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 &#181;L of the 1 mg/mL BODIPY 493/503 stock solution and 900 &#181;L of anhydrous DMSO (final concentration 0.1 mg/mL).</li>
		<li>Aliquot the stock and working solutions, and store at -20 &#176;C. 
		NOTE: Dissolved BODIPY 493/503 is stable for several years at -20 &#176;C. However, the solution has to be protected from moisture and light.</li>
	</ol>
	</li>
	<li>To prepare stock solution for cell boundary visualization, dissolve 25 mg of Cascade Blue dextran (whole vial) in 2.5 mL of deionized water (final concentration 10 mg/mL). Aliquot the stock solution and store at -20 &#176;C protected from light.</li>
	<li>To prepare microscope slide coating solution, dissolve 5 mg of soybean lectin in 5 mL of deionized water (final concentration 1 mg/mL). Aliquot the lectin solution and store at -80 &#176;C. 
	NOTE: The lectin solution is stable for several years at -80 &#176;C. Aliquots currently at use may be stored at -20 &#176;C.</li>
	<li>Prepare cultivation media.
	<ol>
		<li>To prepare 400 mL of complex YES cultivation medium for S. pombe and S. japonicus, dissolve 2 g of yeast extract and 0.1 g of SP supplements (if required for auxotrophic mutants) in 340 mL of deionized water in a 500 mL bottle and autoclave. Add 60 mL of 20% (w/v) of separately autoclaved or filter-sterilized glucose in aseptic conditions.</li>
		<li>To prepare 400 mL of defined EMM cultivation medium for S. pombe and S. japonicus, dissolve 4.9 g of EMM broth without dextrose in 360 mL of deionized water in a 500 mL bottle and autoclave. Add 40 mL of 20% (w/v) of separately autoclaved or filter-sterilized glucose in aseptic conditions. 
		NOTE: For general guidelines on S. pombe and S. japonicus cultivation see25 and26, respectively.</li>
		<li>To prepare 300 mL of complex YPAD cultivation medium for Saccharomyces cerevisiae, dissolve 3 g of yeast extract, 6 g of peptone and 30 mg of adenine sulphate in 270 mL of deionized water in a 500 mL bottle and autoclave. Add 30 mL of 20% (w/v) of separately autoclaved or filter-sterilized glucose in aseptic conditions.</li>
		<li>To prepare 300 mL of defined minimal medium for S. cerevisiae, dissolve 2 g of yeast nitrogen base (without amino acids) in 270 mL of deionized water in a 500 mL bottle and autoclave. Add 30 mL of 20% (w/v) of separately autoclaved or filter-sterilized glucose in aseptic conditions. 
		NOTE: For general guidelines on S. cerevisiae cultivation see27.</li>
	</ol>
	</li>
</ol>
2. Cell Cultivation
<ol>
	<li>Growing S. pombe or S. japonicus to exponential or early stationary phase.
	<ol>
		<li>In the morning, inoculate 5 mL of YES medium with fresh fission yeast biomass. Incubate at 32 &#176;C with shaking (180 rpm) for several hours. 
		NOTE: For all cultivations, use Erlenmeyer flasks having 10 times the volume of culture to ensure proper aeration. Some laboratories prefer to grow fission yeasts at 30 &#176;C, but cultivation temperature of 32 &#176;C results in shorter doubling times without detrimental effects to the cells, thus reducing the total time required to perform an experiment25,28.</li>
		<li>In late afternoon of the same day (after at least 6 hours of cultivation), dilute the culture with fresh YES medium to a 10 mL final culture volume so that it reaches the desired optical density (OD) (or number of cells/mL) the following morning, and incubate at 32 &#176;C with shaking (180 rpm). It is of advantage to know the doubling time of each used strain to accurately determine the dilution factor (use Equation 1). 
		<img alt="Equation 1" src="/files/ftp_upload/59889/59889eq01v3.jpg" /> 
		Where Vculture is the preculture volume needed for dilution, Vfinal is the total volume of the new culture (10 mL for standard cultivations), ODfinal is the desired OD to be reached the following morning, ODcurrent is the currently measured OD of the preculture, t is the time of cell growth until harvesting, tlag is duration of the lag phase (depends on laboratory conditions, needs to be empirically defined) and tDT is the doubling time of the strain. 
		NOTE: When exponential-phase cells are to be analyzed, do not let precultures reach the stationary phase as this dramatically alters cell physiology (including LD content) for several subsequent generations.</li>
		<li>In the morning of imaging day, if the culture reached slightly higher OD than required (in case of exponential-phase cells), dilute it with fresh YES and continue incubation for at least two more doubling times before staining of LDs. Otherwise proceed directly to staining (Section 3).</li>
	</ol>
	</li>
	<li>Growing S. cerevisiae to exponential and stationary phase.
	<ol>
		<li>In the afternoon, inoculate 10 mL of YPAD medium with a small amount of fresh budding yeast biomass and incubate overnight at 30 &#176;C with shaking (180 rpm).</li>
		<li>The morning of imaging day, dilute the culture to OD 0.1 in 10 mL of YPAD medium and grow to the required OD (e.g., OD 1 for exponential phase). Perform any culture dilutions as described in step 2.1.2. Proceed to staining (Section 3).</li>
	</ol>
	</li>
</ol>
3. Lipid Droplet Staining
<ol>
	<li>Prepare a microscope cover slip for each sample to be imaged. Spread 1 &#181;L of slide coating solution onto a clean cover slip using the long side of a horizontally positioned pipette tip. Allow the coating solution to dry completely and store the cover slips in a dust-free environment. 
	NOTE: Glass slides and coverslips can be cleaned prior to use if required. The cleaning procedure consists of washing with dishwashing detergent, rinsing with water, overnight soaking in 3% hydrochloric acid, and washing with distilled water. Cleaned slides and coverslips are stored in pure ethanol until use.</li>
	<li>Measure the OD of cell culture or number of cells/mL, as required. Try to reach similar values among all tested strains to ensure comparable experimental conditions.</li>
	<li>Pipette 1 mL of each cell culture to a 1.5 mL microcentrifuge tube. For S. cerevisiae only, add 5 &#181;L of the slide coating solution, vortex briefly, and incubate at 30 &#176;C with shaking for 5 min.</li>
