Single Cell Analysis of a Bacterial Sender-Receiver System
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{"title"=>"Single Cell Analysis of a Bacterial Sender-Receiver System", "type"=>"journal", "authors"=>[{"first_name"=>"Tiago", "last_name"=>"Ramalho", "scopus_author_id"=>"55612588600"}, {"first_name"=>"Andrea", "last_name"=>"Meyer", "scopus_author_id"=>"22985768200"}, {"first_name"=>"Andrea", "last_name"=>"Mückl", "scopus_author_id"=>"55998901800"}, {"first_name"=>"Korbinian", "last_name"=>"Kapsner", "scopus_author_id"=>"55998967500"}, {"first_name"=>"Ulrich", "last_name"=>"Gerland", "scopus_author_id"=>"26643228900"}, {"first_name"=>"Friedrich C.", "last_name"=>"Simmel", "scopus_author_id"=>"6701513729"}], "year"=>2016, "source"=>"PloS one", "identifiers"=>{"scopus"=>"2-s2.0-84977579951", "sgr"=>"84977579951", "doi"=>"10.1371/journal.pone.0145829", "pui"=>"615996029", "pmid"=>"26808777", "issn"=>"19326203"}, "id"=>"e534384c-4ec0-3525-b8cc-4a37994e610c", "abstract"=>"Monitoring gene expression dynamics on the single cell level provides important information on cellular heterogeneity and stochasticity, and potentially allows for more accurate quantitation of gene expression processes. We here study bacterial senders and receivers genetically engineered with components of the quorum sensing system derived from Aliivibrio fischeri on the single cell level using microfluidics-based bacterial chemostats and fluorescence video microscopy. We track large numbers of bacteria over extended periods of time, which allows us to determine bacterial lineages and filter out subpopulations within a heterogeneous population. We quantitatively determine the dynamic gene expression response of receiver bacteria to varying amounts of the quorum sensing inducer N-3-oxo-C6-homoserine lactone (AHL). From this we construct AHL response curves and characterize gene expression dynamics of whole bacterial populations by investigating the statistical distribution of gene expression activity over time. The bacteria are found to display heterogeneous induction behavior within the population. We therefore also characterize gene expression in a homogeneous bacterial subpopulation by focusing on single cell trajectories derived only from bacteria with similar induction behavior. The response at the single cell level is found to be more cooperative than that obtained for the heterogeneous total population. For the analysis of systems containing both AHL senders and receiver cells, we utilize the receiver cells as ‘bacterial sensors’ for AHL. Based on a simple gene expression model and the response curves obtained in receiver-only experiments, the effective AHL concentration established by the senders and their ‘sending power’ is determined.", "link"=>"http://www.mendeley.com/research/single-cell-analysis-bacterial-senderreceiver-system-1", "reader_count"=>43, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>1, "Researcher"=>6, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>19, "Student > Postgraduate"=>1, "Student > Master"=>3, "Other"=>1, "Student > Bachelor"=>7, "Professor"=>2}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>1, "Researcher"=>6, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>19, "Student > Postgraduate"=>1, "Student > Master"=>3, "Other"=>1, "Student > Bachelor"=>7, "Professor"=>2}, "reader_count_by_subject_area"=>{"Engineering"=>3, "Biochemistry, Genetics and Molecular Biology"=>8, "Agricultural and Biological Sciences"=>20, "Medicine and Dentistry"=>1, "Neuroscience"=>1, "Physics and Astronomy"=>3, "Chemistry"=>4, "Computer Science"=>3}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>3}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Neuroscience"=>{"Neuroscience"=>1}, "Chemistry"=>{"Chemistry"=>4}, "Physics and Astronomy"=>{"Physics and Astronomy"=>3}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>20}, "Computer Science"=>{"Computer Science"=>3}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>8}}, "reader_count_by_country"=>{"United States"=>1, "Germany"=>1, "Spain"=>1}, "group_count"=>1}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2632792"], "description"=>"<p>(A) Histogram of the maximum gene expression <i>α</i><sub><i>max</i></sub> = max<sub><i>t</i></sub><i>α</i>(<i>t</i>) for each single cell trajectory (AHL = 50 nM), and a lognormal MLE fit to <i>P</i>(<i>α</i><sub><i>max</i></sub>) (red line). (B) Plot of the average and standard deviation of the distribution <i>P</i>(<i>α</i><sub><i>max</i></sub>). The red line is a regression curve generated by fitting a Hill function to the data. (C) Histogram of the average expression 〈<i>α</i>(<i>t</i>)〉 for each single cell trajectory (AHL = 50 nM) and a lognormal MLE fit to <i>P</i>(〈<i>α</i>(<i>t</i>)〉). (D) Plot of the average and standard deviation of the distribution <i>P</i>(〈<i>α</i>(<i>t</i>)〉)—the red line represents the best fit with a Hill curve.