Name: Exploring the Regulation of Lipid Droplet Catabolism through Lipophagy (Video) | JoVE Journal
Uploaded: 2025-01-31T12:40:00.000+00:00
Duration: 7 min 20 s
Description: Explore lipophagy and lipid droplet catabolism in cancer cells using BODIPY-based fluorescent imaging and advanced microscopy. This method reveals how autophagy receptor mutations affect lipid droplet dynamics and cancer metabolism.

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The Operetta robotized confocal microscope was financed by Fondo de Equipamiento Mediano (FONDEQUIP) N&#176; EQM220072 grant. C.L. was supported by Vicerrectoria de Investigaci&#243;n y Doctorados (VRID), Universidad San Sebastian PhD scholarship. C.S. was supported by the Agencia Nacional de Investigaci&#243;n y Desarrollo (ANID) scholarship. D.T. and J.C. were supported by the Fondo Nacional de Desarrollo Cient&#237;fico y Tecnol&#243;gico (FONDECYT) N&#176;1221374 grant.

This study was conducted using epithelial adenocarcinoma HeLa cells (CCL2, ATCC). The protocol focuses on studying lipid droplets (LDs) during the induction of lipophagy in live cells to quantify the time course of LD number variation and LD-autophagosome interactions in cells expressing the wild-type (p62/SQSTM1-S182S) and two site-specific mutants of the autophagy receptor p62/SQSTM116. Expression of a phospho-defective form (p62/SQSTM1-S182A) increases the number of LDs, while expression of a phospho-mimicking form (p62/SQSTM1-S182E) reduces the number of LDs16. First, a method for analyzing LDs in live cells using confocal microscopy is described. Then, the protocol for unbiased, fully automated image acquisition and analysis is explained using a robotized confocal microscope. The details of the reagents and equipment used in this study are provided in the Table of Materials.
1. Confocal live cell imaging
<ol>
	<li>Cell culture
	<ol>
		<li>Culture the cells at 37 &#176;C and 5% CO2 in Dulbecco&#39;s Modified Eagle&#39;s Medium (DMEM), supplemented with 10% v/v fetal bovine serum (FBS), 1,000 U/mL penicillin, 100 &#181;g/mL streptomycin, and Amphotericin B. Maintain the cells until they reach no more than 80% confluency.</li>
		<li>Wash the cells with 1 mL of 1x phosphate-buffered saline, collect using 0.25% v/v trypsin/EDTA, and centrifuge at 100 x g for 5 min at room temperature.</li>
		<li>Discard the supernatant using a pipette.</li>
		<li>Resuspend the cells in DMEM culture medium with 10% v/v FBS and seed (0.05 x 106 cells/well) into 35 mm glass-bottom dishes.</li>
		<li>Transfect the cells using TransIT-LT1 transfection or Lipofectamine 2000 reagent. Use 1 &#181;g plasmid DNA of each p62/SQSTM1-mcherry phospho-variants and wild-type p62/SQSTM1-mcherry16.</li>
		<li>Incubate the cells at 37 &#176;C and 5% CO2 for 48 h before proceeding with subsequent treatments. 
		NOTE: At 16-24 h after transfection, p62/SQSTM1 forms large condensates rather than discrete p62/SQSTM1 autophagosomes. These large condensates largely dissipate by the 48-h mark, allowing for more precise observation of autophagosomes.</li>
	</ol>
	</li>
	<li>Organelle labeling
	<ol>
		<li>Wash the cells in a glass-bottom dish twice with 1x PBS (preheated to 37 &#176;C).</li>
		<li>Incubate the cells with BODIPY 493/503 diluted to 0.5 &#181;M in DMEM medium supplemented with 10 mM HEPES and keep for 30 min at 37 &#176;C, 5% CO2. BODIPY 405 and BODIYPY 633 can also be used with the same results.</li>
		<li>Check that expression of the p62/SQSTM1 variants induces changes in LDs number and total fluorescence intensity.</li>
		<li>Wash the cells with 1x PBS (room temperature) and maintain them in red-phenol-free DMEM supplemented with 10 mM ES-qualified HEPES buffer for live-cell imaging. 
		NOTE: BODIPY 493/503 emits bright green fluorescence, making it convenient for double fluorescence labeling. However, under certain conditions, such as repeated excitation, it can emit red fluorescence, potentially leading to erroneous interpretations (false colocalization) when combined with red markers. For more information on 550 nm excitation fluorophores, refer to the article by Ohsaki et al.41.</li>
	</ol>
	</li>
	<li>Confocal microscopy image acquisition in live cell imaging 
	NOTE: Images of LDs are acquired using a confocal microscope with a 63x oil immersion objective (N.A. 1.4). A compatible image acquisition software was used for capturing the images. Temperature is maintained at 37 &#176;C using automated temperature control system. 
	NOTE: Images of lipid droplets (LDs) were acquired using a confocal microscope with a 63x oil immersion objective (N.A. 1.4). Compatible image acquisition software was used to capture the images. The temperature was maintained at 37 &#176;C using an automated temperature control system.
	<ol>
		<li>Adjust the multiline argon gas laser to 10% working power, with the 488 nm laser line at 1%-2% working potency, resulting in an overall laser power of 0.1%-0.2%.</li>
