Mapping the Morphology of DNA on Carbon Nanotubes in Solution Using X-ray Scattering Interferometry.

Single-walled carbon nanotubes (SWCNTs) with adsorbed single-stranded DNA (ssDNA) are applied as sensors to investigate biological systems, with potential applications ranging from clinical diagnostics to agricultural biotechnology. Unique ssDNA sequences render SWCNTs selectively responsive to target analytes such as (GT)n-SWCNTs recognizing the neuromodulator, dopamine. It remains unclear how the ssDNA conformation on the SWCNT surface contributes to functionality, as observations have been limited to computational models or experiments under dehydrated conditions that differ substantially from the aqueous biological environments in which the nanosensors are applied. We demonstrate a direct mode of measuring in-solution ssDNA geometries on SWCNTs via X-ray scattering interferometry (XSI), which leverages the interference pattern produced by AuNP tags conjugated to ssDNA on the SWCNT surface. We employ XSI to quantify distinct surface-adsorbed morphologies for two (GT)n ssDNA oligomer lengths (n = 6, 15) that are used on SWCNTs in the context of dopamine sensing and measure the ssDNA conformational changes as a function of ionic strength and during dopamine interaction. We show that the shorter oligomer, (GT)6, adopts a more periodically ordered ring structure along the SWCNT axis (inter-ssDNA distance of 8.6 ± 0.3 nm), compared to the longer (GT)15 oligomer (most probable 5'-to-5' distance of 14.3 ± 1.1 nm). During molecular recognition, XSI reveals that dopamine elicits simultaneous axial elongation and radial constriction of adsorbed ssDNA on the SWCNT surface. Our approach using XSI to probe solution-phase morphologies of polymer-functionalized SWCNTs can be applied to yield insights into sensing mechanisms and inform future design strategies for nanoparticle-based sensors.

Inferring local molecular dynamics from the global actin network structure: A case study of 2D synthetic branching actin networks.

Cells rely on their cytoskeleton for key processes including division and directed motility. Actin filaments are a primary constituent of the cytoskeleton. Although actin filaments can create a variety of network architectures linked to distinct cell functions, the microscale molecular interactions that give rise to these macroscale structures are not well understood. In this work, we investigate the microscale mechanisms that produce different branched actin network structures using an iterative classification approach. First, we employ a simple yet comprehensive agent-based model that produces synthetic actin networks with precise control over the microscale dynamics. Then we apply machine learning techniques to classify actin networks based on measurable network density and geometry, identifying key mechanistic processes that lead to particular branched actin network architectures. Extensive computational experiments reveal that the most accurate method uses a combination of supervised learning based on network density and unsupervised learning based on network symmetry. This framework can potentially serve as a powerful tool to discover the molecular interactions that produce the wide variety of actin network configurations associated with normal development as well as pathological conditions such as cancer.

Engineered 3D Immuno-Glial-Neurovascular Human Brain Model.

Patient-specific, human-based cellular models that integrate biomimetic BBB, immune, and myelinated neuron components are critically needed to enable translationally relevant and accelerated discovery of neurological disease mechanisms and interventions. By engineering a brain-mimicking 3D hydrogel and co-culturing all six major brain cell types derived from patient iPSCs, we have constructed, characterized, and utilized a multicellular integrated brain (miBrain) immuno-glial-neurovascular model with in vivo- like hallmarks. As proof of principle, here we utilized the miBrain to model Alzheimer's Disease pathologies associated with APOE4 genetic risk. APOE4 miBrains differentially exhibit amyloid aggregation, tau phosphorylation, and astrocytic GFAP. Unlike the co-emergent fate specification of glia and neurons in organoids, miBrains integrate independently differentiated cell types in a modular system with unique utility for elucidating cell-type specific contributions to pathogenesis. We here harness this feature to identify that risk factor APOE4 in astrocytes promotes tau pathogenesis and neuronal dysregulation through crosstalk with microglia. One-Sentence Summary: A novel patient-specific brain model with BBB, neuronal, immune, and glial components was developed, characterized, and harnessed to model Alzheimer's Disease-associated pathologies and APOE4 genetic risk.

Building in vitro models of the brain to understand the role of APOE in Alzheimer's disease.

