Search for: All records

Adeno-associated viruses (AAVs) are a leading vector for gene therapy, yet their clinical utility is limited by the lack of robust quality control methods to distinguish between empty (AAVempty), partially loaded (AAVpartial), and fully DNA loaded (AAVfull) capsids. Current analytical techniques provide partial insights but remain limited in sensitivity, throughput, or resolution. Here we present a multimodal plasmonic nanopore sensor that integrates optical trapping with electrical resistive-pulse sensing to characterize AAV9 capsids at the single-particle level in tens of μL sample volumes and fM range concentrations. As a model system, we employed AAV9 capsids not loaded with DNA, capsids loaded with a self-complementary 4.7 kbp DNA (AAVscDNA), and ones loaded with single-stranded 4.7 kbp DNA (AAVssDNA). Ground-truth validation was performed with analytical ultracentrifugation (AUC). Nanosensor data were acquired concurrently for optical step changes (occurring at AAV trapping and un-trapping) both in transmittance and reflectance geometries, and electrical nanopore resistive pulse signatures, making for a total of five data dimensions. The acquired data was then filtered and clustered by Gaussian mixture models (GMMs), accompanied by spectral clustering stability analysis, to successfully separate between AAV species based on their DNA load status (AAVempty, AAVpartial, AAVfull) and DNA load type (AAVscDNA versus AAVssDNA). The motivation for quantifying the AAVempty and AAVpartial population fractions is that they reduce treatment efficacy and increase immunogenicity. Likewise, the motivation to identify AAVscDNA population fractions is that these have much higher transfection rates. Importantly, the results showed that the nanosensor could differentiate between AAVscDNA and AAVssDNA despite their identical masses. In contrast, AUC could not differentiate between AAVscDNA and AAVssDNA. An equimolar mixture of AAVscDNA, AAVssDNA and AAVempty was also measured with the sensor, and the results showed the expected population fractions, supporting the capacity of the method to differentiate AAV load status in heterogeneous solutions. In addition, less common optical and electrical signal signatures were identified in the acquired data, which were attributed to debris, rapid entry re-entry to the optical trap, or weak optical trap exits, representing critical artifacts to recognize for correct interpretation of the data. Together, these findings establish plasmonic nanopore sensing as a promising platform for quantifying AAV DNA loading status and genome type with the potential to extend ultra-sensitive single-particle characterization beyond the capabilities of existing methods. 

Alternating current (AC) modulation of command voltage applied across a Self-induced Back Action Actuated Nanopore Electrophoresis (SANE) sensor, a type of plasmonic nanopore sensor that we have developed previously, enables acquisition of new data types that could potentially enhance the characterization of nanoparticles (NPs) and single molecules. In particular, AC voltage frequency response provides insight into the charge and dielectric constant of analytes that are normally obfuscated using DC command voltages. We first analyzed Axopatch 200B data to map the frequency response of the empty SANE sensor in terms of phase shift and amplitude modulation, with and without plasmonic excitation. We then tested the frequency response of 20 nm diameter silica NPs and 20 nm gold NPs trapped optically, which made these particles hover over an underlying 25 nm nanopore at the center of the SANE sensor. By applying a DC command voltage with a superimposed AC frequency sweep while keeping the NPs optically trapped in the vicinity of the nanopores’s entrance, we have found that silica and gold NPs to have distinctly different electrical responses. This pilot work demonstrates the feasibility of performing AC measurements with a plasmonic nanopore, which encourages us to pursue more detailed characterization studies with NPs and single molecules in future work. 

Nanopore probing of molecular level transport of proteins is strongly influenced by electrolyte type, concentration, and solution pH. As a result, electrolyte chemistry and applied voltage are critical for protein transport and impact, for example, capture rate ( C R ), transport mechanism ( i.e. , electrophoresis, electroosmosis or diffusion), and 3D conformation ( e.g. , chaotropic vs. kosmotropic effects). In this study, we explored these using 0.5–4 M LiCl and KCl electrolytes with holo-human serum transferrin (hSTf) protein as the model protein in both low (±50 mV) and high (±400 mV) electric field regimes. Unlike in KCl, where events were purely electrophoretic, the transport in LiCl transitioned from electrophoretic to electroosmotic with decreasing salt concentration while intermediate concentrations ( i.e. , 2 M and 2.5 M) were influenced by diffusion. Segregating diffusion-limited capture rate ( R diff ) into electrophoretic ( R diff,EP ) and electroosmotic ( R diff,EO ) components provided an approach to calculate the zeta-potential of hSTf ( ζ hSTf ) with the aid of C R and zeta potential of the nanopore surface ( ζ pore ) with ( ζ pore – ζ hSTf ) governing the transport mechanism. Scrutinization of the conventional excluded volume model revealed its shortcomings in capturing surface contributions and a new model was then developed to fit the translocation characteristics of proteins. 

Abstract Recently, we developed a fabrication method—chemically‐tuned controlled dielectric breakdown (CT‐CDB)—that produces nanopores (through thin silicon nitride membranes) surpassing legacy drawbacks associated with solid‐state nanopores (SSNs). However, the noise characteristics of CT‐CDB nanopores are largely unexplored. In this work, we investigated the 1/fnoise of CT‐CDB nanopores of varying solution pH, electrolyte type, electrolyte concentration, applied voltage, and pore diameter. Our findings indicate that the bulk Hooge parameter (α_b) is about an order of magnitude greater than SSNs fabricated by transmission electron microscopy (TEM) while the surface Hooge parameter (α_s) is ∼3 order magnitude greater. Theα_sof CT‐CDB nanopores was ∼5 orders of magnitude greater than theirα_b, which suggests that the surface contribution plays a dominant role in 1/fnoise. Experiments with DNA exhibited increasing capture rates with pH up to pH ∼8 followed by a drop at pH ∼9 perhaps due to the onset of electroosmotic force acting against the electrophoretic force. The1/fnoise was also measured for several electrolytes and LiCl was found to outperform NaCl, KCl, RbCl, and CsCl. The 1/fnoise was found to increase with the increasing electrolyte concentration and pore diameter. Taken together, the findings of this work suggest the pH approximate 7–8 range to be optimal for DNA sensing with CT‐CDB nanopores.