		<li>To minimize cell damage and probe photobleaching, adjust the 568 nm laser to 3%-5% potency.</li>
		<li>Adjust the image acquisition settings in the software to a resolution of 1024 x 1024 pixels. The system uses a hybrid detector operating at 600 Hz, with an average of 2.</li>
		<li>Adjust the spectral ranges between 478-494 nm (green emission for LDs) and 600-625 nm wavelength (red emission for mCherry).</li>
		<li>Adjust the pinhole size to 1 Airy Unit (AU) for 488 nm wavelength.</li>
		<li>Incubate the cells with 8-Br-cAMP (100 mM) to activate PKA kinase and to increase LDs degradation16.</li>
		<li>Capture fluorescent images at 5 min intervals, 37 &#176;C for 60 min.</li>
	</ol>
	</li>
	<li>Image analysis
	<ol>
		<li>Open a stack file containing individual live image sequences using ImageJ. Use ROI manager for manual cell segmentation. Save regions for the next step.</li>
		<li>Open the stack file and recall saved regions for each image. Adjust LDs channel threshold.</li>
		<li>Measure the total fluorescence intensity by selecting multi-measure on the ROI manager. 
		NOTE: During autophagy, the total fluorescence intensity and the number of LDs can change significantly. Additionally, interactions between LDs and lysosomes increase during autophagy induction.</li>
	</ol>
	</li>
	<li>Contacts and colocalization analysis
	<ol>
		<li>Perform a multispectral capture at 1 s intervals for 5 min at 37 &#176;C.</li>
		<li>Set the multiline argon gas laser to 10% potency, with the 488 nm laser line at 0.1%-0.5% working power to minimize cell damage and probe photobleaching. Adjust 568 nm laser to 3%-5% potency.</li>
		<li>Click on ComDet V: Plugins &#60; ComDet V 0.5.3 &#60; Detect Particles. Define the minimum distance that points must have to be considered colocalized.</li>
		<li>Choose the parameters that define the particle, such as its size (mm or pixels unit) and threshold, for both channels independently. A summary will be provided with the number of particles in each channel for each time and the corresponding colocalization percentage, which can be exported to Excel (see Supplementary File 1). 
		NOTE: ComDet V plugin is recommended for organelles with an oval shape, such as lipid droplets, endosomes, peroxisomes, or lysosomes. It is not recommended for colocalization with organelles like mitochondria. The &#34;ComDet V&#34; plugin (ImageJ FIJI distribution) allows temporal colocalization to be analyzed through particle analysis. To install it in Fiji, copy the URL http://sites.imagej.net/Ekatrukha/, go to Help, Update, select&#160;Manage update sites - Add update site and paste the URL. Apply the changes; it is recommended to use two channels. If there are more channels, separate them and rejoin using Merge.</li>
	</ol>
	</li>
	<li>Dynamic movement of LDs
	<ol>
		<li>Define regions of interest (ROIs) for beads and the cells and save them. Go to plugins and select Tracking and TrackMate.</li>
		<li>Open TrackMate. The plugin&#39;s first window provides information about the time intervals at which the images are captured and the image resolution. To confirm time intervals without making changes, click on NEXT.</li>
		<li>Select LoG detector and then NEXT. In the LoG Detector Configuration, filter particles according to their diameter, from 0.8-1.0 microns, and set an appropriate threshold. Perform a Preview to confirm the proper parameters, and click on NEXT.</li>
		<li>Set the initial Threshold, a quality threshold that limits the number of spots to be analyzed. This is required when following many spots on the plane, which is challenging. Do not use the filter; press NEXT without changing the parameters.</li>
		<li>Select a View: Choose HyperStack Displayer and click on NEXT.</li>
		<li>Select Linear motion LAP tracker for particles with constant speed in the plane.</li>
		<li>Define the maximum distance between two spots when starting a new track. Depending on the images, it is suggested to be between 0.5 -1 micron.</li>
		<li>Set the maximum distance from a predicted position for candidate spots for analysis.</li>
		<li>Set Max Frame Gap as the maximum time to follow a spot that may disappear from the focal plane. We recommend two time points.</li>
		<li>Select Display Options. The summary table, including minimum speed, maximum speed, mean speed, median speed, and track displacement, is exported to Excel (see Supplementary File 2).</li>
		<li>Select Plot Features. To represent the results visually, go to Tracks. Select the measure to represent (velocity and displacement) on the X-axis, and spots are placed on the Y-axis.</li>
		<li>Select an Action to save videos showing the movement of spots. This allows visualization of spots that show significant changes. 
		NOTE: Enhancing autophagy optimizes cellular flux and reduces LD number, highlighting its crucial role in metabolic regulation and cellular homeostasis16. The plugins mentioned are available for ImageJ software (FIJI distribution). Equivalent software can also be used. For all fluorescence intensity measurements, the integrated fluorescence density (&#34;RawIntDen&#34; in ImageJ) was used, which accounts for the total fluorescence across each image pixel, taking the area into consideration.</li>
