Multiplex Solid-Phase Melt Curve Analysis for the Point-of-Care Detection of HIV-1 Drug Resistance.

A point-of-care HIV-1 genotypic resistance assay that could be performed during a clinic visit would enable care providers to make informed treatment decisions for patients starting therapy or experiencing virologic failure on therapy. The main challenge for such an assay is the genetic variability at and surrounding each drug-resistance mutation (DRM). We analyzed a database of diverse global HIV sequences and used thermodynamic simulations to design an array of surface-bound oligonucleotide probe sets with each set sharing distinct 5' and 3' flanking sequences but having different centrally located nucleotides complementary to six codons at HIV-1 DRM reverse transcriptase position 103: AAA, AAC, AAG, AAT, AGA, and AGC. We then performed in vitro experiments using 80-mer oligonucleotides and PCR-amplified DNA from clinical plasma HIV-1 samples and culture supernatants that contained subtype A, B, C, D, CRF01_AE, and CRF02_AG viruses. Multiplexed solid-phase melt curve analysis discriminated perfectly among each of the six reported reverse transcriptase position 103 codons in both 80-mers and clinical samples. The sensitivity and specificity for detecting targets that contained AAC mixed with targets that contained AAA were >98% when AAC was present at a proportion of ≥10%. Multiplexed solid-phase melt curve analysis is a promising approach for developing point-of-care assays to distinguish between different codons in genetically variable regions such as those surrounding HIV-1 DRMs.

Multiplexed identification, quantification and genotyping of infectious agents using a semiconductor biochip.

The emergence of pathogens resistant to existing antimicrobial drugs is a growing worldwide health crisis that threatens a return to the pre-antibiotic era. To decrease the overuse of antibiotics, molecular diagnostics systems are needed that can rapidly identify pathogens in a clinical sample and determine the presence of mutations that confer drug resistance at the point of care. We developed a fully integrated, miniaturized semiconductor biochip and closed-tube detection chemistry that performs multiplex nucleic acid amplification and sequence analysis. The approach had a high dynamic range of quantification of microbial load and was able to perform comprehensive mutation analysis on up to 1,000 sequences or strands simultaneously in <2 h. We detected and quantified multiple DNA and RNA respiratory viruses in clinical samples with complete concordance to a commercially available test. We also identified 54 drug-resistance-associated mutations that were present in six genes of Mycobacterium tuberculosis, all of which were confirmed by next-generation sequencing.

An array-based melt curve analysis method for the identification and classification of closely related pathogen strains.

PCR-based techniques are widely used to identify disease causing bacterial and viral pathogens, especially in point-of-care or near-patient clinical settings that require rapid results and sample-to-answer workflows. However, such techniques often fail to differentiate between closely related species that have highly variable genomes. Here, a homogenous (closed-tube) pathogen identification and classification method is described that combines PCR amplification, array-based amplicon sequence verification, and real-time detection using an inverse fluorescence fluorescence-resonance energy transfer technique. The amplification is designed to satisfy the inclusivity criteria and create ssDNA amplicons, bearing a nonradiating quencher moiety at the 5'-terminus, for all the related species. The array includes fluorescent-labeled probes which preferentially capture the variants of the amplicons and classify them through solid-phase thermal denaturing (melt curve) analysis. Systematic primer and probe design algorithms and empirical validation methods are presented and successfully applied to the challenging example of identification of, and differentiation between, closely related human rhinovirus and human enterovirus strains.

A Fully Integrated CMOS Fluorescence Biochip for DNA and RNA Testing.

Design and successful implementation of a fully-integrated CMOS fluorescence biochip for DNA/RNA testing in molecular diagnostics (MDx) is presented. The biochip includes a 32×32 array of continuous wave fluorescence detection biosensing elements. Each biosensing element is capable of having unique DNA probe sequences, wavelength-selective multi-dielectric emission filter (OD of 3.6), resistive heater for thermal cycling, and a high performance and programmable photodetector. The dimension of each biosensor is 100µm×100µm with a 50µm×50µm Nwell-Psub photodiode acting as the optical transducer, and a ΣΔ modulator based photocurrent sensor. The measured photodetector performance shows ~116 dB detection dynamic range (10fA - 10nA) over the 25°C - 100°C temperature range, while being ~1 dB away from the fundamental shot-noise limit. To empirically demonstrate the compatibility of this biochip with MDx applications, we have successfully utilized the array and its thermal cycling capability to adopt a 7-plex panel for detection of 6 human upper respiratory viruses.

