Experimental Tools to Study Molecular Recognition within the Nanoparticle Corona

Advancements in optical nanosensor development have enabled the design of sensors using synthetic molecular recognition elements through a recently developed method called Corona Phase Molecular Recognition (CoPhMoRe). The synthetic sensors resulting from these design principles are highly selective for specific analytes, and demonstrate remarkable stability for use under a variety of conditions. An essential element of nanosensor development hinges on the ability to understand the interface between nanoparticles and the associated corona phase surrounding the nanosensor, an environment outside of the range of traditional characterization tools, such as NMR. This review discusses the need for new strategies and instrumentation to study the nanoparticle corona, operating in both in vitro and in vivo environments. Approaches to instrumentation must have the capacity to concurrently monitor nanosensor operation and the molecular changes in the corona phase. A detailed overview of new tools for the understanding of CoPhMoRe mechanisms is provided for future applications

Experimental Tools to Study Molecular Recognition within the Nanoparticle Corona

Advancements in optical nanosensor development have enabled the design of sensors using synthetic molecular recognition elements through a recently developed method called Corona Phase Molecular Recognition (CoPhMoRe). The synthetic sensors resulting from these design principles are highly selective for specific analytes, and demonstrate remarkable stability for use under a variety of conditions. An essential element of nanosensor development hinges on the ability to understand the interface between nanoparticles and the associated corona phase surrounding the nanosensor, an environment outside of the range of traditional characterization tools, such as NMR. This review discusses the need for new strategies and instrumentation to study the nanoparticle corona, operating in both in vitro and in vivo environments. Approaches to instrumentation must have the capacity to concurrently monitor nanosensor operation and the molecular changes in the corona phase. A detailed overview of new tools for the understanding of CoPhMoRe mechanisms is provided for future applications.Juvenile Diabetes Research Foundation InternationalMcGovern Institute for Brain Research at MIT. Neurotechnology (MINT) ProgramNational Science Foundation (U.S.) (Postdoctoral Research Fellowship Award DBI-1306229)Burroughs Wellcome Fund (Grant Award 1013994)German Science Foundatio

In vivo biosensing via tissue-localizable near-infrared-fluorescent single-walled carbon nanotubes

Single-walled carbon nanotubes are particularly attractive for biomedical applications, because they exhibit a fluorescent signal in a spectral region where there is minimal interference from biological media. Although single-walled carbon nanotubes have been used as highly sensitive detectors for various compounds, their use as in vivo biomarkers requires the simultaneous optimization of various parameters, including biocompatibility, molecular recognition, high fluorescence quantum efficiency and signal transduction. Here we show that a polyethylene glycol ligated copolymer stabilizes near-infrared-fluorescent single-walled carbon nanotubes sensors in solution, enabling intravenous injection into mice and the selective detection of local nitric oxide concentration with a detection limit of 1 µM. The half-life for liver retention is 4 h, with sensors clearing the lungs within 2 h after injection, thus avoiding a dominant route of in vivo nanotoxicology. After localization within the liver, it is possible to follow the transient inflammation using nitric oxide as a marker and signalling molecule. To this end, we also report a spatial-spectral imaging algorithm to deconvolute fluorescence intensity and spatial information from measurements. Finally, we demonstrate that alginate-encapsulated single-walled carbon nanotubes can function as implantable inflammation sensors for nitric oxide detection, with no intrinsic immune reactivity or other adverse response for more than 400 days.National Institutes of Health (U.S.) (T32 Training Grant in Environmental Toxicology ES007020)National Cancer Institute (U.S.) (Grant P01 CA26731)National Institute of Environmental Health Sciences (Grant P30 ES002109)Arnold and Mabel Beckman Foundation (Young Investigator Award)National Science Foundation (U.S.). Presidential Early Career Award for Scientists and EngineersScientific and Technological Research Council of Turkey (TUBITAK 2211 Research Fellowship Programme)Scientific and Technological Research Council of Turkey (TUBITAK 2214 Research Fellowship Programme)Middle East Technical University. Faculty Development ProgrammeSanofi Aventis (Firm) (Biomedical Innovation Grant

Neurotransmitter Detection Using Corona Phase Molecular Recognition on Fluorescent Single-Walled Carbon Nanotube Sensors

