Detection of hepatitis B virus markers using a biosensor based on imaging ellipsometry

A biosensor based on imaging ellipsometry (BIE) has been developed and validated in 169 patients for detecting five markers of hepatitis B virus (HBV) infection. The methodology has been established to pave the way for clinical diagnosis, including ligand screening, determination of the sensitivity, set-up of cut-off values (CoVs) and comparison with other clinical methods. A matrix assay method was established for ligand screening. The CoVs of HBV markers were derived with the help of receiver operating characteristic curves. Enzyme-linked immunosorbent assay (ELISA) was the reference method. Ligands with high bioactivity were selected and sensitivities of 1 ng/mL and 1 IU/mL for hepatitis B surface antigen (HBsAg) and surface antibody (anti-HBs) were obtained respectively. The CoVs of HBsAg, anti-HBs, hepatitis B e antigen, hepatitis B e antibody and core antibody were as follows: 15%, 18%, 15%, 20% and 15%, respectively, which were the percentages over the values of corresponding ligand controls. BIE can simultaneously detect up to five markers within 1 h with results in acceptable agreement with ELISA, and thus shows a potential for diagnosing hepatitis B with high throughput.

Mechanistic studies of reactions at the single-molecule level using microfluidics with applications in molecular diagnostics

Motivated by needs in molecular diagnostics and advances in microfabrication, researchers started to seek help from microfluidic technology, as it provides approaches to achieve high throughput, high sensitivity, and high resolution. One strategy applied in microfluidics to fulfill such requirements is to convert continuous analog signal into digitalized signal. One most commonly used example for this conversion is digital PCR, where by counting the number of reacted compartments (triggered by the presence of the target entity) out of the total number of compartments, one could use Poisson statistics to calculate the amount of input target.

However, there are still problems to be solved and assumptions to be validated before the technology is widely employed. In this dissertation, the digital quantification strategy has been examined from two angles: efficiency and robustness. The former is a critical factor for ensuring the accuracy of absolute quantification methods, and the latter is the premise for such technology to be practically implemented in diagnosis beyond the laboratory. The two angles are further framed into a “fate” and “rate” determination scheme, where the influence of different parameters is attributed to fate determination step or rate determination step. In this discussion, microfluidic platforms have been used to understand reaction mechanism at single molecule level. Although the discussion raises more challenges for digital assay development, it brings the problem to the attention of the scientific community for the first time.

This dissertation also contributes towards developing POC test in limited resource settings. On one hand, it adds ease of access to the tests by incorporating massively producible, low cost plastic material and by integrating new features that allow instant result acquisition and result feedback. On the other hand, it explores new isothermal chemistry and new strategies to address important global health concerns such as cyctatin C quantification, HIV/HCV detection and treatment monitoring as well as HCV genotyping.

High-fidelity replication of Dammann gratings using soft lithography

We report the experimental results of using the soft lithography method for replication of Dammann gratings. By using an elastomeric stamp, uniform grating structures were transferred to the LTV-curable polymer. To evaluate the quality of the replication, diffraction images and light intensity were measured. Compared with the master devices, the replicas of Dammann gratings show a slight deviation in both surface relief profile and optical performance. Experimental results demonstrated that high-fidelity replication of Dammann gratings is realized by using soft lithography with low cost and high throughput. (C) 2008 Optical Society of America.

18S rRNA V9 metabarcoding for diet characterization: a critical evaluation with two sympatric zooplanktivorous fish species

The potential of the 18S rRNA V9 metabarcoding approach for diet assessment was explored using MiSeq paired-end (PE; 2 9 150 bp) technology. To critically evaluate the method's performance with degraded/digested DNA, the diets of two zooplanktivorous fish species from the Bay of Biscay, European sardine (Sardina pilchardus) and European sprat (Sprattus sprattus), were analysed. The taxonomic resolution and quantitative potential of the 18S V9 metabarcoding was first assessed both in silico and with mock and field plankton samples. Our method was capable of discriminating species within the reference database in a reliable way providing there was at least one variable position in the 18S V9 region. Furthermore, it successfully discriminated diet between both fish species, including habitat and diel differences among sardines, overcoming some of the limitations of traditional visual-based diet analysis methods. The high sensitivity and semi-quantitative nature of the 18S V9 metabarcoding approach was supported by both visual microscopy and qPCR-based results. This molecular approach provides an alternative cost and time effective tool for food-web analysis.