Interface design for CMOS-integrated Electrochemical Impedance Spectroscopy (EIS) biosensors.

Electrochemical Impedance Spectroscopy (EIS) is a powerful electrochemical technique to detect biomolecules. EIS has the potential of carrying out label-free and real-time detection, and in addition, can be easily implemented using electronic integrated circuits (ICs) that are built through standard semiconductor fabrication processes. This paper focuses on the various design and optimization aspects of EIS ICs, particularly the bio-to-semiconductor interface design. We discuss, in detail, considerations such as the choice of the electrode surface in view of IC manufacturing, surface linkers, and development of optimal bio-molecular detection protocols. We also report experimental results, using both macro- and micro-electrodes to demonstrate the design trade-offs and ultimately validate our optimization procedures.

A photovoltaic-driven and energy-autonomous CMOS implantable sensor.

An energy-autonomous, photovoltaic (PV)-driven and MRI-compatible CMOS implantable sensor is presented. On-chip P+/N-well diode arrays are used as CMOS-compatible PV cells to harvest µW's of power from the light that penetrates into the tissue. In this 2.5 mm × 2.5 mm sub-µW integrated system, the in-vivo physiological signals are first measured by using a subthreshold ring oscillator-based sensor, the acquired data is then modulated into a frequency-shift keying (FSK) signal, and finally transmitted neuromorphically to the skin surface by using a pair of polarized electrodes.

FRET-based real-time DNA microarrays.

We present a quantification method for affinity-based DNA microarrays which is based on the real-time measurements of hybridization kinetics. This method, i.e., real-time DNA microarrays, enhances the detection dynamic range of conventional systems by being impervious to probe saturation, washing artifacts, microarray spot-to-spot variations, and other intensity-affecting impediments. We demonstrate in both theory and practice that the time-constant of target capturing is inversely proportional to the concentration of the target analyte, which we take advantage of as the fundamental parameter to estimate the concentration of the analytes. Furthermore, to experimentally validate the capabilities of this method in practical applications, we present a FRET-based assay which enables the real-time detection in gene expression DNA microarrays.

Delivering optical power to subcutaneous implanted devices.

In this paper, a new, easy-to-implement, and MRI-compatible approach for delivering power to implantable devices is presented. The idea is to harvest the energy of light within the therapeutic window wavelengths, where the optical absorption is small, by using subcutaneous photovoltaic (PV) cells. Depending on the application, this energy can then be used to directly drive the embedded electronics of an implanted device or recharge its battery. To show the feasibility of this system, a CMOS chip based on this concept has been implemented and tested. The experimental results demonstrate that µW's of power in ambient light conditions can be harvested using mm(2)-size PV cells. This amount of power is sufficient to address the needs of many low-power applications.

A CMOS Electrochemical Impedance Spectroscopy (EIS) Biosensor Array.

In this paper, we present a fully integrated biosensor 10 × 10 array in a standard complementary metal-oxide semiconducor process, which takes advantage of electrochemical impedance spectroscopy (EIS). We also show that this system is able to detect various biological analytes, such as DNA and proteins, in real time and without the need for molecular labels. In each pixel of this array, we implement a biocompatible Au electrode transducer and embedded sensor circuitry which takes advantage of the coherent detector to measure the impedance of the associated electrode-electrolyte interface. This chip is capable of concurrently measuring admittance values as small as 10(-8) Ω(-1) within the array with the detection dynamic range of more than 90 dB in the frequency range of 10 Hz-50 MHz.

Real-time DNA microarray analysis.

We present a quantification method for affinity-based DNA microarrays which is based on the real-time measurements of hybridization kinetics. This method, i.e. real-time DNA microarrays, enhances the detection dynamic range of conventional systems by being impervious to probe saturation in the capturing spots, washing artifacts, microarray spot-to-spot variations, and other signal amplitude-affecting non-idealities. We demonstrate in both theory and practice that the time-constant of target capturing in microarrays, similar to all affinity-based biosensors, is inversely proportional to the concentration of the target analyte, which we subsequently use as the fundamental parameter to estimate the concentration of the analytes. Furthermore, to empirically validate the capabilities of this method in practical applications, we present a FRET-based assay which enables the real-time detection in gene expression DNA microarrays.

