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Autophagy Analysis Using Object Spot Counting下載
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July 11, 2017
Using Gen5™ to Analyze the Size and Number of Autophagosomes per Nuclei
Authors: Sarah Beckman, PhD, Principal Scientist, BioTek Instruments, Inc., Winooski, VT USA; Nadia A. Rana, PhD, and Wini Luty, Enzo Life Sciences, Inc., Farmingdale, NY USA
Autophagy is critical for the maintenance of cellular homeostasis. However, dysregulated autophagy can lead to death of healthy cells and survival of cancerous cells. Here we describe the use of CYTO-ID® Autophagy Detection Kit in combination with automated object-based spot counting to quantitatively assess the effects of starvation and rapamycin on cellular autophagy by determining the size and number of autophagosomes per cell.
A constant supply of nutrients is required during development to provide the energy necessary for growth, metabolism, and survival. Eukaryotic cells have evolved a variety of mechanisms to adjust their metabolic activities in response to changes in nutrient levels. Nutrient starvation, stress, or reduced availability of growth factors induces eukaryotic cells to adjust their metabolism in order to survive1. One of the key responses to such a stress is autophagy.
Autophagy or “self-eating” is a highly conserved process by which cells break down their intracellular components2. In a healthy cell under physiological conditions, autophagy is protective. In fact, autophagy plays a variety of important roles including maintenance of the amino acid pool during starvation, damaged protein and organelle turnover, prevention of neurodegeneration, tumor suppression, cellular differentiation, clearance of intracellular microbes, and regulation of innate and adaptive immunity3.
The first step of autophagy is formation of the phagophore, a cup shaped double membrane. The edges of this membrane elongate and engulf portions of the cytoplasm, including intracellular material such as damaged organelles and misfolded proteins4. The isolation membrane expands and its open ends fuse to form a doublemembrane structure called the autophagosome. Autophagosomes then fuse with lysosomes to form autolysosomes and the contents inside the autophagosome are degraded by lysosomal hydrolases. The intracellular material is then recycled back into the cytosol5.
Figure 1. Schematic depiction of autophagy.
One of the most well-known inducers of autophagy is starvation. Through autophagy, amino acids and other nutrients are recycled from long-lived proteins, organelles, and other components of the cytoplasm, providing an internal reserve of nutrients. Starvation rapidly induces autophagy, in part by inactivation of the mTOR (mammalian target of rapamycin) substrate S6K6. In a nutrient-rich environment, mTOR inhibitors such as rapamycin can induce autophagy.
Autophagy plays a role in both the pathogenesis and prevention of disease7. This is especially true in cancer, where elimination of damaged intracellular components through autophagy suppresses tissue injury and tumor initiation. However, in an established tumor, autophagy promotes cancer progression by providing substrates for metabolism, maintaining functional mitochondria, and fostering survival8.
Traditional methods of autophagy analysis include electron microscopy and western blot analysis of LC3-II. Electron microscopy is limited by the necessity of specialized expertise, and open to when identifying an autophagosome structure9. Furthermore, flow cytometry or western blot measurements of LC3-II do not always correlate with formation of autophagosomes and do not give per-cell numbers of autophagosomes10. In this application note, we describe the process of using CYTO-ID® Autophagy Detection dye combined with Gen5™ 3.03 software to analyze the effects of serum starvation and rapamycin on autophagosome number in HeLa cells. We perform analysis with Gen5 3.03 with object spot counting capability, which allows us to determine the number of autophagosomes per cell as well as their size.
Materials and Methods
HeLa cells were grown in Advanced Dulbecco’s Modified Eagle’s Medium (DMEM) (Gibco, Grand Island, NY) with 10% FBS (Gibco) and 1x PennStrep-Glutamine (Cellgro, Manassas, VA). Cells were seeded into black sided clear bottom 96-well microplates (Corning, Corning, NY) at 20,000 cells per well.
Cyto-ID® Autophagy Detection Kit
The CYTO-ID Autophagy Detection Kit (donated by Enzo Life Sciences, Farmingdale, NY) was used to assess autophagy levels in HeLa cells. The probe is a cationic amphiphilic tracer (CAT) dye that rapidly partitions into cells. The dye is taken up by passive diffusion across the plasma membrane bilayer, and includes titratable moieties specific for selectively staining autophagic vesicles. HeLa cells were grown in normal media, which was replaced with serum-free media containing 10 μM chloroquine for 2-6 hours to induce autophagy through serumstarvation. Alternatively, cells were treated with 0.1 nM – 10nM rapamycin with or without 10 μM chloroquine for 18 hours to induce autophagy through mTOR inhibition. Following treatment cells were washed 2x with 200 μL assay buffer (1x buffer + 5% FBS). Next, the assay buffer was replaced with 100 μL dual color detection solution (1 mL assay buffer + 1 uL hoechst and 2 μL CYTO-ID® Green Detection Reagent) for 30 minutes at 37 °C in the dark. Finally, the cells were washed with 2x 200 μL assay buffer and this was removed and the sample was imaged in 100 μL assay buffer directly following the wash.
