scholarly journals Fluorescent reporter plasmids for single-cell and bulk-level composition assays in E. faecalis

2019 ◽  
Author(s):  
Kelsey M. Hallinen ◽  
Keanu A. Guardiola-Flores ◽  
Kevin B. Wood
Keyword(s):  
Stationary Phase ◽  
Single Cell ◽  
Opportunistic Pathogen ◽  
Spectral Unmixing ◽  
Model Organisms ◽  
Fluorescent Reporter ◽  
Bacterial Populations ◽  
Fluorescent Reporters ◽  
E Coli ◽  
Scope Of Application

ABSTRACTFluorescent reporters are an important tool for monitoring dynamics of bacterial populations at the single cell and community level. While there are a large range of reporter constructs available–particularly for common model organisms like E. coli–fewer options exist for other species, including E. faecalis, a gram-positive opportunistic pathogen. To expand the potential toolkit available for E. faecalis, we modified a previously developed reporter plasmid (pBSU101) to express one of nine different fluorescent reporters and confirmed that all constructs exhibited detectable fluorescence in single E. faecalis cells and mixed biofilm communities. To identify promising constructs for bulk-level experiments, we then measured the fluorescence spectra from E. faecalis populations in microwell plate (liquid) cultures during different growth phases. Cultures showed density- and reporter-specific variations in fluorescent signal, though spectral signatures of all reporters become clear in late-exponential and stationary-phase populations. Based on these results, we identified six pairs of reporters that can be combined with simple spectral unmixing to accurately estimate population composition in 2-strain mixtures at or near stationary phase. This approach offers a simple and scalable method for selection and competition experiments in simple two-species populations. Finally, we modified the construct to express codon-optimized variants of blue (BFP) and red (RFP) reporters and show that they lead to increased fluorescence in exponentially growing cells. As a whole, the results inform the scope of application of different reporters and identify both single reporters and reporter pairs that are promising for fluorescence-based assays at bulk and single-cell levels in E. faecalis.

scholarly journals Growth and extended survival ofEscherichia coliO157:H7 in soil organic matter

2017 ◽  
Author(s):  
Gitanjali NandaKafle ◽  
Amy A. Christie ◽  
Sébastien Vilain ◽  
Volker S. Brözel
Keyword(s):  
Organic Matter ◽  
Stationary Phase ◽  
Open Systems ◽  
Physiological State ◽  
Principal Component ◽  
Human Infection ◽  
Food Plants ◽  
Bacterial Populations ◽  
E Coli ◽  
2D Gel

AbstractEnterohaemorrhagicEscherichia colisuch as serotype O157:H7 are a leading cause of food-associated outbreaks. While the primary reservoir is associated with cattle, plant foods have been associated as sources of human infection.E. coliis able to grow in the tissue of food plants such as spinach. While fecal contamination is the primary suspect, soil has been underestimated as a potential reservoir. Persistence of bacterial populations in open systems is the product of growth, death, predation, and competition. Here we report thatE. coliO157:H7 can grow using the soluble compounds in soil, and characterize the effect of soil growth in the stationary phase proteome.E. coli933D (stxII-) was cultured in Soil Extracted Soluble Organic Matter (SESOM) and the culturable count determined for 24 d. The proteomes of exponential and stationary phase populations were characterized by 2D gel electrophoresis and protein spots were identified by MALDI-TOF mass spectrometry. While LB controls displayed a death phase, SESOM grown population remained culturable for 24 d, indicating an altered physiological state with superior longevity. This was not due to decreased cell density on entry to stationary phase as 24h SESOM populations concentrated 10-fold retained their longevity. Principal component analysis showed that stationary phase proteomes from SESOM and LB were different. Differences included proteins involved in stress response, motility, membrane and wall composition, nutrient uptake, translation and protein turnover, and anabolic and catabolic pathways, indicating an altered physiological state of soil-grown cells entering stationary phase. The results suggest thatE. colimay be a soil commensal that in absence of predation and competition maintains stable populations in soil.

scholarly journals Droplet-based single cell RNA sequencing of bacteria identifies known and previously unseen cellular states

