Name: Acquiring Fluorescence Time-lapse Movies of Budding Yeast and Analyzing Single-cell Dynamics using GRAFTS (Video) | JoVE Journal
Uploaded: 2013-07-18T13:00:00.000+00:00
Duration: 17 min 1 s
Description: Learn fluorescence time-lapse microscopy of budding yeast using microfluidic chambers and GRAFTS software. This protocol enables single-cell tracking, lineage analysis, and transcription rate measurement for gene regulation studies.

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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.).

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The authors declare that they have no competing financial interests.

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><tbody><tr><td>Y04C Yeast Perfusion Plate</td><td>CellAsic</td><td>Y04C-02</td><td></td></tr><tr><td>ONIX Microfluidic Perfusion Platform</td><td>CellAsic</td><td>EV-262</td><td></td></tr><tr><td>Axio Observer.Z1 Microscope</td><td>Zeiss</td><td></td><td></td></tr><tr><td>Plan-Apochromat 63x/1.40 Oil DIC objective</td><td>Zeiss</td><td>440762-9904-000</td><td></td></tr><tr><td>Cascade II EMCCD camera</td><td>Photometrics</td><td></td><td></td></tr><tr><td>Lumen 200 metal-halide arc lamp</td><td>PRIOR Scientific</td><td></td><td></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></td><td></td></tr><tr><td>MATLAB R2011a</td><td>Mathworks</td><td></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

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...

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 ...

