The ability to efficiently measure and analyze autophagy in living cells is of particular importance when screening for compounds that can potentially modify disease state, such as promoting clearance of misfolded proteins associated with neurodegeneration, or inhibiting drug resistance associated with cancer. In order to develop a novel and efficient method for autophagy detection, new technologies, both in instrumentation and reagents, must be considered to overcome liabilities associated with standard methods. The Cellometer Vision has previously been used for advanced fluorescent cell-based assay,20,21,23
providing a powerful tool for method development. In addition, Cyto-ID®
Green autophagy dye has previously been shown to specifically stain autophagosomes in live cells and further confirmed in this work using fluorescence microscopy to demonstrate the colocalization of RFP-LC3 and Cyto-ID®
Green autophagy dye in a starvation model using HeLa cells. In order to develop the novel autophagy detection method, image-based cytometry was compared with conventional flow cytometry in the measurement of autophagy in nutrient-starved Jurkat cells. The increase in Cyto-ID®
Green autophagy dye fluorescence signals represents the formation of autophagosomes. Both methods showed a strong increase in autophagy in nutrient-starved cells, which decreased for cells that had been allowed to recover by returning them to standard media, but the calculated AAF values obtained with the two analytical platforms differed considerably. The differences may be attributed to the different detection systems implemented in image-based vs. flow cytometry. The FACS Calibur flow cytometer uses a photo-multiplier tube (PMT) while the Cellometer Vision instrument uses a charge coupled device (CCD) for fluorescence measurement. Another reason for the observed differences in quantitative values obtained may arise from differences in data analysis methods. The flow cytometer measures total fluorescence signals from each cell, while the image-based system capture images and measure specific fluorescent intracellular vacuoles, such as autophagosomes, within the cells, which could potentially provide a more accurate representation of the detected signals. The Cellometer software can analyze fluorescence of cells using two methods. The first method involves a summation of the fluorescent pixels in each cell to generate total fluorescence similar to a flow cytometry. The second method involves summation of high intensity fluorescent pixels within each cell which, given the resolution of the system (~1.30 µm2
/pixel), would potentially compare only the fluorescence of stained autophagosomes within the cell. In addition, it has been previously shown that sheer stress of flow cytometry may have some adverse effects on target cells.21
It is important to note that flow cytometry can analyze a much higher number of cells than image-based cytometry, which can improve statistical analysis of the data generated.
The ability to monitor autophagic flux was also an important benchmark for the validation of the novel detection workflow. Besides nutrient starvation, chloroquine was employed to inhibit lysosomal degradation of autophagosomes. The expectation was that autophagic signal would be the greatest for nutrient-starved Jurkat cells in the presence of CQ due to synergistic interaction between the treatments, followed by starvation in the absence of CQ. Jurkat cells in the presence of CQ were expected to display an increase in autophagic signal relative to the control sample, as any autolysosomes generated by basal autophagy would accumulate due to blockage of the distal portion of the autophagic pathway. Experimentally, the addition of CQ showed only a small increase in the fluorescence signal, which likely indicates very low basal autophagy in the unstressed Jurkat cells. Results obtained from both the Cellometer Vision and FACS Calibur showed similar trends, as described above, but the AAF values differed between the two systems, possibly due to the instrumentation differences as detailed above. Despite the quantitative differences in the AAF values, which appear to be instrumentation-specific, these results demonstrated that the image-based cytometric detection method could readily be implemented to examine autophagy.
An essential aspect in developing a rapid autophagy detection method is to demonstrate its ability to analyze samples under multiple conditions, which could potentially be utilized for drug discovery applications. This capability was established using image-based cytometry to measure autophagic levels of Jurkat cells induced with rapamycin at various concentrations, as rapamycin is a small molecule that induces autophagy in an analogous manner as nutrient starvation. Since rapamycin required at least 12 h of incubation in order to observe autophagy induction, the goal was to demonstrate the ability for image-based cytometry to measure dose response effects as a function of incubation period over a relatively long time period. Overall, image-based cytometry was able to detect the differences in autophagic levels across the various incubation periods. In , the autophagic signals (as measured by AAF values) were obviously the highest after 18 h of incubation. However, a slight decrease in AAF values at shorter incubation periods of 8 h was observed compared with 4 h. This could be due to the failure to allow the cells to fully recover after the initial drug treatment. Since many autophagy studies involve the use of adherent cells, the human prostate cancer cell line, PC-3 was selected to benchmark the capability of the image-based cytometry workflow. The resolution of Cellometer Vision was sufficient to image and measure fluorescent autophagosomes (puncta), as indicated in both fluorescent images captured by the system and fluorescence intensity histograms generated.
In addition to measuring time-dependent dose response of rapamycin, it was also important to demonstrate the ability to compare autophagic effects of multiple compounds, which can prove useful in a drug discovery campaigns wherein lead compounds are being selected. Tamoxifen was employed as an alternative to rapamycin in this context. Previously, we determined that 18 h incubation generated a robust tamoxifen response, thus both small molecule compounds were examined for this period of time. The analysis revealed that at the same concentration, rapamycin induced a higher level of autophagy than tamoxifen. However, 100 µM tamoxifen actually proved to be somewhat cytotoxic, leading to Jurkat cell death after 18 h incubation. In this experiment, image-based cytometry was able to verify the cytotoxicity effect of tamoxifen at high concentration, which proved to be useful in eliminating uncertainties from results that only plotted as scatter plots or histograms.
Image-based cytometry has been shown to generate comparable results as standard flow cytometry for fluorescence-based cellular analysis.20,21
Image-based cytometry may offer certain advantages in detection and analysis of response in cell-based assays. For example, cell sample volume requirements typically ranges from 10 to 40 µl for image-based cytometers, which means the number of cells used are significantly reduced compared with a typical flow cytometer, requiring volumes of 300 to 500 µl. Unlike flow cytometer, where the initial setup of PMT voltages or compensation requires running precious cell samples, image-based cytometers generally utilize disposable slides that hold the cells in a stationary state, thus samples are not wasted during initial calibration of signal detection, such as optimization of exposure time adjustment or focusing. More importantly, the ability to capture images allows researchers to visually inspect acquired fluorescence data, such as data generated from starvation and recovery experiments. This can prove useful in order to identify cytotoxicity as a complicating side-effect of a drug treatment regime that leads to autophagy. Since the disposable counting slides are plastic, autofluorescence can occur with the excitation and emission of Cyto-ID®
Green autophagy dye, which may give rise to high background signals. However, the software can automatically remove the background signal to obtain the actual target fluorescence without compromising the AAF calculation. While the fluorescence exposure times used with the Cellometer Vision are longer than instruments using high power lasers or LEDs, fluorescence photobleaching becomes less of an issue when performing cell-based assays. One future improvement to the Cellometer image-based cytometer would be to develop a higher throughput automated system that can analyze more cells for better statistical analysis, as well as the ability to analyze multiple samples, facilitating higher-throughput cell-based drug screening of inhibitors and activators of autophagy, apoptosis, necrosis and other physiological phenomena of interest.