Rapid, femtomolar bioassays in complex matrices combining microfluidics and magnetoelectronics

A significant challenge for all biosensor systems is to achieve high assay sensitivity and specificity while minimizing sample preparation requirements, operational complexity, and sample-to-answer time. We have achieved multiplexed, unamplified, femtomolar detection of both DNA and proteins in complex matrices (including whole blood, serum, plasma, and milk) in minutes using as few as two reagents by labeling conventional assay schemes with micrometer-scale magnetic beads, and applying fluidic force discrimination (FFD). In FFD assays, analytes captured onto a microarray surface are labeled with microbeads, and a controlled laminar flow is then used to apply microfluidic forces sufficient to preferentially remove only nonspecifically bound bead labels. The density of beads that remain bound is proportional to the analyte concentration and can be determined with either optical counting or magnetoelectronic detection of the magnetic labels. Combining FFD assays with chip-based magnetoelectronic detection enables a simple, potentially handheld, platform capable of both nucleic acid hybridization assays and immunoassays, including orthogonal detection and identification of bacterial and viral pathogens, and therefore suitable for a wide range of biosensing applications.     

Silicon chip-based patch-clamp electrodes integrated with PDMS microfluidics

We report on a silicon wafer-based device that can be used for recording macroscopic ion channel protein activities across a diverse group of cell-types. Gigaohm seals were achieved for CHO-K1 and RIN m5F cells, and both cell-attached and whole-cell mode configurations were also demonstrated. Two distinct intrinsic potassium ion channels were recorded in whole-cell mode for HIT-T15 and RAW 264.7 cells. Polydimethylsiloxane (PDMS) microfluidics were also coupled with the micromachined silicon chips in order to demonstrate that a single cell could be selectively directed to a micropore, and membrane protein currents could subsequently be recorded. These silicon chip-based devices have significant advantages over traditional micropipette approaches, and may serve as combinatorial tools for investigating membrane biophysics, pharmaceutical screening, and other bio-sensing tasks.     

Combining multivariate bioassays

Linear multivariate theory is applied to the problem of combining several multivariate bioassays. Results are an asymptotic test of the hypothesis of a common log relative potency; the maximum likelihood estimator of the common log relative potency; and an exact and asymptotic confidence interval estimator for log relative potency.     

Stem cells in microfluidics

With the introduction of microtechnology and microfluidic platforms for cell culture, stem cell research can be put into a new context. Inside microfluidics, microenvironments can be more precisely controlled and their influence on cell fate studied. Microfluidic devices can be made transparent and the cells monitored real time by imaging, using fluorescence markers to probe cell functions and cell fate. This article gives a perspective on the yet untapped utility of microfluidic devices for stem cell research. It will guide the biologists through some basic microtechnology and the application of microfluidics to cell research, as well as highlight to the engineers the cell culture capabilities of microfluidics. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009     

Applying microfluidics to electrophysiology

Microfluidics can be integrated with standard electrophysiology techniques to allow new experimental modalities. Specifically, the motivation for the microfluidic brain slice device is discussed including how the device docks to standard perfusion chambers and the technique of passive pumping which is used to deliver boluses of neuromodulators to the brain slice. By simplifying the device design, we are able to achieve a practical solution to the current unmet electrophysiology need of applying multiple neuromodulators across multiple regions of the brain slice. This is achieved by substituting the standard coverglass substrate of the perfusion chamber with a thin microfluidic device bonded to the coverglass substrate. This was then attached to the perfusion chamber and small holes connect the open-well of the perfusion chamber to the microfluidic channels buried within the microfluidic substrate. These microfluidic channels are interfaced with ports drilled into the edge of the perfusion chamber to access and deliver stimulants. This project represents how the field of microfluidics is transitioning away from proof-of concept device demonstrations and into practical solutions for unmet experimental and clinical needs.     

Clinical applications of microarray-based diagnostic tests

Nearly 15 years have passed since the possibility of analyzing nucleic acid analytes in a massively parallel fashion was proposed using the then new concept of microarrays. A decade ago, proof of principle demonstration projects established the use of high density microarrays to genotype multiple polymorphisms within a large gene [cystic fibrosis transmembrance regulator (CFTR)], to rapidly analyze DNA sequences by hybridization and to ascertain differential gene expression of the entire genome of an organism. The use of microarrays has had an explosive influence on the rate at which new biological information can be learned, including in a nonhypothesis driven manner. The past decade has also seen these research tools applied increasingly to questions of clinical and medical relevance. Genotyping drug metabolizing enzyme genes, resequencing important tumor suppressor genes, and classifying neoplastic disease by differential gene expression profiles are but a few of the many possibilities to provide clinically useful information using microarray-based diagnostic tests.     

