scholarly journals Antibiotics modulate attractive interactions in bacterial colonies affecting survivability under combined treatment

2020 ◽  
Author(s):  
Tom Cronenberg ◽  
Marc Hennes ◽  
Isabelle Wielert ◽  
Berenike Maier
Keyword(s):  
Cell Motility ◽  
Single Cell ◽  
Antibiotic Treatment ◽  
Cell Tracking ◽  
Single Cells ◽  
Combined Treatment ◽  
Cellular Functions ◽  
Mutant Strains ◽  
A Cell ◽  
Cell Volumes

AbstractBiofilm formation protects bacteria from antibiotics. Very little is known about the response of biofilm-dwelling bacteria to antibiotics at the single cell level. Here, we developed a cell-tracking approach to investigate how antibiotics affect structure and dynamics of colonies formed by the human pathogen Neisseria gonorrhoeae. Antibiotics targeting different cellular functions enlarge the cell volumes and modulate within-colony motility. Focusing on azithromycin and ceftriaxone, we identify changes in type 4 pilus (T4P) mediated cell-to-cell attraction as the molecular mechanism for different effects on motility. By using strongly attractive mutant strains, we reveal that the survivability under ceftriaxone treatment depends on motility. Combining our results, we find that sequential treatment with azithromycin and ceftriaxone is synergistic. Taken together, we demonstrate that antibiotics modulate T4P-mediated attractions and hence cell motility and colony fluidity.Author SummaryAggregation into colonies and biofilms can enhance bacterial survivability under antibiotic treatment. Aggregation requires modulation of attractive forces between neighboring bacteria, yet the link between attraction and survivability is poorly characterized. Here, we quantify these attractive interactions and show that different antibiotics enhance or inhibit them. Live-cell tracking of single cells in spherical colonies enables us to correlate attractive interactions with single cell motility and colony fluidity. Even moderate changes in cell-to-cell attraction caused by antibiotics strongly impact on within-colony motility. Vice versa, we reveal that motility correlates with survivability under antibiotic treatment. In summary, we demonstrate a link between cellular attraction, colony fluidity, and survivability with the potential to optimize the treatment strategy of commonly used drug combinations.

scholarly journals Antibiotics modulate attractive interactions in bacterial colonies affecting survivability under combined treatment

2021 ◽  
Vol 17 (2) ◽  
pp. e1009251
Author(s):  
Tom Cronenberg ◽  
Marc Hennes ◽  
Isabelle Wielert ◽  
Berenike Maier
Keyword(s):  
Biofilm Formation ◽  
Cell Tracking ◽  
Combined Treatment ◽  
Human Pathogen ◽  
Cellular Functions ◽  
Cell Level ◽  
Structure And Dynamics ◽  
Mutant Strains ◽  
A Cell ◽  
Cell Volumes

Biofilm formation protects bacteria from antibiotics. Very little is known about the response of biofilm-dwelling bacteria to antibiotics at the single cell level. Here, we developed a cell-tracking approach to investigate how antibiotics affect structure and dynamics of colonies formed by the human pathogen Neisseria gonorrhoeae. Antibiotics targeting different cellular functions enlarge the cell volumes and modulate within-colony motility. Focusing on azithromycin and ceftriaxone, we identify changes in type 4 pilus (T4P) mediated cell-to-cell attraction as the molecular mechanism for different effects on motility. By using strongly attractive mutant strains, we reveal that the survivability under ceftriaxone treatment depends on motility. Combining our results, we find that sequential treatment with azithromycin and ceftriaxone is synergistic. Taken together, we demonstrate that antibiotics modulate T4P-mediated attractions and hence cell motility and colony fluidity.

scholarly journals pcnaDeep: A Fast and Robust Single-Cell Tracking Method Using Deep-Learning Mediated Cell Cycle Profiling