2021 ◽  
Author(s):  
Ryan McNulty ◽  
Duluxan Sritharan ◽  
Shichen Liu ◽  
Sahand Hormoz ◽  
Adam Z. Rosenthal
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Communication ◽  
Growth Conditions ◽  
Bacterial Cells ◽  
Bacterial Populations ◽  
Specialized Cell ◽  
E Coli ◽  
Cellular Processes ◽  
Single Cell Rna Sequencing

AbstractClonal bacterial populations rely on transcriptional variation to differentiate into specialized cell states that increase the community’s fitness. Such heterogeneous gene expression is implicated in many fundamental microbial processes including sporulation, cell communication, detoxification, substrate utilization, competence, biofilm formation, motility, pathogenicity, and antibiotic resistance1. To identify these specialized cell states and determine the processes by which they develop, we need to study isogenic bacterial populations at the single cell level2,3. Here, we develop a method that uses DNA probes and leverages an existing commercial microfluidic platform (10X Chromium) to conduct bacterial single cell RNA sequencing. We sequenced the transcriptome of over 15,000 individual bacterial cells, detecting on average 365 transcripts mapping to 265 genes per cell in B. subtilis and 329 transcripts mapping to 149 genes per cell in E. coli. Our findings correctly identify known cell states and uncover previously unreported cell states. Interestingly, we find that some metabolic pathways segregate into distinct subpopulations across different bacteria and growth conditions, suggesting that some cellular processes may be more prone to differentiation than others. Our high throughput, highly resolved single cell transcriptomic platform can be broadly used for understanding heterogeneity in microbial populations.

scholarly journals Droplet-based single cell RNA sequencing of bacteria identifies known and previously unseen cellular states

2021 ◽  
Author(s):  
Adam Rosenthal ◽  
Ryan McNulty ◽  
Duluxan Sritha ◽  
Shichen Liu ◽  
Sahand Hormoz
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Communication ◽  
Growth Conditions ◽  
Bacterial Cells ◽  
Bacterial Populations ◽  
Specialized Cell ◽  
E Coli ◽  
Cellular Processes ◽  
Single Cell Rna Sequencing

Abstract Clonal bacterial populations rely on transcriptional variation to differentiate into specialized cell states that increase the community’s fitness. Such heterogeneous gene expression is implicated in many fundamental microbial processes including sporulation, cell communication, detoxification, substrate utilization, competence, biofilm formation, motility, pathogenicity, and antibiotic resistance1. To identify these specialized cell states and determine the processes by which they develop, we need to study isogenic bacterial populations at the single cell level2,3. Here, we develop a method that uses DNA probes and leverages an existing commercial microfluidic platform (10X Chromium) to conduct bacterial single cell RNA sequencing. We sequenced the transcriptome of over 15,000 individual bacterial cells, detecting on average 365 transcripts mapping to 265 genes per cell in B. subtilis and 329 transcripts mapping to 149 genes per cell in E. coli. Our findings correctly identify known cell states and uncover previously unreported cell states. Interestingly, we find that some metabolic pathways segregate into distinct subpopulations across different bacteria and growth conditions, suggesting that some cellular processes may be more prone to differentiation than others. Our high throughput, highly resolved single cell transcriptomic platform can be broadly used for understanding heterogeneity in microbial populations.

scholarly journals Spatial transcriptomics of planktonic and sessile bacterial populations at single-cell resolution

Science ◽  
2021 ◽  
Vol 373 (6556) ◽  
pp. eabi4882
Author(s):  
Daniel Dar ◽  
Nina Dar ◽  
Long Cai ◽  
Dianne K. Newman
Keyword(s):  
Single Cell ◽  
Complex Dynamics ◽  
Opportunistic Pathogen ◽  
Microbial Populations ◽  
Bacterial Populations ◽  
Spatially Resolved ◽  
Imaging Approach ◽  
Spatiotemporal Scales ◽  
Metabolic Heterogeneity

