Related Experiment Video
Updated: Jul 23, 2025

A Neonatal Imaging Model of Gram-Negative Bacterial Sepsis
Published on: August 12, 2020
Improved Diagnostics in Bacterial Neonatal Meningitis Using a Next-Generation Sequencing Platform
Alieke van der Hoeven1, Martha T van der Beek2, Vincent Bekker3
1Department of Medical Microbiology, Leiden University Medical Center, Postzone E4-P, Postbus 9600, 2300 RC, Leiden, The Netherlands. A.vanderHoeven@LUMC.nl.
Introduction:
Bacterial meningitis in infants is an infrequent but life-threatening condition. Empiric therapy should begin as soon as meningitis is thought likely. Consequently, the causative microorganisms may not always be detected using culturing techniques, as cerebrospinal fluid (CSF) cultures are influenced by antibiotics. Nucleic acid amplification tests, such as polymerase chain reaction (PCR) (multiplex panels), may overcome this limitation but require a priori knowledge of the likely pathogen present within the sample. With this in mind, we investigated to what extent a culture-free, broad-range 16S rRNA gene next-generation sequencing (NGS) platform (MYcrobiota) could add to the microbiological diagnosis of meningitis.
Methods:
Retrospective cohort study at level III neonatal intensive care unit. Included were all infants with suspected meningitis admitted between 10 November 2017 and 31 December 2020. A comparison was made of the bacterial pathogen detection rate between MYcrobiota and conventional bacterial culture.
Results:
In a 3-year period, 37 CSF samples (diagnostic and follow-up) from 35 infants with proven or possible meningitis were available for MYcrobiota testing. MYcrobiota detected the presence of bacterial pathogens in 11 samples (30%), in contrast with the conventional CSF culture, which detected bacteria in 2 of 36 samples (5.6%).
Conclusion:
Addition of 16S rRNA sequencing to conventional culturing greatly improved the identification of the aetiology of bacterial meningitis compared to culturing of CSF samples alone.
Insights
Next-generation sequencing (NGS) of the 16S rRNA gene significantly enhances bacterial pathogen detection in infant meningitis cases compared to traditional cerebrospinal fluid (CSF) cultures. This advanced molecular method improves diagnostic accuracy for this critical condition.
Area of Science:
- Medical Microbiology
- Neonatal Infectious Diseases
- Molecular Diagnostics
Background:
- Bacterial meningitis in infants is a severe, life-threatening condition requiring prompt empiric treatment.
- Conventional cerebrospinal fluid (CSF) cultures have limitations in detecting bacterial pathogens due to antibiotic influence.
- Nucleic acid amplification tests (NAATs) like PCR require prior knowledge of potential pathogens.
Purpose of the Study:
- To evaluate the diagnostic utility of a culture-free, broad-range 16S rRNA gene next-generation sequencing (NGS) platform (MYcrobiota) for bacterial meningitis in infants.
- To compare the bacterial pathogen detection rate of MYcrobiota with conventional bacterial culture methods.
Main Methods:
- Retrospective cohort study conducted in a level III neonatal intensive care unit.
- Analysis of 37 CSF samples from 35 infants with suspected meningitis between November 2017 and December 2020.
- Comparison of bacterial pathogen detection rates between MYcrobiota (16S rRNA gene NGS) and conventional bacterial culture.
Main Results:
- MYcrobiota detected bacterial pathogens in 11 out of 37 (30%) CSF samples.
- Conventional CSF culture identified bacteria in only 2 out of 36 (5.6%) samples.
- NGS demonstrated a substantially higher detection rate for bacterial pathogens.
Conclusions:
- The addition of 16S rRNA gene sequencing significantly improved the identification of bacterial meningitis etiology.
- Culture-free NGS offers a valuable advancement over conventional CSF culturing for diagnosing bacterial meningitis in neonates.
Related Concept Videos
Modern Molecular Taxonomy
Applications of Molecular Taxonomy
Next-generation Sequencing
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
Methods of Classification and Identification

