sequences as well as hundreds of pathogenic bacteria, fungi, parasites, and helminths. PathoChip provides wide coverage of microbial pathogens in an economical format. PathoChip screening of DNA plus RNA from formalin-fixed, paraffin-embedded tumor tissues demonstrated the utility of this platform, and the detection of oncogenic viruses was validated using independent PCR and sequencing methods. These studies demonstrate that the PathoChip technology is a valuable strategy for detecting the presence of pathogens in human cancers and other diseases.
PMCID: PMC4172075  PMID: 25227467
10.  Development of a High-Throughput Resequencing Array for the Detection of Pathogenic Mutations in Osteogenesis Imperfecta 
PLoS ONE  2015;10(3):e0119553.
Osteogenesis imperfecta (OI) is a rare inherited skeletal disease, characterized by bone fragility and low bone density. The mutations in this disorder have been widely reported to be on various exonal hotspots of the candidate genes, including COL1A1, COL1A2, CRTAP, LEPRE1, and FKBP10, thus creating a great demand for precise genetic tests. However, large genome sizes make the process daunting and the analyses, inefficient and expensive. Therefore, we aimed at developing a fast, accurate, efficient, and cheaper sequencing platform for OI diagnosis; and to this end, use of an advanced array-based technique was proposed.
A CustomSeq Affymetrix Resequencing Array was established for high-throughput sequencing of five genes simultaneously. Genomic DNA extraction from 13 OI patients and 85 normal controls and amplification using long-range PCR (LR-PCR) were followed by DNA fragmentation and chip hybridization, according to standard Affymetrix protocols. Hybridization signals were determined using GeneChip Sequence Analysis Software (GSEQ). To examine the feasibility, the outcome from new resequencing approach was validated by conventional capillary sequencing method.
Overall call rates using resequencing array was 96–98% and the agreement between microarray and capillary sequencing was 99.99%. 11 out of 13 OI patients with pathogenic mutations were successfully detected by the chip analysis without adjustment, and one mutation could also be identified using manual visual inspection.
A high-throughput resequencing array was developed that detects the disease-associated mutations in OI, providing a potential tool to facilitate large-scale genetic screening for OI patients. Through this method, a novel mutation was also found.
PMCID: PMC4350936  PMID: 25742658
11.  Quality assessment and data handling methods for Affymetrix Gene 1.0 ST arrays with variable RNA integrity 
BMC Genomics  2013;14:14.
RNA and microarray quality assessment form an integral part of gene expression analysis and, although methods such as the RNA integrity number (RIN) algorithm reliably asses RNA integrity, the relevance of RNA integrity in gene expression analysis as well as analysis methods to accommodate the possible effects of degradation requires further investigation. We investigated the relationship between RNA integrity and array quality on the commonly used Affymetrix Gene 1.0 ST array platform using reliable within-array and between-array quality assessment measures. The possibility of a transcript specific bias in the apparent effect of RNA degradation on the measured gene expression signal was evaluated after either excluding quality-flagged arrays or compensation for RNA degradation at different steps in the analysis.
Using probe-level and inter-array quality metrics to assess 34 Gene 1.0 ST array datasets derived from historical, paired tumour and normal primary colorectal cancer samples, 7 arrays (20.6%), with a mean sample RIN of 3.2 (SD = 0.42), were flagged during array quality assessment while 10 arrays from samples with RINs < 7 passed quality assessment, including one sample with a RIN < 3. We detected a transcript length bias in RNA degradation in only 5.8% of annotated transcript clusters (p-value 0.05, FC ≥ |2|), with longer and shorter than average transcripts under- and overrepresented in quality-flagged samples respectively. Applying compensatory measures for RNA degradation performed at least as well as excluding quality-flagged arrays, as judged by hierarchical clustering, gene expression analysis and Ingenuity Pathway Analysis; importantly, use of these compensatory measures had the significant benefit of enabling lower quality array data from irreplaceable clinical samples to be retained in downstream analyses.
Here, we demonstrate an effective array-quality assessment strategy, which will allow the user to recognize lower quality arrays that can be included in the analysis once appropriate measures are applied to account for known or unknown sources of variation, such as array quality- and batch- effects, by implementing ComBat or Surrogate Variable Analysis. This approach of quality control and analysis will be especially useful for clinical samples with variable and low RNA qualities, with RIN scores ≥ 2.