The overall goal of this procedure is to obtain fluorescence movies of growing single yeast cells and extract reporter expression time series for each cell. This is accomplished by first culturing a monolayer of cells in a microfluidic chamber. The second step is to perform time-lapse fluorescence microscopy for one or more reporters.Next, the movies are processed using the graft software package to track and measure cells through time. The final step is to analyze the cell measurements and lineage assignments and to export reporter expression time series for each whole cell. Ultimately, an instantaneous transcription rate inferred from expression time series is used to show changes in transcription both over the cell cycle and in response to changing transcription factor levels.This method can help answer key questions in the gene regulation field, such as how quickly do different promoters respond to the sudden addition or some subtraction of an active transcription factor. How variable is this response across a population of cells and how do different cellular characteristics including morphology and cell cycle phase influence the response? Moreover, this method can probe the effects of multiple additions and subtractions of an active transcription factor on gene expression.Prepare yeast cells growing at a nutrient-rich, steady state at a density suitable for loading into the microfluidic device as described in the text protocol. Then prepare a Y zero four C microfluidic culture plate from cell ASIC by rinsing the wells with sterile water following removal of the shipping solution using the onyx perfusion system flow water through wells one through six at six PSI for five minutes to flush the channels, then flow from the loading well eight at six PSI for 10 seconds. Remove water from all wells and replace with 250 microliters of SC media in inlet wells, one through six and 50 microliters of the prepared cell culture in cell loading.Well eight seal the onyx manifold to the plate and begin priming the channels and culture chamber by flowing from Inlet Wells one through six at six PSI for two minutes. Follow this with flowing from only the inlet well holding the starting media for at least five minutes. At six PSI flow this continually until the microscope step tape the plate to the stage mount to prevent its shifting throughout the experiment in order to improve cell tracking during data analysis, following preparation of the microscope as described in the text protocol, placed a few drops of immersion fluid on the objective and mount the microfluidic device securely.In the stage of the inverted microscope, focus on the leftmost third of the first culture chamber using one of the embedded position markers. Turn off flow from the media inlet well and flow from cell loading well eight at six PSI In five second bursts. Move the stage to look around the culture chamber for cells.Increase loading flow time and pressure until desired cell density is achieved, but avoid overloading and clogging the leftmost barrier where fresh media enters the chamber. Begin flowing from the media inlet well containing the starting media at six PSI. In Metamorph or other microscopy automation and control software.Set up a multidimensional acquisition to take multiple images at multiple stage positions over time. Set the number of time points and interval between them. Then select several stage positions with approximately 10 cells.Each.Set up the acquisition so that at each stage position and at each time point, the program will focus on the transmitted light brightfield image using metamorphs built-in autofocus method, implementing the autofocus as a custom written journal at the start of each stage. Position provides greater control over focus accuracy. Also set the program to acquire the brightfield image at plus one micron to the focal plane and to acquire a brightfield out of focus image at minus four microns to the focal plane.Use custom written journals to change the focus as required between images. Finally, set the program. Acquire one gray scale image for each fluorescent reporter at the focal plane.Using settings optimized for rapid acquisition with minimal photo bleaching. Prepare the flow program for the experiment in the onyx control software. Then simultaneously begin the acquisition program in Metamorph and the flow program in the Onyx control software.Run the format data M file in MATLAB to open the data input graphical user interface or GUI. At the top, specify or browse to the folder containing the image files to set the data directory and then use the gooey to specify the data types and image files recorded in the experiment. Proceed to select the segmentation parameters as described in the text protocol.Choosing the proper parameters for segmentation of brightfield images can be difficult and has a significant effect on the resulting time series data quality. We focus on identifying all cell regions with preference given to over segmenting rather than under segmenting regions. During visual data inspection, over segmented regions can be merged together to form whole cell regions, but under segmented regions cannot be split Later.Set the different segmentation parameters and test the segmentation quality by clicking test Successfully. Segmented regions will be green with magenta borders. Be sure to use the slider to check multiple time points in the movie when the segmentation is satisfactory.Select done to return the segmentation parameters to the format data gui. After selecting the color parameters as described in the text, push the auto threshold button to automatically determine the threshold. Using tzu's method, the image will highlight the areas preserved above the threshold.The threshold can be manually adjusted using the edit box. Use the scroll bar to confirm the threshold is appropriate for all time points. When satisfied, push done.To return the threshold to the format data gui, press add and segment. To create the color mask, press format data to read the image files, process the data and create a dot mat file in the specified folder. This file will serve as the input to the process time series gui.Run the process time series M file in MATLAB to open the tracking gui. After opening the gui first set the chamber height. This indicates the height of the chamber in which the cells are trapped.It is used as a constraint in approximating cell volume as a 3D ellipsoid based on the major and minor axis of the cell region. Then set micrometers per pixel. This is the conversion factor used to calibrate pixel to micrometer distance in the images so that cross-sectional area and volume can be calibrated in square micrometers and cubic micrometers respectively.Next, use the filters panel to set the minimum and maximum parameters to filter out junk regions in the mask. The area is the region area in pixels. ECC is the eccentricity factor, which is more circular as the value approaches zero.And SF is the shape factor, which is more circular as the value approaches one click load images in the file input output panel and select the dot mat data file of interest output by the format data gui. The region mask will be applied to each color channel and a number of different measurements are made. The data file is then saved after selecting the track cells parameter as described in the text.Click track cells to track regions from one frame to the next using the region OIDs to assign lineages for newly appearing buds and to save the data, change the parameters in the pop-up windows as necessary. Lineages are automatically assigned by minimizing distances between putative buds and potential mothers. In each frame.Click the button above the selected cell information panel and curate poorly segmented regions and mistakes in ID or lineage assignments using the various gooey tools or shortcut keys which are described in the text protocol to properly curate the data. Merge any over segmented regions, delete any regions not capturing the whole cell and be sure mother bud relationships are properly assigned. Also, eliminate any green IDs by merging regions.Fixing the id, assigning the region to a mother, assigning no mother or deleting the region. Since bud data will be added to that of the mother during the final, final analysis. Do not merge a bud region to its mother.Visually inspect data for each frame up to the last time of interest. It is not required of the entire time series. If not using the later frames, be sure to save changes once all cell regions have been inspected over the time span of interest data for all fluorescence channels and how they change over time are ready to be output.Push time series analysis to analyze the time series for whole cells over time. Calculate cell cycle information and take derivatives. A window will open allowing selection of the measurements of interest and input of analysis parameters Enter the last time point curated in the gooey.The default smoothing and cell cycle assignment parameters work well for haplo and diploid strains imaged in five minute intervals, but these may need to be varied for best results. When all parameters are set, select analyze to export single cell time series data as described in the text protocol, A successfully performed and analyzed experiment will yield mostly continuous time series for single whole cells with realistically assigned but emergence and division times for a halo yeast strain expressing an integrated copy of Ian fluorescent protein or CFP driven by the constituent of a DH one promoter. Whole cell measurements over time were obtained from the time series analysis.The brightfield images show the segmented mother cell in blue and its buds in green. With the contiguous whole cell outlined in red, a dependence on the cell cycle emerges for both volume and expression. Here, grated regions show when the cell is in SG two M and has a bud budding begins at the G one to S transition from white to gray.Shading and cell division occurs at the M to G one transition from gray to white shading after budd formation. The total combined volume increases more quickly than before, but emergence while protein concentration or average intensity decreases slightly. The rise in combined integrated protein also accelerates after budding comparing to the mother region alone.These results demonstrate the importance of properly incorporating bud contributions to the whole cell measurement and highlight the need for proper bud formation and division time assignments. The differentiated time series can be used to calculate an instantaneous relative mRNA level and transcription rate, which both also increase after budding the ability to generate stable time. Derivatives of measurement time series also benefits kinetic studies using the switchable expression system described in the text protocol.A time series analysis shows when the transcription factor is localized to the nucleus and when the target gene starts and stops. Transcription shown here are micrographs from the four indicated acquisition channels for a cell with a switchable YFP fused PHO four Tet R trans activator. The trans activator localizes to the RFP marked nucleus in response to low phosphate and drives expression of a seven X Tet O promoter CFP reporter.The inferred transcription rate changes with localization on average and at the single cell level. Following this procedure, other analyses of the single cell multidimensional time series data can be performed in order to answer additional questions, including how does expression vary across the population over time? Are the effects of extrinsic fluctuations inherited over multiple generations?Do gene activation kinetics change upon reactivation? Does the cell cycle play a role and many other questions requiring gene expression growth and lineage trajectories of single cells.

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Biology

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Applications of pHluorin for Quantitative, Kinetic and High-throughput Analysis of Endocytosis in Budding Yeast

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4D Microscopy of Yeast

Here, we present live-cell imaging which is a non-toxic microscopy method that allows researchers to study protein behavior and nuclear dynamics in living fission yeast cells during mitosis and meiosis.

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Fission yeast is used here as a heterologous host to express bacterial cytoskeletal proteins such as FtsZ and MreB as translational fusion proteins with GFP to visualize their polymerization. Also, compounds that affect polymerization are identified by imaging using a fluorescence microscope.

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Learn fluorescence time-lapse microscopy of budding yeast using microfluidic chambers and GRAFTS software. This protocol enables single-cell tracking, lineage analysis, and transcription rate measurement for gene regulation studies.

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

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