	<li>Add 1 &#181;L of the lipid staining solution to each culture aliquot and vortex briefly. Then add 10 &#181;L of the cell boundary visualization solution and vortex briefly. 
	NOTE: Do not prepare pre-mixed solutions of both stains as this leads to fluorescence quenching of BODIPY 493/503.</li>
	<li>Collect the cells by centrifugation (1,000 x g, 3 min, RT) and remove almost all supernatant (~950 &#181;L). Resuspend the cells in the remaining supernatant.</li>
	<li>Pipette 2 &#181;L of the dense cell suspension on a lectin-coated cover slip and place onto a clean microscope slide. The cells should form a monolayer. Proceed to microscopy (Section 4) as quickly as possible to minimize artefacts in imaging; process maximum of two samples at a time.</li>
</ol>
4. Setting up the Microscope and Imaging
<ol>
	<li>Optimize imaging conditions. 
	NOTE: 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 LD quantification.
	<ol>
		<li>Focus on the cells using phase contrast or differential interference contrast (DIC). 
		NOTE: Phase contrast or DIC images may be taken for reference, but they are not used during the automated image analysis step.</li>
		<li>Set z-stack settings to span the whole cell volume. The total vertical distance depends on the cell size; the number of optical slices depends on the numerical aperture of the objective (point spread function in z-axis). Set the focus to move relative to the central focal plane. 
		NOTE: The optimal number of slices is often set by the microscope control software and does not need to be calculated manually. The typical cell widths are 3-5 &#181;m for S. pombe, 4-7 &#181;m for S. japonicus, and 3-7 &#181;m for S. cerevisiae.</li>
		<li>To image LDs, set light intensity and exposure time in the green channel (excitation and emission maxima of BODIPY are 493 and 503 nm, respectively). 
		NOTE: BODIPY 493/503 is a very bright fluorochrome; however, it may get bleached rapidly with overly strong light intensity. Moreover, LDs are mobile in live cells, thus minimize exposure time and capture the full green-channel z-stack first (before switching to the blue channel) to prevent blurring artifacts. Also, take into account the linear range of the camera for signal intensity to avoid saturated pixels.</li>
		<li>To image cell boundaries, set light intensity and exposure time in the blue channel (excitation and emission maxima of Cascade Blue dextran are 400 and 420 nm, respectively). 
		NOTE: Signal intensity in the blue channel is required for image segmentation, but it is not used for LD quantification itself. Therefore, optimal settings in this channel are not crucial for analysis.</li>
		<li>If possible, create an automated experimental workflow in the microscope control software to facilitate imaging of multiple samples under standardized conditions.</li>
	</ol>
	</li>
	<li>Once imaging conditions have been optimized, image samples to be used for quantification. Focus on the cells and image them in green and blue channels as described in Step 4.1. 
	NOTE: All images must be acquired using the same settings to allow comparison between samples. Image multiple fields of view per sample to obtain robust, representative data.</li>
	<li>Save the blue and green channel z-stack images as 16-bit multi-layer TIFF files (i.e., two files per field of view). Include words &#8220;green&#8221; or &#8220;blue&#8221; in the corresponding file names. Proceed with image analysis (Section 5).</li>
</ol>
5. Image Analysis
<ol>
	<li>Visually check the quality of acquired images.
	<ol>
		<li>Open microscopic images in ImageJ29,30 or other suitable image analysis software.</li>
		<li>Remove any image stacks containing a considerable number of cells that moved during acquisition (and thus created blurring artifacts).</li>
		<li>Remove any image stacks containing highly fluorescent non-cell particles in the blue channel (e.g., dirt on microscope slide or cover slip, impurities in cultivation medium). 
		NOTE: Very bright non-cell objects in the blue channel may create cell detection artifacts or may interfere with detection of cells in their vicinity.</li>
		<li>Remove any image stacks containing a large proportion of dead cells (i.e., cells with increased blue fluorescence compared to live cells). 
		NOTE: While the presence of a small proportion of 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.</li>
	</ol>
	</li>
	<li>Analyze images in the MATLAB software.
	<ol>
		<li>Create a main folder and copy all MATLAB scripts to this location.</li>
		<li>Create a sub-folder (&#8220;pombe&#8221;, &#8220;cerevisiae&#8221; or &#8220;japonicus&#8221;) and copy input TIFF image files to this location.</li>
		<li>Start MATLAB, open script MAIN.m and run it. In the menu select the yeast species to be analyzed and start image processing. 
		NOTE: Some of the parameters required for cell and LD detection are pre-set for the particular species, others are determined automatically during image processing. The pre-set values were determined empirically and depend on several factors such as objective magnification, camera type and sensitivity, and imaging settings. If required, users may edit the script files to change the organism-specific presets to better reflect their experimental setup. Namely, during cell recognition acceptable object sizes are given by the &#8220;minArea&#8221; and &#8220;maxArea&#8221; parameters, and the minimum fraction of filled volume within the object boundaries is given by the &#8220;Solidity&#8221; parameter. For LD recognition, the brightness threshold is given by the &#8220;th&#8221; parameter (its value is affected mostly by image bit depth and fluorescence signal intensity), and maximum acceptable LD size is given by the &#8220;MaxArea&#8221; parameter.</li>
		<li>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 LDs. 
		NOTE: The workflow segments images into background and cell objects, where each cell object may be composed of multiple adjacent cells. Therefore, the output in &#8220;xxxx_cells.csv&#8221; files does not represent single-cell data and should only be used to calculate per-unit-of-cell-volume metrics.</li>
	</ol>
	</li>
</ol>