Bioluminescence regenerative cycle (BRC) system: theoretical considerations for nucleic acid quantification assays.

A novel application of bioluminescence for nucleic acid quantification, the bioluminescence regenerative cycle (BRC), is described in theoretical terms and supported by preliminary experimental data. In the BRC system, pyrophosphate (PPi) molecules are released during biopolymerization and are counted and correlated to DNA copy number. The enzymes ATP-sulfurylase and firefly luciferase are employed to generate photons quantitatively from PPi. Enzymatic unity-gain positive feedback is implemented to amplify photon generation and to compensate for decay in light intensity by self-regulation. The cumulative total of photons can be orders of magnitude higher than in typical chemiluminescent processes. A system level theoretical model is developed, taking into account the kinetics of the regenerative cycle, contamination, and detector noise. Data and simulations show that the photon generation process achieves steady state for the time range of experimental measurements. Based on chain reaction theory, computations show that BRC is very sensitive to variations in the efficiencies of the chemical reactions involved and less sensitive to variations in the quantum yield of the process. We show that BRC can detect attomolar quantities of DNA (10(-18) mol), and that the useful dynamic range is five orders of magnitude. Sensitivity is not constrained by detector performance but rather by background bioluminescence caused by contamination by either PPi or ATP (adenosine triphosphate).

Modeling and simulation of luminescence detection platforms.

Motivated by the design of an integrated CMOS-based detection platform, a simulation model for CCD and CMOS imager-based luminescence detection systems is developed. The model comprises four parts. The first portion models the process of photon flux generation from luminescence probes using ATP-based and luciferase label-based assay kinetics. An optics simulator is then used to compute the incident photon flux on the imaging plane for a given photon flux and system geometry. Subsequently, the output image is computed using a detailed imaging sensor model that accounts for photodetector spectral response, dark current, conversion gain, and various noise sources. Finally, signal processing algorithms are applied to the image to enhance detection reliability and hence increase the overall system throughput. To validate the model, simulation results are compared to experimental results obtained from a CCD-based system that was built to emulate the integrated CMOS-based platform.

Effects of scaling on the SNR and speed of biosensors.

This work presents a stochastic model for the observed signal of biosensors, a model that predicts the signal fluctuation of the system and the SNR associated with it using a Markov chain process. In the process, transition probabilities are derived from the target and probe binding kinetics in view of statistical motion and random walk events. Based on this model, we are able to estimate the settling time, power-spectral density (PSD), and signal to noise ratio (SNR) of general affinity-based biosensors. The effects of scaling from macroscopic to microscopic regimes are also studied, which indicate a fundamental tradeoff between settling time (speed) and signal fluctuation (noise). The model is also applied to analyze the behavior of a DNA hybridization electronic detector.

Maximum a posteriori (MAP) estimator for polymerase chain reaction (PCR) processes.

A maximum a posteriori (MAP) estimator for polymerase chain reaction (PCR) assays is presented. The estimation relies on the replication efficiencies of the PCR assay and the observed end-point concentration of the PCR product at an arbitrary cycle. This derivation is carried out in view of the stochastic progression of the amplicons, and the assumption that the end-point concentrations for any particular initial value have a jointly Gaussian distribution. In addition, we provide an extension for the estimator which can be applied to various quantitative PCR assays.

Statistical modeling of biochemical detection systems.

We study the problem of counting the number of particles in a closed volume where the particles motion are modeled by a Brownian motion process. This problem arises in many biological and chemical sensing experiments, e.g., counting the number of analytes in blood sample, air pollutant measurement, etc. Finding the exact count of particles is challenging in these systems and one generally relies on an estimate based on the sample readouts. We study the statistical properties of the counting process in equilibrium and present the fundamental detection and estimation limitations. In particular, we demonstrate that the count process is inherently noisy and has a quantum-limit signal to noise ratio.