Single-Cell DNA Profiling for Forensic Mixture Deconvolution
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Solution Overview
Problem
Current forensic DNA mixture interpretation methods face challenges in distinguishing contributors, particularly in complex mixtures with overlapping alleles, allele drop-out, and uncertainty in the number of contributors, especially in cases involving related individuals.
Innovation Solution
A method involving single-cell profiling, where cells are individually isolated and genotyped, allowing for the generation of consensus profiles that can be compared directly to reference profiles, eliminating allele overlapping issues and enabling precise identification of contributors without relying on mixture ratios or probabilistic genotyping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard CE-STR analysis with pooled DNA extraction is used, then the process is simple and widely applicable, but the ability to distinguish contributors in complex mixtures deteriorates due to overlapping alleles and uncertainty in number of contributors
Solution Approach 1:
The invention segments the pooled DNA mixture into individual single-cell profiles by isolating and analyzing cells separately. Each cell is genotyped independently, producing discrete contributor profiles that can be directly compared to reference samples, thereby eliminating the overlapping allele problem inherent in pooled DNA analysis.
Solution Approach 2:
The invention introduces single-cell isolation and whole-genome amplification as intermediary steps between DNA extraction and genotyping. These intermediary processes convert the complex pooled DNA mixture into simplified single-cell profiles, making contributor identification straightforward through direct profile comparison.
2Reliability
If single-cell profiling is implemented, then contributor identification accuracy improves by eliminating allele overlapping, but the process complexity increases due to additional isolation and amplification steps
Solution Approach 1:
The invention performs preliminary single-cell isolation and whole-genome amplification before genotyping. By preparing single-cell DNA profiles in advance, the subsequent contributor identification process becomes straightforward and reliable, as each cell's genotype can be directly compared to reference profiles without complex mixture deconvolution.
3Measurement precision
If probabilistic genotyping software is used, then some mixture interpretation is possible, but uncertainty in number of contributors and allele drop-out/drop-in reduces determination confidence
Solution Approach 1:
The invention extracts individual contributor information from the mixture by isolating single cells and generating separate profiles for each. This extraction process removes the confounding effects of allele overlap, drop-out, and drop-in that plague pooled DNA analysis, allowing direct observation of each contributor's true genotype without probabilistic inference.
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 provides a more precise and accurate determination of contributors, reducing uncertainty and improving the reliability of investigative leads, even in complex mixtures like family trios, by analyzing each cell independently and generating consensus profiles that can be matched against DNA databases.
Implementation Method 1
generating amplicons from each cell in the biological sample
Implementation Method 2
detect DNA fragments through Capillary Electrophoresis (CE)
Data Source
AI summary
The present invention includes methods and kits for determining one or more nucleic acid contributors to a sample or specimen from single cells in a sample, comprising: counting isolated cells and cell types; determining a mixture ratio of isolated cells and cell types; generating amplicons from each cell in the sample; calculating from the counted isolated cells, the cell types, the mixture ratio and amplicons from each single cell; comparing the amplicons a reference or known amplicon profile from a subject suspected of contributing nucleic acids; clustering the cells to contributors; identifying a number of contributors to the biological sample or specimen; generating consensus profiles for each contributor, and comparing the consensus profde of each contributor to a reference or known amplicon profde from a subject suspected of contributing nucleic acids to the biological sample or specimen.