Name: analysis of lipid droplet content in fission and budding yeasts using automated image processing
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Description: Watch this Scientific Journal Video about Analysis of Lipid Droplet Content in Fission and Budding Yeasts using Automated Image Processing at JoVE.com

This work was supported by Charles University grants PRIMUS/MED/26, GAUK 1308217 and SVV 260310. We thank Ond&#345;ej &#352;ebesta for help with microscopy and development of the image analysis pipeline. We thank the ReGenEx lab for S. cerevisiae strains, and JapoNet and Hironori Niki&#8217;s lab for S. japonicus strains. The ppc1-88 strain was provided by The Yeast Genetic Resource Center Japan. Microscopy was performed in the Laboratory of Confocal and Fluorescence Microscopy co-financed by the European Regional Development Fund and the state budget of the Czech Republic (Project no. CZ.1.05/4.1.00/16.0347 and CZ.2.16/3.1.00/21515).

1. Preparation of Solutions and Media
<ol>
	<li>Prepare lipid staining solution.
	<ol>
		<li>To prepare stock lipid staining solution dissolve 10 mg of BODIPY 493/503 in 10 mL of anhydrous DMSO (final concentration 1 mg/mL). Dissolve the whole content of a 10 mg BODIPY 493/503 vial to prevent loss of material during weighing. 
		CAUTION: DMSO may pass through the skin. Wear appropriate personal protective equipment.</li>
		<li>Prepare working lipid staining solution by mixing 100 ...

<ol>
	<li>Koch, B., Schmidt, C., Daum, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Storage+lipids+of+yeasts:+a+survey+of+nonpolar+lipid+metabolism+in+Saccharomyces+cerevisiae,+Pichia+pastoris,+and+Yarrowia+lipolytica.">Storage lipids of yeasts: a survey of nonpolar lipid metabolism in Saccharomyces cerevisiae, Pichia pastoris, and Yarrowia lipolytica.</a> FEMS Microbiology Reviews. 38 (5), 892-915 (2014).</li><li>Krahmer, N., Farese, R. V., Walther, T. 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U., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analysis+of+a+genome-wide+set+of+gene+deletions+in+the+fission+yeast+Schizosaccharomyces+pombe.">Analysis of a genome-wide set of gene deletions in the fission yeast Schizosaccharomyces pombe.</a> Nature Biotechnology. 28 (6), 1628-1629 (2010).</li><li>Giaever, G., Nislow, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+yeast+deletion+collection:+a+decade+of+functional+genomics.">The yeast deletion collection: a decade of functional genomics.</a> Genetics. 197 (2), 451-465 (2014).</li><li>Meyers, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+protein+and+neutral+lipid+composition+of+lipid+droplets+isolated+from+the+fission+yeast,+Schizosaccharomyces+pombe.">The protein and neutral lipid composition of lipid droplets isolated from the fission yeast, Schizosaccharomyces pombe.</a> Journal of Microbiology (Seoul, Korea). 55 (2), 112-122 (2017).</li><li>Meyers, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Lipid+Droplets+Form+from+Distinct+Regions+of+the+Cell+in+the+Fission+Yeast+Schizosaccharomyces+pombe.">Lipid Droplets Form from Distinct Regions of the Cell in the Fission Yeast Schizosaccharomyces pombe.</a> Traffic (Copenhagen, Denmark). 17 (6), 657-659 (2016).</li><li>Long, A. 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The understanding of lipid metabolism and its regulation is important for both basic biology, and clinical and biotechnological applications. LD content represents a convenient readout of lipid metabolism state of the cell, with fluorescence microscopy being one of the major methods used for LD content determination. The presented protocol allows automated detection and quantitative description of individual LDs in three different and morphologically distinct yeast species. To our knowledge, no similar tools exist for th...

