Flow Cytometer Event Validation via Time-of-Flight and Peak Height
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Solution Overview
Problem
Flow cytometry faces challenges in capturing noise-free particle events due to optical side-lobes, fluidics drift, baseline drift, and contamination, which result in undesirable signals that contaminate valid cell event data, affecting the quality of cellular analysis.
Innovation Solution
The method involves flowing particles through a flow cytometer, optically interrogating them, extracting putative event features, and using high-resolution time-stamping to distinguish between valid cell events and artifacts, with threshold comparisons to discard invalid events and determine event validity based on time differences and peak heights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional flow cytometry methods are used to detect particle events, then cell counting can be performed, but noise-like signals from optical side-lobes, baseline drift, and fluidics drift contaminate the data and reduce measurement precision
Solution Approach 1:
The patent segments the detection process into multiple independent analysis dimensions: time-of-flight calculation, time-difference analysis, and peak-height comparison. Each dimension independently evaluates putative events and contributes to the final validation decision, allowing noise to be filtered out through multi-criteria assessment rather than relying on a single detection threshold
Solution Approach 2:
The patent introduces time-of-flight calculation as an intermediary parameter that mediates between raw signal detection and final event validation. By calculating the expected time-of-flight based on particle position and velocity, the system creates an intermediate reference frame that helps distinguish true cell events from noise artifacts without directly modifying the detection sensitivity
2Measurement precision
If flow cytometry is performed without high-resolution time-stamping, then the system is simpler to operate, but it cannot effectively distinguish between valid cell events and artifacts from contamination or drift
Solution Approach 1:
The patent implements preliminary time-stamping of all putative events with high-resolution timestamps before validation. This preliminary action creates a time-reference framework that enables subsequent time-difference calculations to quickly identify and reject noise events without requiring complex real-time analysis during the validation phase
Solution Approach 2:
The patent employs dynamic thresholding where the time-difference threshold and peak-height threshold are adjusted based on the specific characteristics of each putative event and its temporal context. Rather than using fixed thresholds, the system dynamically adapts validation criteria to distinguish between genuine cell events and various types of noise artifacts
3Productivity
If multiple putative events are detected in close temporal proximity, then more potential cell events can be captured, but it becomes difficult to determine which are valid events and which are noise artifacts
Solution Approach 1:
The patent implements a feedback mechanism where the validation status of each putative event is determined by comparing its time-difference and peak-height metrics against dynamically adjusted thresholds. The feedback loop validates or rejects events based on multiple criteria, allowing the system to maintain high detection rates while ensuring discrimination accuracy through iterative validation
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 approach effectively isolates small amplitude events near larger cell events, improving the accuracy of cell counting and reducing contamination, thereby enhancing the quality of flow cytometry data by distinguishing between valid cell events and noise-like signals.
Implementation Method 1
optically interrogating the particles flowing through the flow cell
Implementation Method 2
optically interrogating the particles flowing through the flow cell
Data Source
Figure 1A~1B
Figure 2
Figure 3
AI summary
Aspects of the present disclosure include methods for detecting events in a flow cytometer. Also provided are methods of detecting cells in a flow cytometer. Other aspects of the present disclosure include methods for determining a level of contamination in a flow cell. Computer-readable media and systems, e.g., for practicing the methods summarized above, are also provided.