Immunosensing is a bioanalytical technique capable of selective detections of pathogens by utilizing highly specific and strong intermolecular interactions between recognition probes and antigens. Here, we exploited the molecular mechanism in artificial nanopores for selective single-virus identifications. We designed hemagglutinin antibody mimicking oligopeptides with a weak affinity to influenza A virus. By functionalizing the pore wall surface with the synthetic peptides, we rendered specificity to virion−nanopore interactions. The ligand binding thereof was found to perturb translocation dynamics of specific viruses in the nanochannel, which facilitated digital typing of influenza by the resistive pulse bluntness. As amino acid sequence degrees of freedom can potentially offer variety of recognition ability to the molecular probes, this peptide nanopore approach can be used as a versatile immunosensor with single-particle sensitivity that promises wide applications in bioanalysis including bacterial and viral screening to infectious disease diagnosis.
Rapid diagnosis of flu before symptom onsets can revolutionize our health through diminishing a risk for serious complication as well as preventing infectious disease outbreak. Sensor sensitivity and selectivity are key to accomplish this goal as the number of virus is quite small at the early stage of infection. Here we report on label-free electrical diagnostics of influenza based on nanopore analytics that distinguishes individual virions by their distinct physical features. We accomplish selective resistive-pulse sensing of single flu virus having negative surface charges in a physiological media by exploiting electroosmotic flow to filter contaminants at the Si3N4 pore orifice. We demonstrate identifications of allotypes with 68% accuracy at the single-virus level via pattern classifications of the ionic current signatures. We also show that this discriminability becomes >95% under a binomial distribution theorem by ensembling the pulse data of >20 virions. This simple mechanism is versatile for point-of-care tests of a wide range of flu types.
Conventional concepts of resistive pulse analysis is to discriminate particles in liquid by the difference in their size through comparing the amount of ionic current blockage. In sharp contrast, we herein report a proof-of-concept demonstration of the shape sensing capability of solid-state pore sensors by leveraging the synergy between nanopore technology and machine learning. We found ionic current spikes of similar patterns for two bacteria reflecting the closely resembled morphology and size in an ultra-low thickness-to-diameter aspect-ratio pore. We examined the feasibility of a machine learning strategy to pattern-analyse the sub-nanoampere corrugations in each ionic current waveform and identify characteristic electrical signatures signifying nanoscopic differences in the microbial shape, thereby demonstrating discrimination of single-bacterial cells with accuracy up to 90%. This data-analytics-driven microporescopy capability opens new applications of resistive pulse analyses for screening viruses and bacteria by their unique morphologies at a single-particle level.
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