Cell Cycle State Integration for Sequencing and Imaging Data
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Solution Overview
Problem
Current methods for inferring cell cycle states are limited, with few techniques available for classifying cell cycle states from imaging data, despite the importance of accurate cell cycle identification in biological research and medical practice.
Innovation Solution
A computer-implemented method that integrates sequencing data with imaging data by analyzing images to determine cell cycle states and using these states to map cells between sequencing and imaging data, employing machine learning models like CNNs to predict spatial distributions of organelles and correlate cell features with cell cycle states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sequencing data is used for cell cycle inference, then cell cycle state identification is improved, but the method cannot capture spatial and morphological information
Solution Approach 1:
The patent combines sequencing data (for molecular profile) with imaging data (for spatial and morphological information) by integrating them through cell cycle state classification. The machine learning model processes both data types to create a unified cell profile that preserves both molecular and spatial information, resolving the contradiction between molecular accuracy and spatial information retention.
Solution Approach 2:
Cell cycle state classification serves as an intermediary that bridges sequencing data and imaging data. The machine learning model uses cell cycle states as a common framework to map and integrate the two data types, enabling accurate cell cycle inference while preserving spatial and morphological information through the imaging component.
2Loss of information
If imaging data is used for cell cycle classification, then spatial and morphological information is captured, but the number of available methods is limited
Solution Approach 1:
The patent creates a universal framework where machine learning models can classify cell cycle states from imaging data while also integrating with sequencing data. This multi-functional approach allows the same imaging-based classification system to serve both spatial analysis and molecular integration purposes, expanding the number of applicable methods.
Solution Approach 2:
By merging imaging-based cell cycle classification with sequencing data integration through a common machine learning framework, the patent expands the versatility of imaging methods. The unified approach enables imaging data to serve multiple purposes: spatial analysis, morphological characterization, and molecular profile integration.
3Loss of information
If multiple data types are integrated, then comprehensive cell understanding is improved, but data processing complexity increases
Solution Approach 1:
The patent segments the complex data integration process into distinct manageable steps: (1) processing sequencing data to obtain molecular profiles, (2) processing imaging data to obtain spatial and morphological features, (3) classifying cell cycle states using machine learning, and (4) integrating the results. This segmentation reduces overall complexity by breaking down the multi-data integration challenge into sequential, manageable operations.
Solution Approach 2:
Cell cycle state classification acts as an intermediary framework that simplifies the integration of multiple data types. By using cell cycle states as a common reference framework, the patent reduces the complexity of integrating sequencing and imaging data, as both data types can be mapped to the same classification system.
Data Source
AI summary
A system may receive sequencing data for a cell sample, the cell sample comprising a plurality of cells. A system may receive an image of the cell sample. A system may analyze the image to determine a plurality of respective cell cycle states for the plurality of cells in the cell sample. A system may integrate the sequencing data with the image using the plurality of respective cell cycle states.