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Updated: Jan 9, 2026
Sequencing of mRNA from Whole Blood using Nanopore Sequencing
Published on: June 3, 2019
Rapid donor-specific single nucleotide variation detection by nanopore sequencing of ex vivo lung perfusate
Haruchika Yamamoto1,2, Jonathan Allen1, Adam Sundby1
1Toronto Lung Transplant Program and Latner Thoracic Research Laboratories, Toronto General Hospital Research Institute, University Health Network, University of Toronto, Toronto, Ontario, Canada.
Background:
Donor-derived cell-free DNA (ddcfDNA) has been shown to be useful in monitoring lung graft health, and single nucleotide variations (SNVs) between donor and recipient are used to identify ddcfDNA in post-transplant recipient blood. One limitation is the need to map donor or recipient SNVs prior to calculating %ddcfDNA. In this study, we use Nanopore sequencing of ex vivo lung perfusion perfusate cfDNA to map donor SNVs and validate it using standard short-read whole genome sequencing (WGS). Methods: cfDNA was extracted from 11 clinical ex vivo lung perfusion perfusate samples and sequenced using a Nanopore sequencer. SNVs were identified by comparison to a reference genome and then filtered for homozygous calls overlapping the 1000 Genomes SNP database. Matching short-read WGS was performed on 6 matching samples to act as a gold standard. Following mapping, %ddcfDNA was calculated in cell-free DNA (cfDNA) collected from matching post-lung transplant recipient plasma using SNVs called by Nanopore vs SNVs called by short-read WGS and compared for accuracy.
Results:
Nanopore sequencing yielded genomic data with a median coverage of 4.88x (range 2.27-8.79) using a single flow cell with a median run length of 72 hours. The median general error rate of the sequence was 5.55% (range 4.98%-6.49%), and the median number of SNV with a depth > 3 and overlap with the 1000 Genomes SNP database was 246,993 (range 99,357-313,721). The positive predictive value of SNVs identified using 2X to 6X coverage cutoffs ranged from 66.6% to 96.9%, with a median value of 90.3% at 6X coverage. Correlation analysis showed a strong correlation between results by Nanopore sequencing and results by WGS for detecting %ddcfDNA in post-transplant plasma (R^2 = 0.996, p < 0.001).
Conclusions:
Despite the lower sequencing accuracy and depth obtained from Nanopore sequencing, a high positive predictive value can be achieved in a set of donor-specific SNVs when appropriately filtered by read depth and overlap with SNP databases. This demonstrates the potential for the use of Nanopore sequencing to generate personalized donor cfDNA maps for use in post-operative donor-derived plasma cfDNA identification.
