Discriminating single-bacterial shape using low-aspect-ratio pores

Abstract

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.

Analysis Method of the Ion Current-Time Waveform Obtained from Low Aspect Ratio Solid-state Nanopores

“…Analysis using machine learning indicates that viruses and bacteria, unidentified by conventional analysis using a histogram, can be identified with high accuracy via machine learning. [42][43][44][45] It also shows that the physical interpretation of features used in machine learning is possible by combining virus and bacteria multiphysics simulation and machine learning. There are many excellent reviews of solid-state nanopores.…”

“…[38][39][40] The ion current-time waveform obtained with a low aspect ratio nanopore provides more information than that of a high aspect ratio pore, such as the volume, surface structure, surface charge, and flow dynamics of the analyte. [41][42][43][44][45] The analysis of these can complementary be made by the development of multiphysics simulation techniques with finite element methods. 46,47 In order to extract these four types of information from the waveform, it is necessary to analyze the vector quantities representing the waveform, not the scalar quantities of Ip or td.…”

“…42,45 There are not many examples of analysis using machine learning, but those that do exist have demonstrated capability for high-precision discrimination between two types of viruses or two types of bacteria having almost the same volume. [42][43][44][45] In this review, in order to compare high aspect ratio nanopores and low aspect ratio nanopores, we focus on viruses and bacteria that have measurement examples in both classes of solid-state nanopores. First, we outline the flow dynamics that forms the basis for analyzing the ion current-time waveform obtained from both types of solid-state nanopore.…”

“…This is because the purpose herein is to consider the reasons why high accuracy can be obtained through analysis by machine learning based on multiphysics simulations. [42][43][44][45] In order to use solid-state nanopores practically, a large number of waveforms can be obtained in a short time, i.e., a high signal frequency is required. Knowing what determines the signal frequency is essential to controlling the signal frequency.…”

Polarization Induced Electro-Functionalization of Pore Walls: A Contactless Technology

“…Over the last twenty years, functionalized tridimensional pores have emerged as a specific range of biosensors offering sensitivities higher than those of conventional methods [1][2][3][4]. Pore sensing was successfully employed to detect and analyze different types of biomolecules and cells: single-and double-stranded nucleic acids [5], peptides [6], proteins [7], bacteria [8,9], viruses [10,11], and cancer cells [12,13]. Scheme 1 describes the principle of detection of a single biomolecule passing through a 2 of 19 pore: When crossing the pore, the target partially blocks the aperture (Scheme 1A), which is detected by a variation in the ionic current (Scheme 1B).…”