Lipids play crucial roles in cellular energy and carbon metabolism, synthesis of membrane components, and production of bioactive substances. Lipid metabolism is fine-tuned according to environmental conditions, nutrient availability and cell-cycle phase1. In humans, lipid metabolism has been connected to diseases, such as obesity, type II diabetes and cancer2. In industry, lipids produced by microorganisms, such as yeasts, represent a promising source of renewable diesel fuels3. Cells store neutral lipids in so-called lipid droplets (LDs). These evolutionarily conserved bodies are composed of triacylglycerols, steryl esters, an outer phospholipid monolayer and associated proteins1. LDs originate in the endoplasmic reticulum, exert cell-cycle or growth-phase dynamics, and are important for cellular lipid homeostasis1. LD number and morphology can be used as a convenient proxy when assaying lipid metabolism under various growth conditions or when screening a panel of mutants. Given their dynamic nature, techniques capable of analyzing the properties of individual LDs are of particular interest in studies of lipid metabolism.
Various yeast species have been used to describe lipid-related metabolic pathways and their regulation, or used in biotechnology to produce interesting compounds or fuels1. Furthermore, for model yeasts, such as the budding yeast Saccharomyces cerevisiae or the distantly related fission yeast Schizosaccharomyces pombe, genome-wide deletion strain libraries are available that can be used for high-throughput screens4,5. Recently LD composition and dynamics have been described in S. pombe6,7,8,9, and mutants related to lipid metabolism have been isolated in the emerging model yeast Schizosaccharomyces japonicus10.
Numerous techniques are available to study LD content and dynamics. Most employ some kind of staining of LDs with lipophilic dyes such as Nile Red or BODIPY 493/503. The latter shows more narrow excitation and emission spectra, and increased specificity towards neutral lipids (LDs) as opposed to phospholipids (membranes)11. Fluorimetric and flow-cytometry methods have been used successfully in various fungal species to uncover genes and growth conditions that affect storage lipid content12,13,14,15. While these methods are suitable for high-throughput applications, they cannot measure the numbers and morphology of individual LDs in cells, which can differ dramatically between growth conditions and genotypes. Coherent Raman scattering or digital holographic microscopy are label-free methods that yield LD-level data, but require specialized expensive equipment16,17,18. Fluorescence microscopy, on the other hand, can provide detailed data on LD content, while utilizing commonly available instruments and image analysis software tools. Several analysis workflows exist that feature various degrees of sophistication and automation in cell/LD detection from image data, and are optimized for different cell types, such as metazoan cells with large LDs19,20,21, or budding yeasts17,22,23. Some of these approaches only work in 2D (e.g., on maximum projection images), which may fail to reliably describe the cellular LD content. To our knowledge, no tools exist for determination of LD content and morphology from fission yeast microscopic data. Development of automated and robust LD-level analyses would bring increased sensitivity and enhanced statistical power, and provide rich information on neutral lipid content, ideally in multiple yeast species.
We have developed a workflow for LD content analysis from 3D fluorescence microscopy images of yeast cells. Live cells are stained with BODIPY 493/503 and Cascade Blue dextran to visualize LDs and determine cell boundaries, respectively. Cells are immobilized on glass slides and subjected to z-stack imaging using a standard epifluorescence microscope. Images are then processed by an automated pipeline implemented in MATLAB, a widely used (commercial) package for statistical analyses. The pipeline performs image preprocessing, segmentation (cells vs. background, removal of dead cells), and LD identification. Rich LD-level data, such as LD size and fluorescence intensity, are then provided in a tabular format compatible with major spreadsheet software tools. The workflow was used successfully to determine the impact of nitrogen source availability on lipid metabolism in S. pombe24. We now demonstrate the functionality of the workflow in S. pombe, S. japonicus and S. cerevisiae, using growth conditions or mutants that affect cellular LD content.