ABSTRACT: Temporal and spatial changes in neurotransmitter concentrations are central to information processing in neural networks. Therefore, biosensors for neurotransmitters are essential tools for neuroscience. In this work, we applied a new technique, corona phase molecular recognition (CoPhMoRe), to identify adsorbed polymer phases on fluorescent single-walled carbon nanotubes (SWCNTs) that allow for the selective detection of specific neurotransmitters, including dopamine. We functionalized and suspended SWCNTs with a library of different polymers (n = 30) containing phospholipids, nucleic acids, and amphiphilic polymers to study how neurotransmitters modulate the resulting band gap, near-infrared (nIR) fluorescence of the SWCNT. We identified several corona phases that enable the selective detection of neurotransmitters. Catecholamines such as dopamine increased the fluorescence of specific single-stranded DNA- and RNA-wrapped SWCNTs by 58−80 % upon addition of 100 μM dopamine depending on the SWCNT chirality (n,m). In solution, the limit of detection was 11 nM [Kd = 433 nM for (GT)15 DNA-wrapped SWCNTs]. Mechanistic studies revealed that this turn-on response is due to an increase in fluorescence quantum yield and not covalent modification of the SWCNT or scavenging o

Label-free carbon nanotube sensors for glycan and protein detection

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemical Engineering, June 2014.Cataloged from PDF version of thesis.Includes bibliographical references.Nanoengineered glycan sensors may help realize the long-held goal of accurate and rapid glycoprotein profiling without labeling or glycan liberation steps. Current methods of profiling oligosaccharides displayed on protein surfaces, such as liquid chromatography, mass spectrometry, capillary electrophoresis, and microarray methods, are limited by sample pretreatment and quantitative accuracy. Microarrayed platforms can be improved with methods that better estimate kinetic parameters rather than simply reporting relative binding information. These quantitative glycan sensors are enabled by an emerging class of nanoengineered materials that differ in their mode of signal transduction from traditional methods. Platforms that respond to mass changes include a quartz crystal microbalance and cantilever sensors. Electronic response can be detected from electrochemical, field effect transistor, and pore impedance sensors. Optical methods include fluorescent frontal affinity chromatography, surface plasmon resonance methods, and fluorescent single walled carbon nanotubes-(SWNT). Advantages of carbon nanotube sensors include their sensitivity and ability to multiplex. The focus of this work has been to develop carbon nanotube-based sensors for glycans and proteins. Before detailing the development of these new sensors, the thesis will begin with a very brief primer on glycobiology, its connection to medicine, and the advantages and limitations of existing tools for glycan analysis. In the second chapter we model the use of quantitative nanosensors in a weak affinity dynamic microarray (WADM) to simulate practical uses of these sensors in bioprocessing and clinical diagnostics. There is significant interest in developing new detection platforms for characterizing glycosylated proteins, despite the lack of easily synthesized model glycans or high affinity receptors for this analytical problem. In the third chapter we experimentally demonstrate 'proof of concept' of carbon nanotubebased glycan sensors. This is done with a sensor array employing recombinant lectins as glycan recognition sites tethered via Histidine tags to Ni2l complexes that act as fluorescent quenchers for SWNT embedded in a chitosan hydrogel spot to measure binding kinetics of model glycans. We examine as model glycans both free and streptavidin-tethered biotinylated monosaccharides. Two higher-affined glycan-lectin pairs are explored: fucose (Fuc) to PA-IIL and N-acetylglucosamine (GlcNAc) to GafD. The dissociation constants (KD) for these pairs as free glycans (106 and 19 [mu]M respectively) and streptavidin-tethered (142 and 50 [mu]M respectively) were found. The absolute detection limit for the first-generation platform was found to be 2 pg of glycosylated protein or 100 ng of free glycan to 20 pg of lectin. Glycan detection (GlcNAc-streptavidin at 10 [mu]M) is demonstrated at the single nanotube level as well by monitoring the fluorescence from individual SWNT sensors tethered to GafD lectin. Over a population of 1000 nanotubes, 289 of the SWNT sensors had signals strong enough to yield kinetic information (KD of 250 ± 10 [mu]M). We are also able to identify the locations of "strong-transducers" on the basis of dissociation constant (4 sensors with KD 5% quench response). We report the key finding that the brightest SWNT are not the best transducers of glycan binding. SWNT ranging in intensity between 50 and 75% of the maximum show the greatest response. The ability to pinpoint strong-binding, single sensors is promising to build a nanoarray of glycan-lectin transducers as a high throughput method to profile glycans without protein labeling or glycan liberation pretreatment steps. In the fourth chapter we move from detection of model glycoproteins (streptavidin with biotinylated glycans) to a more applied problem: detection of antibodies and their glycosylation. We do this with a second generation array of SWNT nanosensors in an array format. It is widely recognized that an array of addressable