Flow Cytometry Antimicrobial Susceptibility Prediction
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Solution Overview
Problem
Current methods for predicting antibiotic susceptibility in microorganisms using flow cytometry are not robust, as they rely on average fluorescence intensity values and breakpoint concentrations, which can underestimate heterogeneous population signals and fail to discriminate between susceptible, intermediate, and resistant phenotypes effectively.
Innovation Solution
A method involving the calculation of a ratio (Q) from flow cytometry data, which captures heterogeneous profiles by comparing distribution coordinates with and without antibiotic exposure, and a prediction model learning stage using feature vectors from diverse microorganism phenotypes to predict susceptibility, intermediate, or resistance phenotypes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If average fluorescence intensity values and breakpoint concentrations are used for prediction, then the method is simple and fast, but it underestimates heterogeneous population signals and fails to discriminate between phenotypes effectively
Solution Approach 1:
The patent segments the fluorescence intensity distribution into multiple quantiles (e.g., 4 quantiles dividing the distribution into 5 bins) rather than using a single average value. This segmentation allows capture of heterogeneous signals across different subpopulations, improving phenotype discrimination while maintaining computational efficiency. The quantile-based approach divides the population into distinct groups based on fluorescence intensity, enabling detection of subtle variations that would be masked by mean values.
Solution Approach 2:
The patent transitions from one-dimensional average fluorescence intensity measurement to multi-dimensional quantile analysis. By calculating multiple coordinate values at different quantiles (e.g., Q1, Q2, Q3, Q4) and forming ratios between them, the method adds dimensional complexity to the data representation. This dimensional expansion enables better discrimination between susceptible, intermediate, and resistant phenotypes while keeping the overall method computationally tractable.
2Reliability
If breakpoint concentrations are used for antibiotic susceptibility testing, then the method is based on established reference methods, but it does not always correlate with early changes detected by flow cytometry
Solution Approach 1:
The patent changes the concentration parameter from fixed breakpoint values to a range of subinhibitory concentrations. By testing at multiple concentrations below the breakpoint (e.g., 0.5x, 1x, 2x MIC) and analyzing the dose-response relationship through quantile ratios, the method captures early physiological changes that occur before reaching inhibitory concentrations. This parameter variation allows correlation of flow cytometry signals with progressive bacterial response to antibiotics.
Solution Approach 2:
The patent performs preliminary analysis at subinhibitory concentrations before reaching the breakpoint concentration. By measuring fluorescence intensity changes at lower antibiotic concentrations where early physiological effects occur, the method detects phenotypic changes that precede complete growth inhibition. This preliminary measurement allows prediction of susceptibility phenotype based on early response rather than waiting for final inhibitory effect.
3Device complexity
If only mean fluorescence intensity is analyzed, then the data processing is simple, but it masks signals from small portions of heterogeneous populations
Solution Approach 1:
The patent segments the fluorescence distribution into multiple quantile bins, with each bin representing a specific portion of the population ranked by fluorescence intensity. By calculating coordinate values and ratios for each quantile separately, the method preserves information from all subpopulations including small minority populations that would be masked in the mean. This segmentation maintains computational feasibility while recovering lost heterogeneous information.
Solution Approach 2:
The patent replaces the mechanical averaging operation with a quantile-based ranking system. Instead of computing a single mean value that inherently equalizes all observations, the method uses order statistics to identify specific percentile positions in the distribution. This substitution allows differential weighting of different population portions based on their fluorescence intensity rank, preserving heterogeneity information while maintaining algorithmic simplicity.
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
This approach provides a robust and accurate prediction of antibiotic susceptibility phenotypes by capturing subtle variations and heterogeneous responses, improving discrimination between susceptible, intermediate, and resistant strains.
Implementation Method 1
measuring an optical response to said beam of each of the particles, that is to say its fluorescence, its forward-scattered light and its side-scattered light
Implementation Method 2
measuring an optical response to said beam of each of the particles, that is to say its fluorescence, its forward-scattered light and its side-scattered light
Implementation Method 3
bacteria are labeled with a fluorescent marker and analyzed by FCM
Data Source
AI summary
A method for quantifying the sensibility of a test microorganism to a concentration of an antimicrobial agent includes: preparing two liquid samples including the microorganism, one having the antimicrobial agent and one without; for each sample acquiring, by a flow cytometer, a digital set values including a fluorescence, forward, or side scatter distribution, and computing: a first coordinate value corresponding to the acquired distribution main mode and an acquired distribution first area for values greater than the first coordinate value, and a second coordinate value, greater than the first, for which an acquired distribution second area between the values equals a first area predefined percentage over 50%; computing a ratio according to:Q=QT(ATB)-Mode(ATB)QT(noATB)-Mode(noATB)where Mode(ATB) and QT(ATB) are the first and second coordinate values with the antimicrobial agent concentration, and Mode(no ATB) and QT(no ATB) are respectively the first and second coordinate values without the antimicrobial agent concentration.