Single-Cell Clone Selection Using Time-Sequence CNN Imaging
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Solution Overview
Problem
Traditional clone selection methods in cell line development are time-consuming and prone to errors due to the difficulty in accurately identifying single cells within well images, often resulting in false positives and negatives, and are hindered by factors like debris and well wall aberrations.
Innovation Solution
An automated visual inspection system captures digital images at intervals using a convolutional neural network (CNN) to identify candidate object colonies, followed by higher-magnification imaging and classification to determine if a colony formed from a single cell, utilizing machine learning models for accurate classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual visual inspection by analysts is used to identify single cells and colonies, then flexibility in analysis is maintained, but the process becomes extremely time-consuming and labor-intensive
Solution Approach 1:
The patent replaces manual visual inspection with an automated image analysis system that uses computer vision algorithms to detect and classify cells and colonies. The system automatically processes well images, identifies single cells versus colonies, and determines colony origins without human intervention, thereby resolving the contradiction between manual flexibility and automated efficiency
Solution Approach 2:
The system creates digital copies of well images and analyzes them through multiple processing stages. By working with image data rather than physical samples, the system enables rapid, repeatable analysis without the time constraints of manual inspection, improving productivity while maintaining analysis accuracy
2Reliability
If analysts manually review well images to identify single cells, then subjective judgment can be applied, but accuracy decreases due to factors like debris, shadows, and focus issues
Solution Approach 1:
The patent replaces human visual judgment with automated computer vision algorithms that objectively detect and classify cells. The system uses image processing techniques to identify cellular structures, distinguish them from debris and shadows, and determine single-cell versus colony status with consistent, repeatable accuracy不受 human factors influence
Solution Approach 2:
The system incorporates multiple image capture time points and compares changes over time to verify colony formation from single cells. This feedback mechanism allows the system to confirm identification accuracy by observing temporal development patterns, reducing false positives and negatives
3Reliability
If multiple images are captured at different time points to verify single-cell origin, then confidence in clone selection increases, but the time required for analysis increases
Solution Approach 1:
The system captures images at multiple time points continuously during the incubation period, maintaining constant monitoring of cell development. This continuous imaging allows the system to track colony formation in real-time and determine single-cell origin through temporal analysis, achieving high confidence without requiring extended analysis time after the fact
4Measurement precision
If high-magnification imaging is used to accurately identify single cells, then detection precision improves, but the complexity of the imaging system increases
Solution Approach 1:
The patent employs multiple imaging units with different magnification levels, each optimized for specific tasks. Lower-magnification units provide overview and context, while higher-magnification units provide detailed cell identification. This segmentation of imaging functions allows the system to achieve high precision without requiring a single overly complex high-magnification system for all purposes
Data Source
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AI summary
A method for facilitating clone selection includes generating time-sequence images of a well containing a medium, including a first image and a later, second image. The method also includes detecting, by one or more processors analyzing the first image, one or more candidate objects depicted in the first image, and, for each of the candidate objects, determining whether the object is a single cell by analyzing an image of the object using a convolutional neural network. The method further includes detecting, by analyzing the second image with the processor(s), a cell colony depicted in the second image, and determining, by the processor(s), whether the colony was formed from only one cell based at least on whether each candidate object was determined to be a single cell. The method further includes generating, by the processor(s), output data indicating whether the colony was formed from only one cell.