	</ol>
	</li>
</ol>
2. Fully automated confocal image acquisition and image analysis in fixed cells
NOTE: This method allows for assessing several conditions and developing triplicate measurements for each condition, improving the confidence in average measured values and enabling the determination of standard deviation or standard error for statistical differentiation between experiments. The workflow of the method is depicted in the flowchart shown in Figure 1.
<ol>
	<li>Cell Culture
	<ol>
		<li>Culture the cells at 37 &#176;C and 5% CO2 in Dulbecco&#39;s Modified Eagle&#39;s Medium (DMEM), supplemented with 10% v/v fetal bovine serum (FBS), 1,000 U/mL penicillin, 100 &#181;g/mL streptomycin, and Amphotericin B.</li>
		<li>Wash the cells with 1 mL of 1x phosphate-buffered saline (PBS), harvested using 0.25% v/v trypsin/EDTA.</li>
		<li>Collect the cells in a 15 mL centrifuge tube.</li>
		<li>Centrifuge the cells at room temperature at 100 x g for 5 min.</li>
		<li>Aspirate the supernatant carefully with a pipette and keep the cell pellet.</li>
		<li>Resuspend the cell pellet in DMEM culture medium with 10% v/v FBS and seed into optical bottom 96-well plates (0.05 x 106 cells/well). 
		&#8203;NOTE: Cells are incubated at 37 &#176;C and 5% CO2 for 24 h before proceeding with subsequent treatments.</li>
	</ol>
	</li>
	<li>Autophagy induction by serum starvation
	<ol>
		<li>Remove the cell culture medium and incubate the cells in serum-deprived DMEM medium to remove serum and induce autophagy for 6 h.</li>
		<li>Use bafilomycin A1 200 nM to inhibit LD lipophagy. Diacylglycerol acyltransferase-1 (DGAT1) inhibitor T863 (50 &#181;M) inhibits LD biogenesis. Use the Carnitine Palmitoyltransferase I (CPT1) inhibitor Etomoxir (100 &#181;M) to inhibit FFAs conversion to acyl-carnitine. Additionally, lipolysis can be inhibited by using ATGLstatin (10 &#181;M).</li>
	</ol>
	</li>
	<li>Cell fixation
	<ol>
		<li>Wash the cells three times with ice-cold PBS-CM (200 &#181;L/well).</li>
		<li>Fix the cells with 4% paraformaldehyde diluted in PBS supplemented with 0.1 mM CaCl2 and 1 mM MgCl2 (PBS-CM) at room temperature for 15 min16.</li>
		<li>Wash the cells three times with PBS-CM (200 &#181;L/well).</li>
		<li>Permeabilize with 0.2% Triton X-100 in PBS-CM at room temperature for 15 min.</li>
		<li>Wash the cells three times with PBS-CM.</li>
	</ol>
	</li>
	<li>Organelle labeling
	<ol>
		<li>Incubate the cells with BODIPY 493/503 diluted at 0.5 &#181;M and DAPI (125 mg/mL) in PBS-CM for 30 min at 37 &#176;C.</li>
		<li>Wash three times with PBS-CM.</li>
		<li>Keep the fixed and stained cells in 200 &#181;L of PBS per well until image acquisition. 
		&#8203;NOTE: The 96-well plate can be stored at 4 &#176;C for several weeks, protected from light and dehydration. Mounting with commonly used antifade mounting media is not required.</li>
	</ol>
	</li>
	<li>Automated confocal microscopy (fixed cells)
	<ol>
		<li>Perform nuclei staining for automated image segmentation16. 
		&#8203;NOTE: Image acquisition uses a spinning-disk microscope with a 40x (N.A 1.1) water immersion objective. DAPI acquisition is performed using 305 nm LED illumination (Ex: 355-385 nm; Em: 430-500 nm), and BODIPY 493/503 acquisition is performed using 488 nm LED illumination (Ex: 460-490 nm; Em: 550-550 nm).</li>
	</ol>
	</li>
	<li>Image analysis 
	NOTE: Image analysis16 is performed using compatible software with a dedicated LD analysis module. The software used in this study (see Table of Materials) has a ready-made solution (RMS Lipid Droplet Analysis) algorithm combination for image segmentation and quantification.
	<ol>
		<li>Identify the nuclei by selecting the DAPI channel and adjust the threshold based on signal and background.</li>
		<li>Identify the cytoplasm. Select the appropriate method based on the LDs fluorescence channel.</li>
		<li>Identify spots by adjusting parameters: Radius (1-1.5 &#181;m), Contrast (0.2-0.25), Uncorrected Spot to Region Intensity (0.4-0.6), Distance (0.3-0.4 &#181;m), Spot Peak Radius (0.2-0.25 &#181;m).</li>
		<li>Calculate morphology properties: Select population LDs, Region Spot, and standard method, then select Area.</li>
		<li>Calculate intensity properties: Select LDs channel, population LDs, Region Spot, and standard method.</li>
		<li>Calculate properties: Select the Population of all cells, select the method by related population LDs, and then select the number of LDs, area, and intensity (mean and sum; the sum is integrated fluorescence density/intensity). This will yield LD measurements related to the cell.</li>
		<li>Define results by selecting Standard Output, Object Count, number of LDs per cell expressed as mean &#177; SD, Average LDs per area, Total LDs per area, Fractions of LD per area, and Total LDs intensity.</li>
	</ol>
	</li>
</ol>