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W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+ImageJ+ecosystem:+An+open+platform+for+biomedical+image+analysis.">The ImageJ ecosystem: An open platform for biomedical image analysis.</a> Molecular Reproduction and Development. 82 (7-8), 518-529 (2015).</li><li>Schindelin, J., 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=Fiji:+an+open-source+platform+for+biological-image+analysis.">Fiji: an open-source platform for biological-image analysis.</a> Nature Methods. 9 (7), 676-682 (2012).</li><li>Nakamura, T., Pluskal, T., Nakaseko, Y., Yanagida, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Impaired+coenzyme+A+synthesis+in+fission+yeast+causes+defective+mitosis,+quiescence-exit+failure,+histone+hypoacetylation+and+fragile+DNA.">Impaired coenzyme A synthesis in fission yeast causes defective mitosis, quiescence-exit failure, histone hypoacetylation and fragile DNA.</a> Open Biology. 2 (9), 120117 (2012).</li><li>Furuya, K., Niki, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Isolation+of+heterothallic+haploid+and+auxotrophic+mutants+of+Schizosaccharomyces+japonicus.">Isolation of heterothallic haploid and auxotrophic mutants of Schizosaccharomyces japonicus.</a> Yeast. 26 (4), 221-233 (2009).</li><li>Ivnitski-Steele, I., 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=Identification+of+Nile+red+as+a+fluorescent+substrate+of+the+Candida+albicans+ATP-binding+cassette+transporters+Cdr1p+and+Cdr2p+and+the+major+facilitator+superfamily+transporter+Mdr1p.">Identification of Nile red as a fluorescent substrate of the Candida albicans ATP-binding cassette transporters Cdr1p and Cdr2p and the major facilitator superfamily transporter Mdr1p.</a> Analytical Biochemistry. 394 (1), 87-91 (2009).</li><li>Wolinski, H., Kohlwein, S. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Microscopic+analysis+of+lipid+droplet+metabolism+and+dynamics+in+yeast.">Microscopic analysis of lipid droplet metabolism and dynamics in yeast.</a> Methods in Molecular Biology (Clifton, N.J.). 457 (1), 151-163 (2008).</li><li>Graml, V., 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=A+genomic+Multiprocess+survey+of+machineries+that+control+and+link+cell+shape,+microtubule+organization,+and+cell-cycle+progression.">A genomic Multiprocess survey of machineries that control and link cell shape, microtubule organization, and cell-cycle progression.</a> Developmental Cell. 31 (2), 227-239 (2014).</li></ol>