<table>
<tbody>
<tr>
<td>12-bit monochromatic CCD camera Hamamatsu ORCA C4742-80-12AG</td>
<td>Hamamatsu</td>
<td>&nbsp;</td>
<td>or equivalent</td>
</tr>
<tr>
<td>Adenine hemisulfate salt, &ge;99%</td>
<td>Merck</td>
<td>A9126-25G</td>
<td>&nbsp;</td>
</tr>
<tr>
<td>BODIPY 493/503 (4,4-Difluoro-1,3,5,7,8-Pentamethyl-4-Bora-3a,4a-Diaza-s-Indacene)</td>
<td>Thermo Fisher Scientific</td>
<td>D3922</td>
<td>for neutral lipid staining</td>
</tr>
<tr>
<td>D-(+) - Glucose, &ge;99.5%</td>
<td>Merck</td>
<td>G7021</td>
<td>&nbsp;</td>
</tr>
<tr>
<td>Dextran, Cascade Blue, 10,000 MW, Anionic, Lysine Fixable</td>
<td>Thermo Fisher Scientific</td>
<td>D1976</td>
<td>for negative staining of cells</td>
</tr>
<tr>
<td>Dimethyl sulfoxide, &ge;99.5%</td>
<td>Merck</td>
<td>D4540</td>
<td>or higher purity, keep anhydrous on molecular sieves</td>
</tr>
<tr>
<td>EMM broth without dextrose</td>
<td>Formedium</td>
<td>PMD0405</td>
<td>medium may also be prepared from individual components</td>
</tr>
<tr>
<td>Fiji/ImageJ software</td>
<td>NIH</td>
<td>&nbsp;</td>
<td>or equivalent; for visual inspection of microscopic data</td>
</tr>
<tr>
<td>High precision cover glasses, 22x22 mm, No 1.5</td>
<td>VWR</td>
<td>630-2186</td>
<td>use any # 1.5 cover glass</td>
</tr>
<tr>
<td>Image Processing Toolbox for MATLAB, version 10.0</td>
<td>Mathworks</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
</tr>
<tr>
<td>Lectin from Glycine max (soybean)</td>
<td>Merck</td>
<td>L1395</td>
<td>for cell immobilization on slides</td>
</tr>
<tr>
<td>MATLAB software, version 9.2</td>
<td>Mathworks</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
</tr>
<tr>
<td>Microscope slide, 26 x 76 mm, 1 mm thickness</td>
<td>Knittel Glass</td>
<td>L762601.2</td>
<td>use any microscope slide fitting your microscope stage, clean thoroughly before loading cells</td>
</tr>
<tr>
<td>Olympus CellR microscope with automatic z-axis objective movement</td>
<td>Olympus</td>
<td>&nbsp;</td>
<td>or equivalent</td>
</tr>
<tr>
<td>pentaband filter set </td>
<td>Semrock</td>
<td>F66-985</td>
<td>brightfield, green and blue channels are sufficient</td>
</tr>
<tr>
<td>Signal Processing Toolbox for MATLAB, version 7.4</td>
<td>Mathworks</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
</tr>
<tr>
<td>SP supplements</td>
<td>Formedium</td>
<td>PSU0101</td>
<td>&nbsp;</td>
</tr>
<tr>
<td>standard office computer capable of running MATLAB</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
</tr>
<tr>
<td>Statistics and Machine Learning Toolbox for MATLAB, version 11.1</td>
<td>Mathworks</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
</tr>
<tr>
<td>Universal peptone M66 for microbiology</td>
<td>Merck</td>
<td>1070431000</td>
<td>&nbsp;</td>
</tr>
<tr>
<td>UPLSAPO 60XO objective</td>
<td>Olympus</td>
<td>&nbsp;</td>
<td>or equivalent</td>
</tr>
<tr>
<td>Yeast extract</td>
<td>Formedium</td>
<td>YEA03</td>
<td>&nbsp;</td>
</tr>
<tr>
<td>Yeast nitrogen base without amino acids</td>
<td>Formedium</td>
<td>CYN0405</td>
<td>&nbsp;</td>
</tr>
</tbody>
</table>

analysis of lipid droplet content in fission and budding yeasts using automated image processing

