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

Summary

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.

Abstract

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.

Introduction

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 tri....

Protocol

1. Preparation of Solutions and Media

1. Prepare lipid staining solution.
   1. 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.
   2. Prepare working lipid staining solution by mixing 100 µL of the 1 mg/mL BODIPY 493/503 stock solution and 900 µL of anhydrous DMSO (final concentration 0.1 mg/mL).
   3. Aliquot the stock and working s....

Results

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.

Discussion

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.......

Materials

List of materials used in this article

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