scholarly journals Stochasticity of cellular growth: sources, propagation and consequences

2018 ◽  
Author(s):  
Philipp Thomas ◽  
Guillaume Terradot ◽  
Vincent Danos ◽  
Andrea Y. Weiße
Keyword(s):  
Evolutionary Biology ◽  
Cell Model ◽  
Single Cells ◽  
Growth Dynamics ◽  
Cellular Growth ◽  
Bacterial Cells ◽  
Cell Processes ◽  
Cellular Machinery ◽  
Cross Correlations ◽  
Phenotypic Responses

Cellular growth impacts a range of phenotypic responses. Identifying the sources of fluctuations in growth and how they propagate across the cellular machinery can unravel mechanisms that underpin cell decisions. We present a stochastic cell model linking gene expression, metabolism and replication to predict growth dynamics in single bacterial cells. In addition to several population-averaged data, the model quantitatively recovers how growth fluctuations in single cells change across nutrient conditions. We develop a framework to analyse stochastic chemical reactions coupled with cell divisions and use it to identify sources of growth heterogeneity. By visualising cross-correlations we then determine how such initial fluctuations propagate to growth rate and affect other cell processes. We further study antibiotic responses and find that complex drug-nutrient interactions can both enhance and suppress heterogeneity. Our results provide a predictive framework to integrate single-cell and bulk data and draw testable predictions with implications for antibiotic tolerance, evolutionary biology and synthetic biology.

scholarly journals Stochasticity in Colonial Growth Dynamics of Individual Bacterial Cells

2013 ◽  
Vol 79 (7) ◽  
pp. 2294-2301 ◽  
Author(s):  
Konstantinos P. Koutsoumanis ◽  
Alexandra Lianou
Keyword(s):  
Kinetic Parameters ◽  
Single Cells ◽  
Growth Curves ◽  
Growth Dynamics ◽  
Time Lapse ◽  
Initial Population ◽  
Bacterial Cells ◽  
Stochastic Growth ◽  
Bacterial Populations ◽  
Content Type

ABSTRACTConventional bacterial growth studies rely on large bacterial populations without considering the individual cells. Individual cells, however, can exhibit marked behavioral heterogeneity. Here, we present experimental observations on the colonial growth of 220 individual cells ofSalmonella entericaserotype Typhimurium using time-lapse microscopy videos. We found a highly heterogeneous behavior. Some cells did not grow, showing filamentation or lysis before division. Cells that were able to grow and form microcolonies showed highly diverse growth dynamics. The quality of the videos allowed for counting the cells over time and estimating the kinetic parameters lag time (λ) and maximum specific growth rate (μmax) for each microcolony originating from a single cell. To interpret the observations, the variability of the kinetic parameters was characterized using appropriate probability distributions and introduced to a stochastic model that allows for taking into account heterogeneity using Monte Carlo simulation. The model provides stochastic growth curves demonstrating that growth of single cells or small microbial populations is a pool of events each one of which has its own probability to occur. Simulations of the model illustrated how the apparent variability in population growth gradually decreases with increasing initial population size (N0). For bacterial populations withN0of >100 cells, the variability is almost eliminated and the system seems to behave deterministically, even though the underlying law is stochastic. We also used the model to demonstrate the effect of the presence and extent of a nongrowing population fraction on the stochastic growth of bacterial populations.

scholarly journals Nutrient-dependent trade-offs between ribosomes and division protein synthesis control bacterial cell size and growth

2020 ◽  
Author(s):  
Diana Serbanescu ◽  
Nikola Ojkic ◽  
Shiladitya Banerjee
Keyword(s):  
Experimental Data ◽  
Protein Synthesis ◽  
Cell Size ◽  
Bacterial Cell ◽  
Cell Model ◽  
Morphological Changes ◽  
Growth Dynamics ◽  
Cellular Growth ◽  
Trade Off ◽  
Trade Offs

SUMMARYCell size control emerges from a regulated balance between the rates of cell growth and division. In bacteria, simple quantitative laws connect cellular growth rate to ribosome abundance. However, it remains poorly understood how translation regulates bacterial cell size and shapes under growth perturbations. Here we develop a whole-cell model for growth dynamics in rod-shaped bacteria that links ribosomal abundance with cell geometry, division control, and the extracellular environment. Our study reveals that cell size maintenance under nutrient perturbations requires a balanced trade-off between ribosomes and division protein synthesis. Deviations from this trade-off relationship are predicted under translational perturbations, leading to distinct modes of cell morphological changes, in agreement with single-cell experimental data on Escherichia coli. Furthermore, by calibrating our model with experimental data, we predict how combinations of nutrient-, translational- and shape perturbations can be chosen to optimize bacterial growth fitness and antibiotic resistance.

scholarly journals In Silico Geobacter sulfurreducens Metabolism and Its Representation in Reactive Transport Models

