Engineered nanoparticles provide potential opportunities for improving current drug delivery, bioimaging and biosensing modalities. In many cases, a ligand, such as a protein, peptide or nucleic acids, is attached to the nanoparticles surface to serve as a targeting group. However, the nanoconjugation (i.e. covalently bound molecules to a nanocarrier) is not an innocuous reaction. It can change the binding affinity and interfere with the intracellular trafficking of the tethered species. The understanding of this influence to the tethered species is still lacking. Therefore, the main objective of this thesis is to investigate the effect of nanoconjugation to the biological identity of the tethered biomolecules, in terms of cellular uptake, intracellular trafficking and the ultimate biological outcomes. The Epidermal Growth Factor Receptor (EGFR) is a tyrosine kinase that regulates cell proliferation and can cause cancer if dysregulated. Continuous treatment with high doses of EGF can induce apoptosis, in EGFR overexpressing cell lines. In this thesis, Epidermal Growth Factor (EGF) was chosen as the object of investigation. Covalent attachment of EGF to gold nanoparticles (NP-EGF) was found to enhance apoptosis in EGFR overexpressing cell lines (A431, MDA-MB-468) and it is sufficient to induce apoptosis in cell lines exhibiting EGFR expression at physiological levels (HeLa). NP-EGF accumulation through the endosomal pathway was also investigated to assess the impact of nanoconjugation on the spatio-temporal distribution of NP-EGF as potential origin for the observed enhancement of apoptosis. Two orthogonal experimental approaches were applied: (1) isolation of NP-EGF containing endosomes by taking advantage of the increased density of endosomes associated with the uptake of Au NPs; (2) correlated darkfield/fluorescence imaging to map the spatial distribution of NP-EGF in endosomes as a function of time. The studies reveal that nanoconjugation prolongs the dwelling time of phosphorylated receptors in the early endosomes and that the retention of activated EGFR in the early endosomes is accompanied by an EGF mediated apoptosis at effective concentrations that do not induce apoptosis in the case of the free EGF. Investigating the nanoconjugation-enhanced EGF-induced apoptosis improves the current understanding of cell-nanomatieral interactions and provides new opportunities for overcoming apoptosis evasion by cancer cells. / 2017-01-01T00:00:00Z

Localized surface plasmon resonance (LSPR) describes the collective oscillation of conductive electrons in noble metal nanostructures, such as gold, silver and copper; or in selected doped semiconductor nanocrystals. Nanoplasmonics is emerging as a useful and versatile platform that combines the intense and highly tunable optical responses derived from LSPR with the intriguing materials properties at the nanoscale, including high specific surface areas, surface and chemical reactivity, binding affinity, and rigidity. LSPRs in plasmonic nanoparticles (NPs) can provide large optical cross-sections, and can lead to a wide variety of subsequent photophysical responses, such as localization of electric (E-)fields, production of plasmonic hot charge carriers, photothermal heating, etc. Plasmonic NPs can also be combined with other molecular or nanoscale systems into plasmonic heterostructures to further harvest the resonant E-field enhancement or to prolong the lifetime of plasmonic charge carriers. In this dissertation, we study the photophysical properties of plasmonic Ag and Au NPs, particularly E-field localization and hot charge carrier production performances; and illustrate how they can be optimized towards plasmonic photocatalysis, development of nano-antimicrobials, and surface-enhanced Raman spectroscopy (SERS) sensing. We demonstrate that with a lipid-coated noble metal nanoparticle (L-NP) model, the E-field localization properties could be optimized through positioning molecular photosensitizers or photocatalysts within a plasmonic “sweet spot”. This factor renders the plasmonic metal NPs efficient nanoantenna for resonant enhancement of the intramolecular transitions as well as the photocatalytic properties of the molecular photocatalysts. The enhanced photoreactivity have been applied to facilitate fuel cell half reactions for the enhancement of light energy conversion efficiencies; as well as to facilitate the release of broad-band bactericidal compounds that enables plasmonic nano-antimicrobials. Localized E-fields in L-NPs also enhance the inelastic scattering from molecules through SERS. This is utilized to obtain molecular-level information on the configuration of sterol-based, alkyne-containing Raman tags in hybrid lipid membranes, which enables spectroscopic sensing and targeted imaging of biomarker-overexpressing cancer cells at the single-cell level. In contrast to the localized E-field, plasmonic charge carrier generation mechanism relies on non-radiative decay pathways of the excited plasmons that lead to production of ballistic charge carriers. The plasmonic hot charge carriers directly participate in chemical redox processes with degrees of controllability over the nature of the charge carrier produced and direction of their transfers. The implementation and optimization of these mechanisms are explored, and the significances of some relevant applications are discussed.

