SubDivision-Seq UMI Matrix for Rare Mutation Detection
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Solution Overview
Problem
Current Unique Molecular Identifier (UMI) technologies face challenges in detecting rare mutations, particularly in early-stage cancers, due to high levels of false positives and false negatives, and inefficient amplification of rare ctDNA fragments, which are exacerbated by limitations in PCR-based and ligation-based methods.
Innovation Solution
The SubDivision-Seq method forms a two-dimensional matrix of UMIs on DNA molecules, allowing for the subdivision of primary clones into subclones without requiring complementary UMI pairs, enabling high sensitivity and accuracy in amplifying low quantities of DNA while reducing sequencing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If PCR-based methods are used to assign UMIs to target molecules, then UMI assignment can be achieved, but the amplification of rare ctDNA targets is insufficient and redundant UMIs are introduced
Solution Approach 1:
The patent divides the UMI assignment process into two distinct stages: (1) initial UMI assignment using ligation-based methods to attach unique barcodes to target molecules, and (2) subsequent amplification using PCR with UMIs already assigned. This segmentation allows each method to perform its optimal function without compromising the other.
Solution Approach 2:
The patent performs UMI assignment through ligation before PCR amplification. By preliminarily assigning UMIs to target molecules and creating a library structure with adapter sequences, the system ensures that subsequent PCR amplification does not introduce redundant UMIs, as the UMIs are already fixed on the template molecules.
2Loss of substance
If ligation-based methods are used to minimize ctDNA loss, then adapter ligation efficiency is improved, but PCR amplification is still limited and base errors occur during end-repairing
Solution Approach 1:
The patent extracts and removes the problematic end-repairing step from the workflow. By using ligation-based UMI assignment that does not require end-repairing of target molecules, the method eliminates the source of base errors while maintaining high ctDNA recovery through efficient adapter ligation.
Solution Approach 2:
The patent introduces adapter molecules as intermediaries that facilitate UMI assignment through ligation without requiring end-repairing of the target ctDNA molecules. The adapters serve as mediators that can be ligated directly to the target molecules, bypassing the error-prone end-repairing step while still enabling subsequent PCR amplification.
3Measurement precision
If quantitative assignment of UMI to target molecules is enforced, then UMI-to-target ratio is preserved, but target amplification is limited and sampling of rare ctDNA is difficult
Solution Approach 1:
The patent performs UMI assignment and library construction preliminarily before bulk PCR amplification. By assigning UMIs to individual target molecules and constructing libraries with proper adapter structures in advance, the system preserves quantitative information while enabling subsequent large-scale amplification of all library molecules without introducing new UMIs.
Solution Approach 2:
The patent creates library copies with embedded UMIs that can be amplified extensively. Each original target molecule generates library copies containing the original UMI, allowing quantitative preservation through the copying process while enabling sufficient amplification for downstream sequencing by analyzing the distribution of UMI-containing copies.
4Device complexity
If single consensus methods are used to organize target molecules with UMI, then the process is simplified, but random errors cannot be effectively removed
Solution Approach 1:
The patent transitions from one-dimensional UMI organization (single consensus sequence per UMI) to two-dimensional UMI organization (matrix of UMIs across multiple sequences). By arranging UMIs in a matrix structure where multiple sequences share UMIs, the system enables robust error removal through consensus calling across the matrix dimension, significantly improving reliability while maintaining manageable complexity.
Data Source
AI summary
Methods, apparatuses and compositions for generating highly sensitive and accurate sequencing results of massive parallel sequencing (NGS). The methods and compositions may be referred to as SubDivision-Seq, and may comprise two parts. The first part includes making a target-enriched DNA library, organizing the UMIs on DNA molecules to form primary clones and subdividing the primary clones into subclones. The second part includes sequencing the DNA library by NGS, deducing consensus sequence from each subclone, then deducing consensus sequence in each primary clone.