Rapid and robust molecular fingerprinting is critical in biomanufacturing, diagnostics, and environmental monitoring. Nanopore sensing provides single-molecule readouts as transient ionic current pulses; however, conventional analyses depend on handcrafted features that miss informative structural information. We present an interpretable machine learning framework that operates directly on raw pulses, pairing a physics-guided time-frequency transform with a compact neural classifier and feature-attribution maps. We also include conventional feature-based SVMs and a 1D classifier trained on raw pulses as baselines. On two self-assembled DNA nanostructures of similar size but distinct geometry, for which standard pulse features overlap, the method achieves high accuracy and yields physically consistent attributions that highlight discriminative signal motifs. A matched control without the time-frequency transform clarifies when learned filters suffice versus when physics-guided preprocessing improves reliability, leading to a practical "custom-filter" design principle. The workflow is modular, lightweight, and applicable to pulse-based sensing platforms, including virus and exosome analysis, electrochemical monitoring, and industrial fault detection. By combining accuracy with transparency, it lays the groundwork for deployable sensing platforms in regulated, mission-critical settings.

Conventional storage and retrieval of nucleic acid specimens, particularly unstable RNA, rely on costly cold-chain infrastructure and inefficient robotic handling, inhibiting large-scale nucleic acid archives needed for global genomic biobanking. We introduce a scalable room-temperature storage system with minimal physical footprint that enables database-like queries on encapsulated, barcoded, and pooled nucleic acid samples. Queries incorporate numerical ranges, categorical filters, and combinations thereof, advancing beyond previous demonstrations of single-sample retrieval or Boolean classifiers. We evaluate this system on ninety-six mock SARS-CoV-2 genomic samples barcoded with theoretical patient data including age, location, and diagnostic state, demonstrating rapid, scalable retrieval. We further demonstrate storage and sequencing of human patient-derived nucleic acid samples, illustrating applicability to clinical genomic analysis. By avoiding freezer-based storage and retrieval, this approach scales to millions of samples without loss of fidelity or throughput, enabling large-scale pathogen and genomic repositories in under-resourced or isolated regions of the US and worldwide.

Priming rare subdominant precursor B cells in germinal centers (GCs) is a central goal of vaccination to generate broadly neutralizing antibodies (bnAbs) against HIV. Multivalent immunogen display on protein nanoparticle scaffolds can promote such responses, but it also generates scaffold-specific B cells that could theoretically limit bnAb precursor expansion in GCs. We rationally designed DNA origami-based virus-like particles (DNA-VLPs) displaying a germline-targeting HIV envelope protein immunogen, which elicited no scaffold-specific antibody responses. Compared with a state-of-the-art clinical protein nanoparticle, these DNA-VLPs increased the expansion of epitope-specific GC B cells relative to off-target B cells and enhanced expansion of bnAb-lineage B cells in a humanized mouse model of CD4 binding site priming. Thus, minimizing off-target responses enhances bnAb priming and indicates that DNA-VLPs are a promising vaccine platform.

Nucleic acid nanoparticles (NANPs) fabricated by using DNA origami are an emerging delivery vector for nucleic acid therapeutics. Despite their advantages over other nanomaterials that include controlled spatial presentation of targeting ligands such as lipids and sugars, understanding their cell targeting and uptake mechanisms remains limited. Here, we investigated NANP cellular targeting, uptake, and delivery of small interfering RNAs (siRNAs) to liver and neuronal cell models in vitro. Using a rational design approach, we targeted NANPs to two clinically validated receptors, the asialoglycoprotein receptor (ASGPR) and the low-density lipoprotein receptor (LDLR), respectively, using GalNAc and lipidation. We systematically evaluated how the ligand valency, interligand spacing, linker length, and ligand chemistry affected NANP association with on- and off-target liver cell types, revealing the relative roles of the biomolecular corona, receptor engagement, and endocytosis in these targeting strategies. We found that lipidation enhanced NANP uptake into HepG2 cells, a model cell line for hepatocytes, by promoting apolipoprotein recruitment, LDLR engagement, and clathrin-mediated endocytosis and also increased association with nonparenchymal cells. HepG2 uptake was further improved by conjugating NANPs to lipids with higher valency provided that lipids were adequately displayed away from the surface of NANP edges with more lipophilic lipids yielding greater cell association. We then benchmarked the potential for NANPs to deliver siRNAs to HepG2 cells in comparison with lipid nanoparticle and conjugate technologies and explored lipid functionalization as a strategy for nonhepatic NANP targeting to model neuronal cells. Overall, this study advances the foundational understanding of how clinically relevant targeting ligands mediate NANP interactions with both on- and off-target liver cell types in vitro, offering insights into potential design criteria for nucleic acid therapeutic delivery.

