Flow Cytometry Spillover Spreading Characterization Without Population Gating
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Solution Overview
Problem
Conventional methods for quantifying spillover spreading in flow cytometer data require the identification of positive and negative populations, which is error-prone and time-consuming.
Innovation Solution
A method involving partitioning fluorescent flow cytometer data into quantiles based on the intensity of a first fluorochrome, estimating a zero-adjusted standard deviation, and calculating spillover spreading coefficients through linear regression to characterize spillover spreading without the need for population identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to quantify spillover spreading by identifying positive and negative populations, then spillover spreading can be characterized, but the process becomes error-prone and time-consuming
Solution Approach 1:
The patent extracts the spillover spreading quantification process from the conventional population identification approach. It separates the spillover spreading calculation into an independent computation that uses overall distribution statistics (mean and standard deviation) rather than requiring manual or automated population gating, thereby eliminating the time-consuming and error-prone population identification step while maintaining measurement precision
Solution Approach 2:
The method enables the flow cytometer system to automatically calculate spillover spreading coefficients through self-service computation using the overall distribution statistics of the data. The system performs self-characterization of spillover spreading effects without requiring external manual intervention for population identification, reducing both time loss and human error
2Reliability
If population identification is performed to calculate spillover spreading coefficients, then spillover spreading can be quantified, but errors increase and the process becomes more complex
Solution Approach 1:
The patent changes the parameters used for spillover spreading calculation from population-specific metrics to overall distribution statistics (mean and standard deviation of the entire dataset). This parameter change eliminates the need for population identification and gating operations, reducing analytical complexity while improving reliability by avoiding errors associated with population selection and definition
Solution Approach 2:
The method segments the spillover spreading calculation into distinct computational steps: calculating overall mean, calculating overall standard deviation, and deriving the spillover spreading coefficient. This segmentation into discrete mathematical operations simplifies the overall process compared to the integrated and subjective population identification approach, reducing both complexity and error potential
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
Accurately characterizes spillover spreading effects between fluorochromes, reducing errors and simplifying the data analysis process by eliminating the need for population identification, thus enhancing data accuracy and efficiency.
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
Particles, such as molecules, analyte-bound beads, or individual cells, in a fluid suspension are passed by a detection region in which the particles are exposed to an excitation light, typically from one or more lasers, and the light scattering and fluorescence properties of the particles are measured
Data Source
AI summary
Methods for characterizing spillover spreading originating from a first fluorochrome in fluorescent flow cytometer data collected for a second fluorochrome are provided. In some embodiments, methods include partitioning the fluorescent flow cytometer data according to the intensity of the data relative to the first fluorochrome. In embodiments, methods also include estimating with a first linear regression a zero-adjusted standard deviation for the intensity of light collected from the second fluorochrome for each of the partitioned quantiles based on the assumption that the intensity of light collected from the first fluorochrome is zero, and obtaining with a second linear regression a spillover spreading coefficient from the zero-adjusted standard deviations. Systems and computer-readable media for characterizing spillover spreading originating from a first fluorochrome in fluorescent flow cytometer data collected for a second fluorochrome are also provided.