We thank Emily Jackson, Joshua Zeidman, and Nicholas Wren for comments on the software. This work was funded by GM95733 (to N.M.), BBBE 103316 and MIT startup funds (to N.M.).

1. Obtain Fluorescent Microscopy Movies of Single Yeast Cells Growing in a Microfluidic Chamber
<ol>
	<li>Inoculate 1 ml SC media (synthetic defined media with 2% glucose and complete complement of amino acids) with cells from a freshly growing plate, and incubate the culture overnight ~16 hours on a roller drum at 30 &deg;C. Prepare the culture such that the final OD600nm is ~0.1 for early log-phase growth.</li>
	<li>Dilute the starter culture as needed and regrow 1 ml in a fresh test tube an additional 6-8...

<ol>
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The above protocol describes a simple method to obtain and analyze fluorescence time series data with limited experience in microfluidics or in software development. It allows one to obtain time-lapse fluorescence movies of single yeast cells; extract relevant cell size and expression measurements; curate tracking and lineage assignments; and analyze the behavior of whole cells over time using a commercially available microfluidic culture device and a versatile graphical user interface (GUI). While the experimental, segm...

Single-cell analysis of gene expression has furthered our understanding of many aspects of gene regulation. Static snapshots of fluorescent reporter expression using flow cytometry or microscopy provide useful information on the distribution of single-cell expression, but lack the history and evolution of time series data required to directly inform gene expression dynamics. Fluorescence time-lapse microscopy presents a means to obtain both single-cell measurements and their history. Various experimental and analytical techniques have been developed to obtain and quantify movies of fluorescent reporter expression, thus imparting insights into gene regulation features (see 1 for a review) such as cell-to-cell variation 2,3, bacterial persister formation 4, transcription initiation and elongation 5, transcriptional bursting 6,7, cell-cycle dependence 8,9, and heritability 10. However, obtaining quality single-cell fluorescence time series involves significant technical challenges in culturing a monolayer of cells in a controllable environment and in high-throughput quantification of the acquired fluorescence movies. Here, we describe a procedure to obtain and analyze fluorescence movies of S. cerevisiae with no required experience in cell culture device manufacture or in software development (Figure 1).
First, we detail an example protocol to generate fluorescence time series movies for budding yeast expressing one or more fluorescent reporters. Though customized microfluidic culture chambers have been built and successfully employed previously 11-13, we use a commercially available microfluidic device from CellAsic (Hayward, CA). The system confines cells to monolayer growth and allows continual control of the perfusion environment. The microscopy protocol we present is a simple means to obtain fluorescence movies of budding yeast, but any modified experimental protocol (a customized culture device, alternative media conditions, etc.) yielding similar fluorescence movie data of single yeast cells may be substituted.
Next, we outline the analysis of the movies using a graphical user interface (GUI) -based software package in MATLAB (Mathworks, Natick, MA), dubbed the GUI for Rapid Analysis of Fluorescence Time Series (GRAFTS), to extract time series data for single cells. GRAFTS has similar features to the versatile, open-source software package Cell-ID 14 in segmenting and tracking cells and in extracting fluorescence intensity and geometric information. However, GRAFTS provides important additional features. First, it offers easy interactive editing of segmentation and tracking results to verify data accuracy, rather than just statistical gating of outlier region traces after analysis. Moreover, it extends the analysis to automatically designate lineage and cell-cycle points of interest of budding yeast. Determining when mother and daughter divide to form two independent cell regions is crucial to determining whole cell (mother including any connected bud) measurements throughout the cell cycle 8. The suite consists of three modules to accomplish these tasks. The first segments cell regions based on the contrast between focused and unfocused bright field images, and allows the user to define and visually test segmentation parameters. The second tracks (using Blair and Dufresne's MATLAB implementation of the Crocker et al. IDL routine, available at: <a href="http://physics.georgetown.edu/matlab/" target="_blank">http://physics.georgetown.edu/MATLAB/</a>) and measures cell regions through time; automatically assigns lineages; and enables visual inspection and error correction. A simple plotting GUI is included to query single-cell properties. The third module ascribes bud emergence and division times, and outputs whole cell time series data as well as their first and second time derivatives (as discussed in 9). The analysis module outputs the data as a space-delimited text file for subsequent study in the statistical software of choice. Thus, the package enables the user to extract high quality time series data through a graphical interface. We have used this method to estimate real-time transcription rates in single budding yeast cells as a function of the cell cycle 9. While the modules have been optimized for budding yeast, the parameters or, if necessary, the freely available code may be adapted for other organisms and image types. Segmentation, tracking, and lineage assignment algorithms may be specific to types of imaging assigned and the organism in question. The existing algorithms could be replaced, but still retain the GUI interface that allows user-friendly visual inspection and correction of segmentation and tracking errors that invariably occur with any algorithm.