</p>", "links"=>[], "tags"=>["receiver cells", "cell level", "senders", "fluorescence video microscopy", "gene expression dynamics", "bacteria", "induction behavior", "gene expression activity", "gene expression processes", "AHL response curves", "gene expression response", "gene expression model", "Single Cell Analysis"], "article_id"=>1641232, "categories"=>["Biological Sciences"], "users"=>["Tiago Ramalho", "Andrea Meyer", "Andrea Mückl", "Korbinian Kapsner", "Ulrich Gerland", "Friedrich C. Simmel"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0145829.g003", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distribution_of_gene_expression_rates_945_determined_from_single_cell_trajectories_/1641232", "title"=>"Distribution of gene expression rates <i>α</i> determined from single cell trajectories.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-01-28 12:40:35"}
  • {"files"=>["https://ndownloader.figshare.com/files/2632794"], "description"=>"<p>(A)Maximum GFP induction for 7 select experiments as a function of the effective AHL production constant <math><mrow><mi>s</mi><mo>=</mo><mrow><mo>〈</mo><mi>r</mi><mo>〉</mo></mrow><msubsup><mi>t</mi><mrow>max</mrow><mn>2</mn></msubsup></mrow></math>. The sender strength parameter <i>s</i> in the experiments was varied via the sender/receiver ratio <i>r</i>. Nominally, the initial ratios were chosen to be <i>r</i> = 0.067, 0.142, 0.33, and 1 (corresponding to sender <i>fractions</i> of 6.25%, 12.5%, 25%, and 50%, resp.), but due to cell division and dynamics within the bacterial traps, the ratios varied over time—the resulting average ratios 〈<i>r</i>〉 are indicated for the single data points. Error bars represent standard deviations obtained from single cell gene expression histograms as in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0145829#pone.0145829.g003\" target=\"_blank\">Fig 3</a>. (B) Determination of effective AHL concentration for the 7 experiments using the calibration curve acquired in the ‘receiver only’ experiments with constant [AHL]. (C) The parameter <i>s</i> and the effective AHL concentration in the traps can be related as indicated by the broken lines in part A and B of the figure (for the red colored example point). A linear fit to the data following <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0145829#pone.0145829.e019\" target=\"_blank\">Eq (6)</a> is shown as a red line, error bars are obtained from the standard deviations in (A) via error propagation. (D) Example image of a microfluidic trap containing sender (red) and receiver (green) bacteria.</p>", "links"=>[], "tags"=>["receiver cells", "cell level", "senders", "fluorescence video microscopy", "gene expression dynamics", "bacteria", "induction behavior", "gene expression activity", "gene expression processes", "AHL response curves", "gene expression response", "gene expression model", "Single Cell Analysis"], "article_id"=>1641234, "categories"=>["Uncategorised"], "users"=>["Tiago Ramalho", "Andrea Meyer", "Andrea Mückl", "Korbinian Kapsner", "Ulrich Gerland", "Friedrich C. Simmel"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0145829.g004", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_/1641234", "title"=>"Single Cell Analysis of a Bacterial Sender-Receiver System", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-01-28 13:01:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/2632795"], "description"=>"<div><p>Monitoring gene expression dynamics on the single cell level provides important information on cellular heterogeneity and stochasticity, and potentially allows for more accurate quantitation of gene expression processes. We here study bacterial senders and receivers genetically engineered with components of the quorum sensing system derived from <i>Aliivibrio fischeri</i> on the single cell level using microfluidics-based bacterial chemostats and fluorescence video microscopy. We track large numbers of bacteria over extended periods of time, which allows us to determine bacterial lineages and filter out subpopulations within a heterogeneous population. We quantitatively determine the dynamic gene expression response of receiver bacteria to varying amounts of the quorum sensing inducer N-3-oxo-C6-homoserine lactone (AHL). From this we construct AHL response curves and characterize gene expression dynamics of whole bacterial populations by investigating the statistical distribution of gene expression activity over time. The bacteria are found to display heterogeneous induction behavior within the population. We therefore also characterize gene expression in a homogeneous bacterial subpopulation by focusing on single cell trajectories derived only from bacteria with similar induction behavior. The response at the single cell level is found to be more cooperative than that obtained for the heterogeneous total population. For the analysis of systems containing both AHL senders and receiver cells, we utilize the receiver cells as ‘bacterial sensors’ for AHL. Based on a simple gene expression model and the response curves obtained in receiver-only experiments, the effective AHL concentration established by the senders and their ‘sending power’ is determined.