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1.
Appl Environ Microbiol ; 85(18)2019 09 15.
Article in English | MEDLINE | ID: mdl-31324624

ABSTRACT

Here we analyzed the innate fluorescence signature of the single microbial cell, within both clonal and mixed populations of microorganisms. We found that even very similarly shaped cells differ noticeably in their autofluorescence features and that the innate fluorescence signatures change dynamically with growth phases. We demonstrated that machine learning models can be trained with a data set of single-cell innate fluorescence signatures to annotate cells according to their phenotypes and physiological status, for example, distinguishing a wild-type Aspergillus nidulans cell from its nitrogen metabolism mutant counterpart and log-phase cells from stationary-phase cells of Pseudomonas putida We developed a minimally invasive method (confocal reflection microscopy-assisted single-cell innate fluorescence [CRIF] analysis) to optically extract and catalog the innate cellular fluorescence signatures of each of the individual live microbial cells in a three-dimensional space. This technique represents a step forward from traditional techniques which analyze the innate fluorescence signatures at the population level and necessitate a clonal culture. Since the fluorescence signature is an innate property of a cell, our technique allows the prediction of the types or physiological status of intact and tag-free single cells, within a cell population distributed in a three-dimensional space. Our study presents a blueprint for a streamlined cell analysis where one can directly assess the potential phenotype of each single cell in a heterogenous population by its autofluorescence signature under a microscope, without cell tagging.IMPORTANCE A cell's innate fluorescence signature is an assemblage of fluorescence signals emitted by diverse biomolecules within a cell. It is known that the innate fluoresce signature reflects various cellular properties and physiological statuses; thus, they can serve as a rich source of information in cell characterization as well as cell identification. However, conventional techniques focus on the analysis of the innate fluorescence signatures at the population level but not at the single-cell level and thus necessitate a clonal culture. In the present study, we developed a technique to analyze the innate fluorescence signature of a single microbial cell. Using this novel method, we found that even very similarly shaped cells differ noticeably in their autofluorescence features, and the innate fluorescence signature changes dynamically with growth phases. We also demonstrated that the different cell types can be classified accurately within a mixed population under a microscope at the resolution of a single cell, depending solely on the innate fluorescence signature information. We suggest that single-cell autofluoresce signature analysis is a promising tool to directly assess the taxonomic or physiological heterogeneity within a microbial population, without cell tagging.


Subject(s)
Aspergillus fumigatus/physiology , Fluorescence , Machine Learning , Microscopy, Confocal/methods , Pseudomonas putida/physiology , Single-Cell Analysis/methods
2.
Sci Rep ; 6: 32354, 2016 08 31.
Article in English | MEDLINE | ID: mdl-27576448

ABSTRACT

Potassium (K(+)) is an essential macronutrient for all living organisms including cyanobacteria. Cyanobacteria are a group of bacteria performing oxygenic photosynthesis, widely studied in basic and applied sciences. The primary metabolism of the unicellular cyanobacterium Synechocystis sp. PCC 6803 is altered by environmental conditions, and it excretes organic acids and hydrogen under dark, anaerobic conditions. Here we demonstrated that K(+) widely changes the primary carbon metabolism of this cyanobacterium. Succinate and lactate excretion from the cells incubated under dark, anaerobic conditions was enhanced in the presence of K(+), while hydrogen production was repressed. The addition of K(+) and the genetic manipulation of acetate kinase AckA and an RNA polymerase sigma factor SigE additively increased succinate and lactate production to 141.0 and 217.6 mg/L, which are 11 and 46 times, compared to the wild-type strain without K(+), respectively. Intracellular levels of 2-oxoglutarate, succinate, fumarate, and malate increased by K(+) under dark, anaerobic conditions. This study provides the evidence of the considerable effect of K(+) on the biosynthesis of anionic metabolites in a unicellular cyanobacterium.


Subject(s)
Bacterial Proteins/genetics , Carbon/metabolism , Cyanobacteria/metabolism , Potassium/metabolism , Sigma Factor/genetics , Anaerobiosis/genetics , Anions/metabolism , Bacterial Proteins/metabolism , Cyanobacteria/genetics , Gene Expression Regulation, Bacterial , Hydrogen/metabolism , Nitrogen/metabolism , Photosynthesis/genetics , Sigma Factor/metabolism
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