DNA origami holds great potential for advancing therapeutics, but the lack of methods for the precise assessment of structural integrity in vivo prevents its translation. Here we introduce proximity ligation assay for structural tracking and integrity quantification (PLASTIQ) for resolving origami structural integrity with only 1 µl of blood sample and with a detection limit of 0.01 fM. Through PLASTIQ, we could observe and quantify the dynamics of DNA origami degradation during blood circulation and evaluate the effectiveness of PEGylation for slowing this process in a murine model. Additionally, by using a double-layered barrel-like origami structure, we found distinct degradation kinetics of DNA helices depending on their specific location, revealing the slower degradation of internal helices compared with the outer ones. Our results suggest that PLASTIQ offers a quantitative approach for assessing DNA origami integrity in vivo by longitudinal sampling, providing dynamic pharmaceutical-level insights for accelerating the development of DNA-nanostructure-based therapeutic molecules and drugs.

Spatial transcriptomics technologies aim to spatially map gene expression in tissues and typically use oligonucleotide array surfaces that have undergone spatial indexing. These arrays are used to capture nucleic acids diffusing from adjacently placed tissues, allowing subsequent sequencing to reveal both gene and position. Slide-tags is a recently developed method by Russell et al. that inverts this principle. Instead of capturing molecules released from the tissue, probes are detached from a pre-decoded bead array and diffused into tissues, tagging nuclei with spatial barcodes. In this work we reanalyze this data and discover a latent, spatially informative cell-bead network formed incidentally from barcode diffusion and the biophysical properties of the tissue. This allows us to treat Slide-tags as a network-based imaging-by-sequencing approach. By optimizing spatial constraints encoded in the cell-bead network structure, we can achieve unassisted tissue reconstruction, a fundamental shift from classical spatial technologies based on pre-indexed arrays.

Imaging-by-sequencing methods are an emerging alternative to conventional optical micro- or nanoscale imaging. In these methods, molecular networks form through proximity-dependent association between DNA molecules carrying random sequence identifiers. DNA strands record pairwise associations such that network structure may be recovered by sequencing which, in turn, reveals the underlying spatial relationships between molecules comprising the network. Determining the computational reconstruction strategy that makes the best use of the information (in terms of spatial localization accuracy, robustness to noise, and scalability) in these networks is an open problem. We present a graph-based technique for reconstructing a diversity of molecular network classes in 2 and 3 dimensions without prior knowledge of their fundamental generation mechanisms. The model achieves robustness by obtaining an unsupervised sampling of local and global network structure using random walks, making use of minimal prior assumptions. Images are recovered from networks in two stages of dimensionality reduction first with a structural discovery step followed by a manifold learning step. By breaking the process into stages, computational complexity could be reduced leading to fast and accurate performance. Our method represents a means by which diverse molecular network generation scenarios can be unified with a common reconstruction framework.

Stochastic modeling of antibody binding dynamics on patterned antigen substrates suggests the separation distance between adjacent antigens could be a control mechanism for the directed bipedal migration of bound antibodies.

Viruses and bacteria commonly exhibit spatial repetition of the surface molecules that directly interface with the host immune system. However, the complex interaction of patterned surfaces with immune molecules containing multiple binding domains is poorly understood. We developed a pipeline for constructing mechanistic models of antibody interactions with patterned antigen substrates. Our framework relies on immobilized DNA origami nanostructures decorated with precisely placed antigens. The results revealed that antigen spacing is a spatial control parameter that can be tuned to influence the antibody residence time and migration speed. The model predicts that gradients in antigen spacing can drive persistent, directed antibody migration in the direction of more stable spacing. These results depict antibody–antigen interactions as a computational system where antigen geometry constrains and potentially directs the antibody movement. We propose that this form of molecular programmability could be exploited during the co-evolution of pathogens and immune systems or in the design of molecular machines.