PMCID: PMC3557148  PMID: 23324084
Gene expression profiling; Microarray; RNA quality; RNA integrity number; Quality control; ComBat; Surrogate variable analysis; Non-biological experimental variance
12.  Robust methods for accurate diagnosis using pan-microbiological oligonucleotide microarrays 
BMC Bioinformatics  2009;10(Suppl 2):S11.
To address the limitations of traditional virus and pathogen detection methodologies in clinical diagnosis, scientists have developed high-throughput oligonucleotide microarrays to rapidly identify infectious agents. However, objectively identifying pathogens from the complex hybridization patterns of these massively multiplexed arrays remains challenging.
In this study, we conceived an automated method based on the hypergeometric distribution for identifying pathogens in multiplexed arrays and compared it to five other methods. We evaluated these metrics: 1) accurate prediction, whether the top ranked prediction(s) match the real virus(es); 2) four accuracy scores.
Though accurate prediction and high specificity and sensitivity can be achieved with several methods, the method based on hypergeometric distribution provides a significant advantage in term of positive predicting value with two to sixty folds the positive predicting values of other methods.
The proposed multi-specie array analysis based on the hypergeometric distribution addresses shortcomings of previous methods by enhancing signals of positively hybridized probes.
PMCID: PMC2646242  PMID: 19208186
13.  Comparative analysis of copy number detection by whole-genome BAC and oligonucleotide array CGH 
Microarray-based comparative genomic hybridization (aCGH) is a powerful diagnostic tool for the detection of DNA copy number gains and losses associated with chromosome abnormalities, many of which are below the resolution of conventional chromosome analysis. It has been presumed that whole-genome oligonucleotide (oligo) arrays identify more clinically significant copy-number abnormalities than whole-genome bacterial artificial chromosome (BAC) arrays, yet this has not been systematically studied in a clinical diagnostic setting.
To determine the difference in detection rate between similarly designed BAC and oligo arrays, we developed whole-genome BAC and oligonucleotide microarrays and validated them in a side-by-side comparison of 466 consecutive clinical specimens submitted to our laboratory for aCGH. Of the 466 cases studied, 67 (14.3%) had a copy-number imbalance of potential clinical significance detectable by the whole-genome BAC array, and 73 (15.6%) had a copy-number imbalance of potential clinical significance detectable by the whole-genome oligo array. However, because both platforms identified copy number variants of unclear clinical significance, we designed a systematic method for the interpretation of copy number alterations and tested an additional 3,443 cases by BAC array and 3,096 cases by oligo array. Of those cases tested on the BAC array, 17.6% were found to have a copy-number abnormality of potential clinical significance, whereas the detection rate increased to 22.5% for the cases tested by oligo array. In addition, we validated the oligo array for detection of mosaicism and found that it could routinely detect mosaicism at levels of 30% and greater.
Although BAC arrays have faster turnaround times, the increased detection rate of oligo arrays makes them attractive for clinical cytogenetic testing.
PMCID: PMC2909945  PMID: 20587050
14.  The Microbial Detection Array Combined with Random Phi29-Amplification Used as a Diagnostic Tool for Virus Detection in Clinical Samples 
PLoS ONE  2011;6(8):e22631.
A common technique used for sensitive and specific diagnostic virus detection in clinical samples is PCR that can identify one or several viruses in one assay. However, a diagnostic microarray containing probes for all human pathogens could replace hundreds of individual PCR-reactions and remove the need for a clear clinical hypothesis regarding a suspected pathogen. We have established such a diagnostic platform for random amplification and subsequent microarray identification of viral pathogens in clinical samples. We show that Phi29 polymerase-amplification of a diverse set of clinical samples generates enough viral material for successful identification by the Microbial Detection Array, demonstrating the potential of the microarray technique for broad-spectrum pathogen detection. We conclude that this method detects both DNA and RNA virus, present in the same sample, as well as differentiates between different virus subtypes. We propose this assay for diagnostic analysis of viruses in clinical samples.
