Phenotypic heterogeneity within microbial populations at the single-cell level investigated by confocal Raman microspectroscopy

The Analyst ◽  
2009 ◽  
Vol 134 (6) ◽  
pp. 1149 ◽  
Author(s):  
Antje Hermelink ◽  
Angelika Brauer ◽  
Peter Lasch ◽  
Dieter Naumann
Keyword(s):  
Single Cell ◽  
Raman Microspectroscopy ◽  
Phenotypic Heterogeneity ◽  
Microbial Populations ◽  
Single Cell Level ◽  
Cell Level ◽  
Confocal Raman Microspectroscopy ◽  
Confocal Raman

scholarly journals Heteroresistance at the Single-Cell Level: Adapting to Antibiotic Stress through a Population-Based Strategy and Growth-Controlled Interphenotypic Coordination

mBio ◽  
2014 ◽  
Vol 5 (1) ◽  
Author(s):  
Xiaorong Wang ◽  
Yu Kang ◽  
Chunxiong Luo ◽  
Tong Zhao ◽  
Lin Liu ◽  
...  
Keyword(s):  
Growth Rate ◽  
Single Cell ◽  
Task Allocation ◽  
Bacterial Population ◽  
Population Based ◽  
Phenotypic Heterogeneity ◽  
Single Cell Level ◽  
Bacterial Survival ◽  
Cell Level ◽  
Resistant Cells

ABSTRACT Heteroresistance refers to phenotypic heterogeneity of microbial clonal populations under antibiotic stress, and it has been thought to be an allocation of a subset of “resistant” cells for surviving in higher concentrations of antibiotic. The assumption fits the so-called bet-hedging strategy, where a bacterial population “hedges” its “bet” on different phenotypes to be selected by unpredicted environment stresses. To test this hypothesis, we constructed a heteroresistance model by introducing a bla CTX-M-14 gene (coding for a cephalosporin hydrolase) into a sensitive Escherichia coli strain. We confirmed heteroresistance in this clone and that a subset of the cells expressed more hydrolase and formed more colonies in the presence of ceftriaxone (exhibited stronger “resistance”). However, subsequent single-cell-level investigation by using a microfluidic device showed that a subset of cells with a distinguishable phenotype of slowed growth and intensified hydrolase expression emerged, and they were not positively selected but increased their proportion in the population with ascending antibiotic concentrations. Therefore, heteroresistance—the gradually decreased colony-forming capability in the presence of antibiotic—was a result of a decreased growth rate rather than of selection for resistant cells. Using a mock strain without the resistance gene, we further demonstrated the existence of two nested growth-centric feedback loops that control the expression of the hydrolase and maximize population growth in various antibiotic concentrations. In conclusion, phenotypic heterogeneity is a population-based strategy beneficial for bacterial survival and propagation through task allocation and interphenotypic collaboration, and the growth rate provides a critical control for the expression of stress-related genes and an essential mechanism in responding to environmental stresses. IMPORTANCE Heteroresistance is essentially phenotypic heterogeneity, where a population-based strategy is thought to be at work, being assumed to be variable cell-to-cell resistance to be selected under antibiotic stress. Exact mechanisms of heteroresistance and its roles in adaptation to antibiotic stress have yet to be fully understood at the molecular and single-cell levels. In our study, we have not been able to detect any apparent subset of “resistant” cells selected by antibiotics; on the contrary, cell populations differentiate into phenotypic subsets with variable growth statuses and hydrolase expression. The growth rate appears to be sensitive to stress intensity and plays a key role in controlling hydrolase expression at both the bulk population and single-cell levels. We have shown here, for the first time, that phenotypic heterogeneity can be beneficial to a growing bacterial population through task allocation and interphenotypic collaboration other than partitioning cells into different categories of selective advantage.

scholarly journals Generation of genetic tools for gauging multiple gene expression at single cell level

2021 ◽  
Author(s):  
Marta Mellini ◽  
Massimiliano Lucidi ◽  
Francesco Imperi ◽  
Paolo Visca ◽  
Livia Leoni ◽  
...  
Keyword(s):  
Gene Expression ◽  
Image Processing ◽  
Single Cell ◽  
Fluorescent Protein ◽  
Single Cells ◽  
Phenotypic Heterogeneity ◽  
Single Cell Level ◽  
Cell Level ◽  
Multiple Gene ◽  
Genetic Tools