sensors can be multiplexed for the label-free detection of a library of analytes. However, such arrays have useful properties that emerge from the ensemble, even when monofunctionalized. As examples, we show that an array of nanosensors can estimate the mean and variance of the observed dissociation constant (KD), using three different examples of binding IgG with Protein-A as the recognition site, including polyclonal human IgG (KD [mu] = 19 [mu]M, [sigma]2 = 1000 [mu]M2 ). murine IgG (KD = 4.3 [mu]M, 2= 3 [mu]M 2), and human IgG from CHO cells (KD [mu] = 2.5 nM, [sigma]F2 = 0.01 RM2). Second, we show that an array of nanosensors can uniquely monitor weakly-affined analyte interactions via the increased number of observed interactions. One application involves monitoring the metabolically-induced hypermannosylation of human IgG from CHO using PSA-lectin conjugated sensor arrays where temporal glycosylation patterns are measured and compared. Finally, the array of sensors can also spatially map the local production of an analyte from cellular biosynthesis. As an example we rank productivity of IgG-producing HEK colonies cultured directly on the array of nanosensors itself. One great limitation to these practical applications, common to other new sensor developments, are the constraints of large, bulky, and capital-intensive excitation sources, optics, and detectors. In the fifth chapter we detail the design of a lightweight, field-portable detection platform for SWNT based sensors using stock parts with a total cost below $3000. The portable detector is demonstrated with antibody detection in our lab and onsite at a commercial facility 3700 miles away with complex production samples. Along the course of developing these sensors, there was a need to analyze noisy data sets from signal nanotubes (Chapter 3) to determine distinct binding states. NoRSE was developed to analyze highfrequency data sets collected from multi-state, dynamic experiments, such as molecular adsorption and desorption onto carbon nanotubes. As technology improves sampling frequency, these stochastic data sets become increasingly large with faster dynamic events. More efficient algorithms are needed to accurately locate the unique states in each time trace. NoRSE adapts and optimizes a previously published noise reduction algorithm (Chung et al., 1991) and uses a custom peak flagging routine to rapidly identify unique event states. The algorithm is explained using experimental data from our lab and its fitting accuracy and efficiency are then shown with a generalized model of stochastic data sets. The algorithm is compared to another recently published state finding algorithm and is found to be 27 times faster and more accurate over 55% of the generalized experimental space. This work is detailed in Chapter 6. Future uses of these sensors include in vivo reporters of protein biomarkers. In Chapter 7, three-dimensional tracking of single walled carbon nanotubes (SWNT) with an orbital tracking microscope is demonstrated for this purpose. We determine the viscosity regime (above 250 cP) at which the rotational diffusion coefficient can be used for length estimation. We also demonstrate SWNT tracking within live HeLa cells and use these findings to spatially map corral volumes (0.27-1.32 Im 3), determine an active transport velocity (455 nm/s), and calculate local viscosities (54-179 cP) within the cell. With respect to the future use of SWNTs as sensors in living cells, we conclude that the sensor must change the fluorescence signal by at least 4-13% to allow separation of the sensor signal from fluctuations due to rotation of the SWNT when measuring with a time resolution of 32 ms. In the final chapter we draw conclusions from the development of this carbon nanotube-based sensor for glycan analysis and show the start of future work with arrays of SWNT sensors for glycoprofiling.by Nigel F. Reuel.Ph. D

Advancements in Airborne Viral Nucleic Acid Detection with Wearable Devices

Abstract Wearable health sensors for an expanding range of physiological parameters have experienced rapid development in recent years and are poised to disrupt the way healthcare is tracked and administered. The monitoring of environmental contaminants with wearable technologies is an additional layer of personal and public healthcare and is also receiving increased focus. Wearable sensors that detect exposure to airborne viruses can alert wearers of viral exposure and prompt proactive testing and minimization of viral spread, benefitting their own health and decreasing community risk. With the high levels of asymptomatic spread of Coronavirus Disease 2019 (COVID‐19) observed during the pandemic, such devices can dramatically enhance the pandemic response capabilities in the future. To facilitate advancements in this area, this review summarizes recent research on airborne viral detection using wearable sensing devices, as well as technologies suitable for wearables. Since the low concentration of viral particles in the air poses significant challenges to detection, methods for airborne viral particle collection and viral sensing are discussed in detail. A special focus is placed on nucleic acid‐based viral sensing mechanisms due to their enhanced ability to discriminate between viral subtypes. Important considerations for integrating airborne viral collection and sensing on a single wearable device are also discussed

Automated classification of bacterial cell sub-populations with convolutional neural networks.

