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

Podsumowanie

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.

Streszczenie

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.

Wprowadzenie

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: http://physics.georgetown.edu/MATLAB/) 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.

Protokół

1. Obtain Fluorescent Microscopy Movies of Single Yeast Cells Growing in a Microfluidic Chamber

1. 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 °C. Prepare the culture such that the final OD_600nm is ~0.1 for early log-phase growth.
2. Dilute the starter culture as needed and regrow 1 ml in a fresh test tube an additional 6-8 hours to OD_600nm = 0.1. This provides cells growing at a nutrient-rich steady state at a density suitable for loading into the microfluidic device. (Alternatively, replace steps 1.1-1.2 with the procedure necessary to prepare cells as required for the experiment.)
3. Prepare a Y04C microfluidic culture plate from CellAsic: remove the shipping solution; rinse the wells with sterile water; and using the ONIX perfusion system flow water through wells 1-6 at 6 psi for 5 min to flush the channels. Then, flow from the loading well 8 at 6 psi for 10 sec (the loading channel has much higher flow rates and should be flushed separately).
4. Remove water from all wells and replace with 250 μl SC in the inlet wells 1-6 and 50 μl of the culture from step 1.2 in cell loading well 8. (Alternatively, place the desired media in the inlet wells 1-6 for experiments requiring switching between different conditions.)
5. Seal the ONIX manifold to the plate, and begin priming the channels and culture chamber by flowing from inlet wells 1-6 at 6 psi for 2 min followed by at least 5 min at 6 psi from only the inlet well holding the starting media for the experiment. Flow this continually until step 1.7.
6. Prepare the microscope for the experiment. We use a Zeiss Axio Observer.Z1 widefield microscope with a Cascade II back-illuminated EMCCD camera (Photometrics, Tuscon, AZ) and a Lumen 200 metal-halide arc lamp (PRIOR Scientific, Rockland, MA) for fluorescence excitation attenuated to 10% to prevent photobleaching. To minimize the switching time required to acquire in multiple fluorescent channels, we couple a single, triple-bandpass dichroic filter cube to appropriate excitation/emission filters for CFP, YFP, and RFP (Chroma Technology Corp, Bellows Falls, VT; set 89006) set in external, fast-switching filter wheels (Ludl Electronic Products, Novato, CA).
7. Place a few drops of immersion fluid on the objective (if needed, we use a Zeiss Plan-Apochromat 63X/1.40 Oil DIC), and mount the microfluidic device securely in the stage of the inverted microscope. Ensuring the plate does not shift at all in the stage throughout the experiment improves cell tracking during data analysis. We simply tape the plate to the stage mount to prevent its shifting.
8. Focus on the leftmost third of the first culture chamber (where the chamber height is smallest) using one of the embedded position markers. Turn off flow from the media inlet well, and flow from cell loading well 8 at 6 psi in 5 sec 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.
9. Begin flowing from the media inlet well containing the starting media at 6 psi.
10. In MetaMorph (Molecular Devices, Sunnyvale, CA) or other microscopy automation and control software, setup a multi-dimensional acquisition to take multiple images at multiple stage positions over time. Set the number of and interval between time points. Select several stage positions with ~10 cells each (more will overcrowd quickly). We typically use 5 min intervals and image up to 16 positions, which each require ~11 seconds for acquisition at each time point.
11. Setup the acquisition so that at each stage position at each time point the program will: focus on the transmitted light bright field (BF) image using MetaMorph's 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); acquire the BF image at +1 μm to the focal plane (f.p.); acquire a BF out of focus (BFOOF) image at -4 μm to the f.p. (use custom-written journals to change the focus as required between images); and acquire one grayscale image for each fluorescent reporter at the f.p. using settings optimized for rapid acquisition with minimal photobleaching.
12. If necessary, obtain background and shading correction images for each fluorescent reporter in an area of the culture chamber with no cells.
13. Prepare the flow program for the experiment in the ONIX control software. Simultaneously begin the acquisition program in MetaMorph and the flow program in the ONIX control software.
14. At the end of the experiment, correct uneven background and shading in the fluorescent images (if needed), and optionally combine the .tif files for each image channel at each stage position into a .stk movie file (e.g. create BF, BFOOF, CFP, YFP and RFP movies for position 1).

