Predictive Fluorescence Imaging for Rapid Compound Screening
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Solution Overview
Problem
Current compound screening processes are time-consuming and resource-intensive, particularly in determining the effect of compounds on cells without significant biochemical analysis, necessitating a rapid and simple method for assessing compound effects using machine learning image processing.
Innovation Solution
A method utilizing 3D microscopy images without fluorescence labeling, combined with neural networks to generate predicted fluorescence images, enabling the identification and classification of compound effects on cells by comparing against a comparison set of known effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional compound screening methods with fluorescence labeling are used, then measurement precision is improved, but productivity deteriorates and loss of time increases
Solution Approach 1:
The neural network model is trained in advance on a dataset of paired transmitted light and fluorescence images to learn the mapping relationship. This preliminary training enables the model to rapidly generate predicted fluorescence images from transmitted light images without requiring actual fluorescence labeling during screening, thus achieving both high precision and high productivity
Solution Approach 2:
The invention creates a predicted fluorescence image copy based on transmitted light image data through neural network processing. This virtual fluorescence image serves as a substitute for actual fluorescence-labeled images, eliminating the need for time-consuming labeling procedures while maintaining measurement precision comparable to traditional fluorescence imaging
2Measurement precision
If fluorescence labeling is applied to assess compound effects, then measurement precision is improved, but device complexity and loss of substance increase
Solution Approach 1:
The invention extracts and utilizes only the transmitted light imaging modality, removing the need for fluorescence labeling apparatus and associated complexity. The neural network model processes standard transmitted light images to generate predictive fluorescence information, thereby simplifying the overall screening system while maintaining measurement precision
Solution Approach 2:
The neural network model acts as an intermediary that translates transmitted light image data into predicted fluorescence images. This computational mediator bridges the gap between simple transmitted light imaging and complex fluorescence imaging, eliminating the need for physical fluorescence labeling while preserving measurement capabilities
3Productivity
If rapid compound screening is implemented, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The neural network model undergoes comprehensive training in advance on a large dataset of paired images to learn the complex mapping relationships. This preliminary action ensures that during actual screening, the model can rapidly generate accurate predicted fluorescence images without compromising measurement precision, thus achieving both high productivity and high precision simultaneously
Data Source
AI summary
The present invention provides various methods for screening one or more compounds, suitably using non-invasive visual methods and neural networks for generating predicted fluorescence images of cells, to assess an effect of the compound on the cell, as well as to classify a compound or to determine an activity of a compound. Also provided are systems and methods for carrying out such assessments.