Quantification of phenotypic heterogeneity present amongst bacterial cells can be a challenging task. Conventionally, classification and counting of bacteria sub-populations is achieved with manual microscopy, due to the lack of alternative, high-throughput, autonomous approaches. In this work, we apply classification-type convolutional neural networks (cCNN) to classify and enumerate bacterial cell sub-populations (B. subtilis clusters). Here, we demonstrate that the accuracy of the cCNN developed in this study can be as high as 86% when trained on a relatively small dataset (81 images). We also developed a new image preprocessing algorithm, specific to fluorescent microscope images, which increases the amount of training data available for the neural network by 72 times. By summing the classified cells together, the algorithm provides a total cell count which is on parity with manual counting, but is 10.2 times more consistent and 3.8 times faster. Finally, this work presents a complete solution framework for those wishing to learn and implement cCNN in their synthetic biology work

Design and validation of a frugal, automated, solid-phase peptide synthesizer.

Solid phase peptide synthesis (SPPS) has enabled widespread use of synthetic peptides in applications ranging from pharmaceuticals to materials science. The demand for synthetic peptides has driven recent efforts to produce automated SPPS synthesizers which utilize fluid-handling components common to chemistry laboratories to drive costs down to several thousand dollars. Herein, we describe the design and validation of a more 'frugal' SPPS synthesizer that uses inexpensive, consumer-grade fluid-handling components to achieve a prototype price point between US$300 and$600. We demonstrated functionality by preparing and characterizing peptides with a variety of distinct properties including binding functionality, nanoscale self-assembly, and oxidation-induced fluorescence. This system yielded micromoles of peptide at a cost of approximately $1/residue, a cost which may be further reduced by optimization and bulk purchasing

Low-Cost Portable Readout System Design for Inductively Coupled Resonant Sensors

Spiral coil-based LC resonant sensors have seen many applications in agriculture, healthcare, biomanufacturing, and consumer electronics. Traditional techniques to interrogate such sensors using vector network analyzers are expensive and often not portable, whereas custom readouts suffer from either high cost, low range of usable interrogation distances, or low frequency range. This paper proposes a new simple, low-cost, and portable readout design based on the technique of coherent demodulation. A complete theoretical analysis examining how the interrogation distance is related to other circuit parameters is presented. Finally, a complete readout system was implemented using printed circuit board technology and commercially available off-the-shelf components. The system operates between 1-100 MHz and the fabricated system consumes 1.26 W. Measurements show that the system operates reliably and repeatably with interrogation distances up to 5 cm.This is a manuscript of an article published as Roy, Subhanwit, Yee Jher Chan, Nigel F. Reuel, and Nathan M. Neihart. "Low-Cost Portable Readout System Design for Inductively Coupled Resonant Sensors." IEEE Transactions on Instrumentation and Measurement (2022). DOI: 10.1109/TIM.2022.3173277. Copyright 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Posted with permission

Experimental Tools to Study Molecular Recognition within the Nanoparticle Corona

Advancements in optical nanosensor development have enabled the design of sensors using synthetic molecular recognition elements through a recently developed method called Corona Phase Molecular Recognition (CoPhMoRe). The synthetic sensors resulting from these design principles are highly selective for specific analytes, and demonstrate remarkable stability for use under a variety of conditions. An essential element of nanosensor development hinges on the ability to understand the interface between nanoparticles and the associated corona phase surrounding the nanosensor, an environment outside of the range of traditional characterization tools, such as NMR. This review discusses the need for new strategies and instrumentation to study the nanoparticle corona, operating in both in vitro and in vivo environments. Approaches to instrumentation must have the capacity to concurrently monitor nanosensor operation and the molecular changes in the corona phase. A detailed overview of new tools for the understanding of CoPhMoRe mechanisms is provided for future applications