Amplicon Rx(TM), post-PCR clean-up and concentration specifically for forensic DNA multiplex STR PCR

Abstract

Objective: To develop a method to enhance the sensitivity of current multiplex short tandem repeat (STR) DNA profiling without (i) increasing the number of polymerase chain reaction (PCR) cycles, or (ii) developing an entirely new multiplex. This has been accomplished by using a kit that (a) concentrates and (b) purifies all of the generated PCR amplicons from current commercially available multiplex kits such that the efficiency of capillary electrophoresis (CE) electrokinetic injection is dramatically increased, and a signal boost of up to 20 fold (×20) can be achieved. Materials and Methods: The developed kit was initially tested using known positive controls diluted to mimic low template PCR reactions. In addition to these reproducible laboratorygenerated examples, real world low-level DNA samples derived from processing individual latent friction ridge impressions identified with powders (i.e., fingerprints) were also evaluated. Commercial DNA-STR kits from Applied Biosystems and Promega were both tested as were DNA profile mixtures from two contributors. Final electropherograms and DNA profiles before and after post-PCR clean-up with Amplicon Rx(TM) were compared. Results: In all samples tested, both laboratory and forensic, Amplicon Rx(TM) treatment of post-PCR samples provided at least a 5-10 fold boost in electrophoretic peak heights, i.e. a 5-10 fold increase in RFU signal for all alleles. No changes in peak balance were observed for either single source or mixed samples; the increased signal was true to the original electrophoresis. In addition, typed peaks could be recovered from low-level DNA samples using Amplicon Rx(TM). Conclusions: Amplicon Rx(TM) can recover alleles that are present in the original low template PCR sample, but are "invisible" when current protocols, which analyze only 5% of PCR reaction, are used for CE. This method can successfully and efficiently recover useful, probative data from low-level samples