Images were acquired using a 20x objective on the Lionheart™ FX (BioTek Instruments, Winooski, VT) configured with DAPI and GFP light cubes. The DAPI light cube is configured with a 377/50 excitation filter and a 447/60 emission filter. The GFP light cube uses a 469/35 excitation filter and a 525/39 emission filter.
Image pre-processing was used to ensure the best possible detection of nuclei and the best separation between individual autophagosomes. Imaging pre-processing parameters are described in detailed in Table 1. The GFP channel of all the images were pre-processed with a 0.5 μm rolling ball in order to obtain the best separation between individual spots (Figure 2). Image pre-processing should be optimized on a per-experiment basis depending upon the size of particles being analyzed and how spread apart they are.
Figure 2. Autophagy spot counting workflow. (A) Original image. (B) Image after pre-processing. (C) Object Masks highlighting cell area in purple and spots in red.
Object smoothing of 5 cycles was applied to the DAPI channel to facilitate clean masking of the nuclei. Cell counting analysis was applied to the transformed DAPI channel to highlight each individual cell. Next, object spot counting was performed on the GFP channel to determine the size and number of autophagy positive spots per nuclei according to the parameters outlined below in Table 1.
Table 1. Gen5 image analysis software settings. Image analysis parameters for generating a cellular mask in the DAPI channel and an object mask in the GFP channel in order to count autophagic vesicles.
HeLa cells were treated with 0.1 nM – 10 nM rapamycin for 18hr to determine the effect of increasing concentration of rapamycin on the number of autophagy positive vesicles per cell. Rapamycin is an mTOR inhibitor that regulates cell growth and metabolism in response to environmental cues. Rapamycin induces autophagy due to the fact that inhibition of mTOR mimics cellular starvation by blocking signals required for cell growth and proliferation1. Increased CYTO-ID fluorescence indicated autophagosome formation in HeLa cells treated with rapamycin (Figure 3). There is an increase in both the size and the number of autophagic vesicles per cell as a result of increasing rapamycin concentrations (Figure 3).
Figure 3. Autophagy positive spot counts and size increase after treatment with rapamycin. (A) Control (B) 0.1 nM rapamycim (C) 1 nM rapamycin (D) 10 nM rapamycin (E) Autophagosomes per nuclei increase with increasing concentration of rapamycin (F) Autophagosome spot diameter increases with increasing concentrations of rapamycin.
An accumulation of autophagosomes may be indicative of either the increased generation of autophagosomes or a block in autophagosome maturation and completion of the autophagic pathway. Chloroquine is a lysosomal inhibitor that increases the pH of the lysosome, thus preventing the activity of lysosomal acid proteases and causing autophagosomes to accumulate in the cell11. Figure 4 demonstrates an increase in the size and number of autophagy positive spots-per-nuclei in response to combined rapamycin and chloroquine treatment. Notice an increase in both size and number of autophagosomes compared to rapamycin treatment alone (Figure 3 and 4).
Figure 4. Autophagy positive spot counts and size increase after treatment with rapamycin + chloroquine. (A) Control (B) 0.1 nM rapamycim + 10 μM chloroquine (C) 1 nM rapamycin + 10 μM chloroquine (D) 10 nM rapamycin + 10 μM chloroquine (E) Autophagosomes per nuclei increase with increasing concentration of rapamycin and chloroquine (F) Autophagy spot diameter increases with increasing concentrations of rapamycin and chloroquine.
Starvation is one of the most well-known inducers of autophagy. Here, we combined chloroquine treatment and serum starvation for 2-6 hrs. Figure 5 demonstrates that increased length of serum starvation results in increased size and number of autophagosomes per nuclei in HeLa cells.
Figure 5. Autophagy spot count increases with longer serum starvation (A) Control (B) 2hr serum starvation + 10 μM chloroquine (C) 4hr serum starvation + 10 μM chloroquine (D) 6hr serum starvation + 10 μM chloroquine (E) The number of autophagosomes per nuclei increase according to time in serum-free media. (F) Autophagosome spot diameter increases with increasing time in serum-free media.
The ability to efficiently and rapidly analyze autophagy in living cells is critical for many applications such as screening for compounds that can potentially modify disease states. Here we demonstrate that autophagosome number and size increase in response to known autophagy activator rapamycin and serum starvation. Use of CYTOID Autophagy Detection Kit in combination with Gen5™ analysis allows for consistent and precise measurement of object level data including spot count and spot size.
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