Recruitment and expansion of rare precursor B cells in germinal centers (GCs) is a central goal of vaccination to generate broadly neutralizing antibodies (bnAbs) against challenging pathogens such as HIV. Multivalent immunogen display is a well-established method to enhance vaccine-induced B cell responses, typically accomplished by using natural or engineered protein scaffolds. However, these scaffolds themselves are targets of antibody responses, with the potential to generate competitor scaffold-specific B cells that could theoretically limit expansion and maturation of "on-target" B cells in the GC response. Here, we rationally designed T-independent, DNA-origami based virus-like particles (VLPs) with optimal antigenic display of the germline targeting HIV Env immunogen, eOD-GT8, and appropriate T cell help to achieve a potent GC response. In preclinical mouse models, these DNA-VLPs expanded significantly higher frequencies of epitope-specific GC B cells compared with a state-of-the-art clinical protein nanoparticle. Optimized DNA-VLPs primed germinal centers focused on the target antigen and rapidly expanded subdominant broadly neutralizing antibody precursor B cells for HIV with a single immunization. Thus, avoiding scaffold-specific responses augments priming of bnAb precursor B cells, and DNA-VLPs are a promising platform for promoting B cell responses towards challenging subdominant epitopes.

Artificial intelligence (AI) models remain an emerging strategy to accelerate materials design and development. We demonstrate that CNN models can characterize DNA origami nanostructures employed in programmable self-assembly, which is important in many applications such as in biomedicine. Specifically, we benchmark the performance of 9 CNN models, namely, AlexNet, GoogLeNet, VGG16, VGG19, ResNet18, ResNet34, ResNet50, ResNet101, and ResNet152 to characterize the ligation number of DNA origami nanostructures in transmission electron microscopy (TEM) images. We first pretrain CNN models using a large image data set of 720 images from our coarse-grained (CG) molecular dynamics (MD) simulations. Then, we fine-tune the pretrained CNN models, using a small experimental TEM data set with 146 TEM images. All CNN models were found to have similar computational time requirements, although their model sizes and performances are different. We use 20 test MD images to demonstrate that among all of the pretrained CNN models, ResNet50 and VGG16 have the highest and second-highest accuracies. Among the fine-tuned models, VGG16 was found to have the highest agreement with the test TEM images. Thus, we conclude that fine-tuned VGG16 models can quickly characterize the number of ligation sites of nanostructures in large TEM images.

Molecular photonic wires conduct electronic energy via their rapid transport properties. In photosynthesis, nature achieves efficient transport across large distances using delocalized excitons, generated by strong excitonic coupling between chromophores. How, or even whether, delocalization facilitates long-distance energy transport in synthetic systems has been challenging to experimentally test and optimize. Thus, far, studies have been limited to strongly coupled, heterogeneous chromophore aggregates or weakly coupled chromophore monomers. Here, we employed DNA nanostructures to engineer molecular photonic wires constructed from a series of excitonically coupled indocarbocyanine chromophores─achieving the intermediate and strong coupling regimes. Using time-resolved fluorescence spectroscopy and complementary simulations, we demonstrated that an intermediate intermolecular electronic coupling (∼kBT) enables up to 40% faster exciton transport as compared to strongly coupled chromophores. The delocalized excitons generated in the intermediate coupling regime exhibited properties conducive to rapid diffusivity, similar to their monomeric counterparts. Thus, intermediate excitonic coupling, analogous to natural systems, achieves long-distance exciton transport with the high chromophore density required for energy capture.

