Biology subjects

Hoffecker, I. T.

Publications and source records attributed to Hoffecker, I. T..

8 recordsLinked to original sources

bioRxiv2026

Resolving multivalent antibody binding states with defined spatial antigen-patterning

Multivalent interactions are fundamental to many biological systems, including how antibodies bind to their respective antigens. Still, their accumulated binding strength, i.e. avidity, resulting from binding and rebinding events of multiple interacting points, remains difficult to measure. Classical assay platforms like ELISA and SPR lack control over antigen positioning, limiting the resolution of the binding characteristics that shape biological outcomes at nanoscale. Here, we present PANMAP, a planar and plate-based assay inspired by ELISA that uses DNA origami to present antigens at defined nanoscale patterns, enabling direct measurements of spatially resolved binding events, termed avidity profiles. Using IgG antibodies as a model system, PANMAP distinguishes between monovalent and bivalent binding states by combining equilibrium absorbance measurements with a simple biophysical model. The avidity profiles reveal how the balance shifts between monovalent and bivalent interactions as a function of antibody concentration and antigen spacing, with intermediate separation distances favoring bivalent binding events and excluded at both near and far distances. This spatial profiling allows decoupling of affinity dependency from avidity profiles to reveal how spatial constraints influence binding equilibria. Our approach fills a longstanding gap in multivalent interaction measurement and offers a new tool for antibody engineering, development of multivalent reagents, therapeutic screening, and mechanistic immunology.

immunology↗

bioRxiv2024

Hidden network preserved in Slide-tags data allows reference-free spatial reconstruction

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. We reanalyzed this data and discovered 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 new network-based imaging-by-sequencing approach. By optimizing spatial constraints encoded in the cell-bead network structure, we could achieve unassisted tissue reconstruction, a fundamental shift from classical spatial technologies based on pre-indexed arrays.

bioinformatics↗

bioRxiv2024

Spatial Coherence of DNA Barcode Networks

Sequencing-based microscopy is a novel, optics-free method for imaging molecules in biological samples using molecular DNA barcodes, spatial networks, and sequencing technologies. Despite its promise, the principles determining how these networks preserve spatial information are not fully understood. Current validation methods, which rely on comparing reconstructed positions to expected results, would benefit from a deeper understanding of these principles. Here, we introduce the concept of spatial coherence-- a set of fundamental properties of spatial networks that quantifies the alignment between topological relationships and Euclidean geometry. Our findings show that spatial coherence is an effective method for evaluating a networks capacity to maintain spatial fidelity and identify distortions, independent of prior information. This framework provides a cost-effective validation tool for sequencing-based microscopy by taking advantage of the fundamental properties of spatial networks in nanoscale systems.

bioinformatics↗

bioRxiv2022

Image recovery from unknown network mechanisms forDNA sequencing-based microscopy

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 unbiased 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 this structural discovery step followed by the 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 strategies could be unified with a common reconstruction framework.

bioinformatics↗

bioRxiv2020

The geometric determinants of programmed antibody migration and binding on multi-antigen substrates

Viruses and bacteria commonly exhibit spatial repetition of surface molecules that directly interface with the host immune system. However the complex interaction of patterned surfaces with multivalent immune molecules such as immunoglobulins and B-cell receptors is poorly understood, and standard characterization typically emphasizes the monovalent affinity. We developed a mechanistic model of multivalent antibody-antigen interactions as well as a pipeline for constructing such models from a minimal dataset of patterned surface plasmon resonance experiments in which antigen pattern geometries are precisely defined using DNA origami nanostructures. We modeled the change in binding enhancement due to multivalence and spatial tolerance,i.e. the strain-dependent interconversion of bound antibodies from monovalently bound to bivalently bound states at varying antigen separation distances. The parameterized model enables mechanistic post hoc characterization of binding behavior in patterned surface plasmon resonance experiments as well as de novo simulation of transient dynamics and equilibrium properties of arbitrary pattern geometries. Simulation on lattices shows that antigen spacing is a spatial control parameter that can be tuned to determine antibody residence time and migration speed. We found that gradients in antigen spacing are predicted to drive persistent, directed antibody migration toward favorable spacing. These results indicate that antigen pattern geometry can influence antibody interactions, a phenomenon that could be significant during the coevolution of pathogens and immunity in processes like pathogen neutralization or affinity maturation.

