Shotgun Nanopore Signal Analysis for Single-Molecule Phenotyping
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Solution Overview
Problem
Current proteomics research faces challenges with high-throughput and sensitivity in analyzing bulk proteomic extracts due to the inability of Mass Spectrometry to provide single molecule sensing and identify post-translational modifications, and antibody-based assays requiring specific antibodies for different proteins.
Innovation Solution
Nanopore-based shotgun proteomics techniques that utilize nanopore sensors to analyze complex, unlabeled proteomic samples, employing machine learning approaches like convolutional neural networks and clustering models to classify tissue types based on ionic current signatures, reducing the need for resource-intensive alignment and sample preparation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If Mass Spectrometry is used for large-scale proteomics, then throughput is improved, but sensitivity to low abundance proteins and single molecule sensing capability deteriorates
Solution Approach 1:
The patent replaces Mass Spectrometry (a complex mechanical/physical system requiring extensive sample preparation) with nanopore-based electrical sensing. The nanopore system uses ionic current measurements to detect proteins directly in native states, eliminating the need for digestion, labeling, and complex MS instrumentation while achieving both high throughput and single-molecule sensitivity.
Solution Approach 2:
The patent introduces nanopores as intermediary sensing elements that translate protein presence into measurable ionic current changes. These nanopore events serve as mediators between the protein sample and the detection system, enabling direct electrical detection of proteins including low abundance species without the limitations of MS-based approaches.
2Measurement precision
If antibody-based immunohistochemistry assays are used to measure protein abundance, then sensitivity is improved, but device complexity and resource requirements worsen due to needing different antibodies for different proteins
Solution Approach 1:
The patent implements a universal nanopore-based detection platform that can identify and quantify multiple different proteins using the same hardware and methodology. Unlike antibody-based assays requiring specific antibodies for each target, the nanopore system detects proteins through their intrinsic electrical properties and interactions with the nanopore, enabling multi-protein analysis with a single universal tool.
Solution Approach 2:
The patent enables proteins to serve themselves as detection targets without requiring external reagents like antibodies. The native protein molecules interact directly with the nanopore and produce characteristic ionic current signals, eliminating the need for antibody development, validation, and optimization for each protein target.
3Loss of information
If traditional proteomics methods are used, then protein identification is achieved, but loss of time and resources increases due to resource-intensive alignment and sample preparation
Solution Approach 1:
The patent performs preliminary classification of protein types based on nanopore event characteristics before detailed analysis. By using machine learning models to categorize proteins from raw nanopore signals, the system avoids time-consuming alignment and processing steps while maintaining accurate protein identification and classification.
Solution Approach 2:
The patent replaces time-intensive computational alignment processes with direct electrical detection and machine learning-based classification. The nanopore system captures protein information in native states through electrical signals, eliminating the need for protein digestion, separation, and sequence alignment while reducing processing time significantly.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables low-cost, high-throughput protein analysis with single molecule sensitivity, allowing real-time proteomic analysis for applications such as pathogen detection and biomarker discovery without the need for complex sample preparation or sequencing-related processing.
Implementation Method 1
each segmented event of the plurality of segmented events represents ionic current changes during a protein interaction with a nanopore of the plurality of nanopores
Data Source
AI summary
A computer-implemented method of phenotype classification is provided. A computing system receives a plurality of segmented events generated by a plurality of nanopores in response to a sample being applied to the plurality of nanopores, wherein each segmented event of the plurality of segmented events represents ionic current changes during a protein interaction with a nanopore of the plurality of nanopores. The computing system processes the plurality of segmented events to create at least one set of model input data. The computing system provides the at least one set of model input data as input to at least one classifier model to generate a classification of the sample. The computing system transmits the classification for presentation on a display device.