Apparatus and method for predicting cell type enrichment from tissue images using spatially resolved gene expression data
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Solution Overview
Problem
Existing deep learning-based algorithms for tissue classification require laborious visual reading and labeling by experts, lack molecular-level information, and struggle to estimate detailed molecular-functional cellular classifications from simple tissue images.
Innovation Solution
A cell type enrichment prediction model trained on spatially resolved transcriptome information and tissue images, using a communication module, memory, and processor to predict cell type enrichment from general tissue images, leveraging spatially aligned transcriptome and image data to quantify cell group diversity and molecular-functional cells.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If molecularly specific staining methods (immunohistochemical staining or fluorescence in-situ hybridization) are used to identify cell types and obtain biomarkers, then measurement precision of molecular information is improved, but device complexity and loss of substance increase due to additional experimental procedures and materials
Solution Approach 1:
The patent creates a computational copy of molecular staining information by training a deep learning model on spatially resolved transcriptome data (which contains true molecular information) and simple tissue images. The trained model then generates pseudo-molecular staining maps from new tissue images, replicating the information that would otherwise require complex wet lab staining procedures. This copying approach preserves measurement precision while eliminating the need for additional experimental materials and procedures.
Solution Approach 2:
The patent replaces the mechanical/chemical staining system (immunohistochemical staining, fluorescence in-situ hybridization) with an information processing system (deep learning model). Instead of using physical chemicals to bind to molecular targets, the system uses a trained neural network to infer molecular information from morphological features in tissue images, substituting a computational mechanism for a biochemical one.
2Productivity
If deep learning techniques are used to predict labeling of pathological tissue images, then productivity is improved by automating the labeling task, but measurement precision deteriorates because these algorithms lack molecular-level information and require expert visual reading and labeling
Solution Approach 1:
The patent performs preliminary action by pre-training the deep learning model on spatially resolved transcriptome data, which contains ground truth molecular information and expert-labeled cell types. This pre-training phase allows the model to learn the complex mapping between tissue morphology and molecular cell type information before being deployed for automated prediction. By doing the heavy learning work in advance with high-quality training data, the model achieves both automation and precision in subsequent applications.
Solution Approach 2:
The patent introduces spatially resolved transcriptome data as an intermediary during the training phase. This intermediary dataset bridges the gap between simple tissue images and molecular cell type information, allowing the model to learn from paired data where both the image and the true molecular composition are known. The intermediary training data enables the model to develop accurate predictive capabilities without requiring expert labeling during actual use.
3Measurement precision
If spatially resolved transcriptome technology is used to acquire gene expression information, then measurement precision of molecular information is improved, but loss of substance increases due to additional experimental materials and procedures
Solution Approach 1:
The patent creates a computational copy of spatially resolved transcriptome data by training a model on paired datasets of tissue images and spatial transcriptome information. Once trained, the model can generate transcriptome-like molecular information from simple tissue images without requiring actual transcriptome sequencing experiments. This copying approach preserves the measurement precision of true spatial transcriptome data while eliminating the need for consumable experimental materials such as sequencing reagents, probes, and specialized reagents.
Data Source
AI summary
An apparatus for predicting cell type enrichment from tissue images based on spatially resolved gene expression information comprises: a communication module receiving tissue images of a test object; a memory storing a program for predicting cell type enrichment information from the tissue images; and a processor executing the program. The program predicts the cell type enrichment information by inputting the tissue images to a cell type enrichment prediction model which is trained using training data that consists of spatially resolved transcriptome information and tissue images spatially aligned with the spatially resolved transcriptome information. The spatially resolved transcriptome information includes: transcriptome data including spatial information; and tissue image data sharing the spatial information. The spatial information refers to position information about multiple spots arranged in a two-dimensional plane in the tissue image data, and includes the coordinates of each of the spots.