GAN-Based Spatial Omics Imputation from Histopathology Images
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Solution Overview
Problem
Spatial omics data is expensive and time-consuming to obtain, limiting its widespread availability and use in clinical and discovery settings due to the costly equipment and analytics required.
Innovation Solution
An end-to-end computational pipeline using machine learning techniques, specifically a generative adversarial network (GAN) model, to impute spatially resolved analyte concentration from histopathology images, enabling the generation of synthetic spatial omics data from cheaper and more accessible histology images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spatial omics assays are used to obtain spatially resolved molecular data, then measurement precision and spatial context are improved, but cost and time consumption increase significantly
Solution Approach 1:
The patent creates synthetic spatial omics data as a copy of real spatial omics data using GANs. The generator network learns to produce artificial spatial proteomics or transcriptomics data that mimics the statistical properties and spatial patterns of real data, enabling downstream analyses without requiring actual spatial omics measurements
Solution Approach 2:
The patent replaces expensive spatial omics assays with inexpensive histopathology image data. By training GANs on limited real spatial omics data and applying them to routine histology images, the method provides a low-cost alternative that can be applied broadly without requiring costly equipment or reagents
2Loss of information
If spatial omics technology is deployed to characterize tumor microenvironment, then biological insight and spatial pattern characterization are improved, but equipment cost and operational complexity increase
Solution Approach 1:
The patent introduces histopathology images as an intermediary between routine clinical practice and spatial omics analysis. The GANs learn to translate information from histology images into synthetic spatial omics data, serving as a bridge that provides spatial context without requiring direct spatial omics measurements
Solution Approach 2:
The patent generates synthetic spatial omics data that replicates the information content and spatial patterns of real spatial omics measurements. This copying approach preserves the biological insights needed for tumor microenvironment characterization while avoiding the need for complex spatial omics equipment
3Productivity
If real spatial omics data is collected for large cohorts, then statistical power and discovery potential are improved, but cost and resource requirements increase
Solution Approach 1:
The patent uses GANs to generate large volumes of synthetic spatial omics data that can be used for statistical analyses, cohort comparisons, and discovery research. This copying approach enables high-throughput data generation without the proportional increase in resource consumption required for real spatial omics measurements
Solution Approach 2:
The patent replaces expensive spatial omics data with inexpensive synthetic alternatives generated from readily available histology images. This approach enables large-scale studies with many samples without the prohibitive costs associated with collecting real spatial omics data from large cohorts
Data Source
AI summary
The present disclosure relates generally to machine learning techniques, and more specifically to machine learning techniques for generating synthetic spatial omics data based on histopathology image data. An exemplary system for generating synthetic spatial omics images comprises: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for: receiving a histopathology image depicting a diseased region of interest of an input tissue sample; and generating a synthetic spatial omics image depicting one or more stained structures of interest within the diseased region of interest by inputting the histopathology image into a generator of a trained generative adversarial network (GAN) model.