학술논문

Schrödinger’s microbes: Tools for distinguishing the living from the dead in microbial ecosystems
Document Type
article
Source
Microbiome. 5(1)
Subject
Genetics
Biotechnology
Infection
Bacteria
Bacterial Physiological Phenomena
Biomass
Ecosystem
High-Throughput Nucleotide Sequencing
Humans
Metagenomics
Microbial Consortia
Microbial Viability
Real-Time Polymerase Chain Reaction
Sequence Analysis
DNA
DNA sequencing
Flow cytometry
Infectivity
Live/dead
Low biomass
Microbial ecology
PMA
RNA
qPCR
Viability
Ecology
Microbiology
Medical Microbiology
Language
Abstract
While often obvious for macroscopic organisms, determining whether a microbe is dead or alive is fraught with complications. Fields such as microbial ecology, environmental health, and medical microbiology each determine how best to assess which members of the microbial community are alive, according to their respective scientific and/or regulatory needs. Many of these fields have gone from studying communities on a bulk level to the fine-scale resolution of microbial populations within consortia. For example, advances in nucleic acid sequencing technologies and downstream bioinformatic analyses have allowed for high-resolution insight into microbial community composition and metabolic potential, yet we know very little about whether such community DNA sequences represent viable microorganisms. In this review, we describe a number of techniques, from microscopy- to molecular-based, that have been used to test for viability (live/dead determination) and/or activity in various contexts, including newer techniques that are compatible with or complementary to downstream nucleic acid sequencing. We describe the compatibility of these viability assessments with high-throughput quantification techniques, including flow cytometry and quantitative PCR (qPCR). Although bacterial viability-linked community characterizations are now feasible in many environments and thus are the focus of this critical review, further methods development is needed for complex environmental samples and to more fully capture the diversity of microbes (e.g., eukaryotic microbes and viruses) and metabolic states (e.g., spores) of microbes in natural environments.