Deterministic incorporation of colloidal quantum emitters into silicon-based photonic devices would enable major advances in quantum optics and nanophotonics. However, precisely positioning sub-10 nm particles onto micron-sized photonic structures with nanometer-scale accuracy remains an outstanding challenge. Here, we introduce Cavity-Shape Modulated Origami Placement (CSMOP) that leverages the shape programmability of DNA origami to selectively deposit colloidal nanomaterials within lithographically defined resist cavities patterned onto arbitrary photonic devices with high yield and orientation control. Soft-silicification-passivation stabilizes the deposited origami, while preserving their spatially programmable DNA hybridization sites, which enable site-specific attachment of plasmonic gold nanorods (AuNRs) and semiconductor quantum rods (QRs). This offers control over light scattering and emission polarization, respectively, with deterministic integration of individual QRs within silicon nitride waveguides, micro-ring resonators, and bullseye cavities. CSMOP thereby offers a general platform for the integration of colloidal nanomaterials into photonic circuits, with broad potential to empower quantum information science and technology. ### Competing Interest Statement The authors have declared no competing interest.

Complex neuronal circuit functions emerge from local, actively regulated synaptic protein levels that interplay with synaptic neurotransmission across heterogenous synapse populations. Understanding the mechanisms by which chemical and disease-associated genetic perturbations impact neuronal circuit functions requires simultaneous measurement of these factors with single-synapse resolution at population scale. Here, we combine in situ multimodal imaging of local mRNA translation, synaptic multiprotein composition, and synapse activity measured via calcium or glutamate fluxes, within the same spatially resolved synapses. We apply this approach of multimodal synapse profiling to study ketamine plasticity. Results map a causal network of NR2A-depletion-induced changes to synaptic scaffolding and receptor proteins, driven by synaptic activity and local mRNA translation, which translates to Grin2a models of schizophrenia in vitro and in vivo . Thus, multimodal synaptomics can reveal mechanistic neurobiology that underlies chemical and genetic perturbations within the context of scalable neuronal cultures, which can serve as models for human disease and therapeutic development.

Nucleic acid nanoparticles (NANPs) fabricated by using the DNA origami method have broad utility in materials science and bioengineering. Their site-specific, heterovalent functionalization with secondary molecules such as proteins or fluorophores is a unique feature of this technology that drives its utility. Currently, however, there are few chemistries that enable fast, efficient covalent functionalization of NANPs with a broad conjugate scope and heterovalency. To address this need, we introduce synthetic methods to access inverse electron-demand Diels-Alder chemistry on NANPs. We demonstrate a broad conjugate scope, characterize application-relevant kinetics, and integrate this new chemistry with strain-promoted azide-alkyne cycloaddition chemistry to enable heterovalent click reactions on NANPs. We applied these chemistries to formulate a prototypical chemical countermeasure against chemical nerve agents. We envision this additional chemistry finding broad utility in the synthetic toolkit accessible to the nucleic acid nanotechnology community.

Artificial intelligence (AI) models remain an emerging strategy to accelerate materials design and development. We demonstrate that convolutional neural network (CNN) models can characterize DNA origami nanostructures employed in programmable self-assembling, which is important in many applications such as in biomedicine. Specifically, we benchmark the performance of 9 CNN models – viz. AlexNet, GoogLeNet, VGG16, VGG19, ResNet18, ResNet34, ResNet50, ResNet101, and ResNet152 – to characterize the ligation number of DNA origami nanostructures in transmission electron microscopy (TEM) images. We first pre-train CNN models using a large image dataset of 720 images from our coarse-grained (CG) molecular dynamics (MD) simulations. Then, we fine-tune the pre-trained CNN models, using a small experimental TEM dataset with 146 TEM images. All CNN models were found to have similar computational time requirements, while their model sizes and performances are different. We use 20 test MD images to demonstrate that among all of the pre-trained CNN models ResNet50 and VGG16 have the highest and second highest accuracies. Among the fine-tuned models, VGG16 was found to have the highest agreement on the test TEM images. Thus, we conclude that fine-tuned VGG16 models can quickly characterize the ligation number of nanostructures in large TEM images.

