Flow Cytometer Threshold Selector for Dynamic Noise Discrimination
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Solution Overview
Problem
Current flow cytometry systems face challenges in setting optimal thresholds for data logging, as exact values differ based on sample nature, fluorophores, and optical filters, leading to either excessive noise recording or omission of particles of interest, making data analysis inefficient and inaccurate.
Innovation Solution
A flow cytometry system with a real-time trigger module and threshold selector that generates periodic histogram data to automatically set or adjust trigger thresholds, using logarithmic conversion and pattern recognition techniques to distinguish between noise and signal, thereby optimizing data logging and reducing unnecessary data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a fixed trigger threshold value is used in flow cytometry, then the system operation is simple, but the measurement precision deteriorates because exact threshold values differ based on sample nature, fluorophores, and optical filters
Solution Approach 1:
The patent implements dynamic threshold adjustment by continuously monitoring pulse height distributions and automatically adapting the trigger threshold based on real-time sample characteristics. The system transitions from static fixed thresholds to dynamic adaptive thresholds that respond to changes in sample nature, fluorophore properties, and optical filter conditions, thereby maintaining measurement precision across varying experimental conditions while requiring minimal user intervention.
Solution Approach 2:
The system performs self-calibration by automatically analyzing the pulse height histogram and determining optimal trigger thresholds without user input. The flow cytometer autonomously adjusts its triggering parameters based on the statistical distribution of detected events, eliminating the need for manual threshold setting and ensuring accurate discrimination between noise and genuine particle signals across different sample types.
2Quantity of substance
If the trigger threshold is set too low, then more particles are detected, but noise recording increases
Solution Approach 1:
The patent implements feedback control by continuously monitoring the pulse height distribution and adjusting the trigger threshold based on the observed signal characteristics. The system analyzes the statistical properties of detected events in real-time and dynamically modifies the threshold to maintain optimal signal-to-noise ratio, ensuring that genuine particles are detected while minimizing noise incorporation into the data set.
Solution Approach 2:
The system dynamically changes the trigger threshold parameter based on the analyzed pulse height distribution characteristics. By adapting this critical parameter to the specific sample conditions and signal properties, the system optimizes the balance between particle detection sensitivity and noise rejection, allowing flexible adjustment without requiring manual intervention for each new sample type.
3Object-generated harmful factors
If the trigger threshold is set too high, then noise is reduced, but particles of interest are omitted
Solution Approach 1:
The system employs dynamic threshold adaptation that responds to the specific characteristics of each sample. Rather than using a fixed high threshold that might exclude weak signals, the trigger level is continuously adjusted based on the pulse height distribution analysis, ensuring optimal detection sensitivity for particles of interest while maintaining effective noise rejection tailored to each experimental condition.
4Device complexity
If manual threshold setting is used, then the device complexity is low, but the productivity deteriorates due to time-consuming adjustments
Solution Approach 1:
The flow cytometer incorporates automated threshold determination functionality that performs self-calibration by analyzing pulse height histograms and calculating optimal trigger levels without user intervention. This self-service capability eliminates the time-consuming manual adjustment process while adding only moderate computational complexity to the system, significantly improving productivity and enabling rapid adaptation to different sample types.
Solution Approach 2:
The system performs preliminary analysis of the pulse height distribution to pre-determine the optimal trigger threshold before actual particle detection begins. This preliminary action allows the system to be fully prepared and optimized for each sample type, eliminating delays during the measurement process and improving overall throughput without requiring complex manual setup procedures.
Data Source
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AI summary
Disclosed is a threshold selector that selects a threshold by generating a histogram from a detector signal and a flow cytometer. The detector signal includes both data and noise. A histogram is generated, which includes height data from the pulses of the detector signal as well as noise. The flow cytometer can be operated without samples to generate a histogram that includes only noise. The threshold signal can then be selected by selecting an intensity level on the histogram that is between the noise and data.