Neuron Aggregate Imaging for Survival State Classification
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Solution Overview
Problem
Existing methods for analyzing neurons using fluorescent proteins lack the capability to accurately detect protein aggregates and classify neurons based on their survival state, which is crucial for understanding neuronal health and disease progression.
Innovation Solution
A method and device for analyzing neurons using time-series imaging to identify neurite and cell body regions, detect protein aggregates, and classify neurons into groups based on aggregate presence and survival state, utilizing fluorescent proteins tagged with specific proteins like GFP or RFP, and analyzing survival or death states through luminance thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fluorescent protein is introduced into neurons to track aggregates and determine survival states, then the ability to analyze neuronal health and dynamics is improved, but the complexity of the imaging and analysis system increases
Solution Approach 1:
The patent segments the neuron into distinct regions of interest (cell body and neurite processes) and analyzes aggregate presence separately in each region. This segmentation allows precise differentiation between neuronal compartments, enabling accurate survival state classification while maintaining manageable analysis complexity through structured processing
Solution Approach 2:
The patent applies different luminance thresholds and detection parameters to different regions (cell body versus neurite). By customizing detection criteria for each region based on their specific characteristics, the system achieves high measurement precision without requiring a completely complex unified system
2Duration of action of moving object
If time-series imaging is performed to track aggregate dynamics, then the ability to analyze survival states is improved, but the amount of data processing and analysis time increases
Solution Approach 1:
The patent performs preliminary identification of cell body and neurite regions using phase difference images and luminance thresholding before conducting detailed aggregate detection. This preliminary segmentation establishes a framework that guides subsequent time-series analysis, reducing the time required for comprehensive data processing while maintaining full tracking capability
Solution Approach 2:
The patent extracts and focuses analysis on specific regions of interest (cell body and neurite) rather than processing the entire neuron uniformly. By concentrating computational resources on biologically relevant regions where aggregates form, the system reduces overall data processing time while preserving survival state analysis accuracy
3Measurement precision
If multiple luminance thresholds are used to distinguish cell body and neurite regions, then the precision of region identification is improved, but the complexity of threshold determination increases
Solution Approach 1:
The patent uses phase difference images as an intermediary to guide luminance threshold determination. The phase difference data provides structural information about cell body and neurite boundaries, which serves as a mediator to select appropriate luminance thresholds for each region. This approach achieves precise region identification without requiring complex threshold calculation methods
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables precise classification of neurons into groups based on aggregate presence and survival state, providing insights into neuronal health and disease progression by tracking luminance and survival periods.
Implementation Method 1
introducing a gene encoding fluorescent protein into a living cell. As the fluorescent protein, green fluorescent protein (GFP), red fluorescent protein (RFP), and the like are used. These types of fluorescent protein are used as markers indicating localization of genes in cells and protein in cells.
Implementation Method 2
detecting aggregates by detecting, on the basis of the time-series images obtained by imaging, in time series, first fluorescent protein tagged to specific protein expressed in the neuron, presence or absence of aggregates of the specific protein aggregated in the region of the neurite or the region of the cell body identified in the identifying the cell region by using, as a basis, luminance of the first fluorescent protein included in the time-series images
Data Source
AI summary
A method of analyzing neurons includes: identifying a cell region by identifying a region of a neurite or a region of a cell body of a neuron on the basis of time-series images obtained by imaging the neuron in time series; detecting aggregates by imaging, in time series, first fluorescent protein tagged to specific protein expressed in the neuron and detecting presence or absence of aggregates of the specific protein aggregated in the region of the neurite or the region of the cell body identified in the identifying the cell region on the basis of luminance of the first fluorescent protein included in the time-series images; and performing an analysis by classifying the neuron into a plurality of groups on the basis of a detection result of the detecting the aggregates and analyzing a survival state of the neuron for each of the groups.