This work was supported by the following grants to LAS: National Institute of Health (R56HL132209 and 1R01HL140468) and the Miami Heart Research Institute. KY is a recipient of American Heart Association Predoctoral Fellowship (18PRE33960070). HepG2 cells were kindly provided by Dr. Emmanuel Thomas, university of Miami-Miller School of Medicine46,47,48.

1. Seeding Cells in a 24-well Plate
<ol>
	<li>Aspirate media off the cells, wash the cells with 5 mL of Dubelco&#39;s Phosphate Buffered Saline (dPBS), and aspirate the dPBS. For HepG2 cells in a 100 mm dish, use 1.5 mL of 0.25% Trypsin/EDTA, and for HK2 cells or HCAECs use 1.5 mL of 0.05% Trypsin/EDTA solution to detach the cells.</li>
	<li>Incubate the plate in a 37 &#176;C incubator for 4 minutes or until the cells are detached. Neutralize trypsin after a 4 minute incubation by adding 3 mL of complete media for HepG2 and HK2 or 3 mL of trypsin neutralizing solution, dPBS plus 5% fetal bovine serum (FBS), for HCAEC cells.</li>
	<li>Transfer the cells into 15 mL conical tubes and centrifuge at 250 x g for 5 min, aspirate the media, and re-suspend the cell pellet in complete media.</li>
	<li>Filter the cell suspension gently through a 40 &#956;m mesh strainer to break up cell clumps. Do not wash the cells through the strainer.</li>
	<li>Count the cells and plate them at an optimized density. For example, 5,000 cells per well of HepG2 cells or 10,000 cells per well of HK2 cells or HCAECs in a 24-well plate lead to optimal results.</li>
	<li>Incubate the plate overnight at 37 &#176;C to allow cells to attach.</li>
	<li>The next day, change the cell media to base media for the cell line (without FBS) plus 5% Lipo-Protein Deficient Serum (LPDS) or low (2%) FBS media depending on the treatment (see 1.7). Then, continue incubation for 24 h to starve the cells. Use 500 &#956;L of total media per well in a 24-well plate.</li>
	<li>Treat the cells in one of three ways: Add 10 &#181;g/mL of rPCSK9 (or vehicle) and return the cells to the 37 &#176;C incubator for 1 hour, add 40 &#181;M of Dynasore Hydrate (or vehicle, Dimethyl Sulfoxide) and return the cells to the 37 &#176;C incubator for 10 minutes, or add 1 &#181;M Simvastatin (or vehicle, Dimethyl Sulfoxide) and return the cells to the 37 &#176;C incubator for 12, 18 or 24 hours. Use media with 5% LPDS for rPCSK9 or Dynasore treatments. Use low (2%) FBS media or media with 5% LPDS Simvastatin treatments. 
	NOTE: Treating the cells with desired compounds may be done at the time of media change for lipoprotein starvation (step 1.6) for long-term experiments, or prior to the analysis for short-term experiments. Alternatively, customized treatment times may be chosen based on the type and the purpose of the experiments.</li>
	<li>Next, add 5 &#181;L of pHrodo red-labelled LDL (1 mg/mL stock) to each well to obtain a final concentration of 10 &#181;g/mL. Then, carefully remove any bubbles from the wells.</li>
</ol>
2. Live Cell Analysis
<ol>
	<li>Immediately after adding the labelled LDL, place the plate in the live cell analysis system incubator (see Table of Materials) and allow the plate to equilibrate for 15 minutes to reduce condensation in the plate.</li>
	<li>In the meantime, open the software and schedule the scan by adding the vessel holding the plate. Image 16 images per well at 1 hour intervals at 10X for 4 hours using the red and phase channels.</li>
	<li>Create a Plate Map to use for data processing.
	<ol>
		<li>Click on the Properties tab. Choose the Plate Map. Input the cell type and treatments in Compounds tab.</li>
		<li>Click on the Regions tab, select each set of replicates, and save as regions.</li>
	</ol>
	</li>
</ol>
3. Set up Analysis Parameters
<ol>
	<li>Once an experimental run is complete, create an Image Set in the software to train the computer to quantify each parameter included in the counting set.
	<ol>
		<li>In the software, open the plate view, then in the Analysis Job Utilities box choose Create or Add to Image Collection.</li>
		<li>Select New Image Collection, type a name for the image collection, and choose the Red and Phase channels by checking the boxes next to the channels.</li>
		<li>Select 5 more images and add to image collection by adding to the current image collection.</li>
	</ol>
	</li>
	<li>Create a Processing Definition for the cells. Table 1 includes the parameters for the HepG2, HK2, and HCAE cell processing definitions for this LDL influx protocol.
	<ol>
		<li>In the Analysis Job Utilities box, choose New Processing Definition. Choose the Image Collection named in step 2.1.2 from the drop down menu. Input the parameters for the type of cells from Table 1.</li>
		<li>In the Preview box, use the drop down menu to select Preview All.</li>
		<li>In the Analysis Mask box, check the Confluence Mask and the Red Object Mask boxes to view the area included in the analysis for the processing definition. See Figure 1.</li>
		<li>Scroll through the image collection to ensure the cells and LDL are included in the mask. Select File and Save the Processing Definition.</li>
	</ol>
	</li>
	<li>Analyze the set of images from the experimental run.
	<ol>
		<li>Open the experiment in plate view. In the Analysis Job Utilities box, choose Launch New Analysis Job. Select the saved Processing Definition.</li>
		<li>Name the analysis job, choose the time range for analysis, and highlight the experimental wells to analyze. Click the Launch button.</li>
	</ol>
	</li>
</ol>
4. Analysis and Data Processing
<ol>
	<li>Once the Analysis Job is complete, export the data:
	<ol>
		<li>Select the completed analysis and press View. In the Utilities menu, choose Metric/Graph Export.</li>
		<li>In the Regions menu, choose All Wells, and in the Group menu, to obtain the mean values for each set of wells as a group choose Replicates and for exporting the individual values for each well choose None.</li>
		<li>In the Red Object metric menu, choose the Total Red Object Integrated Intensity (RCU x &#181;m2/image). This parameter indicates the sum of the red signal intensity (in RCU) times the area of the red signal (in &#181;m2) in all the images across each well, which corresponds to the total LDL uptake by the cells.</li>
		<li>Click the Data Export button. Check Break data down into individual images. The data is automatically copied on a clipboard and can be pasted to a new spreadsheet file.</li>
		<li>In the Phase Object metric menu, choose the Confluence (Percent). Check Break data down into individual images. Click the Data Export button. The data is automatically copied on a clipboard and can be copied to the spreadsheet file containing Total Red Object Integrated Intensity data.</li>
		<li>Apply the conversion of the percent confluence to total area with the following equation. 
		 