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<li>Martin-Perez, M., Urdiroz-Urricelqui, U., Bigas, C., Benitah, S. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+role+of+lipids+in+cancer+progression+and+metastasis%5BTitle%5D)+AND+%22Cell+Metab%22%5BJournal%5D)">The role of lipids in cancer progression and metastasis.</a> Cell Metab. 34 (11), 1675-1699 (2022).</li>
<li>Shiratori, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Glycolytic+suppression+dramatically+changes+the+intracellular+metabolic+profile+of+multiple+cancer+cell+lines+in+a+mitochondrial+metabolism-dependent+manner%5BTitle%5D)+AND+%22Sci+Rep%22%5BJournal%5D)">Glycolytic suppression dramatically changes the intracellular metabolic profile of multiple cancer cell lines in a mitochondrial metabolism-dependent manner.</a> Sci Rep. 9 (1), 18699(2019).</li>
<li>Danielli, M., Perne, L., Jarc Jovicic, E., Petan, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Lipid+droplets+and+polyunsaturated+fatty+acid+trafficking%3A+Balancing+life+and+death%5BTitle%5D)+AND+%22Front+Cell+Dev+Biol%22%5BJournal%5D)">Lipid droplets and polyunsaturated fatty acid trafficking: Balancing life and death.</a> Front Cell Dev Biol. 11, 1104725(2023).</li>
<li>Butler, L. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Lipids+and+cancer%3A+Emerging+roles+in+pathogenesis%2C+diagnosis+and+therapeutic+intervention%5BTitle%5D)+AND+%22Adv+Drug+Deliv+Rev%22%5BJournal%5D)">Lipids and cancer: Emerging roles in pathogenesis, diagnosis and therapeutic intervention.</a> Adv Drug Deliv Rev. 159, 245-293 (2020).</li>
<li>Nagarajan, S. R., Butler, L. M., Hoy, A. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+diversity+and+breadth+of+cancer+cell+fatty+acid+metabolism%5BTitle%5D)+AND+%22Cancer+Metab%22%5BJournal%5D)">The diversity and breadth of cancer cell fatty acid metabolism.</a> Cancer Metab. 9 (1), 2(2021).</li>
<li>Vasseur, S., Guillaumond, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Lipids+in+cancer%3A+A+global+view+of+the+contribution+of+lipid+pathways+to+metastatic+formation+and+treatment+resistance%5BTitle%5D)+AND+%22Oncogenesis%22%5BJournal%5D)">Lipids in cancer: A global view of the contribution of lipid pathways to metastatic formation and treatment resistance.</a> Oncogenesis. 11 (1), 46(2022).</li>
<li>Yang, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+role+of+lipid+metabolic+reprogramming+in+tumor+microenvironment%5BTitle%5D)+AND+%22Theranostics%22%5BJournal%5D)">The role of lipid metabolic reprogramming in tumor microenvironment.</a> Theranostics. 13 (6), 1774-1808 (2023).</li>
<li>Tan, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Metabolic+reprogramming+from+glycolysis+to+fatty+acid+uptake+and+beta-oxidation+in+platinum-resistant+cancer+cells%5BTitle%5D)+AND+%22Nat+Commun%22%5BJournal%5D)">Metabolic reprogramming from glycolysis to fatty acid uptake and beta-oxidation in platinum-resistant cancer cells.</a> Nat Commun. 13 (1), 4554(2022).</li>
<li>Safi, R., Menendez, P., Pol, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Lipid+droplets+provide+metabolic+flexibility+for+cancer+progression%5BTitle%5D)+AND+%22FEBS+Lett%22%5BJournal%5D)">Lipid droplets provide metabolic flexibility for cancer progression.</a> FEBS Lett. 598 (10), 1301-1327 (2024).</li>
