Cell Production Quality Control With Label-Free Fluorescence Prediction
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Solution Overview
Problem
Existing methods for assessing cell quality during production processes are invasive, requiring fluorescence labeling and disrupting the process, which is not suitable for therapeutic applications.
Innovation Solution
A method using non-invasive 3D microscopy and neural networks to generate predicted fluorescence images of cells, allowing real-time quality assessment without fluorescence labeling, utilizing a bioreactor, microscope, and computing device to identify quality attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fluorescence labeling is used to assess cell quality, then measurement precision is improved, but the cell production process is disrupted and reliability deteriorates
Solution Approach 1:
The patent creates a predicted fluorescence image that copies the information content of a true fluorescence image without using actual fluorescence labeling. The neural network model generates a synthetic fluorescence image from transmitted light microscopy images, allowing quality assessment while maintaining process integrity. This copying approach eliminates the need for invasive fluorescence tags while preserving measurement capability.
Solution Approach 2:
The patent introduces transmitted light microscopy images as an intermediary that bridges the gap between non-invasive imaging and fluorescence-based quality assessment. The neural network acts as a mediator that translates transmitted light images into predicted fluorescence images, enabling indirect but accurate quality measurement without direct fluorescence labeling.
2Measurement precision
If fluorescence labeling is applied to measure cell quality attributes, then measurement precision is improved, but device complexity and ease of operation worsen due to additional labeling steps
Solution Approach 1:
The method creates a predicted fluorescence image that replicates the informational content of actual fluorescence images without requiring fluorescence labeling equipment or procedures. The neural network model generates synthetic fluorescence signals from standard transmitted light microscopy data, eliminating the need for complex fluorescence imaging systems while maintaining measurement precision.
Solution Approach 2:
The patent extracts the essential quality information from transmitted light images that would otherwise require fluorescence labeling. By using the neural network to predict fluorescence signals from non-invasive images, the method removes the need for fluorescence labeling steps and associated complex equipment while preserving the ability to measure quality attributes accurately.
3Measurement precision
If traditional quality assessment methods are used, then measurement precision is improved, but productivity deteriorates due to time-consuming invasive procedures
Solution Approach 1:
The patent performs preliminary action by training the neural network model in advance using paired fluorescence and transmitted light images. Once trained, the model can rapidly generate predicted fluorescence images from new transmitted light images without requiring actual fluorescence labeling or time-consuming image acquisition procedures, enabling fast quality assessment during production.
Solution Approach 2:
The method creates predicted fluorescence images that copy the essential quality information from transmitted light images through neural network processing. This copying approach eliminates the need for slow, sequential fluorescence imaging procedures while maintaining measurement accuracy, thereby significantly improving assessment speed and productivity.
Data Source
AI summary
The present invention provides various methods for easily assessing cell quality of a cell production process, suitably using non-invasive visual methods and neural networks for generating predictive fluorescence images of cells to assess quality attributes. Also provided are systems and methods for carrying out such processes.