Flow Cytometer Entrainment Factor for Clumping Assessment
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Solution Overview
Problem
Flow cytometry faces challenges in efficiently sorting cells due to clumping or aggregation, leading to reduced sorting yield and poor recovery, especially in samples with adherent cells or increased cell-to-cell interaction, where current methods struggle to assess sample behavior in real-time effectively.
Innovation Solution
The method calculates an 'entrainment factor' by comparing observed signal frequencies to expected frequencies based on a Poisson distribution, allowing for corrective actions such as halting or adjusting the flow to address clumping, and using a flow system to compare experimental signal frequencies to predetermined frequencies for purging or resuming signal collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If flow cytometry is used to sort cells, then cell characterization and sorting capability are improved, but clumping or aggregation reduces sorting yield and recovery
Solution Approach 1:
The system performs preliminary assessment of sample behavior by calculating the entrainment factor before sorting begins. This allows identification of clumping issues in advance, enabling corrective actions such as adjusting sample preparation or instrument parameters to prevent yield loss during sorting
Solution Approach 2:
The system continuously monitors sample behavior during sorting by comparing observed signal frequencies to expected Poisson distribution frequencies. This real-time feedback enables dynamic adjustment of sorting parameters to maintain optimal yield despite variations in sample behavior
2Measurement precision
If current methods are used to assess sample behavior, then basic monitoring is possible, but real-time effective assessment of clumping is insufficient
Solution Approach 1:
The system replaces manual or post-sorting assessment methods with automated real-time monitoring using computational analysis. By comparing observed signal frequencies to Poisson distribution expectations, the system provides precise, objective measurement of clumping behavior instantaneously during sorting
Solution Approach 2:
The system autonomously monitors and assesses sample behavior without requiring external intervention. The automated calculation of entrainment factors and real-time comparison to theoretical distributions enables the system to self-diagnose clumping issues and trigger corrective actions independently
Data Source
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AI summary
Methods and systems are disclosed for generating an entrainment factor in a flow cytometry sample. The methods comprise flowing a sample with a series of particles through the flow cytometer, detecting events and calculating an expected frequency of those events based on a distribution, such as a Poisson distribution, and measuring an observed frequency of particle events. An entrainment factor may be generated from a ratio of observed event frequency to expected event frequency. Further adjustment to the flow cytometer maybe performed based on the indicated entrainment factor such as adjusted sorting bias.