Image Activated Cell Sorting Using Neural Network Feature Extraction
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Solution Overview
Problem
Traditional fluorescence activated cell sorting relies heavily on fluorescent markers, which limits morphological information and is time-consuming and biased due to manual gating, and lacks suitability for applications requiring morphological data or not suitable for all applications.
Innovation Solution
An Image Activated Cell Sorting (IACS) workflow using a neural network-based feature encoder to extract features from cell images, cluster cells based on target protein location, and fine-tune a classification network for real-time live sorting, allowing for the identification of dormant and activated cells without relying on fluorescent markers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional fluorescence activated cell sorting (FACS) with manual gating is used, then cells can be sorted based on fluorescent markers, but the process is time-consuming and may be biased
Solution Approach 1:
The system employs unsupervised clustering algorithms that automatically analyze cell images and identify clusters without requiring manual gating. The classification network self-adjusts and fine-tunes based on the clustered data, eliminating the need for time-consuming manual intervention while maintaining sorting accuracy
Solution Approach 2:
The patent replaces the manual mechanical gating process with an automated image-based classification system using neural networks. The system automatically extracts features from cell images, performs clustering, and generates sorting gates without human intervention, substituting the manual mechanical process with an automated computational system
2Measurement precision
If traditional FACS with fluorescent markers is used, then cells can be sorted, but morphological information is limited
Solution Approach 1:
The image-based classification system serves multiple functions: it extracts morphological features, identifies cell clusters, determines sorting gates, and provides comprehensive cell characterization. This multi-functional approach replaces the single-function fluorescent marker-based sorting, making the system adaptable to various applications requiring different types of information
Solution Approach 2:
The patent transitions from one-dimensional fluorescent marker detection to multi-dimensional image analysis, extracting features such as shape, texture, size, and spatial distribution. This dimensional expansion provides rich morphological information that enhances both measurement precision and application versatility
3Measurement precision
If supervised learning with ground truth is used for image-based cell sorting, then classification can be achieved, but ground truth may not be available in many applications
Solution Approach 1:
Instead of using supervised learning that requires ground truth labels, the patent inverts the approach by using unsupervised clustering that works directly with raw cell images. The system clusters cells based on inherent image features without needing pre-labeled training data, making it adaptable to applications where ground truth is unavailable
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
IACS achieves precise and efficient cell sorting with greater than 98.4% precision and 80% recall, enabling the separation of cells based on nuclear translocation of fluorescence signals, overcoming limitations of traditional methods by utilizing image-based sorting and machine learning.
Implementation Method 1
The one or more features comprise a target protein based on a fluorescent dye
Data Source
AI summary
An Image Activated Cell Sorting (IACS) classification workflow includes: employing a neural network-based feature encoder (or extractor) to extract features of cell images; automatically clustering cells based on extracted cell features; identifying a cluster to pick which cluster(s) to sort based on the cell images; fine-tuning a classification network based on the cluster(s) selected; and once refined, the classification network is used to sort cells for real-time live sorting.