Deep RNA sequencing at single base-pair resolution reveals high complexity of the rice transcriptome

Understanding the dynamics of eukaryotic transcriptome is essential for studying the complexity of transcriptional regulation and its impact on phenotype. However, comprehensive studies of transcriptomes at single base resolution are rare, even for modern organisms, and lacking for rice. Here, we present the first transcriptome atlas for eight organs of cultivated rice. Using high-throughput paired-end RNA-seq, we unambiguously detected transcripts expressing at an extremely low level, as well as a substantial number of novel transcripts, exons, and untranslated regions. An analysis of alternative splicing in the rice transcriptome revealed that alternative cis-splicing occurred in similar to 33% of all rice genes. This is far more than previously reported. In addition, we also identified 234 putative chimeric transcripts that seem to be produced by trans-splicing, indicating that transcript fusion events are more common than expected. In-depth analysis revealed a multitude of fusion transcripts that might be by-products of alternative splicing. Validation and chimeric transcript structural analysis provided evidence that some of these transcripts are likely to be functional in the cell. Taken together, our data provide extensive evidence that transcriptional regulation in rice is vastly more complex than previously believed.

Hierarchical electrohydrodynamic structures for surface-enhanced Raman scattering.

Surface enhanced Raman scattering (SERS) is a well-established spectroscopic technique that requires nanoscale metal structures to achieve high signal sensitivity. While most SERS substrates are manufactured by conventional lithographic methods, the development of a cost-effective approach to create nanostructured surfaces is a much sought-after goal in the SERS community. Here, a method is established to create controlled, self-organized, hierarchical nanostructures using electrohydrodynamic (HEHD) instabilities. The created structures are readily fine-tuned, which is an important requirement for optimizing SERS to obtain the highest enhancements. HEHD pattern formation enables the fabrication of multiscale 3D structured arrays as SERS-active platforms. Importantly, each of the HEHD-patterned individual structural units yield a considerable SERS enhancement. This enables each single unit to function as an isolated sensor. Each of the formed structures can be effectively tuned and tailored to provide high SERS enhancement, while arising from different HEHD morphologies. The HEHD fabrication of sub-micrometer architectures is straightforward and robust, providing an elegant route for high-throughput biological and chemical sensing.

Bayesian correlated clustering to integrate multiple datasets.

MOTIVATION: The integration of multiple datasets remains a key challenge in systems biology and genomic medicine. Modern high-throughput technologies generate a broad array of different data types, providing distinct-but often complementary-information. We present a Bayesian method for the unsupervised integrative modelling of multiple datasets, which we refer to as MDI (Multiple Dataset Integration). MDI can integrate information from a wide range of different datasets and data types simultaneously (including the ability to model time series data explicitly using Gaussian processes). Each dataset is modelled using a Dirichlet-multinomial allocation (DMA) mixture model, with dependencies between these models captured through parameters that describe the agreement among the datasets. RESULTS: Using a set of six artificially constructed time series datasets, we show that MDI is able to integrate a significant number of datasets simultaneously, and that it successfully captures the underlying structural similarity between the datasets. We also analyse a variety of real Saccharomyces cerevisiae datasets. In the two-dataset case, we show that MDI's performance is comparable with the present state-of-the-art. We then move beyond the capabilities of current approaches and integrate gene expression, chromatin immunoprecipitation-chip and protein-protein interaction data, to identify a set of protein complexes for which genes are co-regulated during the cell cycle. Comparisons to other unsupervised data integration techniques-as well as to non-integrative approaches-demonstrate that MDI is competitive, while also providing information that would be difficult or impossible to extract using other methods.

Band gap opening of graphene after UV/ozone and oxygen plasma treatments

Graphene grown by Chemical Vapor Deposition (CVD) on nickel subsrate is oxidized by means of oxygen plasma and UV/Ozone treatments to introduce bandgap opening in graphene. The degree of band gap opening is proportional to the degree of oxidation on the graphene. This result is analyzed and confirmed by Scanning Tunnelling Microscopy/Spectroscopy and Raman spectroscopy measurements. Compared to conventional wet-oxidation methods, oxygen plasma and UV/Ozone treatments do not require harsh chemicals to perform, allow faster oxidation rates, and enable site-specific oxidation. These features make oxygen plasma and UV/Ozone treatments ideal candidates to be implemented in high-throughput fabrication of graphene-based microelectronics. © 2011 Materials Research Society.

