Duplex Sequencing for Early Clonal Expansion Detection
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Solution Overview
Problem
Current assays lack sensitivity to detect early stage clonal expansion and neoplastic clonal selection in mixed cell populations, and there is a need for tools to assess successful genome editing without non-targeted nucleic acid alterations.
Innovation Solution
Utilizing Duplex Sequencing to characterize cell populations post-genomic editing by generating error-corrected sequence reads from double-stranded DNA molecules, comparing strand sequences, and analyzing correspondences to determine genomic locus edits and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current assays are used to detect clonal expansion, then the assay simplicity is maintained, but the detection sensitivity is insufficient for early stage selection
Solution Approach 1:
The detection process is segmented into distinct stages: (1) ligating asymmetric adapters to DNA fragments, (2) generating strand-specific copies with unique molecular identifiers, (3) sequencing both strands independently, and (4) comparing sequences to identify true variants. This segmentation allows each step to be optimized for sensitivity while maintaining overall assay manageability through systematic organization of complex procedures
Solution Approach 2:
Asymmetric adapter molecules serve as intermediaries that carry unique molecular identifiers (UMIs) and strand-specific tags. These adapters mediate between the DNA sample and sequencing process, enabling error correction by allowing comparison of both strands while tracking individual molecule origins. The adapter structure includes components that facilitate ligation, amplification, and sequencing while preserving information needed for error correction
2Reliability
If standard sequencing is used for genome editing assessment, then the process is straightforward, but false positives from sequencing errors cannot be distinguished from true edits
Solution Approach 1:
The method applies different treatment and tagging strategies to different strands of the DNA molecule. Each strand receives a unique molecular identifier and strand-specific tag during adapter ligation, allowing local differentiation and tracking. This local quality assignment enables subsequent comparison to distinguish true variants from sequencing errors that would appear randomly on individual strands
Solution Approach 2:
The method implements feedback through comparison of both sequenced strands. Sequencing results from the first strand inform the interpretation of the second strand sequence, and vice versa. True genome editing events will appear as consistent variants on both strands, while sequencing errors will appear as discordant variants that can be identified and corrected through this feedback mechanism
3Measurement precision
If high sensitivity detection is implemented, then low-frequency variants can be detected, but the risk of false positives from sequencing errors increases
Solution Approach 1:
The method converts the harmful effect of sequencing errors into a beneficial feature by using the errors themselves as markers. Since sequencing errors occur randomly and independently on different strands, they create discordant variants that can be identified as false positives. True variants, by contrast, will appear as concordant variants on both strands. This approach transforms the problem of sequencing errors into a solution for distinguishing true signals from noise
Solution Approach 2:
The sequencing method creates a composite information structure by combining data from both DNA strands with their associated unique molecular identifiers and strand-specific tags. This composite data structure allows for sophisticated error correction algorithms that can distinguish true variants from errors by analyzing the consistency and distribution of variants across the composite dataset, thereby improving both sensitivity and reliability
Data Source
AI summary
Methods for characterizing genome editing, clonal expansion and associated reagents for use in such methods are disclosed herein. Some embodiments of the technology are directed to characterizing a population of cells following an engineered genomic editing event, that includes in some embodiments characterizing genomic alterations occurring at both intended and unintended genomic loci within the genome of the populations of cells. Other embodiments are directed to utilizing Duplex Sequencing for assessing a clonal selection in mixed cell populations and/or cell populations following a genomic editing event. Further examples of the present technology are directed to methods for detecting and assessing clonal expansion of cells following a genomic editing event.