Label-Free Dying Cell Phenotype Analysis With ML Encoders
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Solution Overview
Problem
Current methods for analyzing cell death pathways rely heavily on staining and fixation, which can perturb cell phenotypes and hinder further functional or molecular characterization, and lack the ability to objectively quantify cell morphology at high resolution.
Innovation Solution
Employ deep learning and computer vision to analyze phenotypes of dying cells using machine learning encoders to extract ML-based features and computer vision encoders to extract morphometric features, generating multi-dimensional feature vectors for phenotypic differentiation without staining, and utilize microfluidics for real-time sorting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If staining and fixation methods are used to analyze cell death pathways, then detection sensitivity is improved, but cell phenotype is perturbed and viability is compromised
Solution Approach 1:
The patent replaces traditional mechanical/chemical staining and fixation methods with optical imaging combined with machine learning classification. Instead of using physical stains to visualize cell death markers, the system uses brightfield or phase-contrast images processed by deep learning models to identify and classify different cell death states, thereby eliminating the need for penetrating the cell membrane with stains and preserving cell viability.
Solution Approach 2:
The patent creates a computational copy of cell phenotypes through machine learning models that learn to distinguish between different cell death states from image data. The model captures the essential features of cell death pathways (apoptosis, necroptosis, autophagy, etc.) as digital representations, allowing analysis without physical alteration of the cells themselves.
2Ease of operation
If traditional imaging methods are used to analyze cell morphology, then simplicity of operation is maintained, but measurement precision and resolution are insufficient
Solution Approach 1:
The system performs self-service by automatically extracting morphometric features and classifying cell death states without requiring manual intervention. The machine learning model autonomously processes images, identifies morphological features, and determines cell death pathways, eliminating the need for human experts to manually measure and interpret cell morphology while maintaining high precision.
Solution Approach 2:
The patent introduces machine learning models as an intermediary between simple imaging and complex morphological analysis. The model acts as a mediator that takes basic image data and automatically transforms it into precise morphological measurements and cell death classifications, bridging the gap between operational simplicity and measurement precision.
3Measurement precision
If multiple staining markers are used to differentiate cell death pathways, then detection precision is improved, but device complexity and processing steps increase
Solution Approach 1:
The patent applies a universal imaging approach that can detect multiple cell death pathways simultaneously using a single image type. The machine learning model is trained to recognize diverse morphological features characteristic of different death pathways (apoptosis, necroptosis, autophagy, ferroptosis, etc.) within the same image modality, eliminating the need for multiple specialized stains and simplifying the overall processing workflow while maintaining high differentiation precision.
Data Source
AI summary
In some examples, a method includes using a machine learning encoder to extract respective sets of machine learning (ML)-based features from respective images of cells that are dying and unstained. Cells of a first subset are at a first state of dying, and cells of a second subset are at a second state of dying. The method may include using a computer vision encoder to extract respective sets of cell morphometric features from the respective images. The method may include using the respective sets of ML-based features and the respective sets of cell morphometric features to generate respective multi-dimensional feature vectors that represent respective cell phenotypes. The method may include using the respective multi-dimensional feature vectors to correlate, to the first state of dying or to the second state of dying, a phenotypic difference between the cells of the first subset and the cells of the second subset.