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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 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.
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 β-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.
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
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.
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/SQSTM...
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...
The authors have no conflicts of interest to disclose.
The Operetta robotized confocal microscope was financed by Fondo de Equipamiento Mediano (FONDEQUIP) N° EQM220072 grant. C.L. was supported by Vicerrectoria de Investigación y Doctorados (VRID), Universidad San Sebastian PhD scholarship. C.S. was supported by the Agencia Nacional de Investigación y Desarrollo (ANID) scholarship. D.T. and J.C. were supported by the Fondo Nacional de Desarrollo Científico y Tecnológico (FONDECYT) N°1221374 grant.
Name | Company | Catalog Number | Comments |
35 mm glass-bottom dishes | MatTek | P35G-1.5-14-C | |
Bafilomycin A1 | Tocris | 1334 | 200 nM |
BODIPY 493/503 | Invitrogen | D3922 | 0.5 mM |
CaCl2 | Merck | 102378 | 0.1 mM |
ComDet V Plugin | ImageJ | ImageJ FIJI | |
DAPI | Invitrogen | D1306 | 125 mg/mL |
Dulbecco’s Modified Eagle’s Medium (DMEM) | Gibco | 12800017 | |
ES-qualified HEPES buffer | Cytiva HyClone AdvanceSTEM | SH3085101 | 10 mM |
Etomoxir | SigmaAldrich | E1905 | 100 mM |
Fetal Bovine Serum | Cytiva HyClone AdvanceSTEM | SH3039603 | 10% v/v |
Forma Series II Water-Jacketed CO2 Incubator | Thermo Scientific | 3111 | 37 °C, 5% CO2 |
Harmony Phenologic software | Revvity | image analysis software | |
HeLa cells | ATCC | CCL-2 | Maintain cells at a low passage number, ideally between 8 and 10, to ensure optimal cellular characteristics. |
HEPES | Merck | 110110 | 10 mM |
High-speed clinical centrifuge | DLAB | DM0412 | |
Immersion Oil | Leica | 11513859 | |
MgCl2 | Merck | 814733 | 1 mM |
Operetta CLS Live spinning-disk microscope | Revvity | HH16000020 | |
Optical bottom 96-well plates | Thermo Scientific | 165305 | |
Paraformaldehyde | Electron Microscopy Sciences | 157-8 | 4%v/v |
penicillin/streptomycin/Amphotericin B | Biological Industries | 030331b | (1000 µ/mL, 100 mg/mL, 100 mg/mL) |
Phosphate-buffered saline (PBS) | Sartorius | 020235A | 1x |
Red-phenol free DMEM | Gibco | 31053028 | |
T863 | Merck | SML0539 | 50 mM |
TCS SP8 Leica confocal microscope | Leica Microsystems | ||
TransIT-LT1 Transfection Reagent | Mirus | MIR 2304 | |
Triton X-100 | Merck | T9284 | 0.20% |
Trypsin/EDTA | Gibco | 252000056 | 0.25% v/v |
UNO-TEMP controller | Okolab | OK-H401-T-CONTROLLER | 37 °C |
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