ABSTRACTThe surge in nucleic acid analytics requires scalable storage and retrieval systems akin to electronic databases used to organize digital data. Such a system could transform disease diagnosis, ecological preservation, and molecular surveillance of biothreats. Current storage systems use individual containers for nucleic acid samples, requiring single-sample retrieval that falls short compared with digital databases that allow complex and combinatorial data retrieval on aggregated data. Here, we leverage protective microcapsules with combinatorial DNA labeling that enables arbitrary retrieval on pooled biosamples analogous to Structured Query Languages. Ninety-six encapsulated pooled mock SARS-CoV-2 genomic samples barcoded with patient metadata are used to demonstrate queries with simultaneous matches to sample collection date ranges, locations, and patient health statuses, illustrating how such flexible queries can be used to yield immunological or epidemiological insights. The approach applies to any biosample database labeled with orthogonal barcodes, enabling complex post-hoc analysis, for example, to study global biothreat epidemiology.

Natural light-harvesting systems spatially organize densely packed dyes in different configurations to either transport excitons or convert them into charged photoproducts, with high efficiency. In contrast, artificial photosystems like organic solar cells and light-emitting diodes lack this fine structural control, limiting their efficiency. Thus, biomimetic multi-dye systems are needed to organize dyes with the sub-nanometer spatial control required to “sculpt” resulting photoproducts. Here, we synthesize 11 distinct perylene diimide (PDI) dimers integrated into DNA origami nanostructures and identify dimer architectures that offer discrete control over exciton transport versus charge separation. The large structural space and site tunability of origami uniquely provide controlled PDI dimer packing to form distinct excimer photoproducts that are sensitive to interdye configurations. In the future, this platform will enable large-scale programmed assembly of dyes mimicking natural systems to sculpt distinct photophysical products needed for a broad range of optoelectronic devices, including solar energy converters and quantum information processors.

RNA molecules perform a diversity of essential functions for which their linear sequences must fold into higher-order structures. Techniques including crystallography and cryogenic electron microscopy have revealed 3D structures of ribosomal, transfer, and other well-structured RNAs; while chemical probing with sequencing facilitates secondary structure modeling of any RNAs of interest, even within cells. Ongoing efforts continue increasing the accuracy, resolution, and ability to distinguish coexisting alternative structures. However, no method can discover and quantify alternative structures with base pairs spanning arbitrarily long distances - an obstacle for studying viral, messenger, and long noncoding RNAs, which may form long-range base pairs. Here, we introduce the method of Structure Ensemble Ablation by Reverse Complement Hybridization with Mutational Profiling (SEARCH-MaP) and software for Structure Ensemble Inference by Sequencing, Mutation Identification, and Clustering of RNA (SEISMIC-RNA). We use SEARCH-MaP and SEISMIC-RNA to discover that the frameshift stimulating element of SARS coronavirus 2 base-pairs with another element 1 kilobase downstream in nearly half of RNA molecules, and that this structure competes with a pseudoknot that stimulates ribosomal frameshifting. Moreover, we identify long-range base pairs involving the frameshift stimulating element in other coronaviruses including SARS coronavirus 1 and transmissible gastroenteritis virus, and model the full genomic secondary structure of the latter. These findings suggest that long-range base pairs are common in coronaviruses and may regulate ribosomal frameshifting, which is essential for viral RNA synthesis. We anticipate that SEARCH-MaP will enable solving many RNA structure ensembles that have eluded characterization, thereby enhancing our general understanding of RNA structures and their functions. SEISMIC-RNA, software for analyzing mutational profiling data at any scale, could power future studies on RNA structure and is available on GitHub and the Python Package Index.

