Unsupervised Cell Sorting via Neural Network Feature Extraction
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Solution Overview
Problem
Traditional fluorescence activated cell sorting relies heavily on fluorescent markers, which provide limited morphological information and require time-consuming manual gating, making it inefficient and biased, especially when ground truth data is unavailable or not suitable for certain applications.
Innovation Solution
An unsupervised learning framework that uses a neural network-based feature extractor and cluster component for offline and online image-based cell sorting, allowing for clustering and sorting without ground truth data, utilizing convolutional layers and hierarchical density-based spatial clustering to separate and group cells based on morphological features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional fluorescence activated cell sorting is used with manual gating, then cells can be sorted based on fluorescent markers, but the process is time-consuming and may be biased
Solution Approach 1:
The system performs unsupervised clustering automatically without requiring manual gating intervention. The neural network extracts features and the clustering algorithm automatically identifies cell populations, enabling the system to sort cells autonomously based on morphological features from images
Solution Approach 2:
The patent replaces the manual mechanical gating process with an automated computational system. Instead of researchers manually drawing gates on flow cytometry plots, the system uses neural networks for feature extraction and unsupervised clustering algorithms to automatically identify and sort cell populations
2Measurement precision
If fluorescent markers are used for cell sorting, then cells can be identified and sorted, but morphological information is limited
Solution Approach 1:
The patent transitions from one-dimensional fluorescent marker detection to two-dimensional image-based morphological analysis. By capturing and analyzing cell images, the system extracts multiple morphological features (shape, size, texture) that provide much richer information than single-parameter fluorescent markers
Solution Approach 2:
The system changes the measurement parameters from fluorescent intensity to multiple morphological parameters extracted from images. The neural network extracts features such as cell shape, size, and texture, transforming the sorting criteria from chemical markers to physical morphological characteristics
3Measurement precision
If supervised learning with ground truth is used for image based cell sorting, then accurate sorting can be achieved, but ground truth data is not always available
Solution Approach 1:
Instead of using supervised learning that requires ground truth labels, the patent inverts the approach by using unsupervised learning. The system does not require pre-labeled training data but instead automatically discovers cell population structures through clustering algorithms applied to extracted image features
Solution Approach 2:
The system performs self-training and self-organization without external supervision. The unsupervised clustering algorithm automatically identifies cell populations based on the intrinsic structure of the data, enabling the system to adapt to new cell types and conditions without requiring re-labeling or ground truth data
Data Source
AI summary
A framework that includes a feature extractor and a cluster component for clustering is described herein. The framework supports (1) offline image-based unsupervised clustering that replaces time-consuming manual gating; (2) online image-based single cell sorting. During training, one or multiple cell image datasets with or without ground truth are used to train feature extractor, which is based on a neural network including several convolutional layers. Once trained, the feature extractor is used to extract features of cell images for unsupervised cell clustering and sorting. In addition, additional datasets may be used to further refine the feature extractor after it has been trained.