2. Format and Segment Data for Tracking Using the FormatData GUI in MATLAB (Figure 2)

1. Run the FormatData.m file in MATLAB to open the data input GUI. At the top, specify or browse to the folder containing the image files to set the data directory, and then use the GUI to specify the data types and image files recorded in the experiment.
2. To the right, select the radio button next to "Time-lapse" to indicate which analysis type to perform. "Time-lapse" will treat the image series as a time series (e.g. a movie of cells growing in a microfluidic chamber). "Static" will treat each image series as a set of snapshots taken from a population (e.g. multiple images taken at different positions on a single slide sample). In this GRAFTS version, time-lapse data for multiple positions can be processed but as a separate file for each position (see step 2.14).
3. Check the box for image registration to correct for imprecise stage movements by shifting each image in the movies to minimize apparent cell movement within the frame. We strongly recommend this as it greatly improves tracking (reducing manual track curation later), but will add random, "white noise" pixel values to fill in where images have been shifted at the border. It could also be selected after viewing segmented BF images in step 2.7 or at any point until step 2.13.
4. Check the "Bright Field" box in the "Data Channels" panel. Click "Select" to the right to load the BF images. For *.TIF files, select all BF images corresponding to a single movie by holding the Shift key while selecting, then click "Open". For *.STK files, just select the BF stack of interest. If the images are successfully recognized, the file names will be listed in the popup menu to the right in the "Data Recognized" panel. For *.TIF files, be sure the file names appear in the correct order (save files with sequential names during acquisition - BF_t001, etc.).
5. Check the "Bright Field (out of focus)" box and use the "Select" button as in step 2.4 to load the BFOOF files, which will be listed in the popup to the right. (BFOOF obtained as BF -4 μm relative to the f.p. at 63X segments well.)
6. Check the "Cell Mask" box. In the "Mask Source" panel, select the radio button next to "Segment BF". Push the "Parameters" button to open the segmentation GUI. (Alternatively, select a pre-segmented mask movie by selecting the radio button next to the "Select Mask File" button, pressing the latter, and selecting the movie as in step 2.4. In this case, a BFOOF movie is not needed, and can skip to step 2.8.)
7. Set the different segmentation parameters (descriptions of each can be viewed by pressing "Parameter descriptions"), and test the segmentation quality by clicking "Test" (Figure 3). 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 FormatData GUI.
8. Check the "Color Images" box. Enter the color name for the first fluorescent image channel in the text edit box, and then press "Add and Select" to load the color files as in step 2.4. The color name will be added to the list box on the left, and the file names will be added to the table on the right. If a mistake is made in loading the color files, select the color name in the list box on the left and click "Remove Color" to remove the files. Repeat for each color channel.
9. If additional sub-region masks based on one of the fluorescent colors (e.g. fluorescently labeled nucleus → nuclear region mask) are desired, check the "Color Mask" box. If not, skip to step 2.12. In the "Color Mask Source" panel, enter the sub-region mask name in the text edit box. Choose a color from the popup menu in the "Color Mask Source" panel (requires the color to have been added in step 6) and push "Parameters" to open a threshold testing GUI.
10. Push the "Auto-threshold" button to automatically determine the threshold using Otsu's method ¹⁵, and the image will highlight the areas preserved above the threshold (Figure 4). 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 FormatData GUI.
11. Push "Add and Segment" to generate the sub-region mask using the threshold module applied to the selected color images. The color mask name and source will be displayed in the table to the left, with the relevant recognized file names appearing in the table on the right. (Alternatively, push "Add and Select" to load pre-made sub-region mask files.)
12. At the bottom, specify or browse to the desired folder to be used as the save directory.
13. Press "Format Data" to read the image files (using the tiffread2.m code developed by Francois Nedelec, available at: http://www.mathworks.com/MATLABcentral/fileexchange/10298), process the data, and create a *.mat file in the specified folder. This file will serve as the input to the ProcessTimeSeries GUI.
14. Repeat step 2.4-2.13 for the set of movies for each stage position.