The authors have nothing to disclose.

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 ...

analysis-lipid-droplet-content-fission-budding-yeasts-using-automated

Universidad de Cartagena UDEC

Research

JoVE Journal

Immunology and Infection

7.9K Views.  Charles University. 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.

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

Using Luciferase to Image Bacterial Infections in Mice

Imaging is a valuable technique that can be used to monitor biological processes. In particular, the presence of cancer cells, stem cells, specific immune cell types, viral pathogens, parasites and bacteria can be followed in real-time within living animals 1-2. Application of bioluminescence imaging to the study of pathogens has advantages as compared to conventional strategies for analysis of infections in animal models3-4. Infections can be visualized within individual animals over time, without requiring euthanasia to determine the location and quantity of the pathogen. Optical imaging allows comprehensive examination of all tissues and organs, rather than sampling of sites previously known to be infected. In addition, the accuracy of inoculation into specific tissues can be directly determined prior to carrying forward animals that were unsuccessfully inoculated throughout the entire experiment. Variability between animals can be controlled for, since imaging allows each animal to be followed individually. Imaging has the potential to greatly reduce animal numbers needed because of the ability to obtain data from numerous time points without having to sample tissues to determine pathogen load3-4.
This protocol describes methods to visualize infections in live animals using bioluminescence imaging for recombinant strains of bacteria expressing luciferase. The click beetle (CBRLuc) and firefly luciferases (FFluc) utilize luciferin as a substrate5-6. The light produced by both CBRluc and FFluc has a broad wavelength from 500 nm to 700 nm, making these luciferases excellent reporters for the optical imaging in living animal models7-9. This is primarily because wavelengths of light greater than 600 nm are required to avoid absorption by hemoglobin and, thus, travel through mammalian tissue efficiently. Luciferase is genetically introduced into the bacteria to produce light signal10. Mice are pulmonary inoculated with bioluminescent bacteria intratracheally to allow monitoring of infections in real time. After luciferin injection, images are acquired using the IVIS Imaging System. During imaging, mice are anesthetized with isoflurane using an XGI-8 Gas Anethesia System. Images can be analyzed to localize and quantify the signal source, which represents the bacterial infection site(s) and number, respectively. After imaging, CFU determination is carried out on homogenized tissue to confirm the presence of bacteria. Several doses of bacteria are used to correlate bacterial numbers with luminescence. Imaging can be applied to study of pathogenesis and evaluation of the efficacy of antibacterial compounds and vaccines.