Alzheimer's disease (AD) is a devastating, complex, and incurable disease that represents an increasingly problematic global health issue. The etiology of sporadic AD that accounts for a vast majority of cases remains poorly understood, with no effective therapeutic interventions. Genetic studies have identified AD risk genes including the most prominent, APOE, of which the ɛ4 allele increases risk in a dose-dependent manner. A breakthrough discovery enabled the creation of human induced pluripotent stem cells (hiPSCs) that can be differentiated into various brain cell types, facilitating AD research in genetically human models. Herein, we provide a brief background on AD in the context of APOE susceptibility and feature work employing hiPSC-derived brain cell and tissue models to interrogate the contribution of APOE in driving AD pathology. Such models have delivered crucial insights into cellular mechanisms and cell type-specific roles underlying the perturbed biological functions that trigger pathogenic cascades and propagate neurodegeneration. Collectively, hiPSC-based models are envisioned to be an impactful platform for uncovering fundamental AD understanding, with high translational value toward AD drug discovery and testing.

Supervised learning model predicts protein adsorption to carbon nanotubes.

Engineered nanoparticles are advantageous for biotechnology applications including biomolecular sensing and delivery. However, testing compatibility and function of nanotechnologies in biological systems requires a heuristic approach, where unpredictable protein corona formation prevents their effective implementation. We develop a random forest classifier trained with mass spectrometry data to identify proteins that adsorb to nanoparticles based solely on the protein sequence (78% accuracy, 70% precision). We model proteins that populate the corona of a single-walled carbon nanotube (SWCNT)­based nanosensor and study the relationship between the protein's amino acid­based properties and binding capacity. Protein features associated with increased likelihood of SWCNT binding include high content of solvent-exposed glycines and nonsecondary structure­associated amino acids. To evaluate its predictive power, we apply the classifier to identify proteins with high binding affinity to SWCNTs, with experimental validation. The developed classifier provides a step toward undertaking the otherwise intractable problem of predicting protein-nanoparticle interactions.

Nanoparticle cellular internalization is not required for RNA delivery to mature plant leaves.

Rapidly growing interest in the nanoparticle-mediated delivery of DNA and RNA to plants requires a better understanding of how nanoparticles and their cargoes translocate in plant tissues and into plant cells. However, little is known about how the size and shape of nanoparticles influence transport in plants and the delivery efficiency of their cargoes, limiting the development of nanotechnology in plant systems. In this study we employed non-biolistically delivered DNA-modified gold nanoparticles (AuNPs) of various sizes (5-20 nm) and shapes (spheres and rods) to systematically investigate their transport following infiltration into Nicotiana benthamiana leaves. Generally, smaller AuNPs demonstrated more rapid, higher and longer-lasting levels of association with plant cell walls compared with larger AuNPs. We observed internalization of rod-shaped but not spherical AuNPs into plant cells, yet, surprisingly, 10 nm spherical AuNPs functionalized with small-interfering RNA (siRNA) were the most efficient at siRNA delivery and inducing gene silencing in mature plant leaves. These results indicate the importance of nanoparticle size in efficient biomolecule delivery and, counterintuitively, demonstrate that efficient cargo delivery is possible and potentially optimal in the absence of nanoparticle cellular internalization. Overall, our results highlight nanoparticle features of importance for transport within plant tissues, providing a mechanistic overview of how nanoparticles can be designed to achieve efficacious biocargo delivery for future developments in plant nanobiotechnology.

In Planta Nanosensors: Understanding Biocorona Formation for Functional Design.

Climate change and population growth are straining agricultural output. To counter these changes and meet the growing demand for food and energy, the monitoring and engineering of crops are becoming increasingly necessary. Nanoparticle-based sensors have emerged in recent years as new tools to advance agricultural practices. As these nanoparticle-based sensors enter and travel through the complex biofluids within plants, biomolecules including proteins, metabolites, lipids, and carbohydrates adsorb onto the nanoparticle surfaces, forming a coating known as the "bio-corona". Understanding these nanoparticle-biomolecule interactions that govern nanosensor function in plants will be essential to successfully develop and translate nanoparticle-based sensors into broader agricultural practice.

Extraction of Viral Nucleic Acids with Carbon Nanotubes Increases SARS-CoV-2 Quantitative Reverse Transcription Polymerase Chain Reaction Detection Sensitivity.

The global SARS-CoV-2 coronavirus pandemic has led to a surging demand for rapid and efficient viral infection diagnostic tests, generating a supply shortage in diagnostic test consumables including nucleic acid extraction kits. Here, we develop a modular method for high-yield extraction of viral single-stranded nucleic acids by using "capture" ssDNA sequences attached to carbon nanotubes. Target SARS-CoV-2 viral RNA can be captured by ssDNA-nanotube constructs via hybridization and separated from the liquid phase in a single-tube system with minimal chemical reagents, for downstream quantitative reverse transcription polymerase chain reaction (RT-qPCR) detection. This nanotube-based extraction method enables 100% extraction yield of target SARS-CoV-2 RNA from phosphate-buffered saline in comparison to ∼20% extraction yield when using a commercial silica-column kit. Notably, carbon nanotubes enable extraction of nucleic acids directly from 50% human saliva with a similar efficiency as achieved with commercial DNA/RNA extraction kits, thereby bypassing the need for further biofluid purification and avoiding the use of commercial extraction kits. Carbon nanotube-based extraction of viral nucleic acids facilitates high-yield and high-sensitivity identification of viral nucleic acids such as the SARS-CoV-2 viral genome with a reduced reliance on reagents affected by supply chain obstacles.