<li>Iwahashi, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Lipid+droplet+accumulation+independently+predicts+poor+clinical+prognosis+in+high-grade+serous+ovarian+carcinoma%5BTitle%5D)+AND+%22Cancers+%28Basel%29%22%5BJournal%5D)">Lipid droplet accumulation independently predicts poor clinical prognosis in high-grade serous ovarian carcinoma.</a> Cancers (Basel). 13 (20), 5251(2021).</li>
<li>Luo, W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Adding+fuel+to+the+fire%3A+The+lipid+droplet+and+its+associated+proteins+in+cancer+progression%5BTitle%5D)+AND+%22Int+J+Biol+Sci%22%5BJournal%5D)">Adding fuel to the fire: The lipid droplet and its associated proteins in cancer progression.</a> Int J Biol Sci. 18 (16), 6020-6034 (2022).</li>
<li>Ohsaki, Y., Shinohara, Y., Suzuki, M., Fujimoto, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+pitfall+in+using+bodipy+dyes+to+label+lipid+droplets+for+fluorescence+microscopy%5BTitle%5D)+AND+%22Histochem+Cell+Biol%22%5BJournal%5D)">A pitfall in using bodipy dyes to label lipid droplets for fluorescence microscopy.</a> Histochem Cell Biol. 133 (4), 477-480 (2010).</li>
<li>Rambold, A. S., Cohen, S., Lippincott-Schwartz, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Fatty+acid+trafficking+in+starved+cells%3A+Regulation+by+lipid+droplet+lipolysis%2C+autophagy%2C+and+mitochondrial+fusion+dynamics%5BTitle%5D)+AND+%22Dev+Cell%22%5BJournal%5D)">Fatty acid trafficking in starved cells: Regulation by lipid droplet lipolysis, autophagy, and mitochondrial fusion dynamics.</a> Dev Cell. 32 (6), 678-692 (2015).</li>
<li>Nguyen, T. B., Olzmann, J. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Lipid+droplets+and+lipotoxicity+during+autophagy%5BTitle%5D)+AND+%22Autophagy%22%5BJournal%5D)">Lipid droplets and lipotoxicity during autophagy.</a> Autophagy. 13 (11), 2002-2003 (2017).</li>
<li>Giamogante, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+SPLICS+reporter+reveals-synuclein+regulation+of+lysosome-mitochondria+contacts+which+affects+TFEB+nuclear+translocation%5BTitle%5D)+AND+%22Nat+Commun%22%5BJournal%5D)">A SPLICS reporter reveals-synuclein regulation of lysosome-mitochondria contacts which affects TFEB nuclear translocation.</a> Nat Commun. 15 (1), 1516(2024).</li>
<li>Greenspan, P., Mayer, E. P., Fowler, S. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Nile+red%3A+A+selective+fluorescent+stain+for+intracellular+lipid+droplets%5BTitle%5D)+AND+%22J+Cell+Biol%22%5BJournal%5D)">Nile red: A selective fluorescent stain for intracellular lipid droplets.</a> J Cell Biol. 100 (3), 965-973 (1985).</li>
<li>Nguyen, T. B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((DGAT1-dependent+lipid+droplet+biogenesis+protects+mitochondrial+function+during+starvation-induced+autophagy%5BTitle%5D)+AND+%22Dev+Cell%22%5BJournal%5D)">DGAT1-dependent lipid droplet biogenesis protects mitochondrial function during starvation-induced autophagy.</a> Dev Cell. 42 (1), 9-21.e5 (2017).</li>
<li>Murugan, S., Amaravadi, R. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Methods+for+studying+autophagy+within+the+tumor+microenvironment%5BTitle%5D)+AND+%22Adv+Exp+Med+Biol%22%5BJournal%5D)">Methods for studying autophagy within the tumor microenvironment.</a> Adv Exp Med Biol. 899, 145-166 (2016).</li>
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</ol>