Capturing the heterogeneous phenotypes of microbial populations at relevant spatiotemporal scales is highly challenging. Here, we present par-seqFISH (parallel sequential fluorescence in situ hybridization), a transcriptome-imaging approach that records gene expression and spatial context within microscale assemblies at a single-cell and molecule resolution. We applied this approach to the opportunistic pathogen Pseudomonas aeruginosa, analyzing about 600,000 individuals across dozens of conditions in planktonic and biofilm cultures. We identified numerous metabolic- and virulence-related transcriptional states that emerged dynamically during planktonic growth, as well as highly spatially resolved metabolic heterogeneity in sessile populations. Our data reveal that distinct physiological states can coexist within the same biofilm just several micrometers away, underscoring the importance of the microenvironment. Our results illustrate the complex dynamics of microbial populations and present a new way of studying them at high resolution.

scholarly journals Single-Cell E. coli Response to an Instantaneously Applied Chemotactic Signal

2014 ◽  
Vol 107 (3) ◽  
pp. 730-739 ◽  
Author(s):  
Takashi Sagawa ◽  
Yu Kikuchi ◽  
Yuichi Inoue ◽  
Hiroto Takahashi ◽  
Takahiro Muraoka ◽  
...  
Keyword(s):  
Single Cell ◽  
E Coli

Mannose-Binding Activity of Escherichia coli: a Determinant of Attachment and Ingestion of the Bacteria by Macrophages

1980 ◽  
Vol 29 (2) ◽  
pp. 417-424
Author(s):  
Zvi Bar-Shavit ◽  
Rachel Goldman ◽  
Itzhak Ofek ◽  
Nathan Sharon ◽  
David Mirelman
Keyword(s):  
Escherichia Coli ◽  
Stationary Phase ◽  
Pathogenic Bacteria ◽  
Binding Activity ◽  
Exponential Phase ◽  
Restrictive Temperature ◽  
Filamentous Bacteria ◽  
Phagocytic Cells ◽  
E Coli ◽  
Mannose Binding

Recently, it was suggested that a mannose-specific lectin on the bacterial cell surface is responsible for the recognition by phagocytic cells of certain nonopsonized Escherichia coli strains. In this study we assessed the interaction of two strains of E. coli at different phases of growth with a monolayer of mouse peritoneal macrophages and developed a direct method with [ 14 C]mannan to quantitate the bacterial mannose-binding activity. Normal-sized bacteria were obtained from logarithmic and stationary phases of growth. Nonseptated filamentous cells were formed by growing the organisms in the presence of cephalexin or at a restrictive temperature. Attachment to macrophages of all bacterial forms was inhibited by methyl α- d -mannoside and mannan but not by other sugars tested. The attachment of stationary phase and filamentous bacteria to macrophages, as well as their mannose-binding activity, was similar, whereas in the exponential-phase bacteria they were markedly reduced. The results show a linear relation between the two parameters ( R = 0.98, P < 0.001). The internalization of the filamentous cells attached to macrophages during 45 min of incubation was much less efficient (20%) compared to that of exponential-phase, stationary-phase, or antibody-coated filamentous bacteria (90%). The results indicate that the mannose-binding activity of E. coli determines the recognition of the organisms by phagocytes. They further suggest that administration of β-lactam antibiotics may impair elimination of certain pathogenic bacteria by inducing the formation of filaments which are inefficiently internalized by the host's phagocytic cells.

scholarly journals Decay of Fecal Indicator Bacterial Populations and Bovine-Associated Source-Tracking Markers in Freshly Deposited Cow Pats

2013 ◽  
Vol 80 (1) ◽  
pp. 110-118 ◽  
Author(s):  
Adelumola Oladeinde ◽  
Thomas Bohrmann ◽  
Kelvin Wong ◽  
S. T. Purucker ◽  
Ken Bradshaw ◽  
...  
Keyword(s):  
Decay Rate ◽  
Fecal Indicator Bacteria ◽  
Decay Rates ◽  
Indicator Bacteria ◽  
Source Tracking ◽  
Fecal Indicator ◽  
Bacterial Populations ◽  
Content Type ◽  
E Coli ◽  
Decay Dynamic