Rapid SARS-CoV-2 Spike Protein Detection by Carbon Nanotube-Based Near-Infrared Nanosensors.

To effectively track and eliminate COVID-19, it is critical to develop tools for rapid and accessible diagnosis of actively infected individuals. Here, we introduce a single-walled carbon nanotube (SWCNT)-based optical sensing approach toward this end. We construct a nanosensor based on SWCNTs noncovalently functionalized with ACE2, a host protein with high binding affinity for the SARS-CoV-2 spike protein. The presence of the SARS-CoV-2 spike protein elicits a robust, 2-fold nanosensor fluorescence increase within 90 min of spike protein exposure. We characterize the nanosensor stability and sensing mechanism and passivate the nanosensor to preserve sensing response in saliva and viral transport medium. We further demonstrate that these ACE2-SWCNT nanosensors retain sensing capacity in a surface-immobilized format, exhibiting a 73% fluorescence turn-on response within 5 s of exposure to 35 mg/L SARS-CoV-2 virus-like particles. Our data demonstrate that ACE2-SWCNT nanosensors can be developed into an optical tool for rapid SARS-CoV-2 detection.

Rapid SARS-CoV-2 Detection by Carbon Nanotube-Based Near-Infrared Nanosensors.

To effectively track and eliminate COVID-19, it is critical to develop tools for rapid and accessible diagnosis of actively infected individuals. Here, we introduce a single-walled carbon nanotube (SWCNT)-based optical sensing approach towards these ends. We construct a nanosensor based on SWCNTs noncovalently functionalized with ACE2, a host protein with high binding affinity for the SARS-CoV-2 spike protein. Presence of the SARS-CoV-2 spike protein elicits a robust, two-fold nanosensor fluorescence increase within 90 min of spike protein exposure. We characterize the nanosensor stability and sensing mechanism, and passivate the nanosensor to preserve sensing response in saliva and viral transport medium. We further demonstrate that these ACE2-SWCNT nanosensors retain sensing capacity in a surface-immobilized format, exhibiting a 73% fluorescence turn-on response within 5 s of exposure to 35 mg/L SARS-CoV-2 virus-like particles. Our data demonstrate that ACE2-SWCNT nanosensors can be developed into an optical tool for rapid SARS-CoV-2 detection.

Mitigation of Carbon Nanotube Neurosensor Induced Transcriptomic and Morphological Changes in Mouse Microglia with Surface Passivation.

Single-walled carbon nanotubes (SWCNT) are used in neuroscience for deep-brain imaging, neuron activity recording, measuring brain morphology, and imaging neuromodulation. However, the extent to which SWCNT-based probes impact brain tissue is not well understood. Here, we study the impact of (GT)6-SWCNT dopamine nanosensors on SIM-A9 mouse microglial cells and show SWCNT-induced morphological and transcriptomic changes in these brain immune cells. Next, we introduce a strategy to passivate (GT)6-SWCNT nanosensors with PEGylated phospholipids to improve both biocompatibility and dopamine imaging quality. We apply these passivated dopamine nanosensors to image electrically stimulated striatal dopamine release in acute mouse brain slices, and show that slices labeled with passivated nanosensors exhibit higher fluorescence response to dopamine and measure more putative dopamine release sites. Hence, this facile modification to SWCNT-based dopamine probes provides immediate improvements to both biocompatibility and dopamine imaging functionality with an approach that is readily translatable to other SWCNT-based neurotechnologies.

Quantitative Protein Corona Composition and Dynamics on Carbon Nanotubes in Biological Environments.

When nanoparticles enter biological environments, proteins adsorb to form the "protein corona" which alters nanoparticle biodistribution and toxicity. Herein, we measure protein corona formation on DNA-functionalized single-walled carbon nanotubes (ssDNA-SWCNTs), a nanoparticle used widely for sensing and delivery, in blood plasma and cerebrospinal fluid. We characterize corona composition by mass spectrometry, revealing high-abundance corona proteins involved in lipid binding, complement activation, and coagulation. We investigate roles of electrostatic and entropic interactions driving selective corona formation. Lastly, we study real-time protein binding on ssDNA-SWCNTs, obtaining agreement between enriched proteins binding strongly and depleted proteins binding marginally, while highlighting cooperative adsorption mechanisms. Knowledge of protein corona composition, formation mechanisms, and dynamics informs nanoparticle translation from in vitro design to in vivo application.

