Flow Cytometer Coincidence Acceptance Gate for Particle Sorting Yield
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Solution Overview
Problem
Current flow cytometer sorter systems face a reduction in yield of target particles due to their inability to effectively handle coincidences with non-target particles, especially when sorting rare populations, as they often deemphasize yield for purity or vice versa, leading to unnecessary exclusion of particles that could be safely included in the sorted sample.
Innovation Solution
The introduction of a user-definable 'coincidence acceptance gate' or 'null sort gate' that allows for the explicit acceptance or ignoring of certain particle populations detected by the flow cytometer, defined on data histograms using logical operators, enabling the inclusion of non-target particles like sub-cellular debris or marker beads without compromising the purity of the target particles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If thresholding limits are set to high yield mode, then the number of sorted target particles increases, but purity decreases
Solution Approach 1:
The patent segments the sorting decision process into multiple independent components: coincidence evaluation, purity assessment, and yield optimization. By dividing the traditional single thresholding decision into separate evaluative stages, the system can independently optimize for both yield and purity without forcing a trade-off between them.
Solution Approach 2:
The patent introduces new parameters for coincidence evaluation and purity weighting that allow dynamic adjustment of sorting criteria. By changing the parameters from simple thresholding to multi-factor evaluation including coincidence acceptance, the system achieves both high yield and high purity simultaneously.
2Manufacturing precision
If thresholding limits are set to high purity mode, then the purity of sorted particles increases, but yield decreases
Solution Approach 1:
The sorting decision process is segmented into distinct evaluative components, allowing purity requirements to be met through structured coincidence evaluation rather than conservative thresholding. This segmentation enables the system to maintain high purity while avoiding unnecessary rejection of valid target particles.
Solution Approach 2:
The patent changes the purity assessment parameters from simple threshold-based rejection to multi-parameter evaluation including coincidence acceptance criteria. This parameter transformation allows the system to achieve high purity through intelligent discrimination rather than conservative filtering.
3Manufacturing precision
If coincidence with non-target particles is strictly excluded, then purity is maintained, but yield of target particles decreases
Solution Approach 1:
The patent introduces an intermediary coincidence evaluation stage between particle detection and sorting decision. This intermediary layer assesses whether coincident non-target particles actually compromise purity, allowing the system to distinguish between harmful coincidences (which are rejected) and harmless coincidences (which are accepted), thereby maintaining purity while maximizing yield.
Solution Approach 2:
The patent transforms the coincidence handling parameter from binary exclusion to graded evaluation. By changing how coincidence is parameterized and weighted in the sorting decision, the system can accept coincident particles that do not compromise purity, thereby increasing yield without sacrificing purity standards.
Data Source
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AI summary
Provided herein are systems and methods for improving yield of sorted particles. In one embodiment, for example, there is provided a system including: (a) a flow cytometer to analyze a sample, wherein the flow cytometer provides a parameter plot based on the analysis of the sample; (b) a user-interface, wherein a user can define a coincidence acceptance gate in the parameter plot, and wherein the coincidence acceptance gate identifies a non-target particle population in the sample that may be accepted with a target particle in a subsequent sort analysis; and (c) a sort analysis system to sort particles within the sample, while accepting particles defined by coincidence acceptance gate.