2008 ◽  
Vol 75 (1) ◽  
pp. 83-92 ◽  
Author(s):  
E. L. King ◽  
K. Tuncay ◽  
P. Ortoleva ◽  
C. Meile
Keyword(s):  
Reactive Transport ◽  
Cell Model ◽  
Cell Metabolism ◽  
Growth Dynamics ◽  
Growth Efficiency ◽  
Geobacter Sulfurreducens ◽  
Balance Model ◽  
Substrate Uptake ◽  
Elemental Cycling ◽  
Dynamic Cell

ABSTRACT Microbial activity governs elemental cycling and the transformation of many anthropogenic substances in aqueous environments. Through the development of a dynamic cell model of the well-characterized, versatile, and abundant Geobacter sulfurreducens, we showed that a kinetic representation of key components of cell metabolism matched microbial growth dynamics observed in chemostat experiments under various environmental conditions and led to results similar to those from a comprehensive flux balance model. Coupling the kinetic cell model to its environment by expressing substrate uptake rates depending on intra- and extracellular substrate concentrations, two-dimensional reactive transport simulations of an aquifer were performed. They illustrated that a proper representation of growth efficiency as a function of substrate availability is a determining factor for the spatial distribution of microbial populations in a porous medium. It was shown that simplified model representations of microbial dynamics in the subsurface that only depended on extracellular conditions could be derived by properly parameterizing emerging properties of the kinetic cell model.

scholarly journals Evolutionary theory of bacterial quorum sensing: when is a signal not a signal?

2007 ◽  
Vol 362 (1483) ◽  
pp. 1241-1249 ◽  
Author(s):  
Stephen P Diggle ◽  
Andy Gardner ◽  
Stuart A West ◽  
Ashleigh S Griffin
Keyword(s):  
Quorum Sensing ◽  
Evolutionary Theory ◽  
Kin Selection ◽  
Evolutionary Biology ◽  
Bacterial Cells ◽  
Evolutionary Perspective ◽  
Population Response ◽  
Future Action ◽  
Bacterial Quorum Sensing ◽  
Bacterial Quorum

The term quorum sensing (QS) is used to describe the communication between bacterial cells, whereby a coordinated population response is controlled by diffusible molecules produced by individuals. QS has not only been described between cells of the same species (intraspecies), but also between species (interspecies) and between bacteria and higher organisms (inter-kingdom). The fact that QS-based communication appears to be widespread among microbes is strange, considering that explaining both cooperation and communication are two of the greatest problems in evolutionary biology. From an evolutionary perspective, intraspecies signalling can be explained using models such as kin selection, but when communication is described between species, it is more difficult to explain. It is probable that in many cases this involves QS molecules being used as ‘cues’ by other species as a guide to future action or as manipulating molecules whereby one species will ‘coerce’ a response from another. In these cases, the usage of QS molecules cannot be described as signalling. This review seeks to integrate the evolutionary literature on animal signalling with the microbiological literature on QS, and asks whether QS within bacteria is true signalling or whether these molecules are also used as cues or for the coercion of other cells.

scholarly journals Direct observation of frequency modulated transcription in single cells using light activation

eLife ◽  
2013 ◽  
Vol 2 ◽  
Author(s):  
Daniel R Larson ◽  
Christoph Fritzsch ◽  
Liang Sun ◽  
Xiuhau Meng ◽  
David S Lawrence ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Molecule ◽  
Single Cell Analysis ◽  
Cell Model ◽  
Single Cells ◽  
Single Gene ◽  
Gene Activation ◽  
Light Activation ◽  
Dynamic Synthesis ◽  
Single Cell Model

Single-cell analysis has revealed that transcription is dynamic and stochastic, but tools are lacking that can determine the mechanism operating at a single gene. Here we utilize single-molecule observations of RNA in fixed and living cells to develop a single-cell model of steroid-receptor mediated gene activation. We determine that steroids drive mRNA synthesis by frequency modulation of transcription. This digital behavior in single cells gives rise to the well-known analog dose response across the population. To test this model, we developed a light-activation technology to turn on a single steroid-responsive gene and follow dynamic synthesis of RNA from the activated locus.

scholarly journals Extracellular Electrophysiology in the Prostate Cancer Cell Model PC-3

Sensors ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 139 ◽  
Author(s):  
Miguel Cabello ◽  
Haobo Ge ◽  
Carmen Aracil ◽  
Despina Moschou ◽  
Pedro Estrela ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Electrical Activity ◽  
Cell Model ◽  
Treatment Strategies ◽  
Electrode Array ◽  
Cellular Level ◽  
Low Noise ◽  
Cellular Growth ◽  
Male Population ◽  
Depth Analysis