DNA nanotechnology has broad applications in biomedical drug delivery and programmable materials. Characterization of the self-assembly of DNA origami and quantum dots (QDs) is necessary for the development of new DNA-based nanostructures. We use computation and experiment to show that the self-assembly of 3D hierarchical nanostructures can be controlled by programming the binding site number and their positions on DNA origami. Using biotinylated pentagonal pyramid wireframe DNA origamis and streptavidin capped QDs, we demonstrate that DNA origami with 1 binding site at the outer vertex can assemble multimeric origamis with up to 6 DNA origamis on 1 QD, and DNA origami with 1 binding site at the inner center can only assemble monomeric and dimeric origamis. Meanwhile, the yield percentages of different multimeric origamis are controlled by the QD:DNA-origami stoichiometric mixing ratio. DNA origamis with 2 binding sites at the alpha gamma positions (of the pentagon) make larger nanostructures than those with binding sites at the alpha beta positions. In general, increasing the number of binding sites leads to increases in the nanostructure size. At high DNA origami concentration, the QD number in each cluster becomes the limiting factor for the growth of nanostructures. We find that reducing the QD size can also affect the self-assembly because of the reduced access to the binding sites from more densely packed origamis.

Multivalent antigen display is a well-established principle to enhance humoral immunity. Protein-based virus-like particles (VLPs) are commonly used to spatially organize antigens. However, protein-based VLPs are limited in their ability to control valency on fixed scaffold geometries and are thymus-dependent antigens that elicit neutralizing B cell memory themselves, which can distract immune responses. Here, we investigated DNA origami as an alternative material for multivalent antigen display in vivo, applied to the receptor binding domain (RBD) of SARS-CoV2 that is the primary antigenic target of neutralizing antibody responses. Icosahedral DNA-VLPs elicited neutralizing antibodies to SARS-CoV-2 in a valency-dependent manner following sequential immunization in mice, quantified by pseudo- and live-virus neutralization assays. Further, induction of B cell memory against the RBD required T cell help, but the immune sera did not contain boosted, class-switched antibodies against the DNA scaffold. This contrasted with protein-based VLP display of the RBD that elicited B cell memory against both the target antigen and the scaffold. Thus, DNA-based VLPs enhance target antigen immunogenicity without generating off-target, scaffold-directed immune memory, thereby offering a potentially important alternative material for particulate vaccine design.

The Notch signaling pathway is a highly conserved, fundamental process to embryogenesis and neurogenesis. While force-induced conformational change is known to activate Notch receptors, Smyrlaki et al. recently used DNA origami to reveal an additional, force-independent mode of Notch activation via soluble presentation of spatially controlled ligand nanopatterns.

Nucleic acid nanoparticles (NANPs) are increasingly used in preclinical investigations as delivery vectors. Tools that can characterize assembly and assess quality will accelerate their development and clinical translation. Standard techniques used to characterize NANPs, like gel electrophoresis, lack the resolution for precise characterization. Here, we introduce the use of charge detection mass spectrometry (CD-MS) to characterize these materials. Using this technique, we determined the mass of NANPs varying in size, shape, and molecular mass, NANPs varying in production quality due to formulations lacking component oligonucleotides, and NANPs functionalized with protein and nucleic acid-based secondary molecules. Based on these demonstrations, CD-MS is a promising tool to precisely characterize NANPs, enabling more precise assessments of the manufacturing and processing of these materials.

Functionalization of quantum dots (QDs) and quantum rods (QRs) with ligands is essential for their further practical application across various domains. Dehydration-assisted functionalization (DAF) is a versatile method applicable to a wide range of hydrophilic ligands with an affinity to the surface of QDs and QRs. This approach facilitates rapid one-pot ligand exchange and dense modification by efficiently transferring these ligands onto the surface of QDs and QRs. This study demonstrates the efficacy of DAF in preparing chiral QRs, engineering the surface charge of QDs, utilizing QR aggregates, and conjugating dense DNA onto cadmium-free InP/ZnS QDs. DAF therefore offers a versatile solution for hydrophilic ligand functionalization of QDs and QRs applicable to diverse applications.