Methods for bioluminescence imaging of bacterial infections in living animals are decribed. Pathogens are modified to express luciferase allowing optical whole body imaging of infections in live animals. Animal models can be infected with luciferase expressing pathogens and the resulting course of disease visualized in real-time by bioluminescence imaging.

Imaging is a valuable technique that can be used to monitor biological processes.  In particular, the presence of cancer cells, stem cells, specific ...

Determination of Molecular Structures of HIV Envelope Glycoproteins using Cryo-Electron Tomography and Automated Sub-tomogram Averaging

Since its discovery nearly 30 years ago, more than 60 million people have been infected with the human immunodeficiency virus (HIV) <a href="http://www.usaid.gov">(www.usaid.gov)</a>. The virus infects and destroys CD4+ T-cells thereby crippling the immune system, and causing an acquired immunodeficiency syndrome (AIDS) 2. Infection begins when the HIV Envelope glycoprotein "spike" makes contact with the CD4 receptor on the surface of the CD4+ T-cell. This interaction induces a conformational change in the spike, which promotes interaction with a second cell surface co-receptor 5,9. The significance of these protein interactions in the HIV infection pathway makes them of profound importance in fundamental HIV research, and in the pursuit of an HIV vaccine.
The need to better understand the molecular-scale interactions of HIV cell contact and neutralization motivated the development of a technique to determine the structures of the HIV spike interacting with cell surface receptor proteins and molecules that block infection. Using cryo-electron tomography and 3D image processing, we recently demonstrated the ability to determine such structures on the surface of native virus, at &tilde;20 &Aring; resolution 9,14. This approach is not limited to resolving HIV Envelope structures, and can be extended to other viral membrane proteins and proteins reconstituted on a liposome. In this protocol, we describe how to obtain structures of HIV envelope glycoproteins starting from purified HIV virions and proceeding stepwise through preparing vitrified samples, collecting, cryo-electron microscopy data, reconstituting and processing 3D data volumes, averaging and classifying 3D protein subvolumes, and interpreting results to produce a protein model. The computational aspects of our approach were adapted into modules that can be accessed and executed remotely using the Biowulf GNU/Linux parallel processing cluster at the NIH <a href="http://biowulf.nih.gov">(http://biowulf.nih.gov)</a>. This remote access, combined with low-cost computer hardware and high-speed network access, has made possible the involvement of researchers and students working from school or home.

The protocol describes a high-throughput approach to determining structures of membrane proteins using cryo-electron tomography and 3D image processing. It covers the details of specimen preparation, data collection, data processing and interpretation, and concludes with the production of a representative target for the approach, the HIV-1 Envelope glycoprotein. These computational procedures are designed in a way that enables researchers and students to work remotely and contribute to data processing and structural analysis.

Since its discovery nearly 30 years ago, more than 60 million people have been infected with the human immunodeficiency virus (HIV) (www.usaid.gov). The ...

Imaging of HIV-1 Envelope-induced Virological Synapse and Signaling on Synthetic Lipid Bilayers

Human immunodeficiency virus type 1 (HIV-1) infection occurs most efficiently via cell to cell transmission2,10,11. This cell to cell transfer between CD4+ T cells involves the formation of a virological synapse (VS), which is an F-actin-dependent cell-cell junction formed upon the engagement of HIV-1 envelope gp120 on the infected cell with CD4 and the chemokine receptor (CKR) CCR5 or CXCR4 on the target cell 8. In addition to gp120 and its receptors, other membrane proteins, particularly the adhesion molecule LFA-1 and its ligands, the ICAM family, play a major role in VS formation and virus transmission as they are present on the surface of virus-infected donor cells and target cells, as well as on the envelope of HIV-1 virions1,4,5,6,7,13. VS formation is also accompanied by intracellular signaling events that are transduced as a result of gp120-engagement of its receptors. Indeed, we have recently showed that CD4+ T cell interaction with gp120 induces recruitment and phosphorylation of signaling molecules associated with the TCR signalosome including Lck, CD3&zeta;, ZAP70, LAT, SLP-76, Itk, and PLC&gamma;15.
In this article, we present a method to visualize supramolecular arrangement and membrane-proximal signaling events taking place during VS formation. We take advantage of the glass-supported planar bi-layer system as a reductionist model to represent the surface of HIV-infected cells bearing the viral envelope gp120 and the cellular adhesion molecule ICAM-1. The protocol describes general procedures for monitoring HIV-1 gp120-induced VS assembly and signal activation events that include i) bi-layer preparation and assembly in a flow cell, ii) injection of cells and immunofluorescence staining to detect intracellular signaling molecules on cells interacting with HIV-1 gp120 and ICAM-1 on bi-layers, iii) image acquisition by TIRF microscopy, and iv) data analysis. This system generates high-resolution images of VS interface beyond that achieved with the conventional cell-cell system as it allows detection of distinct clusters of individual molecular components of VS along with specific signaling molecules recruited to these sub-domains.