Lipid metabolism and its regulation are of interest to both basic and applied life sciences and biotechnology. In this regard, various yeast species are used as models in lipid metabolic research or for industrial lipid production. Lipid droplets are highly dynamic storage bodies and their cellular content represents a convenient readout of the lipid metabolic state. Fluorescence microscopy is a method of choice for quantitative analysis of cellular lipid droplets, as it relies on widely available equipment and allows analysis of individual lipid droplets. Furthermore, microscopic image analysis can be automated, greatly increasing overall analysis throughput. Here, we describe an experimental and analytical workflow for automated detection and quantitative description of individual lipid droplets in three different model yeast species: the fission yeasts Schizosaccharomyces pombe and Schizosaccharomyces japonicus, and the budding yeast Saccharomyces cerevisiae. Lipid droplets are visualized with BODIPY 493/503, and cell-impermeable fluorescent dextran is added to the culture media to help identify cell boundaries. Cells are subjected to 3D epifluorescence microscopy in green and blue channels and the resulting z-stack images are processed automatically by a MATLAB pipeline. The procedure outputs rich quantitative data on cellular lipid droplet content and individual lipid droplet characteristics in a tabular format suitable for downstream analyses in major spreadsheet or statistical packages. We provide example analyses of lipid droplet content under various conditions that affect cellular lipid metabolism.

The whole procedure is summarized in Figure 1 for the fission yeasts (the budding yeast workflow is analogous), and below we provide examples of how the workflow can be used to study LD content in three different yeast species under various conditions known to affect cellular LD content. Each example represents a single biological experiment.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/59889/59889fig01v2.jpg" /><br /...

Watch this Scientific Journal Video about Analysis of Lipid Droplet Content in Fission and Budding Yeasts using Automated Image Processing at JoVE.com

Analysis of Lipid Droplet Content in Fission and Budding Yeasts using Automated Image Processing

Here, we present a MATLAB implementation of automated detection and quantitative description of lipid droplets in fluorescence microscopy images of fission and budding yeast cells.

Lipid metabolism and its regulation are of interest to both basic and applied life sciences and biotechnology. In this regard, various yeast species are ...

