Multiplex Image Preprocessing for Immune Cell Phenotype Analysis
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Solution Overview
Problem
High-throughput spatial imaging technologies face challenges with image artifacts such as background noise, channel crosstalk, and antibody aggregation, which deteriorate data quality and complicate downstream analysis, particularly in the study of immune microenvironments.
Innovation Solution
An image pre-processing pipeline (IMClean) and a semi-supervised clustering pipeline (IMmuneCite) are employed to remove artifacts and improve image quality. The IMClean pipeline converts image data and removes noise using algorithms, while the IMmuneCite clustering pipeline identifies immune cell phenotypes and characterizes cellular states, utilizing supervised and unsupervised algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If high-throughput spatial imaging technologies are used to detect multiple biomolecules simultaneously, then the productivity and measurement precision are improved, but image artifacts such as background noise, channel crosstalk, and antibody aggregation increase, deteriorating data quality
Solution Approach 1:
The patent applies preliminary action by implementing image pre-processing steps before downstream analysis. The pipeline performs denoising, spillover correction, and artifact removal on individual signal files and combined images prior to cell classification and phenotype analysis, ensuring that artifacts are eliminated in advance to maintain data quality throughout the workflow
Solution Approach 2:
The patent uses an intermediary approach by introducing a dedicated image pre-processing pipeline as an intermediate step between image acquisition and downstream analysis. This pipeline acts as a mediator that processes and cleans the images through multiple algorithms (denoising filters, spillover correction, artifact removal) before the cleaned images are used for cell classification and phenotype identification
2Measurement precision
If image pre-processing is applied to remove artifacts, then the measurement precision and data quality are improved, but the device complexity and processing time increase
Solution Approach 1:
The patent applies segmentation by dividing the complex pre-processing task into separate modular steps: individual signal file processing (denoising, spillover correction), combined image processing (artifact removal), and downstream analysis. Each step handles specific artifacts through dedicated algorithms, making the overall complex process manageable and systematic
Solution Approach 2:
The patent utilizes parameter changes by adjusting processing parameters adaptively. The pipeline modifies denoising thresholds, correction factors, and processing intensity based on image characteristics and artifact levels, allowing the system to optimize measurement precision while managing computational complexity through parameter tuning rather than fixed rigid protocols
3Reliability
If iterative denoising processes are applied to individual signal files, then the artifact removal effectiveness is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by implementing iterative denoising processes that can be adjusted in intensity. The pipeline allows for variable numbers of iteration cycles and adjustable denoising thresholds, enabling the system to apply sufficient processing to remove artifacts effectively while avoiding excessive computation by stopping iterations once adequate artifact removal is achieved
Solution Approach 2:
The patent incorporates feedback mechanisms where the pipeline evaluates image quality and artifact levels at different processing stages. This feedback allows the system to adjust processing intensity dynamically, continuing iterative denoising only as long as artifacts persist, thereby optimizing the balance between artifact removal effectiveness and processing time
Data Source
AI summary
Techniques are disclosed herein that encompass image pre-processing and a semi-supervised clustering for optimization and analysis of immune-enriched single-cell proteomics data generated via multiplexed imaging technologies. This is achieved through an image pre-processing pipeline, which converts image data contained in one type of file (e.g., .mcd) into another type of file (e.g., .tiff) and removes artifact signals from the image data using various algorithms to generate improved image data. Thereafter, a semi-supervised clustering pipeline analyzes the improved image data using various techniques, including implementing a supervised algorithm to identify metaclusters such as general immune phenotypes (e.g., CD4−T-cells, Macrophages, Neutrophils, etc.) as well as non-immune phenotypes while implementing an unsupervised algorithm that enables the identification of specific subclusters and a more in-depth cellular status characterization.