Single-Cell Spatial Relation Prediction via Transcriptome Data
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Solution Overview
Problem
Current methods cannot reconstruct spatial structures of cells or predict cell interactions using single-cell transcriptome sequencing data without a known spatial image, and fail to accurately model ligand-receptor interactions at a single-cell level.
Innovation Solution
A method that calculates a cell-cell interaction intensity matrix based on single-cell transcriptome sequencing data and ligand-receptor interactions, reconstructs a three-dimensional spatial structure by minimizing an objective function using gradient descent, and determines an intercellular distance threshold to create an interaction network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If experimental methods with fluorescent labeling and microscopic imaging are used to obtain spatial distribution information of cells, then spatial organization of cells can be obtained, but the process is complex and requires additional experimental equipment and procedures
Solution Approach 1:
The patent uses single-cell transcriptome sequencing data to create a computational copy of spatial information instead of directly measuring it through complex imaging experiments. The cell-cell interaction intensity matrix serves as a mathematical representation that captures spatial relationships without requiring physical imaging equipment.
Solution Approach 2:
The patent replaces the mechanical/optical system of fluorescent labeling and microscopic imaging with a computational/mathematical system. Instead of using physical markers and microscopes, the invention uses transcriptome data processing and matrix calculations to infer spatial organization.
2Measurement precision
If marker genes are used to map cells in known spatial images, then spatial positions can be determined, but the method cannot reconstruct spatial structures without pre-existing spatial images
Solution Approach 1:
The patent performs preliminary calculations to construct the cell-cell interaction intensity matrix A from transcriptome data before attempting spatial reconstruction. This preliminary processing of interaction data enables the subsequent construction of spatial structures without requiring pre-existing spatial images as input.
Solution Approach 2:
The patent introduces the cell-cell interaction intensity matrix A as an intermediary between raw transcriptome data and spatial structure reconstruction. This matrix serves as a bridge that translates molecular interaction data into spatial relationships, enabling reconstruction without direct imaging input.
3Quantity of substance
If ligand-receptor interactions are analyzed at bulk level, then cell interaction types can be identified, but single-cell level interaction and spatial structure reconstruction cannot be achieved
Solution Approach 1:
The patent segments the bulk ligand-receptor interaction data into single-cell level interactions by constructing the cell-cell interaction intensity matrix A where each element represents interactions between specific cell pairs. This segmentation allows analysis of individual cell interactions rather than aggregated population data.
Solution Approach 2:
The patent changes the resolution parameter of interaction analysis from bulk level to single-cell level. By calculating interaction intensities for each cell pair based on their transcriptome profiles, the method achieves high-resolution single-cell interaction data while utilizing ligand-receptor interaction frameworks.
Data Source
AI summary
A method for predicting the cell spatial relation based on single-cell transcriptome sequencing data includes the steps of obtaining a probability matrix P of a cell-cell interaction strength matrix A based on single-cell transcriptome sequencing data; reconstructing, according to the obtained probability matrix P of the cell-cell interaction strength matrix A, a three-dimensional spatial structure in which cells interact with each other; and for each cell in the reconstructed three-dimensional spatial structure in which cells interact with each other, determining the intercellular distance threshold for each cell to interact with h cells on average to obtain an intercellular interaction network. The method requires only the single-cell transcriptome sequencing data to predict the interaction of the cells in three-dimensional space, which breaks the limitation of the existing technology that needs to obtain the spatial relationship of cells through imaging.