Flow Cytometer Air-Bubble Detection for Accurate Well Identification
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Solution Overview
Problem
High-throughput flow cytometry systems face challenges in accurately identifying individual sample wells due to insufficient temporal distributions in data streams, leading to identification errors.
Innovation Solution
The system utilizes the scatter waveform output of a flow cytometer to detect air bubbles in a continuous fluidic stream, generating a voltage output signal, sampling and recording timestamps and voltage values for air bubble gaps, and synchronizing this data with sample event data to enhance well identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If temporal distribution of particle detection is used to identify sample wells, then high-throughput data acquisition is achieved, but identification accuracy deteriorates due to insufficient temporal distributions
Solution Approach 1:
Air bubbles are introduced as intermediary elements between sample suspensions in the continuous flow stream. These air bubbles serve as physical markers that create distinct temporal gaps in the particle detection signal, enabling reliable segmentation and identification of individual sample wells while maintaining high-throughput continuous flow operation
Solution Approach 2:
The system detects changes in the optical properties of the flow stream by monitoring light scattering signals. Air bubbles produce characteristic scattering patterns that differ from particle suspensions, creating detectable signal transitions that mark well boundaries and enable accurate identification without disrupting the continuous flow
2Reliability
If air bubbles are used to separate samples in continuous flow, then sample separation is achieved, but detection difficulty increases due to signal interference
Solution Approach 1:
The system employs dynamic thresholding and adaptive signal processing that adjusts detection parameters based on the flowing signal characteristics. The detection algorithm dynamically distinguishes between air bubble signals and particle signals by analyzing temporal patterns, signal amplitudes, and transition characteristics, thereby simplifying detection despite the mixed signal environment
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 method improves the accuracy of identifying individual sample wells by delineating well boundaries using air bubble gap detection, reducing errors caused by temporal distribution insufficiencies.
Implementation Method 1
the scatter detector of the flow cytometer...detect scattered light by the scatter detector
Data Source
Figure 1A
Figure 1B
Figure 2
AI summary
A flow cytometer apparatus is provided herein including (a) a flow cell, (b) a fluidic pathway having a second end coupled to a first end of the flow cell, (c) a probe coupled to a first end of the fluidic pathway, (d) at least one sensor configured to detect one or more properties of a fluid in the fluidic pathway and positioned between the probe and the first end of the flow cell, (e) a processor in communication with the at least one sensor, and (f) a non-transitory computer readable medium having stored therein instructions that are executable to cause the processor to perform functions including: (i) receiving, via the processor, the one or more properties of the fluid in the fluidic pathway detected by the at least one sensor, and (ii) determining, based on the detected one or more properties of the fluid in the fluidic pathway, a presence of a separation gas in the fluid in the fluidic pathway.