Surface-enhanced Raman spectroscopy (SERS) is a powerful analytical technique that combines molecular specificity of vibrational fingerprints offered by Raman spectroscopy with single-molecule detection sensitivity from plasmonic hotspots of noble metal nanostructures. Label-free SERS has attracted tremendous interest in bioanalysis over the last two decades due to minimal sample preparation, non-invasive measurement without water background interference, and multiplexing capability from rich chemical information of narrow Raman bands. Nevertheless, significant challenges should be addressed to become a widely accepted technique in bio-related communities. In this dissertation, limitations from different aspects (performance, reliability, and analysis) are articulated with state-of-the-art, followed by how introduced works resolve them. For high SERS performance, SERS substrates consisting of vertically-stacked multiple metal-insulator-metal layers, named nanolaminate, were designed to simultaneously achieve high sensitivity and excellent uniformity, two previously deemed mutually exclusive properties. Two unique factors of nanolaminate SERS substrates were exploited for the improved reliability of label-free in situ classification using living cancer cells, including background refractive index (RI) insensitivity from 1.30 to 1.60, covering extracellular components, and 3D protruding nanostructures that can generate a tight nano-bio interface (e.g., hotspot-cell coupling). Discrete nanolamination by new nanofabrication additionally provides optical transparency, offering backside-excitation, thereby label-free glucose sensing on a skin-phantom model. Towards reliable quantitative SERS analysis, an electronic Raman scattering (ERS) calibration method was developed. ERS from metal is omnipresent in plasmonic constructs and experiences identical hotspot enhancements. Rigorous experimental results support that ERS can serve as internal standards for spatial and temporal calibration of SERS signals with significant potential for complex samples by overcoming intrinsic limitations of state-of-art Raman tags. ERS calibration was successfully applied to label-free living cell SERS datasets for classifying cancer subtypes and cellular drug responses. Furthermore, dual-recognition label-SERS with digital assay revealed improved accuracy in quantitative dopamine analysis. Artificial neural network-based advanced machine learning method was exploited to improve the interpretability of bioanalytical SERS for multiple living cell responses. Finally, this dissertation provides future perspectives with different aspects to design bio-interfaced SERS devices for clinical translation, followed by guidance for SERS to become a standard analytical method that can compete with or complement existing technologies. / Doctor of Philosophy / In photonics, metals were thought to be not very useful, except mirrors. However, at a length scale smaller than wavelength, it has been realized that metallic structures can provide unique ways of light manipulation. Maxwell's equations show that an interface between dielectric and metal can support surface plasmons, resulting in collective oscillations of electrons and light confinement. Surface-enhanced Raman spectroscopy (SERS) is a sensing technique that combines enhanced local fields arising from plasmon excitation with molecular fingerprint specificity of vibrational Raman spectroscopy. The million-fold enhancement of Raman signals at hotspots has driven an explosion of research, providing tons of publications over the last two decades with a broad spectrum of physical, chemical, and biological applications. Nevertheless, significant challenges should be addressed for SERS to become a widely accepted technique, especially in bio-related communities. In this dissertation, limitations from different aspects (performance, reliability, and analysis) are articulated with state-of-the-art, followed by how innovative strategies addressed them. Each chapter's unique approach consists of a combination of five aspects, including nanoplasmonics, nanofabrication, nano-bio interface, cancer biology, statistical machine learning. First, high-performance SERS substrates were designed to simultaneously achieve high sensitivity and excellent uniformity, two previously deemed mutually exclusive properties, by vertically stacking multiple metal-insulator-metal layers (i.e., nanolaminate). Their 3D protruding nanotopography and refractive-index-insensitive SERS response enabled label-free in situ classification of living cancer cells. Tweaked nanofabrication produced discrete nanolamination with optical transparency, enabling label-free glucose sensing on a skin phantom. Towards reliable quantitative SERS analysis, an electronic Raman scattering (ERS) calibration method was developed that can overcome the intrinsic limitations of Raman tags, and it was successfully applied to label-free living cell SERS datasets for classifying cancer subtypes and cellular drug responses. Furthermore, dual-recognition label-SERS with digital assay revealed improved accuracy in quantitative dopamine analysis. Advanced machine learning (artificial neural network) was exploited to improve the interpretability of SERS bioanalysis for multiple cellular drug responses. Finally, this dissertation provides future perspectives with different aspects, including SERS, biology, and statistics, for SERS to potentially become a standard analytical method that can compete with or complement existing technologies.

