Long-Term Single Cell Analysis of S. pombe on a Microfluidic Microchemostat Array
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{"title"=>"Long-term single cell analysis of S. pombe on a microfluidic microchemostat array", "type"=>"journal", "authors"=>[{"first_name"=>"Jean Bernard", "last_name"=>"Nobs", "scopus_author_id"=>"56132379400"}, {"first_name"=>"Sebastian J.", "last_name"=>"Maerkl", "scopus_author_id"=>"6504477610"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"pmid"=>"24710337", "doi"=>"10.1371/journal.pone.0093466", "sgr"=>"84899425060", "isbn"=>"1932-6203", "scopus"=>"2-s2.0-84899425060", "issn"=>"19326203", "pui"=>"372955974"}, "id"=>"c9bc372e-ecc7-34cf-8d96-75436b4737d6", "abstract"=>"Although Schyzosaccharomyces pombe is one of the principal model organisms for studying the cell cycle, surprisingly few methods have characterized S. pombe growth on the single cell level, and no methods exist capable of analyzing thousands of cells and tens of thousands of cell division events. We developed an automated microfluidic platform permitting S. pombe to be grown on-chip for several days under defined and changeable conditions. We developed an image processing pipeline to extract and quantitate several physiological parameters including cell length, time to division, and elongation rate without requiring synchronization of the culture. Over a period of 50 hours our platform analyzed over 100000 cell division events and reconstructed single cell lineages up to 10 generations in length. We characterized cell lengths and division times in a temperature shift experiment in which cells were initially grown at 30°C and transitioned to 25°C. Although cell length was identical at both temperatures at steady-state, we observed transient changes in cell length if the temperature shift took place during a critical phase of the cell cycle. We further show that cells born with normal length do divide over a wide range of cell lengths and that cell length appears to be controlled in the second generation, were large newly born cells have a tendency to divide more rapidly and thus at a normalized cell size. The platform is thus applicable to measure fine-details in cell cycle dynamics, should be a useful tool to decipher the molecular mechanism underlying size homeostasis, and will be generally applicable to study processes on the single cell level that require large numbers of precision measurements and single cell lineages.", "link"=>"http://www.mendeley.com/research/longterm-single-cell-analysis-s-pombe-microfluidic-microchemostat-array", "reader_count"=>78, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>3, "Researcher"=>16, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>25, "Other"=>1, "Student > Master"=>14, "Student > Bachelor"=>12, "Lecturer"=>1, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>3, "Researcher"=>16, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>25, "Other"=>1, "Student > Master"=>14, "Student > Bachelor"=>12, "Lecturer"=>1, "Professor"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>2, "Engineering"=>10, "Biochemistry, Genetics and Molecular Biology"=>11, "Mathematics"=>1, "Agricultural and Biological Sciences"=>40, "Medicine and Dentistry"=>1, "Neuroscience"=>1, "Physics and Astronomy"=>2, "Chemical Engineering"=>1, "Chemistry"=>6, "Computer Science"=>3}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>10}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Neuroscience"=>{"Neuroscience"=>1}, "Chemistry"=>{"Chemistry"=>6}, "Physics and Astronomy"=>{"Physics and Astronomy"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>40}, "Computer Science"=>{"Computer Science"=>3}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>11}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>2}, "Chemical Engineering"=>{"Chemical Engineering"=>1}}, "reader_count_by_country"=>{"South Korea"=>1, "United States"=>1, "United Kingdom"=>2, "France"=>2, "Switzerland"=>4, "Germany"=>1}, "group_count"=>8}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1452840", "https://ndownloader.figshare.com/files/1452841", "https://ndownloader.figshare.com/files/1452842", "https://ndownloader.figshare.com/files/1452843", "https://ndownloader.figshare.com/files/1452845", "https://ndownloader.figshare.com/files/1452846"], "description"=>"<div><p>Although <i>Schyzosaccharomyces pombe</i> is one of the principal model organisms for studying the cell cycle, surprisingly few methods have characterized <i>S. pombe</i> growth on the single cell level, and no methods exist capable of analyzing thousands of cells and tens of thousands of cell division events. We developed an automated microfluidic platform permitting <i>S. pombe</i> to be grown on-chip for several days under defined and changeable conditions. We developed an image processing pipeline to extract and quantitate several physiological parameters including cell length, time to division, and elongation rate without requiring synchronization of the culture. Over a period of 50 hours our platform analyzed over 100000 cell division events and reconstructed single cell lineages up to 10 generations in length. We characterized cell lengths and division times in a temperature shift experiment in which cells were initially grown at 30°C and transitioned to 25°C. Although cell length was identical at both temperatures at steady-state, we observed transient changes in cell length if the temperature shift took place during a critical phase of the cell cycle. We further show that cells born with normal length do divide over a wide range of cell lengths and that cell length appears to be controlled in the second generation, were large newly born cells have a tendency to divide more rapidly and thus at a normalized cell size. The platform is thus applicable to measure fine-details in cell cycle dynamics, should be a useful tool to decipher the molecular mechanism underlying size homeostasis, and will be generally applicable to study processes on the single cell level that require large numbers of precision measurements and single cell lineages.