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Rapid sequencing‐based diagnosis of infectious bacterial species from meningitis patients in Zambia

Publication

Date: 7th November 2019 | Source: Clinical and Translational Immunology

Authors: So Nakagawa, Shigeaki Inoue, Kirill Kryukov, Junya Yamagishi, Ayumu Ohno, Kyoko Hayashida, Ruth Nakazwe, Mox Kalumbi, Darlington Mwenya, Nana Asami, Chihiro Sugimoto, Mable M Mutengo, Tadashi Imanishi.

Objectives
We have developed a portable system for the rapid determination of bacterial composition for the diagnosis of infectious diseases. Our system comprises of a nanopore technology‐based sequencer, MinION, and two laptop computers. To examine the accuracy and time efficiency of our system, we provided a proof‐of‐concept for the detection of the causative bacteria of 11 meningitis patients in Zambia.

Methods
We extracted DNA from cerebrospinal fluid samples of each patient and amplified the 16S rRNA gene regions. The sequencing library was prepared, and the sequenced reads were simultaneously processed for bacterial composition determination using the minimap2 software and the representative prokaryote genomes.

Results
The sequencing results of four of the six culture‐positive samples were consistent with those of conventional culture‐based methods. The dominant bacterial species in each of these samples were identified from the sequencing data within only 3 min. Although the major bacterial species were also detected from the other two culture‐positive samples and five culture‐negative samples, their presence could not be confirmed. Moreover, as a whole, although the number of sequencing reads obtained within a short sequencing run was small, there was no change in the major bacterial species over time with prolonged sequencing. In addition, the processing time strongly correlated with the number of sequencing reads used for the analysis.

Conclusion
Our results suggest that time‐effective analysis could be achieved by determining the number of sequencing reads required for the rapid diagnosis of infectious bacterial species depending on the complexity of bacterial species in a sample.

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