Selectively analyzing and interpreting DNA from multiple donors with a full Single-Cell strategy

Assessing the degree of consistency between genetic profiles from suspects and profiles is, typically, undertaken by probabilistic models that sample over all possible genotype combinations. As the number of contributors increases and the DNA copy numbers decrease, the signal becomes so complex as to render uninformative weights-of-evidence. The application of single-cell techniques, however, has the potential to fill the gap left by a traditional DNA pipeline, but it comes with its own challenges that must first be overcome. Effective single-cell strategies will be reliant upon four factors: 1) efficient methods able to reliably separate and collect single cells, 2) efficient extraction from the cell's matrix, 3) direct-to-PCR and post-PCR processes that render single-copy limit of detection, and 4) interpretation paradigms that consider the nuances associated with single-cell signal. This work shall endeavor to develop optimal procedures for each factor, generating a full single-cell pipeline, based on sound analytical and statistical principles. Specifically, previous work on optimized post-PCR processes will be coupled with low-copy cell separation techniques based on dielectrophoresis (DepArray Technology) and efficient DNA extraction to directly amplify forensically relevant short tandem repeats. The n single-cell electropherograms from an unknown number of contributors will be grouped into m clusters using a variety of techniques, which will be compared by evaluating group misclassification rates. A likelihood ratio for each of the m groups shall be computed using probabilistic methods for the single-cell case and compared against bulk mixture results to identify the samples-types that will likely benefit from single-cell analysis. Note: 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). CA/NCF