Literature DB >> 23892428

Acquiring fluorescence time-lapse movies of budding yeast and analyzing single-cell dynamics using GRAFTS.

Christopher J Zopf1, Narendra Maheshri.   

Abstract

Fluorescence time-lapse microscopy has become a powerful tool in the study of many biological processes at the single-cell level. In particular, movies depicting the temporal dependence of gene expression provide insight into the dynamics of its regulation; however, there are many technical challenges to obtaining and analyzing fluorescence movies of single cells. We describe here a simple protocol using a commercially available microfluidic culture device to generate such data, and a MATLAB-based, graphical user interface (GUI) -based software package to quantify the fluorescence images. The software segments and tracks cells, enables the user to visually curate errors in the data, and automatically assigns lineage and division times. The GUI further analyzes the time series to produce whole cell traces as well as their first and second time derivatives. While the software was designed for S. cerevisiae, its modularity and versatility should allow it to serve as a platform for studying other cell types with few modifications.

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Year:  2013        PMID: 23892428      PMCID: PMC3760216          DOI: 10.3791/50456

Source DB:  PubMed          Journal:  J Vis Exp        ISSN: 1940-087X            Impact factor:   1.355


  18 in total

1.  Control of stochasticity in eukaryotic gene expression.

Authors:  Jonathan M Raser; Erin K O'Shea
Journal:  Science       Date:  2004-05-27       Impact factor: 47.728

2.  Regulated cell-to-cell variation in a cell-fate decision system.

Authors:  Alejandro Colman-Lerner; Andrew Gordon; Eduard Serra; Tina Chin; Orna Resnekov; Drew Endy; C Gustavo Pesce; Roger Brent
Journal:  Nature       Date:  2005-09-18       Impact factor: 49.962

3.  Real-time kinetics of gene activity in individual bacteria.

Authors:  Ido Golding; Johan Paulsson; Scott M Zawilski; Edward C Cox
Journal:  Cell       Date:  2005-12-16       Impact factor: 41.582

4.  Gene regulation at the single-cell level.

Authors:  Nitzan Rosenfeld; Jonathan W Young; Uri Alon; Peter S Swain; Michael B Elowitz
Journal:  Science       Date:  2005-03-25       Impact factor: 47.728

5.  MAPK-mediated bimodal gene expression and adaptive gradient sensing in yeast.

Authors:  Saurabh Paliwal; Pablo A Iglesias; Kyle Campbell; Zoe Hilioti; Alex Groisman; Andre Levchenko
Journal:  Nature       Date:  2007-02-18       Impact factor: 49.962

6.  Single-cell quantification of molecules and rates using open-source microscope-based cytometry.

Authors:  Andrew Gordon; Alejandro Colman-Lerner; Tina E Chin; Kirsten R Benjamin; Richard C Yu; Roger Brent
Journal:  Nat Methods       Date:  2007-01-21       Impact factor: 28.547

7.  Monitoring dynamics of single-cell gene expression over multiple cell cycles.

Authors:  Scott Cookson; Natalie Ostroff; Wyming Lee Pang; Dmitri Volfson; Jeff Hasty
Journal:  Mol Syst Biol       Date:  2005-11-22       Impact factor: 11.429

8.  Cell-cycle dependence of transcription dominates noise in gene expression.

Authors:  C J Zopf; Katie Quinn; Joshua Zeidman; Narendra Maheshri
Journal:  PLoS Comput Biol       Date:  2013-07-25       Impact factor: 4.475

9.  A microfluidic device for temporally controlled gene expression and long-term fluorescent imaging in unperturbed dividing yeast cells.

Authors:  Gilles Charvin; Frederick R Cross; Eric D Siggia
Journal:  PLoS One       Date:  2008-01-23       Impact factor: 3.240

10.  Heritable stochastic switching revealed by single-cell genealogy.

Authors:  Benjamin B Kaufmann; Qiong Yang; Jerome T Mettetal; Alexander van Oudenaarden
Journal:  PLoS Biol       Date:  2007-09       Impact factor: 8.029

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  3 in total

1.  Fluorescence Time-lapse Imaging of the Complete S. venezuelae Life Cycle Using a Microfluidic Device.

Authors:  Susan Schlimpert; Klas Flärdh; Mark Buttner
Journal:  J Vis Exp       Date:  2016-02-28       Impact factor: 1.355

2.  A new protocol for single-cell RNA-seq reveals stochastic gene expression during lag phase in budding yeast.

Authors:  Abbas Jariani; Lieselotte Vermeersch; Bram Cerulus; Gemma Perez-Samper; Karin Voordeckers; Thomas Van Brussel; Bernard Thienpont; Diether Lambrechts; Kevin J Verstrepen
Journal:  Elife       Date:  2020-05-18       Impact factor: 8.140

3.  Cell-cycle dependence of transcription dominates noise in gene expression.

Authors:  C J Zopf; Katie Quinn; Joshua Zeidman; Narendra Maheshri
Journal:  PLoS Comput Biol       Date:  2013-07-25       Impact factor: 4.475

  3 in total

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