2021 ◽  
Author(s):  
Yifan Gui ◽  
Shuang Shuang Xie ◽  
Yanan Wang ◽  
Ping Wang ◽  
Renzhi Yao ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Deep Learning ◽  
Single Cell ◽  
Cell Tracking ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Time Lapse ◽  
Molecular Control ◽  
A Cell ◽  
User Friendly

Motivation: Computational methods that track single-cells and quantify fluorescent biosensors in time-lapse microscopy images have revolutionised our approach in studying the molecular control of cellular decisions. One barrier that limits the adoption of single-cell analysis in biomedical research is the lack of efficient methods to robustly track single-cells over cell division events. Results: Here, we developed an application that automatically tracks and assigns mother-daughter relationships of single-cells. By incorporating cell cycle information from a well-established fluorescent cell cycle reporter, we associate mitosis relationships enabling high fidelity long-term single-cell tracking. This was achieved by integrating a deep-learning based fluorescent PCNA signal instance segmentation module with a cell tracking and cell cycle resolving pipeline. The application offers a user-friendly interface and extensible APIs for customized cell cycle analysis and manual correction for various imaging configurations. Availability and Implementation: pcnaDeep is an open-source Python application under the Apache 2.0 licence. The source code, documentation and tutorials are available at https://github.com/chan-labsite/PCNAdeep.

scholarly journals Proteomic Signatures of Clostridium difficile Stressed with Metronidazole, Vancomycin, or Fidaxomicin

Cells ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 213 ◽  
Author(s):  
Sandra Maaß ◽  
Andreas Otto ◽  
Dirk Albrecht ◽  
Katharina Riedel ◽  
Anke Trautwein-Schult ◽  
...  
Keyword(s):  
Clostridium Difficile ◽  
Antibiotic Treatment ◽  
Antimicrobial Agents ◽  
Cell Envelope ◽  
Protein Biosynthesis ◽  
Cellular Functions ◽  
Antibiotic Resistant ◽  
Incidence And Mortality ◽  
A Cell ◽  
Proteomic Response

The anaerobic pathogen Clostridium difficile is of growing significance for the health care system due to its increasing incidence and mortality. As C. difficile infection is both supported and treated by antibiotics, a deeper knowledge on how antimicrobial agents affect the physiology of this important pathogen may help to understand and prevent the development and spreading of antibiotic resistant strains. As the proteomic response of a cell to stress aims at counteracting the harmful effects of this stress, it can be expected that the pattern of a pathogen’s responses to antibiotic treatment will be dependent on the antibiotic mechanism of action. Hence, every antibiotic treatment is expected to result in a specific proteomic signature characterizing its mode of action. In the study presented here, the proteomic response of C. difficile 630∆erm to vancomycin, metronidazole, and fidaxomicin stress was investigated on the level of protein abundance and protein synthesis based on 2D PAGE. The quantification of 425 proteins of C. difficile allowed the deduction of proteomic signatures specific for each drug treatment. Indeed, these proteomic signatures indicate very specific cellular responses to each antibiotic with only little overlap of the responses. Whereas signature proteins for vancomycin stress fulfil various cellular functions, the proteomic signature of metronidazole stress is characterized by alterations of proteins involved in protein biosynthesis and protein degradation as well as in DNA replication, recombination, and repair. In contrast, proteins differentially expressed after fidaxomicin treatment can be assigned to amino acid biosynthesis, transcription, cell motility, and the cell envelope functions. Notably, the data provided by this study hint also at so far unknown antibiotic detoxification mechanisms.

scholarly journals Single-cell analysis of circadian dynamics in tissue explants

2015 ◽  
Vol 26 (22) ◽  
pp. 3940-3945 ◽  
Author(s):  
Laura Lande-Diner ◽  
Jacob Stewart-Ornstein ◽  
Charles J. Weitz ◽  
Galit Lahav
Keyword(s):  
Circadian Clock ◽  
Single Cell ◽  
Cell Tracking ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Tissue Imaging ◽  
Wide Field ◽  
Isolated Cells ◽  
Peripheral Tissues

