Whole-genome and targeted sequencing of drug-resistant Mycobacterium tuberculosis on the iSeq100 and MiSeq: A performance, ease-of-use, and cost evaluation

Rebecca E Colman, Aurélien Mace, Marva Seifert, Jonathan Hetzel, Haifa Mshaiel, Anita Suresh, Darrin Lemmer, David M Engelthaler, Donald G Catanzaro, Amanda G Young, Claudia M Denkinger, Timothy C Rodwell, Rebecca E Colman, Aurélien Mace, Marva Seifert, Jonathan Hetzel, Haifa Mshaiel, Anita Suresh, Darrin Lemmer, David M Engelthaler, Donald G Catanzaro, Amanda G Young, Claudia M Denkinger, Timothy C Rodwell

Abstract

Background: Accurate, comprehensive, and timely detection of drug-resistant tuberculosis (TB) is essential to inform patient treatment and enable public health surveillance. This is crucial for effective control of TB globally. Whole-genome sequencing (WGS) and targeted next-generation sequencing (NGS) approaches have potential as rapid in vitro diagnostics (IVDs), but the complexity of workflows, interpretation of results, high costs, and vulnerability of instrumentation have been barriers to broad uptake outside of reference laboratories, especially in low- and middle-income countries. A new, solid-state, tabletop sequencing instrument, Illumina iSeq100, has the potential to decentralize NGS for individual patient care.

Methods and findings: In this study, we evaluated WGS and targeted NGS for TB on both the new iSeq100 and the widely used MiSeq (both manufactured by Illumina) and compared sequencing performance, costs, and usability. We utilized DNA libraries produced from Mycobacterium tuberculosis clinical isolates for the evaluation. We conducted WGS on three strains and observed equivalent uniform genome coverage with both platforms and found the depth of coverage obtained was consistent with the expected data output. Utilizing the standardized, cloud-based ReSeqTB bioinformatics pipeline for variant analysis, we found the two platforms to have 94.0% (CI 93.1%-94.8%) agreement, in comparison to 97.6% (CI 97%-98.1%) agreement for the same libraries on two MiSeq instruments. For the targeted NGS approach, 46 M. tuberculosis-specific amplicon libraries had 99.6% (CI 98.0%-99.9%) agreement between the iSeq100 and MiSeq data sets in drug resistance-associated SNPs. The upfront capital costs are almost 5-fold lower for the iSeq100 ($19,900 USD) platform in comparison to the MiSeq ($99,000 USD); however, because of difference in the batching capabilities, the price per sample for WGS was higher on the iSeq100. For WGS of M. tuberculosis at the minimum depth of coverage of 30x, the cost per sample on the iSeq100 was $69.44 USD versus $28.21 USD on the MiSeq, assuming a 2 × 150 bp run on a v3 kit. In terms of ease of use, the sequencing workflow of iSeq100 has been optimized to only require 27 minutes total of hands-on time pre- and post-run, and the maintenance is simplified by a single-use cartridge-based fluidic system. As these are the first sequencing attempts on the iSeq100 for M. tuberculosis, the sequencing pool loading concentration still needs optimization, which will affect sequencing error and depth of coverage. Additionally, the costs are based on current equipment and reagent costs, which are subject to change.

Conclusions: The iSeq100 instrument is capable of running existing TB WGS and targeted NGS library preparations with comparable accuracy to the MiSeq. The iSeq100 has reduced sequencing workflow hands-on time and is able to deliver sequencing results in <24 hours. Reduced capital and maintenance costs and lower-throughput capabilities also give the iSeq100 an advantage over MiSeq in settings of individualized care but not in high-throughput settings such as reference laboratories, where sample batching can be optimized to minimize cost at the expense of workflow complexity and time.

Conflict of interest statement

I have read the journal’s policy and the authors of this manuscript have the following competing interests: DL and DME work for the nonprofit Translational Genomics Research Institute (TGen), which holds the patent on the Next Gen RDST assay that was employed in this study. As of this submission, TGen has not licensed the assay to any commercial party. JH is currently a paid employee of Illumina, where he works as a scientist. AGY is currently an employee of Illumina and a shareholder. CMD served as a Guest Editor on PLOS Medicine’s Special Issue on Tuberculosis.

Figures

Fig 1. Comparison of WGS variant calls.
Fig 1. Comparison of WGS variant calls.
Comparison of variant calls for individual Nextera DNAFlex WGS libraries sequenced on the MiSeq run at Illumina, the MiSeq run at UCSD, and the iSeq100 (green, red, and blue, respectively). Variants were identified using the ReSeqTB UVP. UCSD, University of California, San Diego; UVP, unified variant pipeline; WGS, whole-genome sequencing.
Fig 2. Cost per sample with varying…
Fig 2. Cost per sample with varying batching schemes for MiSeq and iSeq platforms.
WGS depth of coverage calculated assuming M. tuberculosis genome and 2 × 150 bp runs. Minimum of 30× coverage was used for all scenarios. The bar graph depicts the batching size for the different scenarios. Red, brown, green, blue, and purple data depict the iSeq100, MiSeq version 2 kit, MiSeq version 2 Micro kit, MiSeq version 2 Nano kit, and MiSeq version 3 kit, respectively. Prices are based on list price as of October 2018 and are in USD. WGS, whole-genome sequencing.
Fig 3. Cost trajectories with total numbers…
Fig 3. Cost trajectories with total numbers of samples processed.
Cost per sample was calculated for average depth of 50× coverage using 2 × 150 bp runs. Total cost includes capital cost of sequencer and sequencing cost per sample. Red, brown, green, blue, and purple data depict the iSeq100, MiSeq version 2 kit, MiSeq version 2 Micro kit, MiSeq version 2 Nano kit, and MiSeq version 3 kit, respectively.

