Stem Cell Colony Imaging for Non-Invasive Chromosomal Screening
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Solution Overview
Problem
Current methods for detecting chromosomal abnormalities in pluripotent stem cells, such as G-band analysis, are destructive, time-consuming, and costly, and existing image-based techniques focus on single cells rather than colonies, lacking non-invasive and rapid detection methods.
Innovation Solution
An information processing system and method that analyzes image data of pluripotent stem cell colonies to acquire first information on morphology, which is then used to generate second information on chromosomal abnormalities, utilizing trained models and machine learning algorithms to classify cells based on morphological characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If G-band analysis is used to detect chromosomal abnormalities, then measurement precision is improved, but it causes destruction of the cells and requires significant time and cost
Solution Approach 1:
The patent replaces the mechanical/c Chemical process of G-band analysis with an optical imaging system combined with AI analysis. The system captures images of living cell colonies and uses deep learning models to detect chromosomal abnormalities, eliminating the need for cell destruction while maintaining detection capability
Solution Approach 2:
The patent introduces an intermediary AI analysis system that processes optical images to infer chromosomal abnormality information without directly manipulating or destroying the cells. The AI model acts as a mediator between the optical images and the chromosomal abnormality detection, enabling non-invasive measurement
2Measurement precision
If G-band analysis is performed regularly to detect chromosomal abnormalities, then measurement precision is improved, but productivity deteriorates due to time consumption
Solution Approach 1:
The patent replaces the time-consuming G-band analysis process with rapid optical imaging and AI analysis. The system can analyze multiple cell colonies simultaneously and process images much faster than traditional methods, achieving both high precision and high throughput for regular monitoring
Solution Approach 2:
The patent performs preliminary actions by capturing images of cell colonies at early stages and using AI to predict chromosomal abnormalities before they become critical. This enables proactive detection and intervention, improving both precision and productivity by preventing the need for repeated corrective analyses
3Measurement precision
If single cell analysis is performed with fluorescent dyes or chromosome imaging, then measurement precision is improved, but device complexity and ease of operation worsen due to invasive procedures
Solution Approach 1:
The patent merges multiple analysis functions into a single optical imaging system. Instead of requiring separate fluorescent staining and chromosome imaging procedures, the system captures comprehensive images of cell colonies that contain sufficient information for AI analysis, simplifying the operational process while maintaining precision
Solution Approach 2:
The patent creates optical copies (images) of the cell colonies that can be analyzed without touching or altering the original cells. These digital copies contain morphological information that the AI model uses to detect chromosomal abnormalities, enabling non-invasive analysis with operational simplicity
4Measurement precision
If morphological characteristics of colonies are not used for testing, then measurement precision for chromosomal abnormalities is improved through direct chromosome imaging, but device complexity increases due to invasive testing requirements
Solution Approach 1:
The patent extracts useful information (morphological characteristics) from the optical images of cell colonies without requiring invasive procedures. By analyzing features such as colony shape, cell arrangement, and optical properties, the system derives chromosomal abnormality information directly from the existing image data, reducing device complexity
Solution Approach 2:
The patent changes the measurement parameters from direct chromosome structure to colony-level morphological parameters. The AI model is trained to recognize patterns in morphological parameters that correlate with chromosomal abnormalities, enabling precision detection through simpler optical measurements rather than complex chromosome imaging
Data Source
AI summary
Tests regarding chromosomal abnormalities in pluripotent stem cells have involved cellular invasion, and have been unable to be applied to cells that require non-invasive testing, such as cells being cultured. Provided is an information processing system including: an image data acquisition unit configured to acquire image data including a colony of pluripotent stem cells; a first information acquisition unit configured to acquire, based on analysis of the image data, first information that is information regarding a predetermined morphology of the colony of pluripotent stem cells; and a second information generation unit configured to generate, based on the first information, second information that is information regarding chromosomal abnormalities in the pluripotent stem cells.