Detection of mixed-strain infections by FACS and ultra-low input genome sequencing

Gut Microbes. 2020 May 3;11(3):305-309. doi: 10.1080/19490976.2018.1526578. Epub 2018 Oct 5.


The epidemiological tracking of a bacterial outbreak may be jeopardized by the presence of multiple pathogenic strains in one patient. Nevertheless, this fact is not considered in most of the epidemiological studies and only one colony per patient is sequenced. On the other hand, the routine whole genome sequencing of many isolates from each patient would be costly and unnecessary, because the number of strains in a patient is never known a priori. In addition, the result would be biased by microbial culture conditions. Herein we propose an approach for detecting mixed-strain infection, providing C. difficile infection as an example. The cells of the target pathogenic species are collected from the bacterial suspension by the fluorescence activated cell sorting (FACS) and a shallow genome sequencing is performed. A modified sequencing library preparation protocol for low-input DNA samples can be used for low prevalence gut pathogens (< 0.1% of the total microbiome). This FACS-seq approach reduces diagnostics time (no culture is needed) and may promote discoveries of novel strains. Methodological details, possible issues and future directions for the sequencing of these natural pan-genomes are herein discussed.

Keywords: C. difficile; FACS-seq; epidemiology; low-input DNA sequencing; mixed-strain infection.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Clostridioides difficile / classification
  • Clostridioides difficile / cytology
  • Clostridioides difficile / genetics
  • Clostridioides difficile / isolation & purification
  • Clostridium Infections / diagnosis
  • Clostridium Infections / microbiology
  • Coinfection / diagnosis*
  • Coinfection / microbiology
  • DNA, Bacterial / genetics
  • Feces / microbiology
  • Flow Cytometry*
  • Genome, Bacterial / genetics*
  • Humans
  • Microbiological Techniques / methods*
  • Microbiota / genetics
  • RNA, Ribosomal, 16S / genetics
  • Sequence Analysis, DNA


  • DNA, Bacterial
  • RNA, Ribosomal, 16S

Grant support

This work was supported by the Boehringer Ingelheim Fonds [Travel Grant 2014];Generalitat Valenciana [PrometeoII/2014/065]; Instituto de Salud Carlos III [CP09/00049]; Instituto de Salud Carlos III [PIE14/00045]; Ministerio de Economía y Competitividad [SAF 2012-31187]; Ministerio de Economía y Competitividad [AC15/00022]; Ministerio de Economía y Competitividad [SAF2015-65878-R]; Ministerio de Economía y Competitividad [SAF2013-49788-EXP]; Ministerio de Educación, Cultura y Deporte [FPU2010].