PMCID: PMC3154197  PMID: 21853040
15.  Detection of Bacillus anthracis DNA in Complex Soil and Air Samples Using Next-Generation Sequencing 
PLoS ONE  2013;8(9):e73455.
Bacillus anthracis is the potentially lethal etiologic agent of anthrax disease, and is a significant concern in the realm of biodefense. One of the cornerstones of an effective biodefense strategy is the ability to detect infectious agents with a high degree of sensitivity and specificity in the context of a complex sample background. The nature of the B. anthracis genome, however, renders specific detection difficult, due to close homology with B. cereus and B. thuringiensis. We therefore elected to determine the efficacy of next-generation sequencing analysis and microarrays for detection of B. anthracis in an environmental background. We applied next-generation sequencing to titrated genome copy numbers of B. anthracis in the presence of background nucleic acid extracted from aerosol and soil samples. We found next-generation sequencing to be capable of detecting as few as 10 genomic equivalents of B. anthracis DNA per nanogram of background nucleic acid. Detection was accomplished by mapping reads to either a defined subset of reference genomes or to the full GenBank database. Moreover, sequence data obtained from B. anthracis could be reliably distinguished from sequence data mapping to either B. cereus or B. thuringiensis. We also demonstrated the efficacy of a microbial census microarray in detecting B. anthracis in the same samples, representing a cost-effective and high-throughput approach, complementary to next-generation sequencing. Our results, in combination with the capacity of sequencing for providing insights into the genomic characteristics of complex and novel organisms, suggest that these platforms should be considered important components of a biosurveillance strategy.
PMCID: PMC3767809  PMID: 24039948
16.  Reference-unbiased copy number variant analysis using CGH microarrays 
Nucleic Acids Research  2010;38(20):e190.
Comparative genomic hybridization (CGH) microarrays have been used to determine copy number variations (CNVs) and their effects on complex diseases. Detection of absolute CNVs independent of genomic variants of an arbitrary reference sample has been a critical issue in CGH array experiments. Whole genome analysis using massively parallel sequencing with multiple ultra-high resolution CGH arrays provides an opportunity to catalog highly accurate genomic variants of the reference DNA (NA10851). Using information on variants, we developed a new method, the CGH array reference-free algorithm (CARA), which can determine reference-unbiased absolute CNVs from any CGH array platform. The algorithm enables the removal and rescue of false positive and false negative CNVs, respectively, which appear due to the effects of genomic variants of the reference sample in raw CGH array experiments. We found that the CARA remarkably enhanced the accuracy of CGH array in determining absolute CNVs. Our method thus provides a new approach to interpret CGH array data for personalized medicine.
PMCID: PMC2978381  PMID: 20802225
17.  Ancient pathogen DNA in archaeological samples detected with a Microbial Detection Array 
Scientific Reports  2014;4:4245.
Ancient human remains of paleopathological interest typically contain highly degraded DNA in which pathogenic taxa are often minority components, making sequence-based metagenomic characterization costly. Microarrays may hold a potential solution to these challenges, offering a rapid, affordable, and highly informative snapshot of microbial diversity in complex samples without the lengthy analysis and/or high cost associated with high-throughput sequencing. Their versatility is well established for modern clinical specimens, but they have yet to be applied to ancient remains. Here we report bacterial profiles of archaeological and historical human remains using the Lawrence Livermore Microbial Detection Array (LLMDA). The array successfully identified previously-verified bacterial human pathogens, including Vibrio cholerae (cholera) in a 19th century intestinal specimen and Yersinia pestis (“Black Death” plague) in a medieval tooth, which represented only minute fractions (0.03% and 0.08% alignable high-throughput shotgun sequencing reads) of their respective DNA content. This demonstrates that the LLMDA can identify primary and/or co-infecting bacterial pathogens in ancient samples, thereby serving as a rapid and inexpensive paleopathological screening tool to study health across both space and time.
PMCID: PMC3945050  PMID: 24603850
18.  Identification of Upper Respiratory Tract Pathogens Using Electrochemical Detection on an Oligonucleotide Microarray 
PLoS ONE  2007;2(9):e924.
