Flow Cytometry System for Rapid Infectious Agent Identification
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Solution Overview
Problem
Current methods for rapid detection of infectious agents in samples are inefficient and often require extensive empirical testing, lacking the ability to simultaneously identify multiple pathogens and determine antibiotic susceptibility.
Innovation Solution
The development of flow cytometry-based systems that utilize fluorophore-labeled antibodies and unmixing algorithms to identify and quantify infectious agents, integrated with antibiotic susceptibility testing, enabling rapid detection and resistance analysis in a single sample.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional detection methods are used for infectious agents, then detection can be performed, but the process requires extensive empirical testing and is inefficient
Solution Approach 1:
The patent segments the detection process into distinct functional modules: sample preparation unit, incubation unit with temperature control, flow cytometry analysis unit, and automated data processing. Each module performs a specific function, enabling parallel processing and eliminating sequential bottlenecks in traditional methods.
Solution Approach 2:
The system performs preliminary actions by pre-configuring multiple antibody-fluorophore conjugates specific to different pathogens before sample analysis. The flow cytometer is pre-calibrated with reference spectra, and the automated unmixing algorithms are pre-programmed, allowing immediate analysis without empirical testing setup.
2Adaptability or versatility
If multiple pathogens are to be identified simultaneously, then diagnostic coverage is improved, but the complexity of the system increases
Solution Approach 1:
The flow cytometer serves multiple functions: it detects multiple pathogen types simultaneously, performs spectral unmixing, quantifies antibiotic susceptibility, and generates comprehensive diagnostic reports. The single instrument replaces multiple specialized devices, reducing overall system complexity while increasing versatility.
Solution Approach 2:
The patent introduces spectral unmixing algorithms as an intermediary computational layer that processes raw fluorescence data from multiple fluorophores. This mathematical intermediary separates overlapping spectral signatures, enabling simultaneous detection of multiple pathogens without requiring physically separate detection channels for each pathogen.
3Reliability
If antibiotic susceptibility testing is integrated, then resistance determination is improved, but the measurement precision requirements increase
Solution Approach 1:
The system implements feedback through automated data processing where flow cytometry results are immediately fed into susceptibility testing algorithms. The unmixing algorithm continuously refines fluorescence quantification by comparing measured spectra against reference spectra, and susceptibility results feed back into diagnostic decision-making, improving both precision and reliability iteratively.
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 rapid identification and quantification of multiple infectious agents and determination of antibiotic susceptibility, reducing the need for empirical testing and improving diagnostic efficiency.
Implementation Method 1
The optical emission is derived from one or a combination of: (i) one or more fluorophores each conjugated to an antibody bound to the target particle
Data Source
AI summary
Flow cytometry-based methods and systems for rapidly identifying a target particle, such as a target cell, a target particle, or multiple distinct target cells or target particles. The methods and systems herein are capable of distinguishing between and identifying multiple distinct target cells or target particles from a single sample containing a plurality of targets. The methods and systems herein can be used for applications such as but not limited to identification and quantification of infectious agents, e.g., bacteria, viruses, fungi, parasites, etc., in a sample.