M-CGH: Analysing microarray-based CGH experiments

Background  

Microarray-based comparative genomic hybridisation (array CGH) is a technique by which variation in relative copy numbers between two genomes can be analysed by competitive hybridisation to DNA microarrays. This technology has most commonly been used to detect chromosomal amplifications and deletions in cancer. Dedicated tools are needed to analyse the results of such experiments, which include appropriate visualisation, and to take into consideration the physical relation in the genome between the probes on the array.     

Small sample issues for microarray-based classification

In order to study the molecular biological differences between normal and diseased tissues, it is desirable to perform classification among diseases and stages of disease using microarray-based gene-expression values. Owing to the limited number of microarrays typically used in these studies, serious issues arise with respect to the design, performance and analysis of classifiers based on microarray data. This paper reviews some fundamental issues facing small-sample classification: classification rules, constrained classifiers, error estimation and feature selection. It discusses both unconstrained and constrained classifier design from sample data, and the contributions to classifier error from constrained optimization and lack of optimality owing to design from sample data. The difficulty with estimating classifier error when confined to small samples is addressed, particularly estimating the error from training data. The impact of small samples on the ability to include more than a few variables as classifier features is explained.     

Cross-platform comparison of microarray-based multiple-class prediction

High-throughput microarray technology has been widely applied in biological and medical decision-making research during the past decade. However, the diversity of platforms has made it a challenge to re-use and/or integrate datasets generated in different experiments or labs for constructing array-based diagnostic models. Using large toxicogenomics datasets generated using both Affymetrix and Agilent microarray platforms, we carried out a benchmark evaluation of cross-platform consistency in multiple-class prediction using three widely-used machine learning algorithms. After an initial assessment of model performance on different platforms, we evaluated whether predictive signature features selected in one platform could be directly used to train a model in the other platform and whether predictive models trained using data from one platform could predict datasets profiled using the other platform with comparable performance. Our results established that it is possible to successfully apply multiple-class prediction models across different commercial microarray platforms, offering a number of important benefits such as accelerating the possible translation of biomarkers identified with microarrays to clinically-validated assays. However, this investigation focuses on a technical platform comparison and is actually only the beginning of exploring cross-platform consistency. Further studies are needed to confirm the feasibility of microarray-based cross-platform prediction, especially using independent datasets.     

Absence/presence calling in microarray-based CGH experiments with non-model organisms

Structural variations in genomes are commonly studied by (micro)array-based comparative genomic hybridization. The data analysis methods to infer copy number variation in model organisms (human, mouse) are established. In principle, the procedures are based on signal ratios between test and reference samples and the order of the probe targets in the genome. These procedures are less applicable to experiments with non-model organisms, which frequently comprise non-sequenced genomes with an unknown order of probe targets. We therefore present an additional analysis approach, which does not depend on the structural information of a reference genome, and quantifies the presence or absence of a probe target in an unknown genome. The principle is that intensity values of target probes are compared with the intensities of negative-control probes and positive-control probes from a control hybridization, to determine if a probe target is absent or present. In a test, analyzing the genome content of a known bacterial strain: Staphylococcus aureus MRSA252, this approach proved to be successful, demonstrated by receiver operating characteristic area under the curve values larger than 0.9995. We show its usability in various applications, such as comparing genome content and validating next-generation sequencing reads from eukaryotic non-model organisms.     

Multiple-end-point bioassays using microorganisms

Since the 1950s, the numbers of species and chemicals produced have significantly increased. Despite the fact that industrial chemicals have given us numerous benefits, there is no doubt that they have damaged the environment. The chemicals being dispersed on the earth should be carefully controlled to prevent adverse effects. Bioassay is one of the methods to assess chemical safety. In bioassay systems, chemical safety is estimated by monitoring biological responses to environmental pollutants and newly synthesized chemicals. This report introduces multiple-end-point bioassay systems that are based on chemical sensitivities of microorganisms, responses of one kind of organism, and micro-array technology. Multiple-end-point bioassays enable the prediction of chemicals in the environment and the understanding of toxicities of newly synthesized chemicals.     

