Stem Cell Colony Imaging for Non-Destructive Chromosomal Aberration Detection
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Solution Overview
Problem
Conventional chromosomal aberration testing methods for cells are destructive and time-consuming, making real-time, non-destructive testing of cell cultures challenging, especially when targeting cell colonies rather than individual cells.
Innovation Solution
An information processing system and method that utilizes a learned model to analyze image data of pluripotent stem cell colonies, extracting morphological features and determining chromosomal aberrations through machine learning, enabling non-destructive and rapid assessment of chromosomal aberrations in cell colonies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional chromosomal aberration testing methods (G-Band method and FISH method) are used, then chromosomal aberrations can be detected, but the testing is destructive and time-consuming
Solution Approach 1:
The patent uses image data as a copy representation of cell colonies to perform chromosomal aberration detection. Instead of directly analyzing cells through destructive methods, the system creates and analyzes visual copies (images) of cell colonies, enabling non-destructive and rapid assessment while maintaining detection accuracy through machine learning models trained on morphological features
Solution Approach 2:
The patent replaces conventional mechanical/chemical testing methods (G-Band staining, FISH hybridization) with an information processing system that uses machine learning algorithms to analyze cell colony images. This substitution eliminates the need for time-consuming laboratory procedures while achieving comparable or superior detection capability
2Measurement precision
If conventional chromosomal aberration testing methods are used, then chromosomal aberrations can be detected, but the testing is destructive to cells
Solution Approach 1:
The system analyzes images of cell colonies as copies rather than directly manipulating the cells themselves. This allows chromosomal aberration detection without invasive procedures such as cell harvesting, staining, or hybridization that would damage or kill the cells, enabling non-destructive quality control
Solution Approach 2:
The patent introduces image data and machine learning models as intermediary elements between the observer and the cells. Instead of directly testing cells through destructive methods, the system uses visual information and computational algorithms as intermediaries to infer chromosomal aberrations, thereby avoiding direct harmful interaction with the cells
3Measurement precision
If methods targeting single cells are used, then chromosomal aberrations can be detected in individual cells, but applying it to cell colonies requires isolating cells
Solution Approach 1:
The patent segments the cell colony into detectable units by capturing images of individual colonies or groups of cells. The machine learning model then analyzes these segmented visual units to identify chromosomal aberrations, eliminating the need for complex cell isolation procedures while maintaining the ability to detect aberrations at the colony level
Solution Approach 2:
The patent transitions from single-cell resolution analysis to colony-level image analysis, adding a new dimension of observation. By working with images of entire colonies rather than isolated single cells, the system simplifies the workflow while providing sufficient resolution to detect chromosomal aberrations that manifest as colony-level morphological changes
4Object-affected harmful factors
If non-destructive imaging methods are used, then cells remain intact, but traditional imaging cannot provide sufficient information for chromosomal aberration detection
Solution Approach 1:
The patent employs machine learning models that have been trained on labeled data to extract meaningful information from cell colony images. The system uses feedback loops where the model continuously refines its ability to detect chromosomal aberrations by learning from training data and adjusting its feature extraction capabilities, thereby recovering chromosomal information through morphological pattern recognition without destroying cells
Data Source
AI summary
An information processing system, comprises: an image data acquiring section that acquires image data including a colony of pluripotent stem cells; a first information acquiring section that acquires first information regarding morphological features of the colony by inputting the image data to a learned model; and a second information acquiring section that acquires second information regarding a chromosomal aberration of the colony based on the first information. The learned model is obtained by learning with a dataset including image data of a plurality of colonies of pluripotent stem cells having a chromosomal aberration and image data of a plurality of colonies of pluripotent stem cells not having a chromosomal aberration.