Key microbial processes in many bacterial species are heterogeneously expressed in single cells of bacterial populations. However, the paucity of adequate molecular tools for live, real-time monitoring of multiple gene expression at the single cell level has limited the understanding of phenotypic heterogeneity. In order to investigate phenotypic heterogeneity in the ubiquitous opportunistic pathogen Pseudomonas aeruginosa, a genetic tool that allows gauging multiple gene expression at the single cell level has been generated. This tool, named pRGC, consists in a promoter-probe vector for transcriptional fusions that carries three reporter genes coding for the fluorescent proteins mCherry, green fluorescent protein (GFP) and cyan fluorescent protein (CFP). The pRGC vector has been characterized and validated via single cell gene expression analysis of both constitutive and iron-regulated promoters, showing clear discrimination of the three fluorescence signals in single cells of a P. aeruginosa population, without the need of image-processing for spectral crosstalk correction. In addition, two pRGC variants have been generated for either i) integration of the reporter gene cassette into a single neutral site of P. aeruginosa chromosome, that is suitable for long-term experiments in the absence of antibiotic selection, or ii) replication in bacterial genera other than Pseudomonas. The easy-to-use genetic tools generated in this study will allow rapid and cost-effective investigation of multiple gene expression in populations of environmental and pathogenic bacteria, hopefully advancing the understanding of microbial phenotypic heterogeneity. IMPORTANCE Within a bacterial population single cells can differently express some genes, even though they are genetically identical and experience the same chemical and physical stimuli. This phenomenon, known as phenotypic heterogeneity, is mainly driven by gene expression noise and results in the emergence of bacterial sub-populations with distinct phenotypes. The analysis of gene expression at the single cell level has shown that phenotypic heterogeneity is associated with key bacterial processes, including competence, sporulation and persistence. In this study, new genetic tools have been generated that allow easy cloning of up to three promoters upstream of distinct fluorescent genes, making it possible to gauge multiple gene expression at the single cell level by fluorescent microscopy, without the need of advanced image-processing procedures. A proof of concept has been provided by investigating iron-uptake and iron-storage gene expression in response to iron availability in P. aeruginosa.

Identification of water-conditioned Pseudomonas aeruginosa by Raman microspectroscopy on a single cell level

2014 ◽  
Vol 37 (5) ◽  
pp. 360-367 ◽  
Author(s):  
Anja Silge ◽  
Wilm Schumacher ◽  
Petra Rösch ◽  
Paulo A. Da Costa Filho ◽  
Cédric Gérard ◽  
...  
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Single Cell ◽  
Raman Microspectroscopy ◽  
Single Cell Level ◽  
Cell Level

scholarly journals Biochemical principles enabling metabolic cooperativity and phenotypic heterogeneity at the single cell level

2018 ◽  
Vol 8 ◽  
pp. 97-108 ◽  
Author(s):  
Kate Campbell ◽  
Lucia Herrera-Dominguez ◽  
Clara Correia-Melo ◽  
Aleksej Zelezniak ◽  
Markus Ralser
Keyword(s):  
Single Cell ◽  
Phenotypic Heterogeneity ◽  
Single Cell Level ◽  
Cell Level

scholarly journals Measuring phenotypic heterogeneity in isogenic bacterial populations using flow cytometry and Raman spectroscopy

2019 ◽  
Author(s):  
Cristina García-Timermans ◽  
Peter Rubbens ◽  
Jasmine Heyse ◽  
Frederiek-Maarten Kerckhof ◽  
Ruben Props ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Raman Spectroscopy ◽  
Data Analysis ◽  
Single Cell ◽  
Population Level ◽  
Phenotypic Heterogeneity ◽  
Single Cell Level ◽  
Analysis Framework ◽  
Cell Level ◽  
Bacterial Populations