Centrifugal microfluidics for cell analysis

Over the past two decades, centrifugal microfluidic systems have successfully demonstrated their capability for robust, high-performance liquid handling to enable modular, multi-purpose lab-on-a-chip platforms for a wide range of life-science applications. Beyond the handling of homogeneous liquids, the unique, rotationally controlled centrifugal actuation has proven to be specifically advantageous for performing cell and particle handling and assays. In this review we discuss technologies to implement two important steps for cell handling, namely separation and capturing/counting.     

Mathematical modeling of bioassays

The high affinity and specificity of biological receptors determine the demand for and the intensive development of analytical systems based on use of these receptors. Therefore, theoretical concepts of the mechanisms of these systems, quantitative parameters of their reactions, and relationships between their characteristics and ligand–receptor interactions have become extremely important. Many mathematical models describing different bioassay formats have been proposed. However, there is almost no information on the comparative characteristics of these models, their assumptions, and predic- tive insights. In this review we suggested a set of criteria to classify various bioassays and reviewed classical and contempo- rary publications on these bioassays with special emphasis on immunochemical analysis systems as the most common and in-demand techniques. The possibilities of analytical and numerical modeling are discussed, as well as estimations of the minimum concentrations that may be detected in bioassays and recommendations for the choice of assay conditions.     

Label-free reading of microarray-based proteins with high throughput surface plasmon resonance imaging

A simple method is presented discriminating proteins at a gold surface by using an emerging technology, surface plasmon resonance (SPR) imaging. As a high throughput method, the protein array of bovine serum albumin (BSA), poly-l-lysine (PL), casein and lactate dehydrogenase (LDG) was fabricated and SPR imaging enables detection from different kinds of proteins immobilized on the sensor surface. These proteins can be discriminated directly by various reflected intensity or changing the incident angular position of light. Denaturation of these immobilized proteins on SPR sensor by interacting with denaturant 6M GdnHCl solution was also performed and obvious changes in reflected intensity were occurred after denaturation. The observation of denaturation of these proteins further supported the fact that different proteins could be discriminated on protein array before denaturation. On the other hand, the procedure of denaturation provided useful information that any change of molecular structure with the progress of denaturation would result in change of SPR signal. Excellent reproducibility with a chip-to-chip for label-free discriminating various proteins was achieved.     

Improved analytical methods for microarray-based genome-composition analysis

Background  

Whereas genome sequencing has given us high-resolution pictures of many different species of bacteria, microarrays provide a means of obtaining information on genome composition for many strains of a given species. Genome-composition analysis using microarrays, or 'genomotyping', can be used to categorize genes into 'present' and 'divergent' categories based on the level of hybridization signal. This typically involves selecting a signal value that is used as a cutoff to discriminate present (high signal) and divergent (low signal) genes. Current methodology uses empirical determination of cutoffs for classification into these categories, but this methodology is subject to several problems that can result in the misclassification of many genes.     

Integration of microfluidics with a four-channel integrated optical Young interferometer immunosensor

This report describes an optical sensing hybrid system obtained by bonding a microfluidic system to an integrated optical (IO) four-channel Young interferometer (YI) chip. The microfluidic system implemented into a glass plate consists of four microchannels with cross-sectional dimensions of 200 microm x 15 microm. The microfluidic system is structured in such a way that after bonding to the IO chip, each microchannel addresses one sensing window in the four-channel YI sensor. Experimental tests show that the implementation of the microfluidics reduces the response time of the sensor from 100s, as achieved with a bulky cuvette, to 4s. Monitoring the anti-human serum albumine/human serum albumine (alpha-HSA/HSA) immunoreaction demonstrates the feasibility to use the microfluidic sensing system for immunosensing applications. In this case, a better discrimination between the bulk refractive index change and the layer formation can be made, resulting into higher accuracy and offering the prospect of being able to use the kinetics of the immunoreaction. The microfluidic sensing system shows an average phase resolution of 7 x 10(-5) x 2pi for different pairs of channels, which at the given interaction length of 4 mm corresponds to a refractive index resolution of 6 x 10(-8), being equivalent to a protein mass coverage resolution of 20 fg/mm2.