</p></div>", "links"=>[], "tags"=>["biotechnology", "Bioengineering", "cell biology", "Cell processes", "Cell cycle and cell division", "Cell growth", "Molecular cell biology", "developmental biology", "Microbial growth and development", "microbiology", "Mycology", "organisms", "fungi", "yeast", "signal processing", "Image processing", "microfluidic", "microchemostat"], "article_id"=>989385, "categories"=>["Biological Sciences"], "users"=>["Jean-Bernard Nobs", "Sebastian J. Maerkl"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0093466.s001", "https://dx.doi.org/10.1371/journal.pone.0093466.s002", "https://dx.doi.org/10.1371/journal.pone.0093466.s003", "https://dx.doi.org/10.1371/journal.pone.0093466.s004", "https://dx.doi.org/10.1371/journal.pone.0093466.s005", "https://dx.doi.org/10.1371/journal.pone.0093466.s006"], "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Long_Term_Single_Cell_Analysis_of_S_pombe_on_a_Microfluidic_Microchemostat_Array/989385", "title"=>"Long-Term Single Cell Analysis of <i>S. pombe</i> on a Microfluidic Microchemostat Array", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-04-07 03:17:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/1452805"], "description"=>"<p>(A) Example of a single cell growth process and definition of the various parameters the image processing pipeline returns including: birth length, division length, fission length, elongation time, septation time, and elongation rate. Histograms of (B) single cell birth, division, and fission lengths and (C) doubling, elongation, and septation times before and after a temperature shift from 30°C to 25°C.</p>", "links"=>[], "tags"=>["biotechnology", "Bioengineering", "cell biology", "Cell processes", "Cell cycle and cell division", "Cell growth", "Molecular cell biology", "developmental biology", "Microbial growth and development", "microbiology", "Mycology", "organisms", "fungi", "yeast", "signal processing", "Image processing"], "article_id"=>989350, "categories"=>["Biological Sciences"], "users"=>["Jean-Bernard Nobs", "Sebastian J. Maerkl"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0093466.g004", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Single_cell_growth_characterization_/989350", "title"=>"Single-cell growth characterization.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-07 03:17:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/1452802"], "description"=>"<p>(A) Schematic of the <i>S. pombe</i> microchemostat array. Control layer channels are shown in grey, while flow channels are shown in green and cyan. The device consists of 120 microchemostats divided into 8 individually addressable rows. (B–C) A detailed schematic of a single microchemostat chamber. Each growth chamber (4) is supplied with medium through two flow channels (1,6). Sieve channels (2) allow molecules the perfuse the growth chamber while preventing cell escape. Each growth chamber is controlled by a chamber valve (5) and a button membrane (3). (D) A bright-field image of one growth chamber filled to confluence with <i>S. pombe</i> cells.</p>", "links"=>[], "tags"=>["biotechnology", "Bioengineering", "cell biology", "Cell processes", "Cell cycle and cell division", "Cell growth", "Molecular cell biology", "developmental biology", "Microbial growth and development", "microbiology", "Mycology", "organisms", "fungi", "yeast", "signal processing", "Image processing"], "article_id"=>989347, "categories"=>["Biological Sciences"], "users"=>["Jean-Bernard Nobs", "Sebastian J. Maerkl"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0093466.g001", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Microfluidic_device_design_/989347", "title"=>"Microfluidic device design.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-07 03:17:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/1452814"], "description"=>"<p>(A) Box plots of single cell division lengths for cells with a given birth length. (B) The average values of the box plots shown on a scatter plot, with a linear regression fit to the data ranging from 6–10 μm birth lengths. (C) The raw data shown in (A–B). The color of each data point represents the number of single cell division per point. (D–G) Data as in (C) but each data point is now colored by (D) doubling time, (E) elongation time, (F) septation time, and (G) elongation rate.