3. Track Cells and Lineages through Time with ProcessTimeSeries GUI, and Curate ID and Lineage Assignments (Figure 5)

1. Run the ProcessTimeSeries.m file in MATLAB to open the tracking GUI. Set the following parameters after opening the GUI:
   • "Chamber height" ("File I/O" panel, upper left) - 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 as in ⁸.
   • "μm/pixel" ("File I/O" panel) - the conversion factor to calibrate pixel to μm distance in the images so cross-sectional area and volume can be calculated in μm² and μm³, respectively.
   • "Filters panel" (below "File I/O" panel) - set minimum and maximum parameters to filter out junk regions in the mask: "Area" - region area in pixels; "Ecc" - eccentricity factor, more circular as → 0; "SF" - shape factor, more circular as → 1.
2. Click "Load Images" in the "File I/O" panel, and select the *.mat data file of interest output by the FormatData GUI. The region mask (and any sub-region masks) is applied to each color channel, and a number of different measurements are made (Table 1). The data file is then saved. (If loading a file into ProcessTimeSeries from a previous session, it will ask if the analysis should be restarted from the beginning. CAUTION: This will erase any track curation already completed and treat the data as if it were fresh from the FormatData GUI. If accidentally restarted, quickly hit Ctrl+C in the MATLAB command window to prevent the previously curated *.mat file from being overwritten.)
3. Next, track the cells. In the "Track Cells" panel (next to the "Filters" panel), set the following parameters:
   • "Max Displacement" - the maximum distance in pixels a cell can travel between adjacent images before being labeled as a different cell. Higher displacement means the algorithm can account for cells moving more, but it also increases the complexity of matching cell regions in crowds. We start at 12 and decrease if an error occurs in tracking (see step 3.4).
   • "Minimum Length" - minimum number of occurrences for a cell trace to be considered a valid data series. We use 1 to prevent removal of any real traces, but this can be increased if segmentation results in short-lived, spurious region traces.
   • "Frame Memory" - maximum number of consecutive frames a cell region can disappear and be given the same ID when it returns. If it disappears for longer than "Frame Memory", it will be given a new ID upon returning. We use 2 to allow regions to be missed by segmentation for 2 frames before returning.
4. Click "Track Cells" to track regions from one frame to the next using the region centroids (Blair and Dufresne MATLAB implementation of Crocker, Grier, and Weeks IDL routine, available at: http://physics.georgetown.edu/MATLAB/), to assign lineages for newly appearing buds, and to save the data. Lineages are automatically assigned by minimizing distances between putative buds and potential mothers in each frame. (If an error appears in the MATLAB command window due to "difficult combinatorics", decrease "Max Displacement" and repeat tracking.) Change parameters in the popup windows as necessary for your data.
5. Curate poorly segmented regions and mistakes in ID or lineage assignments using the various GUI tools or shortcut keys (shown in a popup window by pressing the button above "Selected Cell Information" panel).
   • Slider, "Prev Image", "Next Image" (Ctrl+left/right) - use to display and move between frames in the image series.
   • "Image #" - displays the frame number of the current image in the left and right axes. Move to a specified frame by typing its number in the right "Image #" edit box.
   • "Delta frame" - shows the difference in frame number between the right and left axes (Δ = right - left).
   • "Hide Text" (Ctrl+T) - toggles between displaying the cell IDs in the right axes or not.
   • "Hide Labels" (Ctrl+L) - toggles between displaying the cell IDs in the left axes or not.
   • "Mask" popup menu - choose between displaying no mask, the cellular mask, or any user-defined sub-masks for display in the left panel.
   • "Overlay" popup menu (Ctrl+1-9) - choose to display BF and color layers in the left frame. Show BF is the only one that toggles the BF layer on or off in both axes. Toggle between display or not by selecting the overlay name in the menu again ("*" denotes overlay is displayed). Ctrl+1 toggles BF overlay, with 2-9 toggling the color overlays in order.
   • "Set Colors" - use to determine color for overlay display. A popup window will query which fluorescence channel to set, and then the color palette will appear for user definition.
   • "Modify Regions and Tracks" panel - these controls effect changes on the cell region mask and trace information:
     • "Update tracks" (Ctrl+U) - use to reassign cell IDs. If no cells are selected in right axes, the GUI will prompt for a cell ID to change. When ID X is changed to Y, all future instances of X will be changed to Y, and any future instances of the current Y, will be switched to X. (Occasionally one cell will switch back and forth between 2 IDs, repeatedly. This is due to the tracking trying to accommodate multiple IDs in an oversegmented region rather than an error in the update ID function.) Multiple cells can be selected and their IDs changed simultaneously, but be sure to give each a unique ID. To assign a region to an ID that does not exist in the current file, enter "0" and one will be generated. Use Ctrl+W to swap the IDs of two highlighted cells.
     • "Delete Selected Tracks" (Ctrl+D) - use to delete the selected cell or multiple cell regions in the current right axes frame. (If no cells are selected, will prompt for ID.) To permanently delete all instances of an ID trace, check the box next to the Delete Selected Tracks button (text will change to "Delete ALL"). This is useful for getting rid of persistent junk regions, but first check future frames to make sure the ID doesn't switch to a valid cell or bud region.
     • "Merge Regions" (Ctrl+A or M) - smooths and combines cell regions. Click a cell or cells in the right axes to select (region will turn red if selected). Merging on one region will morphologically close to fill in cracks or small missing chunks using a disk-shaped element. Merging two or more nearby regions will combine them into one region and give the lowest ID to the new region. This is useful for over-segmented regions (often when cells have large vacuoles).
     • "Draw region" - use the mouse to draw a region in the right axes where desired and double click to finish. Provide a unique ID not present in the data set (between 1 and 65535). Cancel by pressing delete on the keyboard.
   • "Lineage Tracking" panel - after tracking, lineage assignments are automatically generated. When new ID regions appear, the new cell ID appears in pink and a red line is drawn to the mother region. New regions too large to be buds or too far away from potential mothers appear in green and are assigned a mother ID of "NaN" ("not a number", see Table 2). Regions existing at the first time point have a mother ID of "0". To fix individual assignments, enter the mother and bud IDs in the text edit fields and click "Fix". To erase a cell's mother, enter "0" as its mother. A faster method is to select two regions in the right image and press Ctrl+F. This will automatically assign the older region as the mother and the newer region as the daughter, while erasing any previously existing mother assignments for the daughter cell. CAUTION: Clicking "Calculate Lineages" at any time will recalculate all lineage assignments, including those the user has previously curated.
   • "Selected Cell Information" panel - "Get Info" will display some measurements of the regions selected in the right axes. "Measurements…" allows the user to determine which measurements are displayed (as well as define the default list for other operations such as "Export Data"). Can be used to determine correct IDs and lineages (e.g. if cell X has a bud at time t, it most likely does not have another bud at time t + 5 min).
   • "Save Changes" (Ctrl+S) - updates the *.mat file to reflect user changes. USE FREQUENTLY (there is no undo function)!
   • "Generate Plots" - opens the GeneratePlots GUI. Used to quickly plot selected variable information and simple calculations for single-cell regions highlighted in the right axes of the ProcessTimeSeries GUI, their mean, or the mean of all regions in each frame. This GUI can calculate rough first derivatives, but be sure to specify the correct time interval at the top left. Plots may be output to a figure for saving by pressing "Generate Figure".
6. To properly curate data: merge any oversegmented regions; delete any regions not capturing the whole cell; be sure mother-bud relationships are properly assigned (a red line appears between them at the time of bud emergence, when the bud ID is pink, see Table 2); and eliminate any green IDs by fixing the ID, assigning the region a mother, assigning no mother ("0"), or deleting the region. Bud data will be added to that of the mother during the final analysis so do not merge a bud region to its mother (it will lead to inaccurate volume estimation).
7. Visually inspect data for each frame up to the last time of interest, but it is not required of the entire time series if not using the later frames.
8. Be sure to save changes.
9. Repeat steps 3.1-3.7 for the *.mat file for each stage position.