This article describes a method to visualize formation of an HIV-1 envelope-induced virological synapse on glass supported planar bilayers by total internal reflection fluorescence (TIRF) microscopy. The method can also be combined with immunofluorescence staining to detect activation and redistribution of signaling molecules that occur during HIV-1 envelope-induced virological synapse formation.

Human immunodeficiency virus type 1 (HIV-1) infection occurs most efficiently via cell to cell transmission2,10,11. This cell to cell transfer between ...

Facilitating Drug Discovery: An Automated High-content Inflammation Assay in Zebrafish

Zebrafish larvae are particularly amenable to whole animal small molecule screens1,2 due to their small size and relative ease of manipulation and observation, as well as the fact that compounds can simply be added to the bathing water and are readily absorbed when administered in a &lt;1% DMSO solution. Due to the optical clarity of zebrafish larvae and the availability of transgenic lines expressing fluorescent proteins in leukocytes, zebrafish offer the unique advantage of monitoring an acute inflammatory response in vivo. Consequently, utilizing the zebrafish for high-content small molecule screens aiming at the identification of immune-modulatory compounds with high throughput has been proposed3-6, suggesting inflammation induction scenarios e.g. localized nicks in fin tissue, laser damage directed to the yolk surface of embryos7 or tailfin amputation3,5,6. The major drawback of these methods however was the requirement of manual larva manipulation to induce wounding, thus preventing high-throughput screening. Introduction of the chemically induced inflammation (ChIn) assay8 eliminated these obstacles. Since wounding is inflicted chemically the number of embryos that can be treated simultaneously is virtually unlimited. Temporary treatment of zebrafish larvae with copper sulfate selectively induces cell death in hair cells of the lateral line system and results in rapid granulocyte recruitment to injured neuromasts. The inflammatory response can be followed in real-time by using compound transgenic cldnB::GFP/lysC::DsRED26,9 zebrafish larvae that express a green fluorescent protein in neuromast cells, as well as a red fluorescent protein labeling granulocytes.
In order to devise a screening strategy that would allow both high-content and high-throughput analyses we introduced robotic liquid handling and combined automated microscopy with a custom developed software script. This script enables automated quantification of the inflammatory response by scoring the percent area occupied by red fluorescent leukocytes within an empirically defined area surrounding injured green fluorescent neuromasts. Furthermore, we automated data processing, handling, visualization, and storage all based on custom developed MATLAB and Python scripts.
In brief, we introduce an automated HC/HT screen that allows testing of chemical compounds for their effect on initiation, progression or resolution of a granulocytic inflammatory response. This protocol serves a good starting point for more in-depth analyses of drug mechanisms and pathways involved in the orchestration of an innate immune response. In the future, it may help identifying intolerable toxic or off-target effects at earlier phases of drug discovery and thereby reduce procedural risks and costs for drug development.

Here we describe a novel high-content chemically induced inflammation assay aiming at the identification of immune-modulatory bioactives. We have successfully combined automated microscopy with custom developed software scripts enabling automated quantification of the inflammatory response as well as further data processing, analysis, mining, and storage.

Zebrafish larvae are particularly amenable to whole animal small molecule screens1,2 due to their small size and relative ease of manipulation and ...

