Multi-Omics Biomarker Detection With Unified MS and ML Curation
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Solution Overview
Problem
Current biomarker detection methods are limited by the inability to integrate multi-omics data, suffer from low sensitivity and batch-to-batch variability, and lack standardized, high-throughput approaches for biomarker validation across diverse clinical datasets.
Innovation Solution
A unified biomarker detection system utilizing mass spectrometry for the detection of proteins, metabolites, and lipids in a single workflow, leveraging a machine-learning model trained on hundreds of thousands of curated datasets to achieve reproducibility and sensitivity, with a dynamic range spanning 1 ng/L to 100 mg/L, and a two-phase database for iterative refinement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional single-omics detection methods are used, then the detection workflow is simple, but the comprehensive molecular profile coverage is limited
Solution Approach 1:
The patent combines proteomics, metabolomics, and lipidomics detection into a single integrated mass spectrometry workflow. The system simultaneously detects multiple molecular classes (proteins, metabolites, lipids) from the same biological sample using unified sample preparation and a single mass spectrometry instrument, eliminating the need for separate detection systems for each omics layer.
Solution Approach 2:
The mass spectrometry system is configured to perform multiple detection functions across different molecular classes. The same instrument and workflow can detect proteins, metabolites, and lipids, making the system universal for multi-omics analysis rather than requiring specialized equipment for each molecular type.
2Quantity of substance
If untargeted mass spectrometry is used for broad detection, then the detection range is expanded, but the sensitivity and reproducibility decrease
Solution Approach 1:
The detection workflow is segmented into distinct operational phases: sample preparation, mass spectrometry acquisition, and data processing. Within the mass spectrometry phase, the system uses data-independent acquisition (DIA) that segments the mass spectrum into multiple isolation windows, systematically analyzing different mass ranges to maintain both broad coverage and precise quantification.
Solution Approach 2:
The system dynamically adjusts mass spectrometry parameters including collision energy, isolation window width, and scan rate based on the detected analyte characteristics. These parameter changes optimize detection sensitivity and reproducibility across different molecular classes while maintaining broad detection range through adaptive method control.
3Measurement precision
If separate workflows are used for different molecular classes, then the detection specificity is high, but the technical variability increases
Solution Approach 1:
The patent merges sample preparation and detection workflows for proteins, metabolites, and lipids into a single unified process. This eliminates the technical variability introduced by multiple separate workflows while maintaining detection specificity through mass spectrometry's inherent ability to distinguish different molecular classes based on their mass-to-charge ratios.
4Measurement precision
If affinity-based detection methods are used, then the detection sensitivity for known proteins is high, but the scalability for large-scale discovery is limited
Solution Approach 1:
The system replaces affinity-based detection (which relies on physical binding mechanisms like antibodies) with mass spectrometry-based detection. This substitution enables unbiased, large-scale discovery of proteins, metabolites, and lipids without requiring prior knowledge or specific reagents for each analyte, significantly increasing throughput and scalability.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system achieves reproducibility with coefficients of variation below 10%, enabling real-time, scalable biomarker profiling for clinical applications, and supports automated disease classification with high diagnostic accuracy.
Implementation Method 1
Mass spectrometry-based detection of proteins, metabolites, and lipids is performed in a single run or in multiple consecutive runs on the same instrumentation
Data Source
AI summary
The present application provides methods and systems for single-run or minimally sequential detection and quantification of proteins, metabolites, and lipids using a unified instrumentation setup spanning a broad dynamic range (˜1 ng/L to 100 mg/L). In some embodiments, a two-phase database architecture transitions newly observed analytes from a discovery repository into a validated repository, enabling reproducible detection (CV<10%) with precise parameters (e.g., retention time, transitions). A machine-learning pipeline enhances automated peak selection by integrating large-scale manual curation with advanced feature extraction. The disclosed platform supports high-throughput multi-omics profiling of plasma or dried blood spot (DBS) samples and enables correlation with clinical factors such as age, BMI, or genetics. These systems maintain high sensitivity, scalability, and reproducibility, addressing long-standing limitations in clinical proteomics. As a result, the disclosed approach facilitates translational research, remote patient monitoring, and global healthcare implementation.