Bacterial and viral upper respiratory infections (URI) produce highly variable clinical symptoms that cannot be used to identify the etiologic agent. Proper treatment, however, depends on correct identification of the pathogen involved as antibiotics provide little or no benefit with viral infections. Here we describe a rapid and sensitive genotyping assay and microarray for URI identification using standard amplification and hybridization techniques, with electrochemical detection (ECD) on a semiconductor-based oligonucleotide microarray. The assay was developed to detect four bacterial pathogens (Bordetella pertussis, Streptococcus pyogenes, Chlamydia pneumoniae and Mycoplasma pneumoniae) and 9 viral pathogens (adenovirus 4, coronavirus OC43, 229E and HK, influenza A and B, parainfluinza types 1, 2, and 3 and respiratory syncytial virus. This new platform forms the basis for a fully automated diagnostics system that is very flexible and can be customized to suit different or additional pathogens. Multiple probes on a flexible platform allow one to test probes empirically and then select highly reactive probes for further iterative evaluation. Because ECD uses an enzymatic reaction to create electrical signals that can be read directly from the array, there is no need for image analysis or for expensive and delicate optical scanning equipment. We show assay sensitivity and specificity that are excellent for a multiplexed format.
PMCID: PMC1976596  PMID: 17895966
19.  High-throughput microarray technology in diagnostics of enterobacteria based on genome-wide probe selection and regression analysis 
BMC Genomics  2010;11:591.
The Enterobacteriaceae comprise a large number of clinically relevant species with several individual subspecies. Overlapping virulence-associated gene pools and the high overall genome plasticity often interferes with correct enterobacterial strain typing and risk assessment. Array technology offers a fast, reproducible and standardisable means for bacterial typing and thus provides many advantages for bacterial diagnostics, risk assessment and surveillance. The development of highly discriminative broad-range microbial diagnostic microarrays remains a challenge, because of marked genome plasticity of many bacterial pathogens.
We developed a DNA microarray for strain typing and detection of major antimicrobial resistance genes of clinically relevant enterobacteria. For this purpose, we applied a global genome-wide probe selection strategy on 32 available complete enterobacterial genomes combined with a regression model for pathogen classification. The discriminative power of the probe set was further tested in silico on 15 additional complete enterobacterial genome sequences. DNA microarrays based on the selected probes were used to type 92 clinical enterobacterial isolates. Phenotypic tests confirmed the array-based typing results and corroborate that the selected probes allowed correct typing and prediction of major antibiotic resistances of clinically relevant Enterobacteriaceae, including the subspecies level, e.g. the reliable distinction of different E. coli pathotypes.
Our results demonstrate that the global probe selection approach based on longest common factor statistics as well as the design of a DNA microarray with a restricted set of discriminative probes enables robust discrimination of different enterobacterial variants and represents a proof of concept that can be adopted for diagnostics of a wide range of microbial pathogens. Our approach circumvents misclassifications arising from the application of virulence markers, which are highly affected by horizontal gene transfer. Moreover, a broad range of pathogens have been covered by an efficient probe set size enabling the design of high-throughput diagnostics.
PMCID: PMC3017858  PMID: 20964857
20.  Protein kinase substrate identification on functional protein arrays 
BMC Biotechnology  2008;8:22.
Over the last decade, kinases have emerged as attractive therapeutic targets for a number of different diseases, and numerous high throughput screening efforts in the pharmaceutical community are directed towards discovery of compounds that regulate kinase function. The emerging utility of systems biology approaches has necessitated the development of multiplex tools suitable for proteomic-scale experiments to replace lower throughput technologies such as mass spectroscopy for the study of protein phosphorylation. Recently, a new approach for identifying substrates of protein kinases has applied the miniaturized format of functional protein arrays to characterize phosphorylation for thousands of candidate protein substrates in a single experiment. This method involves the addition of protein kinases in solution to arrays of immobilized proteins to identify substrates using highly sensitive radioactive detection and hit identification algorithms.