ABSTRACTUnderstanding the survival of fecal indicator bacteria (FIB) and microbial source-tracking (MST) markers is critical to developing pathogen fate and transport models. Although pathogen survival in water microcosms and manure-amended soils is well documented, little is known about their survival in intact cow pats deposited on pastures. We conducted a study to determine decay rates of fecal indicator bacteria (Escherichia coliand enterococci) and bovine-associated MST markers (CowM3, Rum-2-bac, and GenBac) in 18 freshly deposited cattle feces from three farms in northern Georgia. Samples were randomly assigned to shaded or unshaded treatment in order to determine the effects of sunlight, moisture, and temperature on decay rates. A general linear model (GLM) framework was used to determine decay rates. Shading significantly decreased the decay rate of theE. colipopulation (P< 0.0001), with a rate of −0.176 day−1for the shaded treatment and −0.297 day−1for the unshaded treatment. Shading had no significant effect on decay rates of enterococci, CowM3, Rum-2-bac, and GenBac (P> 0.05). In addition,E. colipopulations showed a significant growth rate (0.881 day−1) in the unshaded samples during the first 5 days after deposition. UV-B was the most important parameter explaining the decay rate ofE. colipopulations. A comparison of the decay behaviors among all markers indicated that enterococcus concentrations exhibit a better correlation with the MST markers thanE. coliconcentrations. Our results indicate that bovine-associated MST markers can survive in cow pats for at least 1 month after excretion, and although their decay dynamic differs from the decay dynamic ofE. colipopulations, they seem to be reliable markers to use in combination with enterococci to monitor fecal pollution from pasture lands.

scholarly journals Prokaryotic Single-Cell RNA Sequencing by In Situ Combinatorial Indexing

2019 ◽  
Author(s):  
Sydney B. Blattman ◽  
Wenyan Jiang ◽  
Panos Oikonomou ◽  
Saeed Tavazoie
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Low Cost ◽  
Bacterial Cells ◽  
Single Experiment ◽  
Bacterial Populations ◽  
Single Cell Rna Sequencing ◽  
Gene Expression Heterogeneity ◽  
Mrna Polyadenylation

AbstractDespite longstanding appreciation of gene expression heterogeneity in isogenic bacterial populations, affordable and scalable technologies for studying single bacterial cells have been limited. While single-cell RNA sequencing (scRNA-seq) has revolutionized studies of transcriptional heterogeneity in diverse eukaryotic systems, application of scRNA-seq to prokaryotes has been hindered by their extremely low mRNA abundance, lack of mRNA polyadenylation, and thick cell walls. Here, we present Prokaryotic Expression-profiling by Tagging RNA In Situ and sequencing (PETRI-seq), a low-cost, high-throughput, prokaryotic scRNA-seq pipeline that overcomes these technical obstacles. PETRI-seq uses in situ combinatorial indexing to barcode transcripts from tens of thousands of cells in a single experiment. PETRI-seq captures single cell transcriptomes of Gram-negative and Gram-positive bacteria with high purity and low bias, with median capture rates >200 mRNAs/cell for exponentially growing E. coli. These characteristics enable robust discrimination of cell-states corresponding to different phases of growth. When applied to wild-type S. aureus, PETRI-seq revealed a rare sub-population of cells undergoing prophage induction. We anticipate broad utility of PETRI-seq in defining single-cell states and their dynamics in complex microbial communities.

scholarly journals Distinguishing different modes of growth using single-cell data

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Prathitha Kar ◽  
Sriram Tiruvadi-Krishnan ◽  
Jaana Männik ◽  
Jaan Männik ◽  
Ariel Amir
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Statistical Methods ◽  
Synthetic Data ◽  
Cellular Growth ◽  
Biological Mechanisms ◽  
E Coli ◽  
High Throughput Data ◽  
Consistent Method ◽  
Cell Data

Collection of high-throughput data has become prevalent in biology. Large datasets allow the use of statistical constructs such as binning and linear regression to quantify relationships between variables and hypothesize underlying biological mechanisms based on it. We discuss several such examples in relation to single-cell data and cellular growth. In particular, we show instances where what appears to be ordinary use of these statistical methods leads to incorrect conclusions such as growth being non-exponential as opposed to exponential and vice versa. We propose that the data analysis and its interpretation should be done in the context of a generative model, if possible. In this way, the statistical methods can be validated either analytically or against synthetic data generated via the use of the model, leading to a consistent method for inferring biological mechanisms from data. On applying the validated methods of data analysis to infer cellular growth on our experimental data, we find the growth of length in E. coli to be non-exponential. Our analysis shows that in the later stages of the cell cycle the growth rate is faster than exponential.

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