Although prostate cancer is one of the most common cancers in the male population, its basic biological function at a cellular level remains to be fully understood. This lack of in depth understanding of its physiology significantly hinders the development of new, targeted and more effective treatment strategies. Whilst electrophysiological studies can provide in depth analysis, the possibility of recording electrical activity in large populations of non-neuronal cells remains a significant challenge, even harder to address in the picoAmpere-range, which is typical of cellular level electrical activities. In this paper, we present the measurement and characterization of electrical activity of populations of prostate cancer cells PC-3, demonstrating for the first time a meaningful electrical pattern. The low noise system used comprises a multi-electrode array (MEA) with circular gold electrodes on silicon oxide substrates. The extracellular capacitive currents present two standard patterns: an asynchronous sporadic pattern and a synchronous quasi-periodic biphasic spike pattern. An amplitude of ±150 pA, a width between 50–300 ms and an inter-spike interval around 0.5 Hz characterize the quasi-periodic spikes. Our experiments using treatment of cells with Gd3⁺, known as an inhibitor for the Ca2⁺ exchanges, suggest that the quasi-periodic signals originate from Ca2⁺ channels. After adding the Gd3⁺ to a population of living PC-3 cells, their electrical activity considerably decreased; once the culture was washed, thus eliminating the Gd3⁺ containing medium and addition of fresh cellular growth medium, the PC-3 cells recovered their normal electrical activity. Cellular viability plots have been carried out, demonstrating that the PC-3 cells remain viable after the use of Gd3⁺, on the timescale of this experiment. Hence, this experimental work suggests that Ca2⁺ is significantly affecting the electrophysiological communication pattern among PC-3 cell populations. Our measuring platform opens up new avenues for real time and highly sensitive investigations of prostate cancer signalling pathways.

scholarly journals Performance Comparison of Reverse Transcriptases for Single-Cell Studies

2019 ◽  
Vol 66 (1) ◽  
pp. 217-228 ◽  
Author(s):  
Daniel Zucha ◽  
Peter Androvic ◽  
Mikael Kubista ◽  
Lukas Valihrach
Keyword(s):  
Single Cell ◽  
Reverse Transcription ◽  
Cell Model ◽  
Single Cells ◽  
Performance Comparison ◽  
Reaction Yield ◽  
Reverse Transcriptases ◽  
Reverse Transcription Pcr ◽  
Cell Experiment ◽  
Almost All

Abstract BACKGROUND Recent advances allowing quantification of RNA from single cells are revolutionizing biology and medicine. Currently, almost all single-cell transcriptomic protocols rely on reverse transcription (RT). However, RT is recognized as a known source of variability, particularly with low amounts of RNA. Recently, several new reverse transcriptases (RTases) with the potential to decrease the loss of information have been developed, but knowledge of their performance is limited. METHODS We compared the performance of 11 RTases in quantitative reverse transcription PCR (RT-qPCR) on single-cell and 100-cell bulk templates, using 2 priming strategies: a conventional mixture of random hexamers with oligo(dT)s and a reduced concentration of oligo(dT)s mimicking common single-cell RNA-sequencing protocols. Depending on their performance, 2 RTases were further tested in a high-throughput single-cell experiment. RESULTS All tested RTases demonstrated high precision (R2 > 0.9445). The most pronounced differences were found in their ability to capture rare transcripts (0%–90% reaction positivity rate) and in their absolute reaction yield (7.3%–137.9%). RTase performance and reproducibility were compared with Z scores. The 2 best-performing enzymes were Maxima H− and SuperScript IV. The validity of the obtained results was confirmed in a follow-up single-cell model experiment. The better-performing enzyme (Maxima H−) increased the sensitivity of the single-cell experiment and improved resolution in the clustering analysis over the commonly used RTase (SuperScript II). CONCLUSIONS Our comprehensive comparison of 11 RTases in low RNA input conditions identified 2 best-performing enzymes. Our results provide a point of reference for the improvement of current single-cell quantification protocols.

scholarly journals Autonomous Growth of Isolated Single Listeria monocytogenes and Salmonella enterica Serovar Typhimurium Cells in the Absence of Growth Factors and Intercellular Contact

2010 ◽  
Vol 76 (8) ◽  
pp. 2600-2606 ◽  
Author(s):  
Barbara Roeder ◽  
Martin Wagner ◽  
Peter Rossmanith
Keyword(s):  
Growth Factors ◽  
Listeria Monocytogenes ◽  
Salmonella Enterica ◽  
Single Cells ◽  
Intercellular Contact ◽  
Bacterial Cells ◽  
Chi Square ◽  
Significant Difference ◽  
Serovar Typhimurium ◽  
Autonomous Growth

