The interpretation of STR signal acquired from mixtures of cells from unknown numbers of contributors continues to be a challenge. In a mixture, the signal from each contributor is obfuscated by other contributors as well as by noise and allele dropout. While it is possible to computationally separate, or deconvolve, the contributor genotypes using peak heights and their ratios, it is difficult to achieve high deconvolution accuracy for complex mixtures, in general. Use of emerging single-cell technologies during the measurement process has the potential to overcome this challenge by disambiguating the DNA signal obtained from each cell. To realize that promise, it is necessary to produce single cell pipelines, optimized for forensic applications, and develop a signal interpretation strategy suitable for determining the number of contributors and match statistics from this new data source.
In this work we will: optimize each step of the laboratory process evaluating experimental data, and couple that optimized process with carefully chosen post-PCR processing parameters designed for single copy signal; explore and validate ultra-low amplification volumes for single-cell analysis; create the statistical tools necessary to interrogate single-cell DNA signals to obtain the number of contributors and match strengths; and, lastly, we shall compare these single-cell methodologies with state-of-the-art bulk-sample interpretation systems to create recommendations for which forensic sample classes single-cell systems are needed. This will be accomplished by comparing the number of contributors' determination and match-strengths obtained from our single-cell measurements against values obtained from probabilistic systems based on bulk processed, non-single cell data.
This project contains a research and/or development component, as defined in applicable law, and complies with Part 200 Uniform Requirements - 2 CFR 200.210(a)(14).