Isolation, Processing and Analysis of Murine Gingival Cells

We have developed a technique to precisely isolate and process murine gingival tissue for flow cytometry and molecular studies. The gingiva is a unique and important tissue to study immune mechanisms because it is involved in host immune response against oral biofilm that might cause periodontal diseases. Furthermore, the close proximity of the gingiva to alveolar bone tissue enables also studying bone remodeling under inflammatory conditions. Our method yields large amount of immune cells that allows analysis of even rare cell populations such as Langerhans cells and T regulatory cells as we demonstrated previously 1. Employing mice to study local immune responses involved in alveolar bone loss during periodontal diseases is advantageous because of the availability of various immunological and experimental tools. Nevertheless, due to their small size and the relatively inconvenient access to the murine gingiva, many studies avoided examination of this critical tissue. The method described in this work could facilitate gingival analysis, which hopefully will increase our understating on the oral immune system and its role during periodontal diseases.

This study describes an efficient technique to isolate and process gingival tissues from the mouse oral cavity in order to produce a single-cell culture. The resulting cells can be further used for flow cytometry analysis and molecular studies.

We have developed a technique to precisely isolate and process murine gingival tissue for flow cytometry and molecular studies. The gingiva is a unique ...

Visualization of Biofilm Formation in Candida albicans Using an Automated Microfluidic Device

Candida albicans is the most common fungal pathogen of humans, causing about 15% of hospital-acquired sepsis cases. A major virulence attribute of C. albicans is its ability to form biofilms, structured communities of cells attached to biotic and abiotic surfaces. C. albicans biofilms can form on host tissues, such as mucosal layers, and on medical devices, such as catheters, pacemakers, dentures, and joint prostheses. Biofilms pose significant clinical challenges because they are highly resistant to physical and chemical perturbations, and can act as reservoirs to seed disseminated infections. Various in vitro assays have been utilized to study C. albicans biofilm formation, such as microtiter plate assays, dry weight measurements, cell viability assays, and confocal scanning laser microscopy. All of these assays are single end-point assays, where biofilm formation is assessed at a specific time point. Here, we describe a protocol to study biofilm formation in real-time using an automated microfluidic device under laminar flow conditions. This method allows for the observation of biofilm formation as the biofilm develops over time, using customizable conditions that mimic those of the host, such as those encountered in vascular catheters. This protocol can be used to assess the biofilm defects of genetic mutants as well as the inhibitory effects of antimicrobial agents on biofilm development in real-time.

Visualization of Biofilm Formation in Candida albicans Using an Automated Microfluidic Device

This protocol describes the use of a customizable automated microfluidic device to visualize biofilm formation in Candida albicans under host physiological conditions.

Candida albicans is the most common fungal pathogen of humans, causing about 15% of hospital-acquired sepsis cases. A major virulence attribute of C. ...

Automated Image-Based Quantification of Neutrophil Extracellular Traps Using NETQUANT

Neutrophil extracellular traps (NETs) are web-like antimicrobial structures consisting of DNA and granule derived antimicrobial proteins. Immunofluorescence microscopy and image-based quantification methods remain important tools to quantitate neutrophil extracellular trap formation. However, there are key limitations to the immunofluorescence-based methods that are currently available for quantifying NETs. Manual methods of image-based NET quantification are often subjective, prone to error and tedious for users, especially non-experienced users. Also, presently available software options for quantification are either semi-automatic or require training prior to operation. Here, we demonstrate the implementation of an automated immunofluorescence-based image quantification method to evaluate NET formation called NETQUANT. The software is easy to use and has a user-friendly graphical user interface (GUI). It considers biologically relevant parameters such as an increase in the surface area and DNA:NET marker protein ratio, and nuclear deformation to define NET formation. Furthermore, this tool is built as a freely available app, and allows for single-cell resolution quantification and analysis.

Here, we present a protocol for generating neutrophil extracellular traps (NETs) and operating NETQUANT, a fully automatic software option for quantification of NETs in immunofluorescence images.

Neutrophil extracellular traps (NETs) are web-like antimicrobial structures consisting of DNA and granule derived antimicrobial proteins. ...