<table border="1">
	<tbody>
		<tr>
			<td>Name of Reagent/Material</td>
			<td>Company</td>
			<td>Catalog Number</td>
			<td>Comments</td>
		</tr>
		<tr>
			<td>Y04C Yeast Perfusion Plate</td>
			<td>CellAsic</td>
			<td>Y04C-02</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>ONIX Microfluidic Perfusion Platform</td>
			<td>CellAsic</td>
			<td>EV-262</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>Axio Observer.Z1 Microscope</td>
			<td>Zeiss</td>
			<td>&nbsp;</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>Plan-Apochromat 63X/1.40 Oil DIC objective</td>
			<td>Zeiss</td>
			<td>440762-9904-000</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>Cascade II EMCCD camera</td>
			<td>Photometrics</td>
			<td>&nbsp;</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>Lumen 200 metal-halide arc lamp</td>
			<td>PRIOR Scientific</td>
			<td>&nbsp;</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>Triple-bandpass dichroic filter cube and excitation and emission filter set</td>
			<td>Chroma Technology Corp</td>
			<td>set #89006</td>
			<td>Used for YFP (Venus/Citrine), CFP (Cerulean), RFP (mCherry/tdTomato)</td>
		</tr>
		<tr>
			<td>MAC 5000 controller and filter wheels</td>
			<td>Ludl Electronic Products</td>
			<td>&nbsp;</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>MATLAB R2011a</td>
			<td>Mathworks</td>
			<td>&nbsp;</td>
			<td>64-bit version handles large data files better than 32-bit</td>
		</tr>
	</tbody>
</table>

acquiring fluorescence time-lapse movies of budding yeast and analyzing single-cell dynamics using grafts

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.

A successfully performed and analyzed experiment will yield mostly continuous time series for single whole cells with realistically assigned bud emergence and division times. As an example, we performed the above protocol with a haploid yeast strain expressing an integrated copy of Cerulean fluorescent protein (CFP) driven by the constitutive ADH1 promoter to observe how growth and global expression may vary over time in single cells (Table 4, Y47). We ran the time series analysis to ob...

Watch this Scientific Journal Video about Acquiring Fluorescence Time-lapse Movies of Budding Yeast and Analyzing Single-cell Dynamics using GRAFTS at JoVE.com

Acquiring Fluorescence Time-lapse Movies of Budding Yeast and Analyzing Single-cell Dynamics using GRAFTS

We present a simple protocol to obtain fluorescence microscopy movies of growing yeast cells, and a GUI-based software package to extract single-cell time series data. The analysis includes automated lineage and division time assignment integrated with visual inspection and manual curation of tracked data.

Fluorescence time-lapse microscopy has become a powerful tool in the study of many biological processes at the single-cell level. In particular, movies ...

acquiring-fluorescence-time-lapse-movies-budding-yeast-analyzing

Research

JoVE Journal

Biology

12.7K visualizações.  Massachusetts Institute of Technology. Nós apresentamos um protocolo simples de se obter filmes de microscopia de fluorescência de crescimento de células de levedura, e um pacote de software baseado em GUI para extrair dados de séries temporais de uma única célula. A análise inclui linhagem automatizada e divisão atribuição tempo integrado com inspeção visual e curadoria manual de dados controladas.