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Tytuł pozycji:

Revisiting single cell analysis in forensic science.

Tytuł:
Revisiting single cell analysis in forensic science.
Autorzy:
Watkins DRL; Forensic and National Security Sciences Institute, Syracuse University, 100 College Place 120 Life Science Building, Syracuse, NY, 13244, USA.
Myers D; Fieldable Forensics, PO Box 141, Skaneateles Falls, NY, 13153, USA.
Xavier HE; Forensic and National Security Sciences Institute, Syracuse University, 100 College Place 120 Life Science Building, Syracuse, NY, 13244, USA.
Marciano MA; Forensic and National Security Sciences Institute, Syracuse University, 100 College Place 120 Life Science Building, Syracuse, NY, 13244, USA. .
Źródło:
Scientific reports [Sci Rep] 2021 Mar 29; Vol. 11 (1), pp. 7054. Date of Electronic Publication: 2021 Mar 29.
Typ publikacji:
Journal Article; Research Support, Non-U.S. Gov't
Język:
English
Imprint Name(s):
Original Publication: London : Nature Publishing Group, copyright 2011-
MeSH Terms:
Forensic Sciences*
Single-Cell Analysis/*methods
Alleles ; DNA Fingerprinting/methods ; Humans ; Polymerase Chain Reaction/methods ; Probability
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Entry Date(s):
Date Created: 20210330 Date Completed: 20211026 Latest Revision: 20211026
Update Code:
20240105
PubMed Central ID:
PMC8007698
DOI:
10.1038/s41598-021-86271-6
PMID:
33782417
Czasopismo naukowe
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 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, varied levels of contribution, and allele masking. 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. 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 in PCR cycle number had less impact. Overall data quality improved notably when increasing PCR from 29 to 30 cycles, less improvement and more volatility was introduced at 31 cycles. The average random match probabilities for the 29, 30, and 31 cycle sets at 150RFU are 1 in 2.4 × 10 18  ± 1.46 × 10 19 , 1 in 1.49 × 10 25  ± 5.8 × 10 25 , and 1 in 1.83 × 10 24  ± 8.09 × 10 24 , respectively. This demonstrates the current power of single cell analysis in removing the need for complex mixture analysis.

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