Engineering at the nano-bio interface: harnessing the protein corona towards nanoparticle design and function.

Unpredictable and uncontrollable protein adsorption on nanoparticles remains a considerable challenge to achieving effective application of nanotechnologies within biological environments. Nevertheless, engineered nanoparticles offer unprecedented functionality and control in probing and altering biological systems. In this review, we highlight recent advances in harnessing the "protein corona" formed on nanoparticles as a handle to tune functional properties of the protein-nanoparticle complex. Towards this end, we first review nanoparticle properties that influence protein adsorption and design strategies to facilitate selective corona formation, with the corresponding characterization techniques. We next focus on literature detailing corona-mediated functionalities, including stealth to avoid recognition and sequestration while in circulation, targeting of predetermined in vivo locations, and controlled activation once localized to the intended biological compartment. We conclude with a discussion of biocompatibility outcomes for these protein-nanoparticle complexes applied in vivo. While formation of the nanoparticle-corona complex may impede our control over its use for the projected nanobiotechnology application, it concurrently presents an opportunity to create improved protein-nanoparticle architectures by exploiting natural or guiding selective protein adsorption to the nanoparticle surface.

Carbon nanocarriers deliver siRNA to intact plant cells for efficient gene knockdown.

Posttranscriptional gene silencing (PTGS) is a powerful tool to understand and control plant metabolic pathways, which is central to plant biotechnology. PTGS is commonly accomplished through delivery of small interfering RNA (siRNA) into cells. Standard plant siRNA delivery methods (Agrobacterium and viruses) involve coding siRNA into DNA vectors and are only tractable for certain plant species. Here, we develop a nanotube-based platform for direct delivery of siRNA and show high silencing efficiency in intact plant cells. We demonstrate that nanotubes successfully deliver siRNA and silence endogenous genes, owing to effective intracellular delivery and nanotube-induced protection of siRNA from nuclease degradation. This study establishes that nanotubes could enable a myriad of plant biotechnology applications that rely on RNA delivery to intact cells.

Graphene Quantum Dot Oxidation Governs Noncovalent Biopolymer Adsorption.

Graphene quantum dots (GQDs) are an allotrope of carbon with a planar surface amenable to functionalization and nanoscale dimensions that confer photoluminescence. Collectively, these properties render GQDs an advantageous platform for nanobiotechnology applications, including optical biosensing and delivery. Towards this end, noncovalent functionalization offers a route to reversibly modify and preserve the pristine GQD substrate, however, a clear paradigm has yet to be realized. Herein, we demonstrate the feasibility of noncovalent polymer adsorption to GQD surfaces, with a specific focus on single-stranded DNA (ssDNA). We study how GQD oxidation level affects the propensity for polymer adsorption by synthesizing and characterizing four types of GQD substrates ranging ~60-fold in oxidation level, then investigating noncovalent polymer association to these substrates. Adsorption of ssDNA quenches intrinsic GQD fluorescence by 31.5% for low-oxidation GQDs and enables aqueous dispersion of otherwise insoluble no-oxidation GQDs. ssDNA-GQD complexation is confirmed by atomic force microscopy, by inducing ssDNA desorption, and with molecular dynamics simulations. ssDNA is determined to adsorb strongly to no-oxidation GQDs, weakly to low-oxidation GQDs, and not at all for heavily oxidized GQDs. Finally, we reveal the generality of the adsorption platform and assess how the GQD system is tunable by modifying polymer sequence and type.

Corona Exchange Dynamics on Carbon Nanotubes by Multiplexed Fluorescence Monitoring.

