Flow Cytometry Histogram Validation Using Distance Functions
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Solution Overview
Problem
Current flow cytometry calibration methods are manual and lack standards for validating the accuracy and reproducibility of measurements, especially for low fluorescence intensities, leading to unreliable test results due to increased uncertainty and variability.
Innovation Solution
A system and method using a distance function with a bin-to-bin dissimilarity matrix to quantify differences in histograms, employing MESF-calibrated beads to construct a dissimilarity matrix that accounts for measurement precision and resolution across the entire fluorescence range, enabling automated validation of cytometer performance and statistical significance analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calibration methods are used, then simplicity of operation is maintained, but measurement precision and reliability deteriorate due to lack of validation standards
Solution Approach 1:
The patent introduces an automated calibration system as an intermediary between the operator and the flow cytometer. This system uses validation standards and statistical algorithms to objectively assess measurement precision, eliminating the need for manual judgment while providing quantifiable metrics for calibration quality.
Solution Approach 2:
The patent replaces manual calibration procedures with an automated computational system that uses statistical validation and distance functions. This substitution transforms the calibration process from a mechanical/manual operation to an automated analytical process, improving precision through systematic validation.
2Reliability
If automated calibration systems are implemented, then measurement reliability is improved, but device complexity increases
Solution Approach 1:
The automated calibration system performs self-validation by comparing measurements against built-in validation standards and statistical criteria. The system automatically detects calibration quality issues and triggers appropriate responses, enabling the instrument to self-monitor and self-validate without external intervention, thereby improving reliability.
Solution Approach 2:
The system implements continuous feedback loops where measurement results are automatically validated against statistical thresholds and calibration standards. When deviations are detected, the system provides feedback to operators or automatically adjusts parameters, ensuring maintained reliability through real-time monitoring and correction.
3Reliability
If validation standards are implemented, then test result reliability is improved, but ease of operation deteriorates due to additional procedural steps
Solution Approach 1:
The validation standards are integrated into the automated calibration system, which performs all validation checks and generates reports automatically. The system serves itself by autonomously assessing calibration quality against predetermined criteria, eliminating the need for operators to manually perform complex validation procedures while maintaining high reliability.
Solution Approach 2:
The patent replaces manual validation procedures with automated computational validation. Statistical algorithms and distance functions automatically assess measurement reliability, substituting complex manual validation steps with streamlined automated processes that maintain reliability while improving ease of operation.
4Measurement precision
If statistical validation is performed, then measurement accuracy is improved, but loss of time increases due to additional analysis steps
Solution Approach 1:
The system performs validation preparations in advance by establishing statistical thresholds, calibration standards, and acceptance criteria before measurements are taken. This preliminary configuration enables rapid real-time validation during operation, improving measurement accuracy without adding significant time delays during the actual measurement process.
Solution Approach 2:
The statistical validation process operates continuously and concurrently with measurement acquisition rather than as a separate sequential step. The system continuously monitors measurement quality and performs validation in real-time, maintaining measurement accuracy while minimizing time loss by eliminating idle validation periods.
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 automated, statistically valid calibration and validation of flow cytometry measurements, ensuring reliable results by accounting for measurement precision and resolution, and detecting potential instrument failures, thus improving the reliability and efficiency of flow cytometry operations.
Implementation Method 1
The laser 14 emits excitation light
Implementation Method 2
fluorescent chemicals found in the particle or attached to the particle may be excited into emitting luminescence (fluorescence, phosphorescence)
Implementation Method 3
light scattering, fluorescence, and absorbance measurements
Implementation Method 4
an optical system including a collection lens 20
Implementation Method 5
The detectors listed above are aimed at the point where the fluid stream passes through the light beam... the emitted light is detected by the fluorescence detectors 44
Data Source
AI summary
A system and method of validating differences between measured values of fluorescence intensities obtained from a fluorescence-based instrument, including: generating calibration histograms for calibration beads run through the instrument; calibrating a distance function configured to measure distances between the histograms by: constructing a metric using the distance function, which includes a bin-to-bin dissimilarity matrix; and populating the dissimilarity matrix to maximize the following conditions: (A) the distance function of two histograms representing two sets of different beads equals the difference of their MESF values; and (B) the distance function of two histograms representing fluorescence of two sets of identical beads is zero; applying the metric to flow cytometry histograms generated for biological samples, to determine distances between the histograms; constructing a statistical test using the metric to determine a statistical significance of the distances; and determining whether the histogram results from the biological samples are reliable based on the statistical significance.