Owing to their unique physical, chemical, and mechanical properties, nanoparticles (NPs) have been used, or are being evaluated for use, in many fields (e.g., personal care and cosmetics, pharmaceutical, energy, electronics, food and textile). However, concerns regarding the environmental and biological implications of NPs are raised alongside the booming nanotechnology industry. Numerous studies on the biological effect of NPs have been done in the last decade, and many mechanisms have been proposed. In brief, mechanisms underlying the adverse biological effect caused by NPs can be summarized as: (i) indirect adverse effect induced by reactive oxygen species (ROS) generated by NPs, (ii) indirect adverse effect induced by released toxic ions, and (iii) adverse effect induced by direct interactions of NPs with biological systems. Up to now, most efforts have been focused on the first two mechanisms. In contrast, adverse biological effects induced by direct nano-bio interactions are the least researched. This is largely because of the complexity and lack of suitable techniques for characterizing the nano-bio interface. This dissertation aims at advancing our understanding of the nano-bio interactions leading to the adverse biological effect of NPs. Specifically, it is comprised of three parts. Firstly, because the aggregation of NPs alters particle size and other physicochemical properties of NPs, the property of NPs reaching and interacting with biological cells is very likely different from that of what we feed initially. Consequently, as the first step and an essential prerequisite for understanding the biological effect of NPs, NP aggregation is investigated and models are developed for predicting the stability and the extent of aggregation of NPs. Secondly, interactions between NPs and cell membrane are studied with paramecium as the model cell. Due to the lack of cell wall, the susceptible cell membrane of paramecium is directly exposed to NPs in the medium. The extent and strength of direct nano-cell membrane interaction is evaluated and quantified by calculating the interfacial force/interaction between NPs and cell membrane. A correlation is further established between the nano-cell membrane interaction and the lethal acute toxicity of NPs. We find NPs that have strong association or interaction with the cell membrane tend to induce strong lethal effects. Lastly, we demonstrate systematic experimental approaches based on atomic force microscope (AFM), which allows us to characterize nano-bio interfaces on the single NP and single-molecular level, coupled with modeling approaches to probe the nano-DNA interaction. Using quantum dots (QDs) as a model NP, we have examined, with the novel application of AFM, the NP-to-DNA binding characteristics including binding mechanism, binding kinetics, binding isotherm, and binding specificity. We have further assessed the binding affinity of NPs for DNA by calculating their interaction energy on the basis of the DLVO models. The modeling results of binding affinity are validated by the NP-to-DNA binding images acquired by AFM. The investigation of the relationship between the binding affinity of twelve NPs for DNA with their inhibition effects on DNA replication suggests that strong nano-DNA interactions result in strong adverse genetic effects of NPs. In summary, this dissertation has furthered our understanding of direct nano-bio interactions and their role in the biological effect of NPs. Furthermore, the models developed in this dissertation lay the basis for building an “ultimate” predictive model of biological effects of NPs that takes into account multiple mechanisms and their interactions, which would save a lot of testing costs and time in evaluating the risk of NPs.

