Single-Cell Genomic DNA Barcoding for SNV and CNV Detection
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Solution Overview
Problem
Current genomic analysis methods, such as next-generation sequencing, struggle to identify heterogeneous cell populations in cancer due to masked genetic heterogeneity at the individual cellular level, leading to less effective cancer treatment regimens.
Innovation Solution
A method for performing single-cell analysis that directly determines cellular genotypes from genomic DNA by barcoding, amplifying, and sequencing DNA-derived amplicons, allowing for the simultaneous identification of SNVs and CNVs, thereby overcoming the limitations of indirect methods like cDNA sequencing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If bulk measurement sequencing is used, then sequencing coverage is achieved, but genetic heterogeneity at individual cellular level is masked
Solution Approach 1:
The patent segments the bulk tumor sample into individual single cells for separate genomic analysis. Each cell is isolated, barcoded, and sequenced independently, allowing detection of genetic heterogeneity at the cellular level rather than averaging signals across the entire population. This segmentation enables identification of rare mutant cells and subclonal populations that would be masked in bulk sequencing.
Solution Approach 2:
The patent introduces cell barcodes as an intermediary element that links individual cells to their sequenced DNA. The barcodes are incorporated during single-cell encapsulation and amplification, allowing computational assignment of each sequence read to its cell of origin. This intermediary enables reconstruction of cellular-level genetic information from bulk sequencing data.
2Measurement precision
If indirect methods (cDNA sequencing) are used, then cellular genotype determination is achieved, but reverse transcription errors and artifacts are introduced
Solution Approach 1:
The patent extracts and eliminates the reverse transcription step from the single-cell sequencing workflow. By developing methods that work directly with genomic DNA or mRNA without converting to cDNA, the patent removes the source of reverse transcription errors and artifacts. This extraction of the problematic step while maintaining the ability to determine cellular genotype directly improves measurement precision.
3Measurement precision
If single-cell analysis is performed, then cellular heterogeneity is detected, but analysis complexity and cost increase
Solution Approach 1:
The patent develops universal single-cell sequencing methods that can simultaneously determine multiple genotypic features (SNVs, CNVs, structural variants) from the same single-cell data. The same barcoded single-cell library can be analyzed for different mutation types using integrated computational approaches, reducing the need for separate experimental workflows and decreasing overall complexity.
Solution Approach 2:
The patent merges the detection of different mutation types (SNVs, CNVs, structural variants) into a unified single-cell analysis pipeline. By combining multiple analytical approaches and integrating computational methods, the patent reduces the number of separate experiments needed, thereby simplifying the overall workflow and reducing costs despite maintaining high measurement precision.
Data Source
AI summary
Single-cell analysis of a population of cells reveals cellular genotypes of individual cells. Accordingly, methods for performing single-cell analyses for a plurality of cells to determine cellular genotypes of individual cells are described. Generally, the single-cell Also described are methods of analysis involving targeted DNA-seq to generate sequence reads derived from genomic DNA that are used to determine the cell genotype. Methods described also include determining a cell genotype, particularly in distinguishing a genotype amongst a heterogenous population of cells, through analysis of different classes of cell mutations such as short-sequence mutations (e.g., SNVs) in combination with structural variants (e.g., CNVs). Reagents, materials, and kits for performing the same are also described. The identification of subpopulations of cells is informative for improving the understanding of cellular biology, especially in the context of diseases such as cancer, and is further informative for the better design of diagnostics and therapies.