Naticchia, M. R. | Search bioRxiv

bioRxiv2026

A rapid, sensitive, and quantitative high plex biomarker digital detection platform enabled by Hypercoding

Low-cost, multiplexed, and automated assays are needed to make omic technologies more broadly accessible in clinical, research, and commercial settings. We present Hypercoding, a scalable technology for detection and quantitation of multi-omic targets. Drawing from data reliability methods in the telecommunications field, Hypercoding uses fluorescent signals from hybridization with an error-correcting code to enable detection of high-plexity targets from biological samples, such as human DNA. In the presence of the target, a linear DNA construct is circularized, immobilized, and amplified to enable single-molecule detection of a target via rapid readout cycles within a 96-well plate. We demonstrate capability for >10,000 code plexity and accurate (98.7%) genotyping of 209 pharmacogenomic variants. Furthermore, we show computation of copy number variation with whole chromosome and sub-gene resolution, as well as quantitation of target abundance down to 10 fM sensitivity with a dynamic range of up to 10 logs.

genomics↗

bioRxiv2021

Mucin-mimetic glycan arrays integrating machine learning for analyzing receptor pattern recognition by influenza A viruses

Influenza A viruses (IAVs) exploit host glycans in airway epithelial mucosa to gain entry and initiate infection. Rapid detection of changes in IAV specificity towards host glycan classes can provide early indication of virus transmissibility and infection potential. IAVs use hemagglutinins (HA) to bind sialic acids linked to larger glycan structures and a switch in HA specificity from 2,3-to 2,6-linked sialoglycans is considered a prerequisite for viral transmission from birds to humans. While the changes in HA structure associated with the evolution of binding phenotype have been mapped, the influence of glycan receptor presentation on IAV specificity remains obscured. Here, we describe a glycan array platform which uses synthetic mimetics of mucin glycoproteins to model how receptor presentation in the mucinous glycocalyx, including glycan type and valency of the glycoconjugates and their surface density, impact IAV binding. We found that H1N1 virus produced in embryonated chicken eggs, which recognizes both receptor types, exclusively engaged mucin-mimetics carrying 2,3-linked sialic acids in their soluble form. The virus was able utilize both receptor structures when the probes were immobilized on the array; however, increasing density in the mucin-mimetic brush diminished viral adhesion. Propagation in mammalian cells produced a change in receptor pattern recognition by the virus, without altering its HA affinity, toward improved binding of 2,6-sialylated mucin mimetics and reduced sensitivity to surface crowding of the probes. Application of a support vector machine (SVM) learning algorithm as part of the glycan array binding analysis efficiently characterized this shift in binding preference and may prove useful to study the evolution of viral responses to a new host.

microbiology↗