Selective detections of single-viruses using solid-state nanopores

Abstract

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.

Figure 1

Single-influenza-virion detections using a solid-state nanopore. (a) Schematic illustration depicting nanopore measurements. Individual influenza virions in chorioallantoic fluid were passed through a Si₃N₄ nanopore via electrophoresis under the applied voltage V_b and associated resistive pulses were recorded by tracing a temporal change in the cross-membrane ionic current I_ion. (b) Influenza virion consisting of a spherical capsid covered with envelope and protein spikes such as haemagglutinin and neuraminidase protruding from the surface. This figure was created using the protein structure of the haemagglutinin (https://www.rcsb.org/structure/1ru7) and neuraminidase (https://www.rcsb.org/structure/5hun) from the Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank (PDB) website. (c) Three types of influenza viruses employed for nanopore sensing (Top: A(H1N1); middle: B; bottom: A(H3N2)).

Figure 2

Virion-derived resistive pulses. (a) Ionic current (I_ion) traces recorded in chorioallantoic fluid containing A(H1N1) (red), A(H3N2) (green), or B (blue) using 300 nm-sized Si₃N₄ nanopore under the bias dc voltage V_b = +0.1 V. Each spike-like I_ion change signifies electrophoretic translocation of single-virion through the pore channel. (b) A magnified view of a resistive pulse. The open pore current is offset to zero. Insets describe the viral motion upon transit through the conduit. Contaminants in the chorioallantoic solution depicted as transparent particles are repelled from the nanopore via electroosmotic flow during the virus sensing under the applied positive voltage.

Figure 3

Machine learning based single-pulse identifications. (a) Resistive pulse features used for single-virus discriminations. (b) F-measure score, F_meas, deduced for cases of distinguishing A(H1N1) and B (top); A(H1N1) and A(H3N2) (middle); and A(H3N2) and B (bottom). The distributions are constructed with 4260 F_meas data output by 71 classifiers and 60 feature vectors utilized in the single-pulse analysis. (c) The highest F_meas, F_max. (d) Dependence of the influenza type discriminability P_s on the number of detected virions n. Color coding is the same as that in (c).

Figure 4

Dissecting resistive-pulses to elucidate physical features of single-viruses. Recall P_rec to discriminate A(H1N1) and A(H3N2) (red) or A(H1N1) and B (blue) plotted as a function of the feature parameters. Each feature reflects distinct physical characteristics of single-virus translocated through a nanopore, i.e. the translocation dynamics (yellow), the size (orange), and the shape (pink). The higher P_rec with the dynamics-reflecting features suggests that the machine learning algorithm discriminated influenza types according to a difference in their surface charge densities rather than the morphologies.