AbstractInvestigating phenotypic heterogeneity can help to better understand and manage microbial communities. However, characterizing phenotypic heterogeneity remains a challenge, as there is no standardized analysis framework. Several optical tools are available, which often describe properties of the individual cell. In this work, we compare Raman spectroscopy and flow cytometry to study phenotypic heterogeneity in bacterial populations. The growth phase of E. coli populations was characterized using both technologies. Our findings show that flow cytometry detects and quantifies shifts in phenotypic heterogeneity at the population level due to its high-throughput nature. Raman spectroscopy, on the other hand, offers a much higher resolution at the single-cell level (i.e. more biochemical information is recorded). Therefore, it is capable of identifying distinct phenotypic populations when coupled with standardized data analysis. In addition, it provides information about biomolecules that are present, which can be linked to cell functionality. We propose an automated workflow to distinguish between bacterial phenotypic populations using Raman spectroscopy and validated this approach with an external dataset. We recommend to apply flow cytometry to characterize phenotypic heterogeneity at the population level, and Raman spectroscopy to perform a more in-depth analysis of heterogeneity at the single-cell level.ImportanceSingle-cell techniques are frequently applied tools to study phenotypic characteristics of bacterial populations. As flow cytometry and Raman spectroscopy gain popularity in the field, there is a need to understand their advantages and limitations, as well as to create a more standardized data analysis framework. Our work shows that flow cytometry allows to study and quantify shifts at the bacterial population level, but since its resolution is limited for microbial purposes, distinct phenotypic populations cannot be distinguished at the single-cell level. Raman spectroscopy, combined with appropriate data analysis, has sufficient resolving power at the single-cell level, enabling the identification of distinct phenotypic populations. As regions in a Raman spectrum are associated with specific (bio)molecules, it is possible to link these to the cell state and/or its function.

Using Stable Isotope Probing and Raman Microspectroscopy to measure growth rates of heterotrophic bacteria

2021 ◽  
Author(s):  
Felix Weber ◽  
Tatiana Zaliznyak ◽  
Virginia P. Edgcomb ◽  
Gordon T. Taylor
Keyword(s):  
Stable Isotope ◽  
Single Cell ◽  
Bacterial Growth ◽  
Growth Rates ◽  
Heterotrophic Bacteria ◽  
Raman Microspectroscopy ◽  
Stable Isotope Probing ◽  
Single Cell Level ◽  
Cell Level ◽  
E Coli

The suitability of stable isotope probing (SIP) and Raman microspectroscopy to measure growth rates of heterotrophic bacteria at the single-cell level was evaluated. Label assimilation into E. coli biomass during growth on a complex 13 C-labeled carbon source was monitored in time course experiments. 13 C-incorporation into various biomolecules was measured by spectral “red shifts” of Raman-scattered emissions. The 13 C- and 12 C-isotopologues of the amino acid phenylalanine (Phe) proved to be a quantitatively accurate reporter molecules of cellular isotopic fractional abundances ( f cell ). Values of f cell determined by Raman microspectroscopy and independently by isotope-ratio mass spectrometry (IRMS) over a range of isotopic enrichments were statistically indistinguishable. Progressive labeling of Phe in E. coli cells among a range of 13 C/ 12 C organic substrate admixtures occurred predictably through time. Relative isotopologue abundances of Phe determined by Raman spectral analysis enabled accurate calculation of bacterial growth rates as confirmed independently by optical density (OD) measurements. Results demonstrate that combining stable isotope probing (SIP) and Raman microspectroscopy can be a powerful tool for studying bacterial growth at the single-cell level when grown on defined or complex organic 13 C-carbon sources even in mixed microbial assemblages. Importance: Population growth dynamics and individual cell growth rates are the ultimate expressions of a microorganism’s fitness to its environmental conditions, whether natural or engineered. Natural habitats and many industrial settings harbor complex microbial assemblages. Their heterogeneity in growth responses to existing and changing conditions is often difficult to grasp by standard methodologies. In this proof of concept study, we tested whether Raman microspectroscopy can reliably quantify assimilation of isotopically-labeled nutrients into E. coli cells and enable determination of individual growth rates among heterotrophic bacteria. Raman-derived growth rate estimates were statistically indistinguishable from those derived by standard optical density measurements of the same cultures. Raman microspectroscopy also can be combined with methods for phylogenetic identification. We report development of Raman-based techniques that enable researchers to directly link genetic identity to functional traits and rate measurements of single cells within mixed microbial assemblages, currently a major technical challenge in microbiological research.

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