biophysics↗

bioRxiv2019

A DNA nanoassembly-based approach to map membrane protein nanoenvironments

Super-resolution imaging has revealed that most proteins at the plasma membrane are not uniformly distributed but localize to dynamic domains of nanoscale dimensions. To investigate their functional relevance, there is a need for methods that enable comprehensive mapping of the compositions and spatial organizations of membrane protein nanodomains in cell populations. However, current superresolution methods are limited to analysing small, preselected subsets of proteins, at very low sampling fractions. Here we describe the development of a non-microscopy based super-resolution method for unbiased ensemble analysis of membrane protein nanodomains. The method, termed NANOscale DEciphEring of membrane Protein nanodomains (NanoDeep), is based on the use of DNA nanoassemblies to translate membrane protein organization information into a DNA sequencing readout. Using NanoDeep, we characterized the nanoenvironments of Her2, a membrane receptor of critical relevance in cancer. We found that the occupancies of Her2, Her3 and EGFR in the nanoenvironments surrounding Her2 were similar in two cell lines with vastly different expression levels of Her2. Further, we found that adding Heregulin-{beta}1 to cancer cells led to increased occupancy of Her2 and Her3, and to a lesser extent EGFR, in Her2 nanoenvironments. NanoDeep has the potential to provide new insights into the roles of the composition and spatial organization of protein nanoenvironments in the regulation of membrane protein function.

bioengineering↗

bioRxiv2019

Tuning intercellular cohesion with membrane-anchored oligonucleotides

Cohesive interactions between cells play an integral role in development, differentiation, and regeneration. Existing methods for controlling cell-cell cohesion by manipulating protein expression are constrained by biological interdependencies, e.g. coupling of cadherins to actomyosin force-feedback mechanisms. We use oligonucleotides conjugated to PEGylated lipid anchors (ssDNAPEGDPPE) to introduce artificial cell-cell cohesion that is largely decoupled from the internal cytoskeleton. We describe cell-cell doublets with a mechanical model based on isotropic, elastic deformation of spheres to estimate the cohesion at the cell-cell interface. Physical manipulation of cohesion by modulating PEG-lipid to ssDNAPEGDPPE ratio, and conversely treatment with actin-depolymerizing cytochalsin-D, resulted respectively in decreases and increases in doublet contact area. Our data are relevant to the ongoing discussion over mechanisms of tissue surface tension and in agreement with models based on opposing cortical and cohesive forces. PEG-lipid modulation of doublet geometries resulted in a well-defined curve indicating continuity, enabling prescriptive calibration for controlling doublet geometry. Our study demonstrates tuning of basic doublet cohesion, laying the foundation for more complex multicellular cohesion control independent of protein expression.

biophysics↗

bioRxiv2018

A Computational Framework for DNA Sequencing-Based Microscopy

Barcoded DNA polony amplification techniques provide a means to impart a unique sequence identity onto specific locations of a surface wafer or chip. We describe a method whereby micro-scale spatial information such as the relative positions of biomolecules on a surface can be transferred to a sequence-based format and reconstructed into images without the use of conventional optics. The principle is based on the pair-wise association of uniquely tagged and spatially adjacenct polonies. The network of polonies connected by shared borders forms a graph whose topology can be reconstructed from a set of edges derived from pairs of barcodes associated with each other during a polony crosslinking phase, the sequences of which could be determined by isolation of the DNA and next-gen sequencing. We developed a mathematical and computational framework for this principle called Polony Adjacency Reconstruction for Spatial Inference and Topology and show that Euclidean spatial data may be partially stored and transmitted in the form of untethered graph topology. This effect may be exploited to form images by transferring molecular information from a surface of interest, which we demonstrated in silico by reconstructing images formed from stochastic transfer of hypothetical red, green, and blue molecular markers. The theory developed here could serve as a guide for a highly automated, multiplexable, and potentially super resolution imaging method based on molecular information encoding and transmission.

biophysics↗