References

    1. World; Health Organization (WHO). Global Tuberculosis Report 2018. Geneva, Switzerland: World Health Organization (WHO); 2018.
    1. (WHO) WHO. Implementing the End TB Strategy: the essentials; 2015. Report No.: WHO/HTM/TB/2015.31.
    1. Miotto P, Tessema B, Tagliani E, Chindelevitch L, Starks AM, Emerson C, et al. A standardised method for interpreting the association between mutations and phenotypic drug resistance in Mycobacterium tuberculosis. The European respiratory journal. 2017;50(6). 10.1183/13993003.01354-2017 .
    1. Consortium CR, the GP, Allix-Beguec C, Arandjelovic I, Bi L, Beckert P, et al. Prediction of Susceptibility to First-Line Tuberculosis Drugs by DNA Sequencing. The New England journal of medicine. 2018;379(15):1403–15. 10.1056/NEJMoa1800474 .
    1. (WHO) WHO. The use of next-generation sequencing technologies for the detection of mutations associated with drug resistance in Mycobacterium tuberculosis complex: technical guide; 2018. Report No.: WHO/CDS/TB/2018.19.
    1. MacCannell D. Next Generation Sequencing in Clinical and Public Health Microbiology. Clinical Microbiology Newsletter. 2016;38(21):169–76. 10.1016/j.clinmicnews.2016.10.001.
    1. Dolinger DL, Colman RE, Engelthaler DM, Rodwell TC. Next-generation sequencing-based user-friendly platforms for drug-resistant tuberculosis diagnosis: A promise for the near future. International journal of mycobacteriology. 2016;5 Suppl 1:S27–S8. 10.1016/j.ijmyco.2016.09.021 .
    1. Smith S. Global Next Generation Sequencing (NGS) Market—Analysis and Forecast (2017–2024). PR Newswire [Internet]. 2017.
    1. Illumina. iSeq100 Sequencing System Guide. Illumina. 2018;1000000036024 v03.
    1. Rodwell TC, Valafar F, Douglas J, Qian L, Garfein RS, Chawla A, et al. Predicting extensively drug-resistant Mycobacterium tuberculosis phenotypes with genetic mutations. J Clin Microbiol. 2014;52(3):781–9. 10.1128/JCM.02701-13 .
    1. Hillery N, Groessl EJ, Trollip A, Catanzaro D, Jackson L, Rodwell TC, et al. The Global Consortium for Drug-resistant Tuberculosis Diagnostics (GCDD): design of a multi-site, head-to-head study of three rapid tests to detect extensively drug-resistant tuberculosis. Trials. 2014;15:434 10.1186/1745-6215-15-434 .
    1. Illumina. Nextera DNA Flex Library Prep. Illumina. 2017;1000000025416 v00.
    1. Ezewudo M, Borens A, Chiner-Oms A, Miotto P, Chindelevitch L, Starks AM, et al. Integrating standardized whole genome sequence analysis with a global Mycobacterium tuberculosis antibiotic resistance knowledgebase. Sci Rep. 2018;8(1):15382 10.1038/s41598-018-33731-1 .
    1. Colman RE, Schupp JM, Hicks ND, Smith DE, Buchhagen JL, Valafar F, et al. Detection of Low-Level Mixed-Population Drug Resistance in Mycobacterium tuberculosis Using High Fidelity Amplicon Sequencing. PLoS ONE. 2015;10(5):e0126626 10.1371/journal.pone.0126626 .
    1. Colman RE, Anderson J, Lemmer D, Lehmkuhl E, Georghiou SB, Heaton H, et al. Rapid Drug Susceptibility Testing of Drug-Resistant Mycobacterium tuberculosis Isolates Directly from Clinical Samples by Use of Amplicon Sequencing: a Proof-of-Concept Study. J Clin Microbiol. 2016;54(8):2058–67. 10.1128/JCM.00535-16 .
    1. Illumina. MiSeq System Guide. Illumina. 2018;1000000061014 v00.
    1. Tagliani E, Hassan MO, Waberi Y, De Filippo MR, Falzon D, Dean A, et al. Culture and Next-generation sequencing-based drug susceptibility testing unveil high levels of drug-resistant-TB in Djibouti: results from the first national survey. Sci Rep. 2017;7(1):17672 10.1038/s41598-017-17705-3 .

Source: PubMed

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