Microbial heterogeneity affects bioprocess robustness: dynamic single-cell analysis contributes to understanding of microbial populations

Biotechnol J. 2014 Jan;9(1):61-72. doi: 10.1002/biot.201300119. Epub 2013 Oct 23.

Abstract

Heterogeneity or segregation of microbial populations has been the subject of much research, but the real impact of this phenomenon on bioprocesses remains poorly understood. The main reason for this lack of knowledge is the difficulty in monitoring microbial population heterogeneity under dynamic process conditions. The main concepts resulting in microbial population heterogeneity in the context of bioprocesses have been summarized by two distinct hypotheses. The first involves the individual history of microbial cells or the "path" followed during their residence time inside the process equipment. The second hypothesis involves a coordinated response by the microbial population as a bet-hedging strategy, in order to cope with process-related stresses. The respective contribution of each hypothesis to microbial heterogeneity in bioprocesses is still unclear. This illustrates the fact that, although microbial phenotypic heterogeneity has been thoroughly investigated at a fundamental level, the implications of this phenomenon in the context of microbial bioprocesses are still subject to debate. At this time, automated flow cytometry is the best technique for investigating microbial heterogeneity under process conditions. However, dedicated software and relevant biomarkers are needed for the proper integration of flow cytometry as a bioprocess control tool.

Keywords: Bioreactor heterogeneity; Microbial stress; Scale-up; Single cell; Stress biosensor.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Bacteria / cytology*
  • Biomarkers
  • Biosensing Techniques
  • Flow Cytometry / instrumentation*
  • Flow Cytometry / methods
  • Fungi / cytology*
  • Phenotype
  • Single-Cell Analysis / instrumentation*
  • Single-Cell Analysis / methods
  • Software
  • Stress, Physiological

Substances

  • Biomarkers