Single-Cell Data Pipelines for Faster Flow Cytometry Analysis
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Solution Overview
Problem
Current methods for single-cell analysis and data management are inefficient, requiring manual data transfer and redundant experiment setup, leading to a manual analysis bottleneck and excessive processor usage, especially in life science experiments where experiments often share common characteristics but require repetitive tasks.
Innovation Solution
A networked link between an acquisition computer and a remote analysis computer for real-time data transfer, along with modular experiment templates and an automated pipeline that allows for efficient data management and analysis, reducing redundant tasks and automating common experimental elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data transfer using mobile disk drives is used, then data can be transferred between acquisition instrument and analysis computer, but data transfer time and manual effort increase significantly
Solution Approach 1:
The patent replaces the mechanical data transfer system (USB drives, manual copying) with an automated electronic data transfer system. The acquisition computer automatically transfers data files to the analysis computer over a network, eliminating the need for physical media and manual intervention. This substitution of mechanical processes with automated electronic processes resolves the contradiction by maintaining reliable data transfer while dramatically reducing transfer time and manual effort.
Solution Approach 2:
The system implements self-service automation where the acquisition computer autonomously performs data transfer operations without requiring scientist intervention. The computer automatically identifies acquired data files, initiates transfer to the analysis computer, and monitors transfer completion. This self-service capability eliminates manual data management tasks while ensuring reliable automated data transfer.
2Ease of manufacture
If conventional experiment templates are used, then experiment setup can be standardized, but redundant tasks must still be performed manually for each new experiment
Solution Approach 1:
The patent implements preliminary action by pre-configuring experiment templates with all necessary analysis parameters, gating strategies, and processing workflows before experiments are acquired. When new experimental data arrives, the pre-configured templates are automatically applied without requiring manual setup. This preliminary preparation resolves the contradiction by making experiment setup easy through standardization while increasing throughput through automated application of pre-configured templates.
Solution Approach 2:
The system creates universal experiment templates that can be applied across multiple different experiments and data types. A single template configuration can handle various experimental conditions by automatically adapting to different datasets. This universality allows one template to serve multiple functions across different experiments, eliminating redundant manual setup tasks while maintaining ease of use through standardized interfaces.
3Reliability
If conventional templates process all samples, then complete analysis is performed, but excessive processor bandwidth is consumed on unnecessary samples
Solution Approach 1:
The patent applies partial action by implementing intelligent filtering that processes only the necessary subset of samples through the complete analysis pipeline. The system identifies and flags samples that meet predefined quality criteria or show interesting features, then applies full template processing only to those selected samples. Other samples receive streamlined processing or are excluded from resource-intensive analysis steps. This partial processing approach maintains analysis reliability for relevant samples while dramatically reducing processor bandwidth consumption by avoiding unnecessary complete analysis of all samples.
4Measurement precision
If manual clustering is performed for each experiment, then accurate cell population identification is achieved, but significant manual analysis bottleneck occurs
Solution Approach 1:
The system implements feedback mechanisms where initial automated clustering results are evaluated against quality criteria and expert-defined parameters. The feedback loop allows automated algorithms to refine their clustering based on predefined performance metrics and biological knowledge embedded in the template configurations. This feedback-driven automation maintains measurement precision for cell population identification while eliminating the manual analysis bottleneck by replacing iterative manual clustering with automated feedback-based clustering pipelines.
Data Source
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AI summary
Disclosed herein are a number of example embodiments for data management and analysis in connection with life science operations such as flow cytometry. For example, disclosed herein are (1) a networked link between an acquisition computer and a computer performing analysis on the acquired data, (2) modular experiment templates that can be divided into individual components for future use in multiple experiments, and (3) an automated pipeline of experiment elements.