Flow Cytometry Quality Scoring for Event-Level Measurement Uncertainty
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Solution Overview
Problem
Flow cytometry data lacks information about measurement uncertainty at event-specific or parameter-specific levels, making it difficult to differentiate true biological variability from measurement error, which affects the confidence of classification decisions.
Innovation Solution
A method for estimating event-specific measurement uncertainty is introduced, including identifying measurement uncertainty associated with detected light from a sample, generating quality scores for each particle, and using integrated circuit devices and non-transitory computer readable storage mediums to implement this method.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If flow cytometry measures optical signals from particles to characterize biological material, then particle classification and sorting capability is improved, but measurement uncertainty cannot be determined and confidence in classification decisions deteriorates
Solution Approach 1:
The system introduces feedback by measuring a population of reference particles with known characteristics and using this information to calculate measurement uncertainty. The uncertainty calculation feeds back into the classification process, allowing the system to account for measurement error when making classification decisions about test particles.
Solution Approach 2:
The patent introduces an intermediary computational process that bridges the gap between raw optical signal measurements and reliable classification. This intermediary step calculates measurement uncertainty by comparing reference particle measurements against their known characteristics, then applies this uncertainty information to improve classification confidence.
2Productivity
If flow cytometry data is collected without measurement uncertainty information, then data collection speed is improved, but ability to differentiate true biological variability from measurement error deteriorates
Solution Approach 1:
The system performs preliminary measurements on a population of reference particles with known characteristics before analyzing test particles. This preliminary action establishes the measurement uncertainty baseline, which is then used to interpret test particle measurements without slowing down the actual data collection process.
Solution Approach 2:
The patent uses reference particles that copy or represent the characteristics of test particles but with known ground truth values. By measuring these reference copies and comparing against their known characteristics, the system determines measurement uncertainty that applies to the actual test particles.
3Measurement precision
If manual measurement and calculation of measurement uncertainty is required, then measurement uncertainty can be estimated, but ease of operation deteriorates and time consumption increases
Solution Approach 1:
The system performs self-service by automatically calculating and reporting measurement uncertainty without requiring manual intervention. The flow cytometer itself collects reference particle data, computes uncertainty metrics, and integrates this information into the classification process, eliminating the need for practitioners to manually measure or calculate uncertainty.
Solution Approach 2:
The patent replaces manual mechanical calculation methods with automated computational processing. Instead of practitioners manually measuring and calculating uncertainty, the system uses computer-based algorithms to automatically process reference particle data and generate uncertainty metrics that are directly integrated into the analysis.
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
Improves the accuracy of flow cytometry by differentiating true biological variability from measurement error, enhancing sort purity and yield, and providing confidence in classification decisions.
Implementation Method 1
the flow stream is irradiated with light. Variations in the materials in the flow stream, such as morphologies or the presence of fluorescent labels, may cause variations in the observed light
Implementation Method 2
the presence of fluorescent labels, may cause variations in the observed light
Data Source
Figure 1A
Figure 1B
Figure 1C
AI summary
Aspects of the present disclosure include methods for identifying measurement uncertainty associated with light detected from a sample. Methods according to the present disclosure include introducing a sample into a flow cytometer, flowing the introduced sample in a flow stream, irradiating the sample in the flow stream with a light source, detecting light from particles in the sample flowing in the flow stream and, identifying measurement uncertainty associated with the detected light. the present disclosure the present disclosure, measurement uncertainty is identified corresponding to individual particles in the sample. the present disclosure, measurement uncertainty is identified for individual parameters of detected light for particles in the sample. Methods according to the present disclosure further comprise generating a quality score for each particle based on the measurement uncertainty for each particle. Systems, integrated circuit devices (e.g., a field programmable gate array) and non-transitory computer readable storage mediums for practicing the subject methods are also provided.