Clustering Multiplexed Spatially Resolved Biological Data
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Solution Overview
Problem
Current methods fail to simultaneously analyze large, multiplexed protein measurements across various length scales in biomedicine, particularly in capturing subcellular distribution linked to cellular phenotypic states and microenvironments in a high-throughput manner while preserving sample quality.
Innovation Solution
A method involving the processing of multiplexed image data from biological samples through clustering, where images are registered, pixel profiles are generated, and similar profiles are grouped to create cluster images, allowing for reduced dimensionality and visualization of similar structures, along with the generation of interaction maps to depict cluster interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution intracellular imaging of multiple proteins is performed, then subcellular distribution information is obtained, but data complexity and processing difficulty increase
Solution Approach 1:
The patent segments the complex multiplexed image data into individual protein channel images, then processes each channel separately through registration and pixel profile extraction before clustering. This segmentation approach breaks down the overwhelming complexity of analyzing 40+ protein channels simultaneously into manageable sequential steps, allowing high-resolution subcellular measurement while controlling processing complexity through systematic data decomposition
Solution Approach 2:
The patent introduces pixel profiles as an intermediary representation that bridges the gap between raw high-dimensional image data and meaningful biological interpretation. By converting spatially corresponding pixels across multiple channels into unified pixel profiles, the system creates an intermediate data structure that preserves subcellular distribution information while reducing complexity for subsequent clustering analysis
2Quantity of substance
If multiple images with different stains are recorded simultaneously, then comprehensive biological information is captured, but data volume and processing time increase
Solution Approach 1:
The patent performs preliminary spatial registration of all multiplexed images to a common coordinate system before any analysis. This preliminary alignment action ensures that subsequent processing steps work with pre-organized data, eliminating the need for repeated registration operations and significantly reducing overall processing time while maintaining comprehensive capture of biological information across all stained structures
Solution Approach 2:
The patent merges spatially corresponding pixels from multiple stained images into unified pixel profiles that contain information from all channels. This merging operation consolidates the large volume of separate image data into a more compact representation that preserves all biological information while reducing the dimensionality and processing burden for downstream clustering analysis
3Measurement precision
If spatial registration of images is performed to identify corresponding pixels, then accurate structure comparison is enabled, but computational complexity increases
Solution Approach 1:
The patent creates a reference coordinate system based on one image and generates corresponding coordinate mappings for all other images. This copying approach to spatial registration allows accurate identification of corresponding pixels across all multiplexed images without requiring complex iterative optimization, as each image is transformed to match the reference frame through pre-determined transformation parameters
Data Source
AI summary
The invention relates to a method for processing large multiplexed image data of a biological sample, the method comprising the steps of, recording a plurality of images of a biological sample, wherein the plurality of images comprises images having a different entity of the biological sample targeted with a predefined stain, determining spatially corresponding image pixels in the plurality of registered images, associating the spatially corresponding image pixels to a pixel profile, wherein each pixel profile comprises the pixel values of the spatially corresponding pixels and wherein the pixel profile is associated with the respective image coordinate of the spatially corresponding pixels, pooling the pixel profiles by means of a clustering method configured to determine pixel profiles with similar values, and thereby generating a plurality of clusters, each comprising pixel profiles with similar pixel values, for each cluster assigning a cluster value to the image coordinate of the pixel profiles comprised by said cluster and thereby generating a cluster image with cluster pixels.