Shotgun sequencing of clinical biofilm following scanning electron microscopy identifies bacterial community composition

Blaine Fritz, Camilla Stavnsbjerg, Merete Markvart, Peter De Barros Damgaard, Sofie Holtsmark Nielsen, Lars Bjørndal, Klaus Qvortrup, Thomas Bjarnsholt*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

4 Citations (Scopus)

Abstract

Bacterial biofilm infections often involve aggregates of bacteria heterogeneously distributed throughout a tissue or on a surface (such as an implanted medical device). Identification of a biofilm infection requires direct visualization via microscopy, followed by characterization of the microbial community by culturing or sequencing-based approaches. A sample, therefore, must be divided prior to analysis, often leading to inconsistent results. We demonstrate a combined approach, using scanning electron microscopy and next-generation shotgun sequencing, to visually identify a biofilm and characterize the microbial community, without dividing the sample. A clinical sample recovered from a patient following a dental root-filling procedure was prepared and visualized by scanning electron microscopy. DNA was then extracted from the sample several years later and analyzed by shotgun sequencing. The method was subsequently validated on in vitro cultures of Pseudomonas aeruginosa biofilm. Between 19 and 21 different genera and species were identified in the clinical sample with an estimated relative abundance greater than 1% by two different estimation approaches. Only eight genera identified were not associated with endodontic infections. This provides a proof-of-concept for a dual, microscopy and sequencing-based approach to identify and characterize bacterial biofilms, which could also easily be implemented in other scientific fields.

Original languageEnglish
Article numberftz013
JournalPathogens and Disease
Volume77
Issue number1
Number of pages7
ISSN2049-632X
DOIs
Publication statusPublished - 2019

Keywords

  • biofilm
  • chronic infection
  • implanted device
  • metagenomics
  • microbiome
  • SEM

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