4. Exporting Data for Analysis

1. To simply export raw region measurements for each cell through time, push "Export Data". The measurements of interest can be selected in the GUI that opens, and they will be output in a space-delimited *.txt file with variable names as column headers.
2. To analyze the time series for whole cells over time, calculate cell-cycle information, and take derivatives, push "Time Series Analysis". A window will open allowing selection of the measurements of interest and input of analysis parameters (Figure 6).
3. Enter the last time point curated in the GUI (all subsequent frames will be ignored). The default smoothing and cell-cycle assignment parameters work well for our haploid and diploid strains imaged in 5 min intervals, but these may need to be varied for best results. (Check that the parameter values are appropriate by manually determining budding and division time for a test data set and comparing to the performance of the automated algorithm.)
4. When all parameters are set, push "Analyze" to:
   1. Construct arrays for each raw measurement and subtract the background (measured as the average intensity of the non-cell area of the first frame in each fluorescent movie, or can be specified to account for average autofluorescence per cell pixel).
   2. Fit a smoothing spline to the selected measurements to eliminate high-frequency measurement noise (as discussed in ⁹).
   3. Automatically determine bud emergence and cell division times for each cell based on changes in the slope of the volume time series as discussed in ⁹. The bud measurements will then be added to the mother's measurements between emergence and division each cycle for a contiguous whole cell time series. A normalized cell-cycle progression will also be calculated ranging from 0 to 2 from division to division.
   4. Fit a smoothing spline to take stable 1^st and 2^nd derivatives of the selected measurements. (For the purpose of well-defined derivatives, time series are made continuous across division by shifting data for the following cell cycle to meet the endpoint for the previous cycle.)
   5. Output raw and smoothed time series for all regions, and whole cell smoothed, continuous, and differentiated time series as an array for each measurement. The first element is a color index for background subtraction, the rest of the first row holds cell IDs, the rest of the first column is the frame number, and the remaining elements hold the time series for each single cell in a column with each row corresponding to the frame in the first column. A similar array of cell-cycle progression time series and lineage information will also be output. The array "LineageArray" contains a table with each row representing a mother-bud relationship and the first through fifth columns are mother ID, bud ID, frame of first bud region, estimated budding frame, and estimated division frame. Data is exported to a *.mat file (MATLAB data file).

Wyniki

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...

Dyskusje

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...

Materiały

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