Lineage Sequencing for Single-Cell Somatic Mutation Detection
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Solution Overview
Problem
Current methods for somatic mutation detection in mammalian and bacterial cells are limited by their inability to accurately and quantitatively analyze genome-wide somatic mutations, especially in terms of sensitivity, specificity, and temporal resolution, leading to incomplete understanding of mutation accumulation and its impact on health.
Innovation Solution
The method of lineage sequencing, which involves isolating single cells from a clonal population, sequencing their genomic DNA, and tracing mutations across cell lineages to determine true somatic mutations with high accuracy and minimal selection biases, allowing for the reconstruction of mutation history and correlation analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If molecular consensus sequencing methods with barcodes are used to detect rare alleles in bulk samples, then detection sensitivity is improved, but protocol complexity increases and genome-wide analysis becomes challenging
Solution Approach 1:
The method segments the bulk sample into individual single cells, each containing a unique molecular barcode. This segmentation allows tracking of mutations through cell lineages without requiring complex consensus sequencing protocols, as each cell's mutations can be independently identified and traced back to its parent cell.
Solution Approach 2:
The method performs preliminary action by incorporating unique molecular barcodes into cells before the sequencing process. These barcodes are inherited through cell divisions, allowing retrospective tracking of mutation history without requiring complex post-sequencing analysis or ultra-deep sequencing to achieve accurate detection.
2Measurement precision
If single cells are isolated and cloned for sequencing to enrich for somatic variants, then variant enrichment is improved, but uncertainty in mutation rate calculations increases due to unknown cell division events
Solution Approach 1:
The method performs preliminary action by recording the complete lineage history of each single cell through time-lapse imaging before sequencing. This creates a digital pedigree that tracks every cell division event, allowing accurate correlation of mutations with specific generation numbers and eliminating uncertainty in mutation rate calculations.
Solution Approach 2:
The method creates a digital copy of the lineage information through automated imaging and tracking systems. This digital pedigree serves as a permanent record of cell division history that can be directly correlated with sequencing data, preserving lineage information that would otherwise be lost in traditional cloning approaches.
3Measurement precision
If mutation accumulation experiments run for hundreds of generations in bacterial cells, then mutation enrichment is improved, but the method becomes impractical for mammalian cells due to extended time requirements
Solution Approach 1:
The method changes the key parameter of generation number by utilizing the much shorter cell cycle time of bacteria (minutes) compared to mammalian cells (days). This parameter change allows achieving the same level of mutation enrichment in a practical time frame, making the approach feasible for both bacterial and mammalian systems without requiring hundreds of generations.
Solution Approach 2:
The method substitutes the mechanical constraint of time with a computational solution by using automated imaging and digital tracking to monitor and record lineage information. This replacement of time-intensive manual tracking with automated systems enables efficient monitoring of mutation accumulation across multiple generations without proportionally increasing experiment duration.
4Reliability
If direct single-cell methods are used for mutation detection, then selection bias is reduced, but sensitivity and accuracy of variant detection decrease compared with bulk approaches
Solution Approach 1:
The method segments the analysis into individual cell lineages while preserving the ability to perform comparative analysis across multiple lineages. By tracking mutations through unique barcodes and recorded pedigrees, the method maintains the advantages of direct single-cell analysis (no selection bias) while achieving bulk-level detection accuracy through statistical power from multiple independent lineage measurements.
Solution Approach 2:
The method implements feedback by using the recorded lineage information to guide and validate mutation detection. The known pedigree structure provides a framework for interpreting sequencing data, allowing verification of detected variants against expected inheritance patterns and improving overall detection accuracy while maintaining the unbiased nature of direct single-cell analysis.
Data Source
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AI summary
Most somatic mutations are harmless, but some lead to a phenotypic consequence. The present invention provides methods and tools to elucidate exactly how mutations appear in the genome using detection of mutations in a cell lineage. The present invention also provides for determining the effect of environmental stimuli and drugs on somatic mutations. Finally, the present invention provides for personalized medicine by determining the effect of drugs on somatic mutations in cells obtained from a subject in need thereof.