GIST Spatial Transcriptomics Cell-Type Mapping
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Solution Overview
Problem
Current spatial transcriptomics methods cannot effectively combine AI-annotated pathology images with spatial transcriptomics data to improve cell-type composition inference.
Innovation Solution
The Guiding-Image Spatial Transcriptomics (GIST) methodology leverages AI-annotated tissue images and spatial transcriptomics data to generate feature maps of tissue samples, identifying cell types by tissue region through a mapping estimate derived from image data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spatial transcriptomics data alone is used for cell-type identification, then the method is simple and straightforward, but the accuracy of cell-type composition inference is insufficient
Solution Approach 1:
The patent combines spatial transcriptomics data with AI-annotated pathology images to create a joint analysis framework. The image data provides morphological context while transcriptomics data provides molecular information, and their integration through the GIST model improves cell-type composition inference accuracy beyond what either modality achieves alone.
Solution Approach 2:
The patent introduces a Bayesian statistical model as an intermediary that integrates image-derived prior information with spatial transcriptomics data. This mediator combines the two data sources in a mathematically rigorous way, allowing the image data to inform the transcriptomics analysis without directly processing both raw data types together.
2Measurement precision
If AI-annotated pathology images are used alone for cell-type identification, then the spatial distribution information is available, but the molecular characterization accuracy is insufficient
Solution Approach 1:
The patent merges image data that contains spatial distribution information with spatial transcriptomics data that contains molecular characterization. The integration ensures that spatial information from images is preserved and enhanced by molecular data, rather than being lost or replaced.
Solution Approach 2:
The patent performs preliminary annotation of pathology images with AI models to extract cell-type information before integrating with spatial transcriptomics data. This preliminary action prepares the image data in a form that can be effectively combined with molecular data, ensuring spatial information is captured early in the process.
3Adaptability or versatility
If traditional spatial transcriptomics methods are used, then the workflow is established and standardized, but the ability to capture cellular interactions and spatial distributions is limited
Solution Approach 1:
The patent creates a multi-functional methodology that can identify cell types, map their spatial distributions, and infer cellular interactions within a single integrated framework. The GIST model serves multiple purposes simultaneously, making the approach versatile for various spatial biology questions beyond what traditional methods achieve.
Data Source
AI summary
An exemplary embodiment of the present disclosure provides systems and methods for mapping a location of cell types within a tissue sample. The system may include one or more processors, a non-transient memory in communication with the one or more processors storing instructions that when executed by the one or more processors are configured to perform method steps. The method may include receiving a tissue sample, capturing image data of the tissue sample, extracting a plurality of nucleic molecules from the tissue sample, and generating spatially resolved transcriptomic data from the extracted plurality of cellular analytes. The method may include determining cell-type reference data and generating a feature map of the tissue sample that includes a final inferred cell type compositional map for each tissue region of the tissue sample based on the spatially resolved transcriptomics data, the cell-type reference data, and the mapping estimate of cell types.