To date, the factors required for optimal performance of protein array-based kinase substrate identification have not been described. In the current study, we have carried out a detailed characterization of the protein array-based method for kinase substrate identification, including an examination of the effects of time, buffer compositions, and protein concentration on the results. The protein array approach was compared to standard solution-based assays for assessing substrate phosphorylation, and a correlation of greater than 80% was observed. The results presented here demonstrate how novel substrates for protein kinases can be quickly identified from arrays containing thousands of human proteins to provide new clues to protein kinase function. In addition, a pooling-deconvolution strategy was developed and applied that enhances characterization of specific kinase-substrate relationships and decreases reagent consumption.
Functional protein microarrays are an important new tool that enables multiplex analysis of protein phosphorylation, and thus can be utilized to identify novel kinase substrates. Integrating this technology with a systems biology approach to cell signalling will help uncover new layers in our understanding of this essential class of enzymes.
PMCID: PMC2270825  PMID: 18307815
21.  Modular Nucleic Acid Assembled p/MHC Microarrays for Multiplexed Sorting of Antigen-Specific T Cells 
The human immune system consists of a large number of T cells capable of recognizing and responding to antigens derived from various sources. The development of peptide-major histocompatibility (p/MHC) tetrameric complexes has enabled the direct detection of these antigen-specific T cells. With the goal of increasing throughput and multiplexing of T cell detection, protein microarrays spotted with defined p/MHC complexes have been reported, but studies have been limited due to the inherent instability and reproducibility of arrays produced via conventional spotted methods. Herein, we report on a platform for the detection of antigen-specific T cells on glass substrates that offers significant advantages over existing surface-bound schemes. In this approach, called “Nucleic Acid Cell Sorting (NACS)”, single-stranded DNA oligomers conjugated site-specifically to p/MHC tetramers are employed to immobilize p/MHC tetramers via hybridization to a complementary-printed substrate. Fully assembled p/MHC arrays are used to detect and enumerate T cells captured from cellular suspensions, including primary human T cells collected from cancer patients. NACS arrays outperform conventional spotted arrays assessed in key criteria such as repeatability and homogeneity. The versatility of employing DNA sequences for cell sorting is exploited to enable the programmed, selective release of target populations of immobilized T cells with restriction endonucleases for downstream analysis. Because of the performance, facile and modular assembly of p/MHC tetramer arrays, NACS holds promise as a versatile platform for multiplexed T cell detection.
PMCID: PMC2720314  PMID: 19552409
22.  ArrayInitiative - a tool that simplifies creating custom Affymetrix CDFs 
BMC Bioinformatics  2011;12:136.
Probes on a microarray represent a frozen view of a genome and are quickly outdated when new sequencing studies extend our knowledge, resulting in significant measurement error when analyzing any microarray experiment. There are several bioinformatics approaches to improve probe assignments, but without in-house programming expertise, standardizing these custom array specifications as a usable file (e.g. as Affymetrix CDFs) is difficult, owing mostly to the complexity of the specification file format. However, without correctly standardized files there is a significant barrier for testing competing analysis approaches since this file is one of the required inputs for many commonly used algorithms. The need to test combinations of probe assignments and analysis algorithms led us to develop ArrayInitiative, a tool for creating and managing custom array specifications.
ArrayInitiative is a standalone, cross-platform, rich client desktop application for creating correctly formatted, custom versions of manufacturer-provided (default) array specifications, requiring only minimal knowledge of the array specification rules and file formats. Users can import default array specifications, import probe sequences for a default array specification, design and import a custom array specification, export any array specification to multiple output formats, export the probe sequences for any array specification and browse high-level information about the microarray, such as version and number of probes. The initial release of ArrayInitiative supports the Affymetrix 3' IVT expression arrays we currently analyze, but as an open source application, we hope that others will contribute modules for other platforms.
ArrayInitiative allows researchers to create new array specifications, in a standard format, based upon their own requirements. This makes it easier to test competing design and analysis strategies that depend on probe definitions. Since the custom array specifications are easily exported to the manufacturer's standard format, researchers can analyze these customized microarray experiments using established software tools, such as those available in Bioconductor.
PMCID: PMC3113937  PMID: 21548938
23.  Reproducibility of quantitative RT-PCR array in miRNA expression profiling and comparison with microarray analysis 
BMC Genomics  2009;10:407.