Dioxin-like components in human breast milk collected from Hong Kong and Guangzhou

The H4IIE rat hepatoma cell line was employed as a cell model to screen 7-ethoxyresorufin O-deethylase (EROD)-TCDD equivalents (EROD-TEQ) of human breast milk samples collected from Hong Kong and Guangzhou, China. The screening methods employed a 96-well plate spectrofluorometer-EROD assay. For cell-line validation, our results demonstrated a dose-dependent increase in the Ah receptor-mediated response (i.e., CYP1A1 mRNA and EROD) of the cells upon exposure to a number of known Ah receptor agonists, including 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), 2,3,7,8-tetrachlorodibenzothiophene, benzo[a]pyrene, and beta-naphthaflavone. TCDD induced CYP1A1 mRNA and EROD was in a close positive correlation (r = 0.98). For the screening of dioxin-like compounds, breast milk samples collected during lactation weeks 3-5 were used. One hundred (from Hong Kong) and 48 (from Guangzhou) breast milk samples were assayed, of which 65% and 68% of the samples, respectively, showed detectable dioxin-like activities using the H4IIE cell EROD screening method. For sixty-five samples from Hong Kong the mean EROD-TEQ values ranged from 58.1 to 96.5 pg/g of milk fat for those aged 21-36 years while 32 samples from Guangzhou had mean values of 98.8-202.1 pg/g of milk fat. In comparisons of the EROD-TEQ values for different age groups from both cities, there were no significant differences (P < 0.05). However, the mean and median EROD-TEQ values of the Guangzhou population were in general higher than those of the Hong Kong population. The results of the present study indicate that it is feasible to use the H4IIE cell-line as a model for screening dioxin-like compounds in human breast milk. In addition, the method is rapid and cost-effective, particularly for a routine and high-throughput sample screening analysis, compared to the costly and time-intensive chemical analytical techniques. (C) 2003 Elsevier Inc. All rights reserved.

Soil microbial community analysis using denaturing gradient gel electrophoresis

The most biological diversity on this planet is probably harbored in soils. Understanding the diversity and function of the microbiological component of soil poses great challenges that are being overcome by the application of molecular biological approaches. This review covers one of many approaches being used: separation of polymerase chain reaction (PCR) amplicons using denaturing gradient gel electrophoresis (DGGE). Extraction of nucleic acids directly from soils allows the examination of a community without the limitation posed by cultivation. Polymerase chain reaction provides a means to increase the numbers of a target for its detection on gels. Using the rRNA genes as a target for PCR provides phylogenetic information on populations comprising communities. Fingerprints produced by this method have allowed spatial and temporal comparisons of soil communities within and between locations or among treatments. Numerous samples can be compared because of the rapid high throughput nature of this method. Scientists now have the means to begin addressing complex ecological questions about the spatial, temporal, and nutritional interactions faced by microbes in the soil environment.

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry with a matrix of carbon nanotubes for the analysis of low-mass compounds in environmental samples

The use of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) for environmental analysis has been mainly focused on qualitative analysis of high-mass molecules, such as toxins, humic acid, and microorganisms. Herein,we describe a novel MALDI-TOF-MS method with a matrix of oxidized carbon nanotubes for analysis of low-mass compounds in environmental samples. A number of chemicals in the environment were qualitatively analyzed by the present method, and it was found that most of them, especially the highly polar chemicals, were measurable with high sensitivity. With the intrinsic ability to measure high-mass chemicals, this method can compensate for the current shortage of methods for environmental analysis for the measurement of highly polar or high-mass chemicals. For sample analysis, arsenic speciation in Chinese traditional medicines was qualified and diphenylolpropane in water samples was quantified. With the relatively high tolerance of the method to interfering molecules, a simple pretreatment or even no pretreatment could be employed before MS detection. Furthermore, this method can be employed in a high-throughput format.