Non-invasive and continuous spatiotemporal pathogen monitoring at biological interfaces (e.g., human tissue) holds promise for transformative applications in personalized healthcare (e.g., wound infection monitoring) and environmental surveillance (e.g., airborne virus surveillance). Despite notable progress, current receptor-based biosensors encounter inherent limitations, including inadequate long-term performance, restricted spatial resolutions and length scales, and challenges in obtaining multianalyte information. Surface-enhanced Raman spectroscopy (SERS) has emerged as a robust analytical method, merging the molecular specificity of Raman spectroscopy's vibrational fingerprinting with the enhanced detection sensitivity from strong light-matter interaction in plasmonic nanostructures. As a receptor-free and noninvasive detection tool capable of capturing multianalyte chemical information, SERS holds the potential to actualize bio-interfaced spatiotemporal pathogen monitoring. Nonetheless, several challenges must be addressed before practical adoption, including the development of plasmonic bio-interfaces, sensitive capture of multianalyte information from pathogens, regeneration of nanogap hotspots for long-term sensing, and extraction of meaningful information from spatiotemporal SERS datasets. This dissertation tackles these fundamental challenges. Plasmonic bio-interfaces were created using innovative nanoimprint lithography-based scalable nanofabrication methods for reliable bio-interfaced spatiotemporal measurements. These plasmonic bio-interfaces feature sensitive, dense, and uniformly distributed plasmonic transducers (e.g., plasmonic nano dome arrays, optically-coupled plasmonic nanodome and nanohole arrays, self-assembled nanoparticle micro patches) on ultra-flexible and porous platforms (e.g., biomimetic polymeric meshes, textiles). Using these plasmonic bio-interfaces, advancements were made in SERS signal transduction, machine-learning-enabled data analysis, and sensor regeneration. Large-area multianalyte spatiotemporal monitoring of bacterial biofilm components and pH was demonstrated in in-vitro biofilm models, crucial for wound biofilm diagnostics. Additionally, novel approaches for sensitive virus detection were introduced, including monitoring spectral changes during viral infection in living biofilms and direct detection of decomposed viral components. Spatiotemporal SERS datasets were analyzed using unsupervised machine-learning methods to extract biologically relevant spatiotemporal information and supervised machine-learning tools to classify and predict biological outcomes. Finally, a sensor regeneration method based on plasmon-induced nanocavitation was developed to enable long-term continuous detection in protein-rich backgrounds. Through continuous implementation of spatiotemporal SERS signal transduction, machine-learning-enabled data analysis, and sensor regeneration in a closed loop, our solution has the potential to enable spatiotemporal pathogen monitoring at the bio-interface. / Doctor of Philosophy / Continuous monitoring of pathogens within our bodies and surrounding environments is indispensable for various applications in personalized healthcare (e.g., monitoring wound infections) and environmental surveillance (e.g., airborne virus tracking). To accomplish this, we require sensors capable of seamlessly interfacing with biological systems, such as human tissue, and consistently providing pathogen-related information (e.g., spatial location and pathogen type) over prolonged periods. Our research relies on Surface-enhanced Raman spectroscopy (SERS) to address this challenge. SERS enables noninvasive sensing by providing unique fingerprints of molecules near the sensor's surface. SERS holds the potential to enable bio-interfaced spatiotemporal pathogen monitoring, but several challenges must be tackled before practical adoption. In this dissertation, we address various fundamental challenges in SERS, including constructing SERS devices that can seamlessly interface with biological systems while maintaining performance, sensitively capturing pathogen-related information, extracting meaningful insights from SERS datasets, and continuously regenerating the sensor surface to ensure long-term performance. We developed SERS devices capable of seamlessly interfacing with biological systems using innovative scalable nanofabrication methods. These devices contain sensitive, dense, and uniformly distributed SERS sensors on flexible and porous platforms, such as polymeric scaffolds and textiles. Leveraging these SERS devices, we made advancements in pathogen sensing, data analysis, and sensor regeneration. We demonstrated large-area spatiotemporal monitoring of biofilm components and pH in lab-grown biofilm models, critical for wound biofilm diagnostics. Additionally, we introduced novel approaches for sensitive virus detection, including monitoring changes in SERS signals during viral infection in living biofilms and directly detecting decomposed viral components. The SERS datasets were analyzed using machine learning models to extract biologically relevant spatial and temporal information, such as the spatial location of pathogen components and the temporal stage of pathogen growth, and to predict biological outcomes. Finally, we developed a sensor regeneration method to enable long-term continuous detection in complex backgrounds, such as blood. By continuously performing spatiotemporal pathogen sensing, data analysis, and sensor regeneration in a closed loop, our solution has the potential to realize bio-interfaced spatiotemporal pathogen monitoring.