ABSTRACT The aim of this study was to observe growth of isolated single bacterial cells in the absence of growth factors and intercellular contact. In order to exclude stochastic uncertainties induced by dilution series, a new micromanipulation method was developed to ensure explicit results under visual control. This was performed with particular care for production of single prokaryotic cells and subsequent investigation of their autonomous growth. Over 450 single isolated Listeria monocytogenes and Salmonella enterica subsp. enterica serovar Typhimurium cells in lag, log, and stationary growth phases were investigated by this method, which included thoroughly washing the cells. The proportion of living cells within the initial cultures was compared to the proportion of positive samples after enrichment of the separated single cells. This resulted in P values of ≥0.05 using the chi-square test for statistical analysis, indicating no significant difference, and clearly demonstrates reproduction of isolated single bacterial cells without the need for growth factors or intercellular contact. Ease of handling of the apparatus and good performance of the cleaning procedures were achieved, as was validation of the method, demonstrating its suitability for routine laboratory use.

scholarly journals Digital Microfluidics for Single Bacteria Capture and Selective Retrieval Using Optical Tweezers

Micromachines ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 308 ◽  
Author(s):  
Phalguni Tewari Kumar ◽  
Deborah Decrop ◽  
Saba Safdar ◽  
Ioannis Passaris ◽  
Tadej Kokalj ◽  
...  
Keyword(s):  
Optical Tweezers ◽  
Cell Sorting ◽  
Magnetic Beads ◽  
Single Cells ◽  
Digital Microfluidics ◽  
Cell Behavior ◽  
Bacterial Cells ◽  
Microfluidic Platforms ◽  
Dynamic Cell ◽  
Downstream Analysis

When screening microbial populations or consortia for interesting cells, their selective retrieval for further study can be of great interest. To this end, traditional fluorescence activated cell sorting (FACS) and optical tweezers (OT) enabled methods have typically been used. However, the former, although allowing cell sorting, fails to track dynamic cell behavior, while the latter has been limited to complex channel-based microfluidic platforms. In this study, digital microfluidics (DMF) was integrated with OT for selective trapping, relocation, and further proliferation of single bacterial cells, while offering continuous imaging of cells to evaluate dynamic cell behavior. To enable this, magnetic beads coated with Salmonella Typhimurium-targeting antibodies were seeded in the microwell array of the DMF platform, and used to capture single cells of a fluorescent S. Typhimurium population. Next, OT were used to select a bead with a bacterium of interest, based on its fluorescent expression, and to relocate this bead to a different microwell on the same or different array. Using an agar patch affixed on top, the relocated bacterium was subsequently allowed to proliferate. Our OT-integrated DMF platform thus successfully enabled selective trapping, retrieval, relocation, and proliferation of bacteria of interest at single-cell level, thereby enabling their downstream analysis.

Seeing is believing: Can biofilm diagnostics guide individualized therapy in endocarditis?

2020 ◽  
Author(s):  
Judith Kikhney ◽  
Laura Kursawe ◽  
Swb Eichinger ◽  
Walter Eichinger ◽  
Julia Schmidt ◽  
...  
Keyword(s):  
Antibiotic Therapy ◽  
Biofilm Formation ◽  
Heart Valves ◽  
Single Cells ◽  
Intergenic Spacer ◽  
Rrna Gene ◽  
Bacterial Cells ◽  
Intergenic Spacer Region ◽  
Causative Microorganism ◽  
The Impact

<p><strong>Introduction</strong></p> <p>In Infective Endocarditis (IE), early diagnosis of the causative microorganism is crucial for correct antibiotic therapy, which improves the patients’ outcome.</p> <p><strong>Objectives</strong></p> <p>We studies the impact of biofilm formation in IE samples.</p> <p><strong>Materials & methods</strong></p> <p>We used Fluorescence in situ Hybridization (FISH) combined with 16S rRNA-gene PCR and sequencing to visualize and identify the infectious agents in native as well as prosthetic valves and to study any biofilm formation. The signal intensity of the fluorescence-labelled FISH probes correlates to a high ribosome content of the bacteria indicating metabolic activity at the time point of surgery. We developed a spacer FISH assay for the detection of the 16S-23S intergenic spacer region that is only present in actively transcribing cells to detect the activity of bacterial cells more precisely on a single cell level.</p> <p><strong>Results</strong></p> <p>FISH visualized bacteria in the heart valves ranging from single cells to highly organized biofilms. Interestingly, we found FISH positive bacteria in culture negative samples and samples from patients under antibiotic therapy. Using the spacer FISH, we visualized positive microbial cells in heart valves of patients under adequate therapy. Preliminary data point to a correlation between the biofilm state and treatment inefficiency.</p> <p><strong>Conclusion</strong></p> <p>FISH/PCR not only allows timely identification of the pathogens in IE, but also biofilm-staging and visualization of the effect of antimicrobial therapy at time of surgery. The technique provides crucial information for successful targeted antibiotic therapy, and it might guide therapeutical decisions in relation to biofilm state in the future.</p>

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