The EXACT description of biomedical protocols

Soldatova, L. N., Aubrey, W., King, R. D., Clare, A. J. (2008). The EXACT description of biomedical protocols. Bioinformatics, 24 (13), i295-i303 Sponsorship: BBSRC / RAEng / EPSRC specialissue: ISMB

Integration of relational and hierarchical network information for protein function prediction

BACKGROUND:In the current climate of high-throughput computational biology, the inference of a protein's function from related measurements, such as protein-protein interaction relations, has become a canonical task. Most existing technologies pursue this task as a classification problem, on a term-by-term basis, for each term in a database, such as the Gene Ontology (GO) database, a popular rigorous vocabulary for biological functions. However, ontology structures are essentially hierarchies, with certain top to bottom annotation rules which protein function predictions should in principle follow. Currently, the most common approach to imposing these hierarchical constraints on network-based classifiers is through the use of transitive closure to predictions.RESULTS:We propose a probabilistic framework to integrate information in relational data, in the form of a protein-protein interaction network, and a hierarchically structured database of terms, in the form of the GO database, for the purpose of protein function prediction. At the heart of our framework is a factorization of local neighborhood information in the protein-protein interaction network across successive ancestral terms in the GO hierarchy. We introduce a classifier within this framework, with computationally efficient implementation, that produces GO-term predictions that naturally obey a hierarchical 'true-path' consistency from root to leaves, without the need for further post-processing.CONCLUSION:A cross-validation study, using data from the yeast Saccharomyces cerevisiae, shows our method offers substantial improvements over both standard 'guilt-by-association' (i.e., Nearest-Neighbor) and more refined Markov random field methods, whether in their original form or when post-processed to artificially impose 'true-path' consistency. Further analysis of the results indicates that these improvements are associated with increased predictive capabilities (i.e., increased positive predictive value), and that this increase is consistent uniformly with GO-term depth. Additional in silico validation on a collection of new annotations recently added to GO confirms the advantages suggested by the cross-validation study. Taken as a whole, our results show that a hierarchical approach to network-based protein function prediction, that exploits the ontological structure of protein annotation databases in a principled manner, can offer substantial advantages over the successive application of 'flat' network-based methods.

Identification of RNA editing in the human exome and development of DARNED DatabaseSF

RNA editing is a biological phenomena that alters nascent RNA transcripts by insertion, deletion and/or substitution of one or a few nucleotides. It is ubiquitous in all kingdoms of life and in viruses. The predominant editing event in organisms with a developed central nervous system is Adenosine to Inosine deamination. Inosine is recognized as Guanosine by the translational machinery and reverse-transcriptase. In primates, RNA editing occurs frequently in transcripts from repetitive regions of the genome. In humans, more than 500,000 editing instances have been identified, by applying computational pipelines on available ESTs and high-throughput sequencing data, and by using chemical methods. However, the functions of only a small number of cases have been studied thoroughly. RNA editing instances have been found to have roles in peptide variants synthesis by non-synonymous codon substitutions, transcript variants by alterations in splicing sites and gene silencing by miRNAs sequence modifications. We established the Database of RNA EDiting (DARNED) to accommo-date the reference genomic coordinates of substitution editing in human, mouse and fly transcripts from published literatures, with additional information on edited genomic coordinates collected from various databases e.g. UCSC, NCBI. DARNED contains mostly Adenosine to Inosine editing and allows searches based on genomic region, gene ID, and user provided sequence. The Database is accessible at http://darned.ucc.ie RNA editing instances in coding region are likely to result in recoding in protein synthesis. This encouraged me to focus my research on the occurrences of RNA editing specific CDS and non-Alu exonic regions. By applying various filters on discrepancies between available ESTs and their corresponding reference genomic sequences, putative RNA editing candidates were identified. High-throughput sequencing was used to validate these candidates. All predicted coordinates appeared to be either SNPs or unedited.

Explicit DNase sequence bias modeling enables high-resolution transcription factor footprint detection.

DNaseI footprinting is an established assay for identifying transcription factor (TF)-DNA interactions with single base pair resolution. High-throughput DNase-seq assays have recently been used to detect in vivo DNase footprints across the genome. Multiple computational approaches have been developed to identify DNase-seq footprints as predictors of TF binding. However, recent studies have pointed to a substantial cleavage bias of DNase and its negative impact on predictive performance of footprinting. To assess the potential for using DNase-seq to identify individual binding sites, we performed DNase-seq on deproteinized genomic DNA and determined sequence cleavage bias. This allowed us to build bias corrected and TF-specific footprint models. The predictive performance of these models demonstrated that predicted footprints corresponded to high-confidence TF-DNA interactions. DNase-seq footprints were absent under a fraction of ChIP-seq peaks, which we show to be indicative of weaker binding, indirect TF-DNA interactions or possible ChIP artifacts. The modeling approach was also able to detect variation in the consensus motifs that TFs bind to. Finally, cell type specific footprints were detected within DNase hypersensitive sites that are present in multiple cell types, further supporting that footprints can identify changes in TF binding that are not detectable using other strategies.