This protocol facilitates quantitative small-to large-scale analyses of cellular storage lipid content in a variety of yeast species and does so in an unbiased and standardized manner. The technique allows for the rapid processing of microscopic images and provides detailed quantitative outputs to easily compare samples such as various mutants and cells grown under diverse conditions. To begin, follow along in the accompanying text protocol to prepare the staining solutions, culture media, and cells.Ensure that the cells are healthy and in the desired growth phase prior to running the experiment. Next, prepare a microscope coverslip for each sample to be imaged. Spread one microliter of slide coating solution onto a clean coverslip using the long side of a horizontally positioned pipette tip.Allow the coating solution to dry completely, and then store the coverslips in a dust-free environment. Next, measure the optical densities of the cell cultures, and for cultures to be analyzed in the same growth phase, try to reach similar values among all tested cultures to ensure comparable experimental conditions. Then, pipette one milliliter of the cell culture into a 1.5-milliliter microcentrifuge tube.For the S.cerevisiae cells only, add five microliters of the slide coating solution to the tube as well. Then, briefly vortex all of the microcentrifuge tubes, and incubate them at 30 degrees Celsius with shaking for five minutes. Next, add one microliter of the lipid staining solution to each culture aliquot, and vortex them briefly.Then, add 10 microliters of the cell boundary visualization solution, and vortex them briefly a second time. Collect the cells by centrifuging them at 1, 000 times gravity for three minutes at room temperature. When finished spinning, remove 950 microliters from the supernatant and resuspend the cells in the remaining supernatant using a pipette.Next, pipette two microliters of the dense cell suspension onto a lectin-coated coverslip. Then, place the coverslip onto a clean microscope slide to form a cell monolayer, and proceed to microscopy as quickly as possible to minimize artifacts in imaging. Setting up the microscope requires long exposures to strong light sources that could cause damage to the sample and skew results.Therefore, set up the imaging conditions using a dedicated sample slide that will not be further used for lipid droplet quantification. Place the dedicated sample onto the stage of a phase contrast or differential interference contrast microscope setup, and focus on the cells. In the microscope's software, set the Z-stack settings so that they span the whole cell volume.The total vertical distance depends on the cell size, and the number of optical slices depends on the numerical aperture of the objective. Next, set the focus to move relative to the central focal plane. To image lipid droplets, experimentally set the light intensity and exposure time in the green channel.Be cautious when imaging, as BODIPY is a very bright fluorochrome that may be rapidly photobleached. Additionally, minimize the exposure time to avoid oversaturation. Capture the full green channel Z-stack first before switching to the blue channel to prevent blurring the mobile lipid droplets.To image cell boundaries, experimentally set the light intensity and exposure time in the blue channel. If possible, set up and use an automated experimental workflow in the microscope control software to facilitate imaging of multiple samples under standardized conditions. Importantly, all images must be acquired using the same settings to allow for appropriate comparison between samples.Once imaging conditions have been optimized, focus on the cells and image them in both the green and blue channels. Image multiple fields of view per sample to obtain robust, representative data. Save the blue and green channel Z-stack images as 16-bit, multilayer TIFF files.Be sure to include the words green or blue in the corresponding file names. Open microscopic images in ImageJ, and remove any image stacks containing a considerable number of cells that moved during acquisition. These appear at different positions in individual Z sections.Next, remove any image stacks containing highly fluorescent non-cell particles in the blue channel. This is often caused by dirt on the imaging surface or impurities in the cultivation medium and may interfere with detection of cells in their vicinity. Also, remove any image stacks containing a large proportion of dead cells.These images will display cells with increased blue fluorescence compared to the live cells. While the presence of a few dead cells in the sample is typically not a problem and these cells are automatically discarded during analysis, some dead or dying cells may occasionally be recognized as live cells by the segmentation algorithm and thus skew the reported results. To begin analysis in MATLAB, first create a main folder and copy all MATLAB scripts to this location.Next, create subfolders with the names of the various yeast species, and copy the respective TIFF images to these locations. Now, start MATLAB, open the script MAIN. m, and run the script.In the menu, select the yeast species to be analyzed and start image processing. Inspect and process the output files as required using a spreadsheet editor or statistical package. The workflow produces semicolon-separated CSV files and segmented TIFF files with detected cell objects and lipid droplets.Shown here are wild-type S.pombe cells that were grown in either the complex YES medium or the defined EMM medium. Compared to cells grown in the YES medium, fewer lipid droplets and higher staining intensity per unit of cell volume were detected in the EMM medium. Moreover, individual lipid droplets formed in EMM medium were larger and displayed an increased total staining intensity.This is in agreement with previous findings of increased stored lipid content in cells grown in EMM. Next, these images show S.japonicus cells from exponential and early-stationary cultures grown in YES medium. Cells entering stationary phase showed a markedly decreased number of lipid droplets per unit of cell volume while volume-normalized lipid droplet fluorescence intensity decreased slightly between the two conditions.The early stationary-phase lipid droplets were typically moderately larger in size and had moderately higher total fluorescence intensity compared to droplets from exponentially growing cells. In S.cerevisiae cells grown to exponential versus stationary phase, stationary cells contained somewhat fewer lipid droplets per unit of volume compared to exponentially growing cells. However, their volume-normalized droplet fluorescence intensity almost doubled.This sharp increase in overall lipid droplet content was due to the much higher fluorescence intensity and volume of individual lipid droplets in stationary phase. Following this procedure, output data can be aggregated in a statistical package such as R to create plots of summarized results and run statistical tests on any observed differences in lipid droplet content. In principle, the image processing pipeline can be used for analysis of lipid droplet content in other microorganisms or to analyze other dot-like subcellular structures.

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