</p></div>", "links"=>[], "tags"=>["receiver cells", "cell level", "senders", "fluorescence video microscopy", "gene expression dynamics", "bacteria", "induction behavior", "gene expression activity", "gene expression processes", "AHL response curves", "gene expression response", "gene expression model", "Single Cell Analysis"], "article_id"=>1641235, "categories"=>["Biological Sciences"], "users"=>["Tiago Ramalho", "Andrea Meyer", "Andrea Mückl", "Korbinian Kapsner", "Ulrich Gerland", "Friedrich C. Simmel"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0145829", "stats"=>{"downloads"=>6, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Single_Cell_Analysis_of_a_Bacterial_Sender_Receiver_System_/1641235", "title"=>"Single Cell Analysis of a Bacterial Sender-Receiver System", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2016-01-28 12:40:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/2632789"], "description"=>"<p>(A) Total cell area <i>A</i> in pixels as a function of time for acquisitions with different AHL concentrations. The cells are in exponential growth for at least 450 <i>min</i>. After this time, the bacteria completely fill the microfluidic traps and the measured area stays constant. (B) Total colony fluorescence <i>F</i> as a function of time. (C) Maximum gene expression rates calculated as <math><mrow><mi>F</mi><mo>˙</mo><mo>/</mo><mi>A</mi></mrow></math> (cf. Eqs <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0145829#pone.0145829.e001\" target=\"_blank\">1</a>–<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0145829#pone.0145829.e005\" target=\"_blank\">3</a>). The solid response curve is a Hill fit to the data with <i>n</i> = 0.95±0.2 and <i>K</i> = 5.3±1.4 nM. (D) Snapshots taken from a time-lapse microscopy video of a bacterial colony growing in a microfluidic trap, which is connected to a supply channel on the right. The images shown are overlays of bright-field and fluorescence data. In the example, the bacteria were induced with 12 nM AHL.</p>", "links"=>[], "tags"=>["receiver cells", "cell level", "senders", "fluorescence video microscopy", "gene expression dynamics", "bacteria", "induction behavior", "gene expression activity", "gene expression processes", "AHL response curves", "gene expression response", "gene expression model", "Single Cell Analysis"], "article_id"=>1641229, "categories"=>["Biological Sciences"], "users"=>["Tiago Ramalho", "Andrea Meyer", "Andrea Mückl", "Korbinian Kapsner", "Ulrich Gerland", "Friedrich C. Simmel"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0145829.g001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Bulk_analysis_of_gene_expression_in_microfluidic_chemostats_/1641229", "title"=>"Bulk analysis of gene expression in microfluidic chemostats.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-01-28 12:40:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/2632791"], "description"=>"<p>(A) Evolution of the bacterial fluorescence per area <i>F</i>/<i>A</i> as a function of time. Clusters of ‘late inducers’ are visible. (B) Histogram of <i>F</i>/<i>A</i> at time <i>t</i> = 600 min using a logarithmic scale on the x-axis. Solid lines represent the two Gaussian distributions resulting from the Gaussian mixture fitting procedure used to separate out the dominant, homogeneous fraction of cells. (C) Parametric plot of the square of the coefficient of variation (CV) of <i>p</i> = <i>F</i>/<i>A</i>, i.e., <math><mrow><msubsup><mi>σ</mi><mi>p</mi><mn>2</mn></msubsup><mo>/</mo><mrow><mo>〈</mo><mi>p</mi><mo>〉</mo></mrow><mn>2</mn></mrow></math>, as a function of 〈<i>p</i>〉. The noise is dynamically ramping up until the cells reach roughly 1/3 of their maximal expression level. After this, <i>CV</i><sup>2</sup> stays approximately constant, indicating the dominance of extrinsic noise in gene expression.</p>", "links"=>[], "tags"=>["receiver cells", "cell level", "senders", "fluorescence video microscopy", "gene expression dynamics", "bacteria", "induction behavior", "gene expression activity", "gene expression processes", "AHL response curves", "gene expression response", "gene expression model", "Single Cell Analysis"], "article_id"=>1641231, "categories"=>["Uncategorised"], "users"=>["Tiago Ramalho", "Andrea Meyer", "Andrea Mückl", "Korbinian Kapsner", "Ulrich Gerland", "Friedrich C. Simmel"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0145829.g002", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Single_cell_gene_expression_histogram_and_trajectories_for_AHL_50_nM_/1641231", "title"=>"Single cell gene expression histogram and trajectories for AHL = 50 nM.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-01-28 12:40:37"}

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Relative Metric

{"start_date"=>"2016-01-01T00:00:00Z", "end_date"=>"2016-12-31T00:00:00Z", "subject_areas"=>[]}
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