Automated Behavioral Analysis of Large C. elegans Populations Using a Wide Field-of-view Tracking Platform

Caenorhabditis elegans is a well-established animal model in biomedical research, widely employed in functional genomics and ageing studies. To assess the health and fitness of the animals under study, one typically relies on motility readouts, such as the measurement of the number of body bends or the speed of movement. These measurements usually involve manual counting, making it challenging to obtain good statistical significance, as time and labor constraints often limit the number of animals in each experiment to 25 or less. Since high statistical power is necessary to obtain reproducible results and limit false positive and negative results when weak phenotypic effects are investigated, efforts have recently been made to develop automated protocols focused on increasing the sensitivity of motility detection and multi-parametric behavioral profiling. In order to extend the limit of detection to the level needed to capture the small phenotypic changes that are often crucial in genetic studies and drug discovery, we describe here a technological development that enables the study of up to 5,000 individual animals simultaneously, increasing the statistical power of the measurements by about 1,000-fold compared to manual assays and about 100-fold compared to other available automated methods.

Automated Behavioral Analysis of Large C. elegans Populations Using a Wide Field-of-view Tracking Platform

We describe protocols for using the wide field-of-view nematode tracking platform (WF-NTP), which enables high-throughput phenotypic characterization of large populations of Caenorhabditis elegans. These protocols can be used to characterize subtle behavioral changes in mutant strains or in response to pharmacological treatment in a highly scalable fashion.

Caenorhabditis elegans is a well-established animal model in biomedical research, widely employed in functional genomics and ageing studies. To assess the ...

Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy

Particle tracking on a video sequence and the posterior analysis of their trajectories is nowadays a common operation in many biological studies. Using the analysis of cell membrane receptor clusters as a model, we present a detailed protocol for this image analysis task using Fiji (ImageJ) and Matlab routines to: 1) define regions of interest and design masks adapted to these regions; 2) track the particles in fluorescence microscopy videos; 3) analyze the diffusion and intensity characteristics of selected tracks. The quantitative analysis of the diffusion coefficients, types of motion, and cluster size obtained by fluorescence microscopy and image processing provides a valuable tool to objectively determine particle dynamics and the consequences of modifying environmental conditions. In this article we present detailed protocols for the analysis of these features. The method described here not only allows single-molecule tracking detection, but also automates the estimation of lateral diffusion parameters at the cell membrane, classifies the type of trajectory and allows complete analysis thus overcoming the difficulties in quantifying spot size over its entire trajectory at the cell membrane.

Here, we present a protocol for single particle tracking image analysis that allows quantitative evaluation of diffusion coefficients, types of motion and cluster sizes of single particles detected by fluorescence microscopy.

Particle tracking on a video sequence and the posterior analysis of their trajectories is nowadays a common operation in many biological studies. Using ...

Analysis of Interactions between Endobiotics and Human Gut Microbiota Using In Vitro Bath Fermentation Systems

Human intestinal microorganisms have recently become an important target of research in promoting human health and preventing diseases. Consequently, investigations of interactions between endobiotics (e.g., drugs and prebiotics) and gut microbiota have become an important research topic. However, in vivo experiments with human volunteers are not ideal for such studies due to bioethics and economic constraints. As a result, animal models have been used to evaluate these interactions in vivo. Nevertheless, animal model studies are still limited by bioethics considerations, in addition to differing compositions and diversities of microbiota in animals vs. humans. An alternative research strategy is the use of batch fermentation experiments that allow evaluation of the interactions between endobiotics and gut microbiota in vitro. To evaluate this strategy, bifidobacterial (Bif) exopolysaccharides (EPS) were used as a representative xenobiotic. Then, the interactions between Bif EPS and human gut microbiota were investigated using several methods such as thin-layer chromatography (TLC), bacterial community compositional analysis with 16S rRNA gene high-throughput sequencing, and gas chromatography of short-chain fatty acids (SCFAs). Presented here is a protocol to investigate the interactions between endobiotics and human gut microbiota using in vitro batch fermentation systems. Importantly, this protocol can also be modified to investigate general interactions between other endobiotics and gut microbiota.

Described here is a protocol to investigate the interactions between endobiotics and human gut microbiota using in vitro batch fermentation systems.

Human intestinal microorganisms have recently become an important target of research in promoting human health and preventing diseases. Consequently, ...

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

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Automated Image-Based Quantification of Neutrophil Extracellular Traps Using NETQUANT

Automated Behavioral Analysis of Large C. elegans Populations Using a Wide Field-of-view Tracking Platform

Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy

Analysis of Interactions between Endobiotics and Human Gut Microbiota Using In Vitro Bath Fermentation Systems