The authors have no conflicts of interest to disclose.

Quantitative imaging techniques, such as confocal microscopy and image analysis protocols, have provided valuable insights into the dynamics of LDs during lipophagy16,42,43. These technologies enable real-time visualization and quantification of LDs, allowing for the analysis of their number, size, and interactions with other organelles16. However, one of the most critical steps in this protocol is the co...

Autophagy is a general term used to describe catabolic processes in which the cell transports its components to the lysosome for degradation. To date, three types of autophagy have been identified: microautophagy, macroautophagy, and chaperone-mediated autophagy1,2,3. Macroautophagy, hereafter referred to as autophagy, is an essential pathway for regulating cellular homeostasis. Disruption of this balance can lead to the development of pathological conditions4.
Autophagy is a complex process that involves multiple steps. The first step is autophagy induction, triggered by various stimuli such as the withdrawal of growth factors (insulin and insulin-like growth factors), pathogenic infections, reduced cellular energy levels (ATP), extracellular or intracellular stress (e.g., hypoxia, endoplasmic reticulum (ER) stress, oxidative stress), and nutrient deficiency (amino acids, glucose)5. The second step involves the formation of the phagophore, where membrane isolation is initiated from the ER, plasma membrane, and mitochondria. De novo formation involves conserved machinery of cytosolic proteins that are sequentially recruited6, such as the Ser/Thr kinase Unc-51-like kinase-1 complex (ULK1: ATG1 in yeast), Beclin-1, and VPS347. After the formation of the ULK1 complex, the class III phosphatidylinositol 3-kinase (PI3K) complex I is recruited to the isolated membrane (IM), which functions in the initial sequestering of cargos8. Furthermore, the ULK1 complex has the ability to recruit ATG9 to the isolation membrane (IM), an essential step since ATG9 vesicles are recognized as membrane carriers that facilitate IM expansion9.
Two ubiquitin-like (Ubl) conjugation systems are critical for the expansion process: the microtubule-associated protein 1 light chain 3 (LC3-I) system and the ATG12 system10. Prior to conjugation, the LC3 precursor undergoes cleavage. The cytosolic LC3-I is then conjugated to phosphatidylethanolamine (PE) to produce the membrane-associated LC3-PE (LC3-II), which facilitates autophagosome formation11. The cargo must be internalized into the forming double-membrane autophagosome during this process. Autophagy can internalize random targets for degradation or capture selective cargos through specific autophagy receptors such as p62/SQSTM112. The last step is the fusion of the formed autophagosome with lysosomes, leading to autolysosome formation. Although the precise mechanism for autolysosome formation remains elusive, membrane-tethering complexes, the RAS-related GTP-binding protein, and the soluble-N-ethylmaleimide-sensitive factor attachment protein receptors (SNARE) proteins are involved in this fusion process13. Furthermore, the microtubule cytoskeleton system is essential for trafficking mature autophagosomes and lysosomes from random initiation sites toward the perinuclear area for autolysosome formation14,15,16. In the autolysosome, the randomly or selectively sequestered cargos are degraded proteolytically by lysosomal proteases17.
The autophagy process is conserved across all eukaryotic organisms and is crucial in regulating intracellular conditions through cytoplasmic turnover. It removes misfolded or aggregated proteins, eliminates intracellular pathogens, and clears damaged organelles. Several organelles, including the endoplasmic reticulum, mitochondria, peroxisomes, lysosomes, nucleus, and LDs, have been reported as targets of autophagy16,18,19,20,21,22. LDs originate from the ER and are essential storage organelles central to lipid and energy homeostasis. Their distinct architecture consists of a hydrophobic core of neutral lipids enclosed by a phospholipid monolayer embedded with specific proteins. These droplets can interact with various cellular organelles through membrane contact sites23. Additionally, autophagy helps recycle primary resources to maintain optimal cellular conditions. For example, the degradation of LDs can lead to ATP production through fatty acid &#946;-oxidation24.
Autophagy is associated with various diseases, including neurodegenerative diseases, metabolic disorders, and cancer17. Autophagy can promote or inhibit tumor growth in cancer, depending on the context25,26. For instance, Beclin 1 +/- mice exhibit a high incidence of spontaneous lymphomas and carcinomas in organs such as the lung, liver, and mammary tissue. Conversely, the loss of the autophagy-related gene Atg7 in intestinal epithelial cells attenuates tumor growth driven by the loss of the primary tumor suppressor in colorectal cancer, adenomatous polyposis coli (APC)27,28. Thus, the loss of autophagy-related genes can have opposing effects on tumor growth.
Cancer cells must produce energy to sustain their growth, division, and survival29. They have high avidity for lipids, which are used for the biosynthesis of structural components and energy production30. Cancer cells can adapt their metabolism to environmental conditions. For example, when glycolysis is suppressed in cervical cancer-derived HeLa cells, oxidative phosphorylation is increased to obtain the necessary ATP for survival31. Lipids in a cell do not exist as non-esterified FFAs due to their potential cytotoxicity at high concentrations. Instead, cells store FFAs and cholesterol as neutral, inert biomolecules such as sterol esters and triglycerides in LDs32. Consequently, lipophagy can contribute to cancer metabolism by supplying FFAs to produce energy, an emerging field in cancer research. However, the pathways that upregulate mitochondrial FA oxidation in cancer cells remain poorly understood. FFAs uptake and accumulation have been shown to enhance the aggressiveness of different cancer types33,34,35. Lipid metabolism reprogramming is a hallmark of cancer metabolic reprogramming, playing a pivotal role as an adaptive response to manage adverse physiological scenarios in the tumor microenvironment36,37. Indeed, LDs accumulation has been observed in many human cancers, including lung, breast, and prostate cancers, and is associated with aggressiveness and poor clinical prognosis38,39,40.
Given the relevance of autophagy and LDs in cancer metabolism and the poorly understood mechanisms, it is essential to establish protocols for studying their contribution to cancer development. This study describes a protocol to evaluate lipophagy through confocal imaging acquisition and quantitative imaging analysis protocols to investigate lipid metabolic changes in cancer cells.