</p>", "links"=>[], "tags"=>["biotechnology", "Bioengineering", "cell biology", "Cell processes", "Cell cycle and cell division", "Cell growth", "Molecular cell biology", "developmental biology", "Microbial growth and development", "microbiology", "Mycology", "organisms", "fungi", "yeast", "signal processing", "Image processing"], "article_id"=>989358, "categories"=>["Biological Sciences"], "users"=>["Jean-Bernard Nobs", "Sebastian J. Maerkl"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0093466.g006", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Cellular_length_homeostasis_/989358", "title"=>"Cellular length homeostasis.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-07 03:17:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/1452809"], "description"=>"<p>Each data point is a single cell division event, and the color indicates the percentage of the division time that has elapsed after the temperature shift. The generation dividing during the temperature shift and the first generation after the temperature shift are shown. Black lines indicate the moving average and the red lines the standard deviation. (A) Division lengths, (B) birth lengths, (C) number of divisions per time bin, (D) doubling times, (E) elongation times, and (F) septation times are shown.</p>", "links"=>[], "tags"=>["biotechnology", "Bioengineering", "cell biology", "Cell processes", "Cell cycle and cell division", "Cell growth", "Molecular cell biology", "developmental biology", "Microbial growth and development", "microbiology", "Mycology", "organisms", "fungi", "yeast", "signal processing", "Image processing", "plotted"], "article_id"=>989355, "categories"=>["Biological Sciences"], "users"=>["Jean-Bernard Nobs", "Sebastian J. Maerkl"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0093466.g005", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Various_single_cell_parameters_plotted_over_time_and_in_respect_to_the_temperature_shift_0_min_/989355", "title"=>"Various single cell parameters plotted over time and in respect to the temperature shift (0 min).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-07 03:17:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/1452804"], "description"=>"<p>(A) A five generation lineage tree reconstructed by our image processing pipeline. (B) The number of lineages that could be traced over a given number of generations during a single experiment covering 50 hours of cell growth. (C) The number of complete lineage trees 1, 2, or 3 generations in length. (D) A schematic describing our definition of percent completeness of a lineage tree and the corresponding average completeness of lineage trees of a given number of generations in length (E).</p>", "links"=>[], "tags"=>["biotechnology", "Bioengineering", "cell biology", "Cell processes", "Cell cycle and cell division", "Cell growth", "Molecular cell biology", "developmental biology", "Microbial growth and development", "microbiology", "Mycology", "organisms", "fungi", "yeast", "signal processing", "Image processing", "lineage"], "article_id"=>989349, "categories"=>["Biological Sciences"], "users"=>["Jean-Bernard Nobs", "Sebastian J. Maerkl"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0093466.g003", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Large_scale_lineage_tracking_/989349", "title"=>"Large-scale lineage tracking.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-07 03:17:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/1452803"], "description"=>"<p>(A) A flow diagram showing all analysis steps of the image processing pipeline. First, each growth chamber is divided into lanes as defined by the microfluidic ‘highway’ structures. Next, individual cells are segmented (B) and tracked using either a local (C) or a global (D) tracker. All scale bars are 10 μm.</p>", "links"=>[], "tags"=>["biotechnology", "Bioengineering", "cell biology", "Cell processes", "Cell cycle and cell division", "Cell growth", "Molecular cell biology", "developmental biology", "Microbial growth and development", "microbiology", "Mycology", "organisms", "fungi", "yeast", "signal processing", "Image processing", "pipeline"], "article_id"=>989348, "categories"=>["Biological Sciences"], "users"=>["Jean-Bernard Nobs", "Sebastian J. Maerkl"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0093466.g002", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Image_processing_pipeline_and_cell_tracking_/989348", "title"=>"Image processing pipeline and cell tracking.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-07 03:17:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/1452818"], "description"=>"<p>Sister pair data points are in both cases colored according to mother division length.</p>", "links"=>[], "tags"=>["biotechnology", "Bioengineering", "cell biology", "Cell processes", "Cell cycle and cell division", "Cell growth", "Molecular cell biology", "developmental biology", "Microbial growth and development", "microbiology", "Mycology", "organisms", "fungi", "yeast", "signal processing", "Image processing", "doubling", "times", "elongation"], "article_id"=>989362, "categories"=>["Biological Sciences"], "users"=>["Jean-Bernard Nobs", "Sebastian J. Maerkl"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0093466.g007", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_sister_cell_A_doubling_times_and_B_elongation_rates_/989362", "title"=>"Comparison of sister cell (A) doubling times and (B) elongation rates.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-07 03:17:50"}

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Relative Metric

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