Label-Free Cell Sorting Using ML Image Classification
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Solution Overview
Problem
Current cell sorting methods rely on expensive and time-consuming label-based techniques that can stress and damage cells, and there is a need for more efficient, label-free methods.
Innovation Solution
A method and system using machine learning to identify cells of interest through image classification, combined with mechanical devices to separate them based on timing and flow rate data, without the need for labels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If label-based sorting techniques are used, then cell identification accuracy is improved, but cost and time consumption increase
Solution Approach 1:
The patent extracts and removes the fluorescent labels from the cell sorting process, relying instead on intrinsic cell properties (morphology, size, refractive index) that are naturally visible to the imaging system. This eliminates the need for label synthesis, conjugation, and washing steps while maintaining identification accuracy through machine learning classification of intrinsic cellular features.
Solution Approach 2:
The method enables cells to be identified and sorted based on their own intrinsic characteristics without external labeling. The machine learning model analyzes natural cellular features such as shape, size, and optical properties, allowing the cells themselves to provide the identification information needed for sorting without requiring additional reagents or tags.
2Measurement precision
If fluorescent labels are applied to cells, then cell identification capability is improved, but cell stress and damage increase
Solution Approach 1:
The patent removes the harmful fluorescent labels from the system, eliminating the physical and chemical stress they impose on cells during the sorting process. Cell identification is achieved through analysis of intrinsic cellular properties using machine learning, avoiding the need for external labeling that causes cellular stress and potential damage.
Solution Approach 2:
The method allows cells to be identified through their own intrinsic features without the imposition of foreign labeling materials. The machine learning algorithm analyzes natural cellular morphology and optical characteristics, enabling identification without the harmful effects of fluorescent tags on cell viability and function.
3Measurement precision
If multiple fluorescent labels are synthesized for different cell types, then sorting specificity is improved, but manufacturing complexity and cost increase
Solution Approach 1:
The patent employs a single machine learning-based imaging system that can identify and sort multiple cell types simultaneously based on their intrinsic characteristics. This universal approach replaces the need for multiple specialized fluorescent labels, allowing one system to handle diverse cell types through software classification rather than requiring separate labeling protocols for each cell type.
Solution Approach 2:
The method eliminates the complex manufacturing process of synthesizing and conjugating different fluorescent labels for various cell types. Instead, the system uses a universal imaging and machine learning platform that automatically distinguishes between cell types based on their inherent properties, significantly simplifying the overall manufacturing and operational complexity.
4Measurement precision
If cells are given time to interact with labels and excess labels are removed, then labeling accuracy is improved, but process time increases
Solution Approach 1:
The patent extracts and eliminates the time-consuming labeling and washing steps from the cell sorting process. By relying on intrinsic cell properties that are immediately visible to the imaging system, the method achieves accurate identification without requiring cells to interact with labels or undergo subsequent washing to remove excess labels.
Solution Approach 2:
The method enables immediate identification of cells based on their intrinsic features without requiring time for label binding or washing. The machine learning model analyzes natural cellular characteristics in real-time, eliminating the need for preliminary labeling steps and subsequent washing processes that would extend the overall process time.
Data Source
AI summary
Provided herein are techniques for label free cell sorting. The systems and methods provided herein may use machine learning based image classification techniques to identify cells of interest within a sample of cells. The cells of interest may then be separated from the sample using mechanical, pneumatic, piezoelectric, and/or electronic devices.