<table><tbody><tr><td>35 mm glass-bottom dishes</td><td>MatTek</td><td>P35G-1.5-14-C</td><td></td></tr><tr><td>Bafilomycin A1</td><td>Tocris</td><td>1334</td><td>200 nM</td></tr><tr><td>BODIPY 493/503</td><td>Invitrogen</td><td>D3922</td><td>0.5 mM</td></tr><tr><td>CaCl&lt;sub&gt;2&lt;/sub&gt;</td><td>Merck</td><td>102378</td><td>0.1 mM</td></tr><tr><td>ComDet V Plugin</td><td>ImageJ</td><td>ImageJ FIJI</td><td></td></tr><tr><td>DAPI</td><td>Invitrogen</td><td>D1306</td><td>125 mg/mL</td></tr><tr><td>Dulbecco&amp;rsquo;s Modified Eagle&amp;rsquo;s Medium (DMEM)</td><td>Gibco</td><td>12800017</td><td></td></tr><tr><td>ES-qualified HEPES buffer</td><td>Cytiva HyClone AdvanceSTEM</td><td>SH3085101</td><td>10 mM</td></tr><tr><td>Etomoxir</td><td>SigmaAldrich</td><td>E1905</td><td>100 mM</td></tr><tr><td>Fetal Bovine Serum</td><td>Cytiva HyClone AdvanceSTEM</td><td>SH3039603</td><td>10% v/v</td></tr><tr><td>Forma Series II Water-Jacketed CO2 Incubator</td><td>Thermo Scientific</td><td>3111</td><td>37 &amp;deg;C, 5% CO&lt;sub&gt;2&lt;/sub&gt;</td></tr><tr><td>Harmony Phenologic software</td><td>Revvity</td><td></td><td>image analysis software</td></tr><tr><td>HeLa cells</td><td>ATCC</td><td>CCL-2</td><td>Maintain cells at a low passage number, ideally between 8 and 10, to ensure optimal cellular characteristics.</td></tr><tr><td>HEPES</td><td>Merck</td><td>110110</td><td>10 mM</td></tr><tr><td>High-speed clinical centrifuge</td><td>DLAB</td><td>DM0412</td><td></td></tr><tr><td>Immersion Oil</td><td>Leica</td><td>11513859</td><td></td></tr><tr><td>MgCl&lt;sub&gt;2&lt;/sub&gt;</td><td>Merck</td><td>814733</td><td>1 mM</td></tr><tr><td>Operetta CLS Live spinning-disk microscope</td><td>Revvity</td><td>HH16000020</td><td></td></tr><tr><td>Optical bottom 96-well plates</td><td>Thermo Scientific</td><td>165305</td><td></td></tr><tr><td>Paraformaldehyde</td><td>Electron Microscopy Sciences</td><td>157-8</td><td>4%v/v</td></tr><tr><td>penicillin/streptomycin/Amphotericin B</td><td>Biological Industries</td><td>030331b</td><td>(1000 &amp;micro;/mL, 100 mg/mL, 100 mg/mL)</td></tr><tr><td>Phosphate-buffered saline (PBS)</td><td>Sartorius</td><td>020235A</td><td>1x</td></tr><tr><td>Red-phenol free DMEM</td><td>Gibco</td><td>31053028</td><td></td></tr><tr><td>T863</td><td>Merck</td><td>SML0539</td><td>50 mM</td></tr><tr><td>TCS SP8 Leica confocal microscope</td><td>Leica Microsystems</td><td></td><td></td></tr><tr><td>TransIT-LT1 Transfection Reagent</td><td>Mirus</td><td>MIR 2304</td><td></td></tr><tr><td>Triton X-100</td><td>Merck</td><td>T9284</td><td>0.20%</td></tr><tr><td>Trypsin/EDTA</td><td>Gibco</td><td>252000056</td><td>0.25% v/v</td></tr><tr><td>UNO-TEMP controller</td><td>Okolab</td><td>OK-H401-T-CONTROLLER</td><td>37 &amp;deg;C</td></tr></tbody></table>

exploring the regulation of lipid droplet catabolism through lipophagy

Exploring the Regulation of Lipid Droplet Catabolism through Lipophagy

Macroautophagy, commonly referred to as autophagy, is a highly conserved cellular process responsible for the degradation of cellular components. This process is particularly prominent under conditions such as fasting, cellular stress, organelle damage, cellular damage, or aging of cellular components. During autophagy, a segment of the cytoplasm is enclosed within double-membrane vesicles known as autophagosomes, which then fuse with lysosomes. Following this fusion, the contents of autophagosomes undergo non-selective bulk degradation facilitated by lysosomes. However, autophagy also exhibits selective functionality, targeting specific organelles, including mitochondria, peroxisomes, lysosomes, nuclei, and lipid droplets (LDs). Lipid droplets are enclosed by a phospholipid monolayer that isolates neutral lipids from the cytoplasm, protecting cells from the harmful effects of excess sterols and free fatty acids (FFAs). Autophagy is implicated in various conditions, including neurodegenerative diseases, metabolic disorders, and cancer. Specifically, lipophagy -- the autophagy-dependent degradation of lipid droplets -- plays a crucial role in regulating intracellular FFA levels across different metabolic states. This regulation supports essential processes such as membrane synthesis, signaling molecule formation, and energy balance. Consequently, impaired lipophagy increases cellular vulnerability to death stimuli and contributes to the development of diseases such as cancer. Despite its significance, the precise mechanisms governing lipid droplet metabolism regulated by lipophagy in cancer cells remain poorly understood. This article aims to describe confocal imaging acquisition and quantitative imaging analysis protocols that enable the investigation of lipophagy associated with metabolic changes in cancer cells. The results obtained through these protocols may shed light on the intricate interplay between autophagy, lipid metabolism, and cancer progression. By elucidating these mechanisms, novel therapeutic targets may emerge for combating cancer and other metabolic-related diseases.