Noncovalent adsorption of DNA on nanoparticles has led to their widespread implementation as gene delivery tools and optical probes. Yet, the behavior and stability of DNA-nanoparticle complexes once applied in biomolecule-rich, in vivo environments remains unpredictable, whereby biocompatibility testing usually occurs in serum. Here, we demonstrate time-resolved measurements of exchange dynamics between solution-phase and adsorbed corona-phase DNA and protein biomolecules on single-walled carbon nanotubes (SWCNTs). We capture real-time binding of fluorophore-labeled biomolecules, utilizing the SWCNT surface as a fluorescence quencher, and apply this corona exchange assay to study protein corona dynamics on ssDNA-SWCNT-based dopamine sensors. We study exchange of two blood proteins, albumin and fibrinogen, adsorbing to and competitively displacing (GT)6 vs (GT)15 ssDNA from ssDNA-SWCNTs. We find that (GT)15 binds to SWCNTs with a higher affinity than (GT)6 and that fibrinogen interacts with ssDNA-SWCNTs more strongly than albumin. Albumin and fibrinogen cause a 52.2% and 78.2% attenuation of the dopamine nanosensor response, coinciding with 0.5% and 3.7% desorption of (GT)6, respectively. Concurrently, the total surface-adsorbed fibrinogen mass is 168% greater than that of albumin. Binding profiles are fit to a competitive surface exchange model which recapitulates the experimental observation that fibrinogen has a higher affinity for SWCNTs than albumin, with a fibrinogen on-rate constant 1.61-fold greater and an off-rate constant 0.563-fold smaller than that of albumin. Our methodology presents a generic route to assess real-time corona exchange on nanoparticles in solution phase and more broadly motivates testing of nanoparticle-based technologies in blood plasma rather than the more ubiquitously tested serum conditions.

Chemometric Approaches for Developing Infrared Nanosensors To Image Anthracyclines.

Generation, identification, and validation of optical probes to image molecular targets in a biological milieu remain a challenge. Synthetic molecular recognition approaches leveraging the intrinsic near-infrared fluorescence of single-walled carbon nanotubes are promising for long-term biochemical imaging in tissues. However, generation of nanosensors for selective imaging of molecular targets requires a heuristic approach. Here, we present a chemometric platform for rapidly screening libraries of candidate single-walled carbon nanotube nanosensors against biochemical analytes to quantify the fluorescence response to small molecules, including vitamins, neurotransmitters, and chemotherapeutics. We further show this method can be applied to identify biochemical analytes that selectively modulate the intrinsic near-infrared fluorescence of candidate nanosensors. Chemometric analysis thus enables identification of nanosensor-analyte "hits" and also nanosensor fluorescence signaling modalities such as wavelength shifts that are optimal for translation to biological imaging. Through this approach, we identify and characterize a nanosensor for the chemotherapeutic anthracycline doxorubicin (DOX), which provides a ≤17 nm fluorescence red-shift and exhibits an 8 µM limit of detection, compatible with peak circulatory concentrations of doxorubicin common in therapeutic administration. We demonstrate the selectivity of this nanosensor over dacarbazine, a chemotherapeutic commonly co-injected with doxorubicin. Lastly, we establish nanosensor tissue compatibility for imaging of doxorubicin in muscle tissue by incorporating nanosensors into the mouse hindlimb and measuring the nanosensor response to exogenous DOX administration. Our results motivate chemometric approaches to nanosensor discovery for chronic imaging of drug partitioning into tissues and toward real-time monitoring of drug accumulation.

Stochastic Simulation of Dopamine Neuromodulation for Implementation of Fluorescent Neurochemical Probes in the Striatal Extracellular Space.

Imaging the dynamic behavior of neuromodulatory neurotransmitters in the extracelluar space that arise from individual quantal release events would constitute a major advance in neurochemical imaging. Spatial and temporal resolution of these highly stochastic neuromodulatory events requires concurrent advances in the chemical development of optical nanosensors selective for neuromodulators in concert with advances in imaging methodologies to capture millisecond neurotransmitter release. Herein, we develop and implement a stochastic model to describe dopamine dynamics in the extracellular space (ECS) of the brain dorsal striatum to guide the design and implementation of fluorescent neurochemical probes that record neurotransmitter dynamics in the ECS. Our model is developed from first-principles and simulates release, diffusion, and reuptake of dopamine in a 3D simulation volume of striatal tissue. We find that in vivo imaging of neuromodulation requires simultaneous optimization of dopamine nanosensor reversibility and sensitivity: dopamine imaging in the striatum or nucleus accumbens requires nanosensors with an optimal dopamine dissociation constant (Kd) of 1 µM, whereas Kds above 10 µM are required for dopamine imaging in the prefrontal cortex. Furthermore, as a result of the probabilistic nature of dopamine terminal activity in the striatum, our model reveals that imaging frame rates of 20 Hz are optimal for recording temporally resolved dopamine release events. Our work provides a modeling platform to probe how complex neuromodulatory processes can be studied with fluorescent nanosensors and enables direct evaluation of nanosensor chemistry and imaging hardware parameters. Our stochastic model is generic for evaluating fluorescent neurotransmission probes, and is broadly applicable to the design of other neurotransmitter fluorophores and their optimization for implementation in vivo.