Optical Recognition of Discrete Entities in 3D Cell Cultures
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Solution Overview
Problem
Current methods struggle to repeatedly identify and track individual discrete entities, such as rare cells, in large numbers within scaffold-based suspension 3D cell cultures, due to the inability to handle and recognize each entity effectively in a liquid environment.
Innovation Solution
A method and device for optically recognizing target discrete entities by acquiring optical read-outs, generating sets of representations, and comparing them for similarity and statistical confidence, allowing for the identification and isolation of rare cells in a liquid environment using markers like fluorescent microbeads or nanorulers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If optical read-out and representation comparison methods are implemented to identify individual discrete entities, then identification reliability and tracking capability are improved, but device complexity and measurement difficulty increase
Solution Approach 1:
The identification process is segmented into distinct stages: optical read-out acquisition, representation generation from read-out data, and comparison operations. This segmentation allows each stage to be optimized independently and facilitates implementation using standard computational components, thereby improving reliability without proportionally increasing overall system complexity
Solution Approach 2:
The method creates digital representations (copies) of the optical read-outs that can be stored and compared without requiring complex real-time processing of raw optical data. These representations serve as simplified models that retain the essential identifying features while reducing computational complexity
2Reliability
If optical read-out and representation comparison methods are implemented to identify individual discrete entities, then identification reliability is improved, but measurement precision requirements increase
Solution Approach 1:
The system performs preliminary actions by generating and storing representations of optical read-outs before the actual identification comparison is needed. These pre-processed representations capture the essential features of each discrete entity, allowing for more robust matching that is less sensitive to variations in optical read-out precision during actual identification operations
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
Enables fast and reliable identification of individual discrete entities within a large number of pooled entities, facilitating ultra-high throughput analysis and downstream processing, including bioprocessing and therapeutic applications.
Implementation Method 1
using markers like fluorescent microbeads or nanorulers
Data Source
AI summary
A method for optically recognizing at least one target discrete entity from a plurality of discrete entities includes acquiring a first optical read-out of a marker of a first discrete entity that defines the target discrete entity, generating a first set of representations of the marker of the first discrete entity based on the first optical read-out, associating the first set of representations with the target discrete entity, acquiring a second optical read-out of a marker of at least one discrete entity from the plurality of discrete entities, generating a second set of representations of the marker of the at least one discrete entity based on the second optical read-out, comparing the second set of representations to the first set of representations, and recognizing the at least one discrete entity as the target discrete entity upon determining that the second set of representations matches the first set of representations.