DEPArray™ single-cell technology: A validation study for forensic applications.

DEPArray™ technology DNA mixture deconvolution Image-based digital cell sorting Single-cell genotyping Validation

Journal

Forensic science international. Genetics
ISSN: 1878-0326
Titre abrégé: Forensic Sci Int Genet
Pays: Netherlands
ID NLM: 101317016

Informations de publication

Date de publication:
16 Feb 2024
Historique:
received: 12 10 2023
revised: 17 01 2024
accepted: 14 02 2024
medline: 28 2 2024
pubmed: 28 2 2024
entrez: 27 2 2024
Statut: aheadofprint

Résumé

In forensics investigations, it is common to encounter biological mixtures consisting of homogeneous or heterogeneous components from multiple individuals and with different genetic contributions. One promising mixture deconvolution strategy is the DEPArray™ technology, which enables the separation of cell populations before genetic analysis. While technological advances are fundamental, their reliable validation is crucial for successful implementation and use for casework. Thus, this study aimed to 1) systematically validate the DEPArray™ system concerning specificity, sensitivity, repeatability, and contamination occurrences for blood, epithelial, and sperm cells, and 2) evaluate its potential for single-cell analysis in the field of forensic science. Our findings confirmed the effective identification of different cell types and the correct assignment of successfully genotyped single cells to their respective donor(s). Using the NGM Detect™ Amplification Kit, the average profile completeness for diploid cells was approximately 80%, with ∼ 290 RFUs. In contrast, haploid sperm analysis yielded an average completeness of 51% referring to the haploid reference profile, accompanied by mean peak heights of ∼ 176 RFUs. Although certain alleles of heterozygous loci in diploid cells showed strong imbalances, the overall peak balances yielded acceptable values above ≥ 60% with a mean value of 72% ± 0.21, a median of 77%, but with a maximum imbalance of 9% between heterozygous peaks. Locus dropouts were considered stochastic events, exhibiting variations among donors and cell types, with a notable failure incidence observed for TH01. Within the wet-lab experimentation with >500 single cells for the validation, profiling was performed using the consensus approach, where profiles were selected randomly from all data to better mirror real casework results. Nevertheless, complete profiles could be achieved with as few as three diploid cells, while the average success rate increased to 100% when using profiles of 6-10 cells. For sperms, however, a consensus profile with completeness >90% of the autosomal diploid genotype could be attained using ≥15 cells. In addition, the robustness of the consensus approach was evaluated in the absence of the respective reference profile without severe deterioration. Here, increased stutter peaks (≥ 15%) were found as the main artifact in single-cell profiles, while contamination and drop-ins were ascertained as rare events. Lastly, the technique's potential and limitations are discussed, and practical guidance is provided, particularly valuable for cold cases, multiple perpetrator rapes, and analyses of homogeneous mixed evidence.

Identifiants

pubmed: 38412740
pii: S1872-4973(24)00020-6
doi: 10.1016/j.fsigen.2024.103026
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

103026

Informations de copyright

Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare they have no conflict of interest.

Auteurs

Janine Schulte (J)

Institute of Forensic Medicine, University Basel, Pestalozzistrasse 22, Basel 4056, Switzerland.

Amke Caliebe (A)

Institute of Medical Informatics and Statistics, Kiel University and University-Hospital Schleswig-Holstein, Brunswiker Str. 10, Kiel 24105, Germany.

Michael Marciano (M)

Forensic & National Security Sciences Institute, Syracuse University, 900 S Crouse Ave, Syracuse, NY 13244 , USA.

Pia Neuschwander (P)

Departement of Clinical Research, c/o Universitätsspital Basel, Spitalstrasse 8/12, Basel 4031, Switzerland.

Ilona Seiberle (I)

Institute of Forensic Medicine, University Basel, Pestalozzistrasse 22, Basel 4056, Switzerland.

Eva Scheurer (E)

Institute of Forensic Medicine, University Basel, Pestalozzistrasse 22, Basel 4056, Switzerland.

Iris Schulz (I)

Institute of Forensic Medicine, University Basel, Pestalozzistrasse 22, Basel 4056, Switzerland. Electronic address: Iris.schulz@bs.ch.

Classifications MeSH