Tracking molecular dynamics in single cells in vivo is instrumental to understanding how cells act and interact in tissues. Current tissue imaging approaches focus on short-term observation and typically nonendogenous or implanted samples. Here we develop an experimental and computational setup that allows for single-cell tracking of a transcriptional reporter over a period of >1 wk in the context of an intact tissue. We focus on the peripheral circadian clock as a model system and measure the circadian signaling of hundreds of cells from two tissues. The circadian clock is an autonomous oscillator whose behavior is well described in isolated cells, but in situ analysis of circadian signaling in single cells of peripheral tissues is as-yet uncharacterized. Our approach allowed us to investigate the oscillatory properties of individual clocks, determine how these properties are maintained among different cells, and assess how they compare to the population rhythm. These experiments, using a wide-field microscope, a previously generated reporter mouse, and custom software to track cells over days, suggest how many signaling pathways might be quantitatively characterized in explant models.

scholarly journals Cell-to-cell diversification in ERBB-RAS-MAPK signal transduction that produces cell-type specific growth factor responses

2019 ◽  
Author(s):  
Hiraku Miyagi ◽  
Michio Hiroshima ◽  
Yasushi Sako
Keyword(s):  
Growth Factors ◽  
Single Cell ◽  
Cell Fate ◽  
Single Cells ◽  
Bet Hedging ◽  
Cell Type ◽  
Cell Responses ◽  
Single Cell Responses ◽  
Specific Growth Factor ◽  
A Cell

AbstractGrowth factors regulate cell fates, including their proliferation, differentiation, survival, and death, according to the cell type. Even when the response to a specific growth factor is deterministic for collective cell behavior, significant levels of fluctuation are often observed between single cells. Statistical analyses of single-cell responses provide insights into the mechanism of cell fate decisions but very little is known about the distributions of the internal states of cells responding to growth factors. Using multi-color immunofluorescent staining, we have here detected the phosphorylation of seven elements in the early response of the ERBB–RAS–MAPK system to two growth factors. Among these seven elements, five were analyzed simultaneously in distinct combinations in the same single cells. Although principle component analysis suggested cell-type and input specific phosphorylation patterns, cell-to-cell fluctuation was large. Mutual information analysis suggested that cells use multitrack (bush-like) signal transduction pathways under conditions in which clear cell fate changes have been reported. The clustering of single-cell response patterns indicated that the fate change in a cell population correlates with the large entropy of the response, suggesting a bet-hedging strategy is used in decision making. A comparison of true and randomized datasets further indicated that this large variation is not produced by simple reaction noise, but is defined by the properties of the signal-processing network.Author SummaryHow extracellular signals, such as growth factors (GFs), induce fate changes in biological cells is still not fully understood. Some GFs induce cell proliferation and others induce differentiation by stimulating a common reaction network. Although the response to each GF is reproducible for a cell population, not all single cells respond similarly. The question that arises is whether a certain GF conducts all the responding cells in the same direction during a fate change, or if it initially stimulates a variety of behaviors among single cells, from which the cells that move in the appropriate direction are later selected. Our current statistical analysis of single-cell responses suggests that the latter process, which is called a bet-hedging mechanism is plausible. The complex pathways of signal transmission seem to be responsible for this bet-hedging.

scholarly journals Single cell network analysis with a mixture of Nested Effects Models

2018 ◽  
Author(s):  
Martin Pirkl ◽  
Niko Beerenwinkel
Keyword(s):  
Single Cell ◽  
New Technologies ◽  
Single Cells ◽  
R Package ◽  
Supplementary Information ◽  
Data Sets ◽  
Cell Network ◽  
A Cell ◽  
Supplementary Material ◽  
Cell Data

