Droplet Sorting Modules with Automated Decision Logic

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Solution Overview

Problem

Current droplet sorting technologies in flow cytometry face challenges in accurately and efficiently sorting droplets based on the presence and position of target and non-target particles, often requiring manual adjustments and relying heavily on human input, which can lead to reduced accuracy and increased operational costs.

Innovation Solution

The implementation of droplet sorting modules with a processor and memory that utilize event match logic and sort decision logic to determine an optimal sort decision unit for each droplet, applying masks to identify target and non-target particles and ranking decision units based on match values and count thresholds to optimize sorting.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If manual adjustments and human input are used for droplet sorting decisions, then the system is easier to operate, but the accuracy and efficiency of sorting deteriorates

Engineering Contradiction:

Improveease of operationVSAvoidsorting accuracy

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The droplet sorting system automatically makes sorting decisions by processing particle detection data through multiple sort decision units with different masks, eliminating the need for manual adjustments and human input while improving sorting accuracy and efficiency

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual human decision-making with an automated computational system that uses event match logic, sort decision logic, and processor-based algorithms to determine optimal sorting decisions for each droplet

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If multiple sort decision units with different masks are implemented, then the sorting precision is improved, but the device complexity increases

Engineering Contradiction:

Improvesorting precisionVSAvoiddevice complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The sorting system is divided into multiple independent sort decision units, each equipped with specific masks for different particle types. This segmentation allows parallel processing of different particle categories, improving overall sorting precision while maintaining manageable complexity through modular design

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

Multiple sort decision units with different masks are implemented within a single droplet sorting system, allowing the device to handle various particle types (target and non-target particles) simultaneously through a unified multi-functional platform

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Productivity

If automated decision-making with multiple sort decision units is used, then the productivity is improved, but the device complexity increases

Engineering Contradiction:

ImprovethroughputVSAvoiddevice complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

Sort decision units are pre-configured with different masks and criteria before operation. This preliminary setup enables rapid automated decision-making during droplet sorting without requiring complex real-time calculations, thereby improving throughput while managing device complexity

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system uses event match logic to compare detected particles against predefined masks in sort decision units, creating a feedback mechanism that automatically determines optimal sorting decisions. This automated feedback loop improves productivity by eliminating manual intervention while the modular mask-based approach keeps complexity manageable

Inventive Principle:

Principle #23Feedback

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 enhances the accuracy and efficiency of droplet sorting by automating the decision-making process, reducing reliance on human input, and improving the precision of particle separation and collection, thereby facilitating high-throughput laboratory testing and research applications.

Implementation Method 1

Droplets are passed through an electrostatic field and are deflected based on polarity and magnitude of charge on the droplet into one or more collection containers

Methodology Applied

Scientific EffectElectrostatic field deflection: Electrostatics

Data Source

PatentUS20240402065A1Droplet Sorting Decision Modules, Systems and Methods of Use Thereof

Publication Date: 2024.12.05 BECTON DICKINSON & CO

AI summary

Droplet sorting modules for sorting droplets of a flow stream are described. Droplet sorting modules according to certain embodiments include a plurality of droplet sort decision units and a processor with memory operably coupled to the processor, where the memory includes instructions stored thereon, which when executed by the processor, cause the processor to determine an optimal droplet sort decision unit from the plurality of sort decision units for sorting each droplet of the flow stream. Particle (e.g., cells) sorting systems and methods for sorting droplets of a flow stream are also described. Kits having one or more of the subject droplet sorting modules are also provided.