MicroRNAs (miRNAs) have critical functions in various biological processes. MiRNA profiling is an important tool for the identification of differentially expressed miRNAs in normal cellular and disease processes. A technical challenge remains for high-throughput miRNA expression analysis as the number of miRNAs continues to increase with in silico prediction and experimental verification. Our study critically evaluated the performance of a novel miRNA expression profiling approach, quantitative RT-PCR array (qPCR-array), compared to miRNA detection with oligonucleotide microchip (microarray).
High reproducibility with qPCR-array was demonstrated by comparing replicate results from the same RNA sample. Pre-amplification of the miRNA cDNA improved sensitivity of the qPCR-array and increased the number of detectable miRNAs. Furthermore, the relative expression levels of miRNAs were maintained after pre-amplification. When the performance of qPCR-array and microarrays were compared using different aliquots of the same RNA, a low correlation between the two methods (r = -0.443) indicated considerable variability between the two assay platforms. Higher variation between replicates was observed in miRNAs with low expression in both assays. Finally, a higher false positive rate of differential miRNA expression was observed using the microarray compared to the qPCR-array.
Our studies demonstrated high reproducibility of TaqMan qPCR-array. Comparison between different reverse transcription reactions and qPCR-arrays performed on different days indicated that reverse transcription reactions did not introduce significant variation in the results. The use of cDNA pre-amplification increased the sensitivity of miRNA detection. Although there was variability associated with pre-amplification in low abundance miRNAs, the latter did not involve any systemic bias in the estimation of miRNA expression. Comparison between microarray and qPCR-array indicated superior sensitivity and specificity of qPCR-array.
PMCID: PMC2753550  PMID: 19715577
24.  Food Microbial Pathogen Detection and Analysis Using DNA Microarray Technologies 
Foodborne Pathogens and Disease  2008;5(4):531-550.
Culture-based methods used for microbial detection and identification are simple to use, relatively inexpensive, and sensitive. However, culture-based methods are too time-consuming for high-throughput testing and too tedious for analysis of samples with multiple organisms and provide little clinical information regarding the pathogen (e.g., antibiotic resistance genes, virulence factors, or strain subtype). DNA-based methods, such as polymerase chain reaction (PCR), overcome some these limitations since they are generally faster and can provide more information than culture-based methods. One limitation of traditional PCR-based methods is that they are normally limited to the analysis of a single pathogen, a small group of related pathogens, or a small number of relevant genes. Microarray technology enables a significant expansion of the capability of DNA-based methods in terms of the number of DNA sequences that can be analyzed simultaneously, enabling molecular identification and characterization of multiple pathogens and many genes in a single array assay. Microarray analysis of microbial pathogens has potential uses in research, food safety, medical, agricultural, regulatory, public health, and industrial settings. In this article, we describe the main technical elements of microarray technology and the application and potential use of DNA microarrays for food microbial analysis.
PMCID: PMC3186690  PMID: 18673074
25.  Absolute quantification of microbial proteomes at different states by directed mass spectrometry 
The developed, directed mass spectrometry workflow allows to generate consistent and system-wide quantitative maps of microbial proteomes in a single analysis. Application to the human pathogen L. interrogans revealed mechanistic proteome changes over time involved in pathogenic progression and antibiotic defense, and new insights about the regulation of absolute protein abundances within operons.
The developed, directed proteomic approach allowed consistent detection and absolute quantification of 1680 proteins of the human pathogen L. interrogans in a single LC–MS/MS experiment.The comparison of 25 extensive, consistent and quantitative proteome maps revealed new insights about the proteome changes involved in pathogenic progression and antibiotic defense of L. interrogans, and about the regulation of protein abundances within operons.The generated time-resolved data sets are compatible with pattern analysis algorithms developed for transcriptomics, including hierarchical clustering and functional enrichment analysis of the detected profile clusters.This is the first study that describes the absolute quantitative behavior of any proteome over multiple states and represents the most comprehensive proteome abundance pattern comparison for any organism to date.
Over the last decade, mass spectrometry (MS)-based proteomics has evolved as the method of choice for system-wide proteome studies and now allows for the characterization of several thousands of proteins in a single sample. Despite these great advances, redundant monitoring of protein levels over large sample numbers in a high-throughput manner remains a challenging task. New directed MS strategies have shown to overcome some of the current limitations, thereby enabling the acquisition of consistent and system-wide data sets of proteomes with low-to-moderate complexity at high throughput.
