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Review
. 2024 Aug;38(15-16):e25090.
doi: 10.1002/jcla.25090. Epub 2024 Aug 19.

Application of Second-Generation Sequencing Technology in Lower Respiratory Tract Infection

Chong Wang  1   2 , Shuo Yang  1   2 , Qi Liu  1   2 , Hongchao Liu  1   2 , Shangjia Jin  1   2 , Jiajia Zheng  1   2 , Xiumei Xiao  1   2 , Xin Hou  1   2 , Jing Li  1   2 , Sisi Ma  1   2 , Liyan Cui  1   2
Affiliations
Review

Application of Second-Generation Sequencing Technology in Lower Respiratory Tract Infection

Chong Wang et al. J Clin Lab Anal. 2024 Aug.

Abstract

Background: Lower respiratory tract infection (LRTI) has long been an important threat to people's life and health, so the rapid diagnosis of LRTI is of great significance in clinical treatment. In recent years, the development of the sequencing technology provides a new direction for the rapid diagnosis of LRTI. In this review, the advantages and disadvantages of second-generation sequencing techniques represented by metagenomics next-generation sequencing (mNGS) and droplet digital polymerase chain reaction (ddPCR) in LRTI were reviewed. Furthermore, it offers insights into the future trajectory of this technology, highlighting its potential to revolutionise the field of respiratory infection diagnostics.

Objective: This review summarises developments in mechanistic research of second-generation sequencing technology their relationship with clinical practice, providing insights for future research.

Methods: Authors conducted a search on PubMed and Web of Science using the professional terms 'Lower respiratory tract infection' and 'droplet digital polymerase chain reaction' and 'metagenomics next generation sequencing'. The obtained literature was then roughly categorised based on their research content. Similar studies were grouped into the same sections, and further searches were conducted based on the keywords of each section.

Results: Different studies discussed the application of second-generation sequencing technology in LRTI from different angles, including the detection of pathogens of LRTI by mNGS and ddPCR, the prediction ability of drug-resistant bacteria, and comparison with traditional methods. We try to analyse the advantages and disadvantages of the second-generation sequencing technology by combing the research results of mNGS and ddPCR. In addition, the development direction of the second-generation sequencing technology is prospected.

Keywords: droplet digital polymerase chain reaction; lower respiratory tract infection; metagenomics next‐generation sequencing; pathogen analysis; prediction of drug resistance.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

FIGURE 1
FIGURE 1
Basic flow chart of mNGS.
FIGURE 2
FIGURE 2
Basic flow chart of ddPCR.
See this image and copyright information in PMC

References

    1. GBD 2015 LRI Collaborators , “Estimates of the Global, Regional, and National Morbidity, Mortality, and Aetiologies of Lower Respiratory Tract Infections in 195 Countries: A Systematic Analysis for the Global Burden of Disease Study 2015,” Lancet Infectious Diseases 17, no. 11 (2017): 1133–1161, 10.1016/S1473-3099(17)30396-1. - DOI - PMC - PubMed
    1. Zhu Y.‐G., Tang X. D., Lu Y. T., Zhang J., and Qu J.‐M., “Contemporary Situation of Community‐Acquired Pneumonia in China: A Systematic Review,” Journal of Translational Internal Medicine 6, no. 1 (2018): 26–31, 10.2478/jtim-2018-0006. - DOI - PMC - PubMed
    1. Bielicki J. A., Cromwell D. A., and Sharland M., “Fifteen‐Minute Consultation: The Complexities of Empirical Antibiotic Selection for Serious Bacterial Infections‐A Practical Approach,” Archives of Disease in Childhood. Education and Practice Edition 102, no. 3 (2017): 117–123, 10.1136/archdischild-2016-310527. - DOI - PubMed
    1. Hwang E. J., Park S., Jin K.‐N., et al., “Development and Validation of a Deep Learning‐Based Automated Detection Algorithm for Major Thoracic Diseases on Chest Radiographs,” JAMA Network Open 2, no. 3 (2019): e191095, 10.1001/jamanetworkopen.2019.1095. - DOI - PMC - PubMed
    1. Rajapaksha P., Elbourne A., Gangadoo S., Brown R., Cozzolino D., and ChapmanAnalyst J., “A Review of Methods for the Detection of Pathogenic Microorganisms,” Analyst 144, no. 2 (2019): 396–411, 10.1039/c8an01488d. - DOI - PubMed

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