Clone Selection Imaging for Single-Cell Colony Verification
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Solution Overview
Problem
Traditional clone selection processes 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 or negatives, especially when distinguishing between single cells and doublets or debris.
Innovation Solution
An automated visual inspection system captures digital images at intervals during the incubation period, using convolutional neural networks (CNNs) to detect and classify cell colonies, followed by higher-magnification imaging to confirm the origin of candidate objects as single cells, thereby improving accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual visual inspection is used to identify single cells, then flexibility and adaptability are maintained, but time consumption and human effort increase significantly
Solution Approach 1:
The patent replaces manual visual inspection with an automated image analysis system using convolutional neural networks (CNNs). The system captures images of well plates at multiple time points and uses deep learning algorithms to automatically identify single cells, track their division, and determine colony origins, eliminating the need for manual analyst review while significantly reducing processing time
Solution Approach 2:
The system creates digital copies (images) of the well plate contents at multiple time points during incubation. These image copies are then analyzed by CNNs to track cell division events and determine colony origins, replacing the need for analysts to physically examine and compare multiple microscope slides or well images manually
2Reliability
If manual inspection is used to distinguish single cells from doublets or debris, then human judgment is applied, but measurement precision and accuracy decrease due to false positives and negatives
Solution Approach 1:
The patent replaces human visual judgment with CNN-based image analysis that objectively identifies single cells by analyzing image features at multiple time points. The system tracks cell division events through automated image comparison, determining whether colonies originated from single cells or multiple cells/debris with high precision and without human error
Solution Approach 2:
The system uses feedback from multiple time-point images to continuously refine its identification of single cells. By comparing images taken at different incubation stages, the CNN can track cell division events and confirm whether observed colonies originated from single cells, providing ongoing verification that reduces false positives and negatives
3Measurement precision
If multiple time-point images are captured and analyzed, then accuracy of clone selection improves, but device complexity and processing requirements increase
Solution Approach 1:
The patent segments the image analysis process into distinct functional modules: image capture at multiple time points, pre-processing to enhance image quality, CNN-based single cell identification, colony detection, and origin determination. This segmentation allows each module to be optimized independently while working together to achieve high accuracy in clone selection
Solution Approach 2:
The system performs preliminary actions by capturing and storing multiple images during the incubation period before final analysis. These pre-captured images serve as input data for the CNN, enabling accurate retrospective analysis of cell division events and colony origins without requiring complex real-time processing during the critical identification phase
Data Source
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.