Abstract
Background
Forensic science has yet to take full advantage of single cell analysis. Its greatest benefit is the ability to alleviate the challenges associated with DNA mixture analysis, which, despite the emergence of probabilistic genotyping, remains a significant hurdle in forensic science. Many of the factors that cause complexity in mixture interpretation are absent in single cell analyses — multiple contributors and varied levels of contribution and allele masking. Recent technological innovations have been developed within the scientific community that mitigate the historical risks of single cell analysis in forensic casework. This study revisits single cell analyses in the context of forensic identification, introducing previously unseen depth to the characterization of data generated from single cells using a novel pipeline that includes recovery of single cells using the DEPArray™ NxT and amplification using the PowerPlex Fusion 6c kit with varied PCR cycles (29, 30, and 31); the resulting allelic signal was assessed using analytical thresholds of 10, 100 and 150RFU.
Results
The mean peak heights across the sample sets generally increased as cycle number increased, 75.0 ± 85.3, 147.1 ± 172.6, and 226.1 ± 298.2 RFU, for 29, 30, and 31 cycles, respectively. The average proportion of allele/locus dropout was most significantly impacted by changes in the detection threshold, whereas increases f PCR cycle number had less impact. Overall data quality improved notably when increasing PCR from 29 to 30 cycles; however, less improvement and more volatility introduced at 31 cycles. The average random match probabilities (RMP) for the 29, 30, and 31 cycle sets at 150RFU are 1 in 2.4 × 1018 ± 1.46 × 1019, 1 in 1.49 × 1025 ± 5.8 × 1025, and 1 in 1.83 × 1024 ± 8.09 × 1024, respectively.
Conclusions
This study introduces and optimizes an analytical pipeline to generate ample high quality data from single cells that can be used in forensic analyses, thus removing the need for complex mixture interpretation. An analysis of the impact of PCR cycles on single cells revealed a significant overall improvement in the amount and quality of interpretable data when increasing from 29 to 30 cycles, while no such improvement was noted when increasing to 31 cycles.
Microfluidics-based single cell analysis reveals drug-dependent motility changes in trypanosomes