In this study, we applied this integrated, two-stage MS strategy to investigate global proteome changes in the human pathogen L. interrogans. In the initial discovery phase, 1680 proteins (out of around 3600 gene products) could be identified (Schmidt et al, 2008) and, by focusing precious MS-sequencing time on the most dominant, specific peptides per protein, all proteins could be accurately and consistently monitored over 25 different samples within a few days of instrument time in the following scoring phase (Figure 1). Additionally, the co-analysis of heavy reference peptides enabled us to obtain absolute protein concentration estimates for all identified proteins in each perturbation (Malmström et al, 2009). The detected proteins did not show any biases against functional groups or protein classes, including membrane proteins, and span an abundance range of more than three orders of magnitude, a range that is expected to cover most of the L. interrogans proteome (Malmström et al, 2009).
To elucidate mechanistic proteome changes over time involved in pathogenic progression and antibiotic defense of L. interrogans, we generated time-resolved proteome maps of cells perturbed with serum and three different antibiotics at sublethal concentrations that are currently used to treat Leptospirosis. This yielded an information-rich proteomic data set that describes, for the first time, the absolute quantitative behavior of any proteome over multiple states, and represents the most comprehensive proteome abundance pattern comparison for any organism to date. Using this unique property of the data set, we could quantify protein components of entire pathways across several time points and subject the data sets to cluster analysis, a tool that was previously limited to the transcript level due to incomplete sampling on protein level (Figure 4). Based on these analyses, we could demonstrate that Leptospira cells adjust the cellular abundance of a certain subset of proteins and pathways as a general response to stress while other parts of the proteome respond highly specific. The cells furthermore react to individual treatments by ‘fine tuning' the abundance of certain proteins and pathways in order to cope with the specific cause of stress. Intriguingly, the most specific and significant expression changes were observed for proteins involved in motility, tissue penetration and virulence after serum treatment where we tried to simulate the host environment. While many of the detected protein changes demonstrate good agreement with available transcriptomics data, most proteins showed a poor correlation. This includes potential virulence factors, like Loa22 or OmpL1, with confirmed expression in vivo that were significantly up-regulated on the protein level, but not on the mRNA level, strengthening the importance of proteomic studies. The high resolution and coverage of the proteome data set enabled us to further investigate protein abundance changes of co-regulated genes within operons. This suggests that although most proteins within an operon respond to regulation synchronously, bacterial cells seem to have subtle means to adjust the levels of individual proteins or protein groups outside of the general trend, a phenomena that was recently also observed on the transcript level of other bacteria (Güell et al, 2009).
The method can be implemented with standard high-resolution mass spectrometers and software tools that are readily available in the majority of proteomics laboratories. It is scalable to any proteome of low-to-medium complexity and can be extended to post-translational modifications or peptide-labeling strategies for quantification. We therefore expect the approach outlined here to become a cornerstone for microbial systems biology.
Over the past decade, liquid chromatography coupled with tandem mass spectrometry (LC–MS/MS) has evolved into the main proteome discovery technology. Up to several thousand proteins can now be reliably identified from a sample and the relative abundance of the identified proteins can be determined across samples. However, the remeasurement of substantially similar proteomes, for example those generated by perturbation experiments in systems biology, at high reproducibility and throughput remains challenging. Here, we apply a directed MS strategy to detect and quantify sets of pre-determined peptides in tryptic digests of cells of the human pathogen Leptospira interrogans at 25 different states. We show that in a single LC–MS/MS experiment around 5000 peptides, covering 1680 L. interrogans proteins, can be consistently detected and their absolute expression levels estimated, revealing new insights about the proteome changes involved in pathogenic progression and antibiotic defense of L. interrogans. This is the first study that describes the absolute quantitative behavior of any proteome over multiple states, and represents the most comprehensive proteome abundance pattern comparison for any organism to date.
PMCID: PMC3159967  PMID: 21772258
absolute quantification; directed mass spectrometry; Leptospira interrogans; microbiology; proteomics

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