Clinical metagenomics is an emerging method in the diagnosis of infectious diseases that uses next generation sequencing (NGS) technology to identify the etiologic agents to allow for more effective and targeted treatment of infectious diseases. Conventional diagnostic methods are mainly based on basic morphologic, phenotypic and genotypic analyses which can be insensitive and/or time consuming. Metagenomic NGS (mNGS) can be performed with only a small amount of nucleic acid from the specimen and not only can the pathogen be identified and characterized, but also its antimicrobial susceptibility can be inferred. Although tremendous advancements were made in the speed, throughput, and cost of NGS in recent years, the application of clinical metagenomics in routine diagnosis of infectious diseases is not yet practical because of its much higher cost compared to conventional microbiological tests, complex laboratory workflows and computational challenges.
Still, the application of this approach as an adjunct to conventional methods may benefit patients that are critically ill with infectious diseases of unknown etiology. There is significant morbidity and mortality from infections of the respiratory tract, urinary tract and clinical sterile body sites such as the blood, cerebrospinal fluid, pleural fluid, pericardial fluid, joint fluid or tissues but the etiology of these infections remains unknown in a large proportion of cases. The sequences from mNGS can also reveal more than the pathogen alone, as it contains host genome and gene expression data as well as the microbiome which are additional factors that can modulate the disease or influence the interpretation of the laboratory findings.
However, the mNGS approach is not without its limitations in infectious disease diagnostics. mNGS generates a wide range of organisms and microbial profiles which can be difficult to interpret and definitively identify the causative agent. As clinical microbiology laboratories move towards the accreditation of mNGS for routine clinical use, we also need to turn our attention towards standardization by developing standard operating procedures for reproducible sample handling, sequencing, quality control, and downstream bioinformatic analyses.
This research topic welcomes articles highlighting advances from sample processing to data analysis to clinical applications of mNGS in infectious disease diagnosis and treatment.
More specifically, topics of interest include, but are not limited to:
1. Challenges and progress in culture-independent metagenomic sequencing for infectious diseases
2. Diagnosis of sterile body site, respiratory tract, and urinary tract infections using mNGS
3. Metagenomic approaches to infer antibiotic resistance
4. Identification of novel or uncommon pathogens and their association with diseases using mNGS
5. Address specific challenges of mNGS, including sample processing to improve diagnostic yield, contamination, quality control, bioinformatic approaches, clinical interpretation and contamination and quality control mechanisms.
Clinical metagenomics is an emerging method in the diagnosis of infectious diseases that uses next generation sequencing (NGS) technology to identify the etiologic agents to allow for more effective and targeted treatment of infectious diseases. Conventional diagnostic methods are mainly based on basic morphologic, phenotypic and genotypic analyses which can be insensitive and/or time consuming. Metagenomic NGS (mNGS) can be performed with only a small amount of nucleic acid from the specimen and not only can the pathogen be identified and characterized, but also its antimicrobial susceptibility can be inferred. Although tremendous advancements were made in the speed, throughput, and cost of NGS in recent years, the application of clinical metagenomics in routine diagnosis of infectious diseases is not yet practical because of its much higher cost compared to conventional microbiological tests, complex laboratory workflows and computational challenges.
Still, the application of this approach as an adjunct to conventional methods may benefit patients that are critically ill with infectious diseases of unknown etiology. There is significant morbidity and mortality from infections of the respiratory tract, urinary tract and clinical sterile body sites such as the blood, cerebrospinal fluid, pleural fluid, pericardial fluid, joint fluid or tissues but the etiology of these infections remains unknown in a large proportion of cases. The sequences from mNGS can also reveal more than the pathogen alone, as it contains host genome and gene expression data as well as the microbiome which are additional factors that can modulate the disease or influence the interpretation of the laboratory findings.
However, the mNGS approach is not without its limitations in infectious disease diagnostics. mNGS generates a wide range of organisms and microbial profiles which can be difficult to interpret and definitively identify the causative agent. As clinical microbiology laboratories move towards the accreditation of mNGS for routine clinical use, we also need to turn our attention towards standardization by developing standard operating procedures for reproducible sample handling, sequencing, quality control, and downstream bioinformatic analyses.
This research topic welcomes articles highlighting advances from sample processing to data analysis to clinical applications of mNGS in infectious disease diagnosis and treatment.
More specifically, topics of interest include, but are not limited to:
1. Challenges and progress in culture-independent metagenomic sequencing for infectious diseases
2. Diagnosis of sterile body site, respiratory tract, and urinary tract infections using mNGS
3. Metagenomic approaches to infer antibiotic resistance
4. Identification of novel or uncommon pathogens and their association with diseases using mNGS
5. Address specific challenges of mNGS, including sample processing to improve diagnostic yield, contamination, quality control, bioinformatic approaches, clinical interpretation and contamination and quality control mechanisms.