Cell Motion Characterization via Optical Segmentation
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Solution Overview
Problem
Current methods for characterizing cell motion, particularly in cardiomyocyte cultures, face challenges such as variations in culture density, health state, and sensitivity to plating density, leading to inefficient and costly characterization, especially in capturing dynamics of cardiomyocyte beating and handling impurities in culture.
Innovation Solution
A method and system for characterizing cell motion that involves receiving image data from cell cultures, segmenting cell subpopulations, determining resting and single-peak motion signals, and extracting motion features, allowing for phenotypic expression assessment over time without direct electrophysiological measurement, and enabling characterization of cardiomyocyte beating motion at a subpopulation level.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional methods are used to characterize cell motion in cardiomyocyte cultures, then measurement capability is achieved, but efficiency is low and cost is high due to variations in culture density, health state, and sensitivity to plating density
Solution Approach 1:
The patent segments the cell culture into multiple subpopulations based on their motion characteristics. By dividing the heterogeneous cell population into distinct groups with similar beating patterns, the system can characterize each subpopulation separately, improving both efficiency and accuracy while accounting for variations in culture density and health state
Solution Approach 2:
The patent transforms the characterization approach by changing from direct electrophysiological measurement to optical motion detection. This parameter change enables non-invasive monitoring of cell beating through image analysis, improving efficiency and reducing costs while maintaining measurement capability through alternative physical parameters
2Measurement precision
If direct electrophysiological measurement is used, then accurate electrical activity data is obtained, but the process is invasive and complex
Solution Approach 1:
The patent replaces the mechanical/electrical measurement system with an optical system. Instead of using electrodes to directly measure electrical activity, the system uses image capture and analysis to detect motion caused by cell beating, thereby substituting a complex electrophysiological measurement system with a simpler optical detection system
Solution Approach 2:
The patent introduces motion as an intermediary parameter between electrical activity and measurement. Rather than measuring electrical activity directly, the system measures the mechanical motion resulting from electrical stimulation, using this intermediate physical quantity to indirectly characterize the electrophysiological state of the cells
3Loss of information
If cell cultures are monitored manually, then detailed analysis is possible, but time consumption is high and automation is low
Solution Approach 1:
The patent implements self-service through automated image analysis algorithms that independently process and characterize cell motion without human intervention. The system automatically captures images, processes them through analysis algorithms, and generates characterization results, enabling the system to serve itself and eliminating time-consuming manual analysis while preserving detailed information
Solution Approach 2:
The patent incorporates feedback mechanisms where the automated analysis system continuously monitors cell cultures and adjusts characterization parameters based on observed motion patterns. This feedback loop enables the system to maintain detailed analysis capability while operating autonomously, reducing time loss through iterative self-correction and optimization
Data Source
AI summary
A method and system for characterizing cell motion comprising: receiving image data corresponding to a set of images of a cell culture captured at a set of time points; segmenting, from at least one image of the set of images, a cell subpopulation from the cell culture; determining a resting signal for the cell subpopulation; determining a single-peak motion signal based upon the set of images, the set of time points, and the resting signal; detrending the single-peak motion signal of the cell subpopulation based upon the resting signal; determining values of a set of motion features of the cell subpopulation, thereby characterizing cell motion; and clustering the cell subpopulation with at least one other cell subpopulation based upon at least one of the single-peak motion signal and a value of the set of motion features.