Digital Polymerase Chain Reaction for Assessment of Mutant Mitochondrial Carry-over after Nuclear Transfer for In Vitro Fertilization.


Journal

Clinical chemistry
ISSN: 1530-8561
Titre abrégé: Clin Chem
Pays: England
ID NLM: 9421549

Informations de publication

Date de publication:
06 07 2021
Historique:
received: 23 09 2020
accepted: 21 01 2021
pubmed: 7 4 2021
medline: 7 4 2022
entrez: 6 4 2021
Statut: ppublish

Résumé

The quantification of mitochondrial DNA heteroplasmy for the diagnosis of mitochondrial disease or after mitochondrial donation, is performed mainly using next-generation sequencing strategies (NGS). Digital PCR (dPCR) has the potential to offer an accurate alternative for mutation load quantification. We assessed the mutation load of 23 low-input human samples at the m.11778 locus, which is associated with Leber's hereditary optic neuropathy (LHON) using 2 droplet digital PCR platforms (Stilla Naica and Bio-Rad QX200) and the standard NGS strategy. Assay validation was performed by analyzing a titration series with mutation loads ranging from 50% to 0.01%. A good concordance in mutation rates was observed between both dPCR techniques and NGS. dPCR established a distinctly lower level of background noise compared to NGS. Minor alleles with mutation loads lower than 1% could still be detected, with standard deviations of the technical replicates varying between 0.07% and 0.44% mutation load. Although no significant systematic bias was observed when comparing dPCR and NGS, a minor proportional bias was detected. A slight overestimation of the minor allele was observed for the NGS data, most probably due to amplification and sequencing errors in the NGS workflow. dPCR has proven to be an accurate tool for the quantification of mitochondrial heteroplasmy, even for samples harboring a low mutation load (<1%). In addition, this alternative technique holds multiple benefits compared to NGS (e.g., less hands-on time, more straightforward data-analysis, and a lower up-front capital investment).

Sections du résumé

BACKGROUND
The quantification of mitochondrial DNA heteroplasmy for the diagnosis of mitochondrial disease or after mitochondrial donation, is performed mainly using next-generation sequencing strategies (NGS). Digital PCR (dPCR) has the potential to offer an accurate alternative for mutation load quantification.
METHODS
We assessed the mutation load of 23 low-input human samples at the m.11778 locus, which is associated with Leber's hereditary optic neuropathy (LHON) using 2 droplet digital PCR platforms (Stilla Naica and Bio-Rad QX200) and the standard NGS strategy. Assay validation was performed by analyzing a titration series with mutation loads ranging from 50% to 0.01%.
RESULTS
A good concordance in mutation rates was observed between both dPCR techniques and NGS. dPCR established a distinctly lower level of background noise compared to NGS. Minor alleles with mutation loads lower than 1% could still be detected, with standard deviations of the technical replicates varying between 0.07% and 0.44% mutation load. Although no significant systematic bias was observed when comparing dPCR and NGS, a minor proportional bias was detected. A slight overestimation of the minor allele was observed for the NGS data, most probably due to amplification and sequencing errors in the NGS workflow.
CONCLUSION
dPCR has proven to be an accurate tool for the quantification of mitochondrial heteroplasmy, even for samples harboring a low mutation load (<1%). In addition, this alternative technique holds multiple benefits compared to NGS (e.g., less hands-on time, more straightforward data-analysis, and a lower up-front capital investment).

Identifiants

pubmed: 33822904
pii: 6211385
doi: 10.1093/clinchem/hvab021
doi:

Substances chimiques

DNA, Mitochondrial 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

968-976

Subventions

Organisme : Special Research Funding
Organisme : Ghent University
ID : BOF18/DOC/200
Organisme : Flemish Fund for Scientific Research
ID : G051017N

Commentaires et corrections

Type : CommentIn

Informations de copyright

© American Association for Clinical Chemistry 2021.

Auteurs

Olivier Tytgat (O)

Laboratory of Pharmaceutical Biotechnology, Ghent University, Ghent, Belgium.
Department of Life Science Technologies, Imec, Leuven, Belgium.

Mao-Xing Tang (MX)

Ghent-Fertility and Stem Cell Team (G-FaST), Department for Reproductive Medicine, Ghent University Hospital, Ghent, Belgium.

Willem van Snippenberg (W)

Department of Internal Medicine and Pediatrics, HIV Cure Research Center, Ghent University, Ghent, Belgium.

Annekatrien Boel (A)

Ghent-Fertility and Stem Cell Team (G-FaST), Department for Reproductive Medicine, Ghent University Hospital, Ghent, Belgium.

Ramesh Reddy Guggilla (RR)

Ghent-Fertility and Stem Cell Team (G-FaST), Department for Reproductive Medicine, Ghent University Hospital, Ghent, Belgium.

Yannick Gansemans (Y)

Laboratory of Pharmaceutical Biotechnology, Ghent University, Ghent, Belgium.

Michiel Van Herp (M)

Department of Morphology, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium.

Sofie Symoens (S)

Department of Biomolecular Medicine, Center for Medical Genetics Ghent (CMGG), Ghent University Hospital, Ghent, Belgium.

Wim Trypsteen (W)

Department of Internal Medicine and Pediatrics, HIV Cure Research Center, Ghent University, Ghent, Belgium.

Dieter Deforce (D)

Laboratory of Pharmaceutical Biotechnology, Ghent University, Ghent, Belgium.

Björn Heindryckx (B)

Ghent-Fertility and Stem Cell Team (G-FaST), Department for Reproductive Medicine, Ghent University Hospital, Ghent, Belgium.

Paul Coucke (P)

Department of Biomolecular Medicine, Center for Medical Genetics Ghent (CMGG), Ghent University Hospital, Ghent, Belgium.

Ward De Spiegelaere (W)

Department of Morphology, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium.

Filip Van Nieuwerburgh (F)

Laboratory of Pharmaceutical Biotechnology, Ghent University, Ghent, Belgium.

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Classifications MeSH