AbstractMotivationNew technologies allow for the elaborate measurement of different traits of single cells. These data promise to elucidate intra-cellular networks in unprecedented detail and further help to improve treatment of diseases like cancer. However, cell populations can be very heterogeneous.ResultsWe developed a mixture of Nested Effects Models (M&NEM) for single-cell data to simultaneously identify different cellular sub-populations and their corresponding causal networks to explain the heterogeneity in a cell population. For inference, we assign each cell to a network with a certain probability and iteratively update the optimal networks and cell probabilities in an Expectation Maximization scheme. We validate our method in the controlled setting of a simulation study and apply it to three data sets of pooled CRISPR screens generated previously by two novel experimental techniques, namely Crop-Seq and Perturb-Seq.AvailabilityThe mixture Nested Effects Model (M&NEM) is available as the R-package mnem at https://github.com/cbgethz/mnem/[email protected], [email protected] informationSupplementary data are available.online.

SIFTER: Electrophoretic complex fractionation protocol

2021 ◽  
Author(s):  
Julea Vlassakis ◽  
Louise L Hansen ◽  
Amy E Herr
Keyword(s):  
Single Cell ◽  
Protein Interaction ◽  
Gel Electrophoresis ◽  
Protein Complexes ◽  
Single Cells ◽  
Polyacrylamide Gel Electrophoresis ◽  
Simultaneous Detection ◽  
Single Cell Protein ◽  
Cell Protein ◽  
A Cell

Abstract We introduce micro-arrayed, differential detergent fractionation for the simultaneous detection of protein complexes in 100s of individual cells with SIFTER (Single-cell protein Interaction Fractionation Through Electrophoresis and immunoassay Readout). Size-based fractionation of protein complexes is accomplished with five assay steps. First, a cell suspension generated by trypsinization is introduced onto a microwell array, and single cells are settled into the microwells by gravity. Cells are lysed in F-actin stabilization buffer that is delivered by a hydrogel lid. Second, the protein complexes are fractionated from the smaller monomers by polyacrylamide gel electrophoresis. Monomers are electrophoresed into the gel and are immobilized using a UV-induced covalent reaction to benzophenone. Third, a protein-complex depolymerization buffer is introduced by another hydrogel lid. Fourth, the recently depolymerized complexes are electrophoresed into a region of the gel separate from the immobilized monomers, where the complex fraction are in turn immobilized. Fifth, in-gel immunoprobing detects the immobilized populations of monomer and depolymerized complexes. These general steps are built on previously published protocols for bulk actin studies, single-cell western blotting, and bidirectional separations1-4.

scholarly journals Single Cells Exhibit Differing Behavioral Phases during Early Stages of Pseudomonas aeruginosa Swarming

2019 ◽  
Vol 201 (19) ◽  
Author(s):  
Chinedu S. Madukoma ◽  
Peixian Liang ◽  
Aleksandar Dimkovikj ◽  
Jianxu Chen ◽  
Shaun W. Lee ◽  
...  
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Cell Motility ◽  
Single Cell ◽  
High Cell Density ◽  
Single Cells ◽  
High Cell ◽  
Wild Type ◽  
Content Type ◽  
Type Iv ◽  
The Many

ABSTRACT Pseudomonas aeruginosa is among the many bacteria that swarm, where groups of cells coordinate to move over surfaces. It has been challenging to determine the behavior of single cells within these high-cell-density swarms. To track individual cells within P. aeruginosa swarms, we imaged a fluorescently labeled subset of the larger population. Single cells at the advancing swarm edge varied in their motility dynamics as a function of time. From these data, we delineated four phases of early swarming prior to the formation of the tendril fractals characteristic of P. aeruginosa swarming by collectively considering both micro- and macroscale data. We determined that the period of greatest single-cell motility does not coincide with the period of greatest collective swarm expansion. We also noted that flagellar, rhamnolipid, and type IV pilus motility mutants exhibit substantially less single-cell motility than the wild type. IMPORTANCE Numerous bacteria exhibit coordinated swarming motion over surfaces. It is often challenging to assess the behavior of single cells within swarming communities due to the limitations of identifying, tracking, and analyzing the traits of swarming cells over time. Here, we show that the behavior of Pseudomonas aeruginosa swarming cells can vary substantially in the earliest phases of swarming. This is important to establish that dynamic behaviors should not be assumed to be constant over long periods when predicting and simulating the actions of swarming bacteria.

