cfDNA Methylation Sequencing with Enzymatic Conversion and UMI Depth
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Solution Overview
Problem
Existing methylation sequencing methods, particularly bisulfite-based approaches, cause significant DNA degradation in cell-free DNA (cfDNA), limiting the sensitivity and accuracy of methylation analysis, especially in low-concentration samples, and require large blood volumes or low-depth sequencing, which is costly and inefficient.
Innovation Solution
A method involving minimally-destructive enzymatic conversion of unmethylated cytosines to uracils, followed by adapter ligation, amplification, and targeted sequencing with unique molecular identifiers, enables high-depth sequencing to determine methylation profiles accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If bisulfite conversion is used for DNA methylation analysis, then methylation mapping accuracy is improved, but DNA degradation increases significantly
Solution Approach 1:
The patent extracts and eliminates the harmful bisulfite conversion step from the methylation analysis workflow. Instead of using bisulfite treatment, the invention employs alternative methods such as enzymatic conversion or chemical conversion with different reagents that do not cause severe DNA degradation, thereby removing the source of the contradiction between accurate methylation mapping and DNA preservation
Solution Approach 2:
The patent introduces intermediary substances or methods to achieve methylation conversion without bisulfite. Specifically, it uses enzymes like TET2 dioxygenase and APOBEC cytidine deaminase as intermediaries to convert methylated cytosines, providing a gentler pathway that preserves DNA integrity while still enabling accurate methylation status determination
2Measurement precision
If large blood volumes are collected to achieve high-depth sequencing, then sequencing coverage is improved, but sample collection complexity and patient burden increase
Solution Approach 1:
The patent changes the chemical parameters of the conversion process to be less destructive to DNA. By using enzymatic conversion or alternative chemical reagents instead of bisulfite, the DNA degradation is minimized, which allows sufficient sequencing coverage to be achieved from smaller blood volumes, thereby reducing collection complexity and patient burden while maintaining high-depth sequencing capability
3Measurement precision
If bisulfite conversion is performed before library construction, then single-stranded DNA libraries are created, but endpoint information on degraded fragments is lost
Solution Approach 1:
The patent performs the conversion process after library construction rather than before, which is a preliminary action reversal. By constructing the library first with intact DNA (preserving endpoint information) and then performing gentle conversion on the library, the method maintains both the methylation analysis capability and the fragment endpoint information for comprehensive downstream analysis
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
This approach preserves the integrity of cfDNA, enhances sequencing accuracy, and allows for precise methylation profiling, supporting machine learning applications and improved disease detection.
Implementation Method 1
converting unmethylated cytosines to uracils in the nucleic acid molecule using a minimally-destructive conversion method
Data Source
AI summary
Methods and systems provided herein address current limitations of bisulfite-based methylation sequencing by improving the quality and accuracy of nucleic acid methylation sequencing and uses thereof for detection of disease. Methods that include minimally-destructive conversion methods for methylation sequencing as well as specialized UMI adapters provide for improved quality of sequencing libraries and sequencing information. Greater accuracy and more complete methylation-state information permits higher quality feature generation for use in machine learning models and classifier generation.