Confocal live cell imaging 
LDs are dynamic and transiently interact with p62/SQSTM1-positive autophagosomes. When lipophagy is induced, these interactions decrease the number of LDs and their total fluorescent intensity. This protocol used phospho-mutant versions of the autophagy receptor p62/SQSTM1 to examine these effects16.
The number and fluorescence intensity of LDs are regulated by lipophagy, dependent on the expression variants of p62/SQSTM1....

Lipophagy is a selective form of autophagy that involves the degradation of lipid droplets. Dysfunctions in this process are associated with cancer development. However, the precise mechanisms are not yet fully understood. This protocol describes quantitative imaging approaches to better understand the interplay between autophagy, lipid metabolism, and cancer progression.

Macroautophagy, commonly referred to as autophagy, is a highly conserved cellular process responsible for the degradation of cellular components. This ...

exploring-the-regulation-of-lipid-droplet-catabolism-through-lipophagy

Research

JoVE Journal

Cancer Research

Two techniques for isolating cellular lipid droplets from 1) yeast cells and 2) human placentas are presented. The centerpiece of both procedures is density gradient centrifugation, where the resulting floating layer containing the droplets can be readily visualized by eye, extracted, and quantified by Western Blot analysis for purity.

Isolation of Cellular Lipid Droplets: Two Purification Techniques Starting from Yeast Cells and Human Placentas

Lipid droplets are important organelles for the replication of several pathogens, including the Hepatitis C Virus (HCV). We describe a method to isolate lipid droplets for quantitative mass spectrometry of associated proteins; it can be used under a variety of conditions, such as virus infection, environmental stress, or drug treatment.

Lipid Droplet Isolation for Quantitative Mass Spectrometry Analysis

Here, we describe a protocol to obtain the lipid droplet index (LD index) to study the dynamics of triacylglycerols in cells cultured in high-throughput experiments. The LD index assay is an easy and reliable method that uses BODIPY 493/503. This assay does not need dispendious lipid extraction or microscopy analysis.

Lipid Index Determination by Liquid Fluorescence Recovery in the Fungal Pathogen Ustilago Maydis

This protocol establishes a novel method of lipid droplet isolation and purification from mouse livers, using a well-established endoplasmic reticulum isolation kit.

Rapid Lipid Droplet Isolation Protocol Using a Well-established Organelle Isolation Kit

Increasing evidence indicates that excessive infiltration of lipids inside skeletal muscle results in lipotoxicity and diabetes. Here, we present a complete protocol, including tissue processing, staining with Bodipy, image acquisition, and analysis, to quantify the size, density, and subcellular distribution of lipid droplets in a fiber-type specific manner.

Fiber Type and Subcellular-Specific Analysis of Lipid Droplet Content in Skeletal Muscle

Triglyceride lipolysis in adipocytes is an important metabolic process resulting in the liberation of free fatty acids and glycerol. Here, we provide a detailed protocol to measure basal and stimulated lipolysis in adipocytes and ex vivo adipose tissue from mice.

Measuring the Rate of Lipolysis in Ex Vivo Murine Adipose Tissue and Primary Preadipocytes Differentiated In Vitro

Herein, we demonstrate an optimized BODIPY 493/503 fluorescence-based protocol for lipid droplet characterization in liver tissue. Through the use of orthogonal projections and 3D reconstructions, the fluorophore allows for successful discrimination between microvesicular and macrovesicular steatosis and may represent a complementary approach to the classical histological protocols for hepatic steatosis assessment.

Analysis of Fluorescent-Stained Lipid Droplets with 3D Reconstruction for Hepatic Steatosis Assessment

The present protocol describes how to use oil red O to dye lipid droplets (LDs), calculate the size and number of LDs in a fatty acid-induced fatty hepatocyte model, and use BODIPY 493/503 to observe the process of small LDs fusing into large LDs by live cell imaging.

Evaluation of Lipid Droplet Size and Fusion in Bovine Hepatic Cells

This protocol enables reproducible visualization of lipid droplets and organelle dynamics during mitosis using synchronized hepatoma cells.

A Fluorescence-Based Method To Visualize Lipid Droplet And Organelle Dynamics During Mitosis in Hepatoma Cells

Here we present addressable droplet microarrays (ADMs), a droplet array based method able to determine absolute protein abundance in single cells. We demonstrate the capability of ADMs to characterize the heterogeneity in expression of the tumor suppressor protein p53 in a human cancer cell line.

Counting Proteins in Single Cells with Addressable Droplet Microarrays

Explore lipophagy and lipid droplet catabolism in cancer cells using BODIPY-based fluorescent imaging and advanced microscopy. This method reveals how autophagy receptor mutations affect lipid droplet dynamics and cancer metabolism.

Exploring the Regulation of Lipid Droplet Catabolism through Lipophagy

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