scholarly journals Cell explosions: single cell death triggers an avoidance response in local populations of the ecologically prominent phytoplankton genus Micromonas

2019 ◽  
Author(s):  
Richard Henshaw ◽  
Jonathan Roberts ◽  
Marco Polin
Keyword(s):  
Cell Death ◽  
Avoidance Response ◽  
Single Cell ◽  
Single Cells ◽  
Local Environment ◽  
Warning Signal ◽  
Predator Prey ◽  
A Cell ◽  
Observed Behaviour ◽  
Micromonas Pusilla

The global phytoplankton community, comprised of aquatic photosynthetic organisms, is acknowledged for being responsible for half of the global oxygen production Prominent among these is the pico-eukaryote Micromonas commoda (formally Micromonas pusilla of the genus Micromonas), which can be found in marine and coastal environments across the globe. Cell death of phytoplankton has been identified as contributing to the largest carbon transfers on the planet moving 109 tonnes of carbon in the oceans every day. During a cell death organic matter is released into the local environment which can act as both a food source and a warning signal for nearby organisms. Here we present a novel motility response to single cell death in populations of Micromonas sp., where the death of a single cell releases a chemical patch triggers surrounding cells to escape the immediate affected area. These so-called “burst events” are then modelled and compared with a spherically symmetric diffusing patch which is found to faithfully reproduce the observed behaviour. Finally, laser ablation of single cells reproduces the observed avoidance response, confirming that Micromonas sp. has evolved a specific motility response in order to escape harmful environments for example nearby predator-prey interactions or virus lysis induced cell death.

scholarly journals Functional single cell proteomic profiling of cells with abnormal DNA damage response dynamics

2021 ◽  
Author(s):  
Pin-Rui Su ◽  
Li You ◽  
Cecile Beerens ◽  
Karel Bezstarosti ◽  
Jeroen Demmers ◽  
...  
Keyword(s):  
Dna Damage ◽  
Single Cell ◽  
Dna Damage Response ◽  
Cell Tracking ◽  
Single Cells ◽  
Proteomic Profiling ◽  
Response Dynamics ◽  
Isolated Cells ◽  
Cellular Dynamics ◽  
Damage Response

Tumor heterogeneity is an important source of cancer therapy resistance. Single cell proteomics has the potential to decipher protein content leading to heterogeneous cellular phenotypes. Single-Cell ProtEomics by Mass Spectrometry (SCoPE-MS) is a recently developed, promising, unbiased proteomic profiling techniques, which allows profiling several tens of single cells for >1000 proteins per cell. However, a method to link single cell proteomes with cellular behaviors is needed to advance this type of profiling technique. Here, we developed a microscopy-based functional single cell proteomic profiling technology, called FUNpro, to link the proteome of individual cells with phenotypes of interest, even if the phenotypes are dynamic or the cells of interest are sparse. FUNpro enables one i) to screen thousands of cells with subcellular resolution and monitor (intra)cellular dynamics using a custom-built microscope, ii) to real-time analyze (intra)cellular dynamics of individual cells using an integrated cell tracking algorithm, iii) to promptly isolate the cells displaying phenotypes of interest, and iv) to single cell proteomically profile the isolated cells. We applied FUNpro to proteomically profile a newly identified small subpopulation of U2OS osteosarcoma cells displaying an abnormal, prolonged DNA damage response (DDR) after ionizing radiation (IR). With this, we identified PDS5A and PGAM5 proteins contributing to the abnormal DDR dynamics and helping the cells survive after IR.

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