		Total Phase Area (&#181;m2/image) = Confluence (%) &#215; (Image Height (Pixels) &#215; Resolution) &#215; (Image Width (Pixels) &#215; Resolution) 
		 
		NOTE:The confluence (%) parameter indicates the percent confluence of the phase area per image that corresponds to the area of the cells in each well. This metric should be converted into total phase area for each individual image once exported. The image specifications for phase channel (image height, width and resolution) to be used with the above formula for each experimental vessel can be found by referring to Vessel Properties under Image Channels.</li>
		<li>Normalize Total Red Object Integrated Intensity to the Total Phase Area as calculated in 4.1.6 using the following formula for each individual image to eliminate the variability in the cell density across the wells.
		<ol>
			<li>Divide the Total Red Object Integrated Intensity (RCU x &#181;m2/image) values of each image by its corresponding Total Phase Area (&#181;m2/image) to obtain LDL Uptake (RCU) values per image.</li>
			<li>Then, average the LDL Uptake (RCU) data of all images of each well to obtain the average LDL uptake in each well and then average the LDL uptake values of all the replicate wells to obtain group means. These data are the final LDL uptake values and may be used for illustration and statistical analysis using the software of choice.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>

The authors declare that they have no competing financial interests.

In the current protocol, we demonstrate the utilization of live cell imaging as a new and more effective method for measuring real time LDL uptake over a time course in various human cell lines. Human hepatic carcinoma (HepG2) cells are commonly used in studies screening for cholesterol-lowering therapeutics21,22,23,24,25,26,39,40. Therefore, we chose this cell type for testing the capability of a live cell imaging system for LDL influx studies. Our results indicate that HepG2 cells are compatible with this new technique and result in a sigmoid-like curve indicating continuous LDL uptake for the duration of the influx assay until 4.33 h as the final endpoint (Figure&#160;2A and Figure 3).
Cholesterol homeostasis plays a major role in the pathophysiology of various nephropathies. Indeed, cholesterol accumulation in renal tissue is a major contributor to renal fibrosis leading to chronic kidney disease and is a major pathology in various nephropathies28,29,30,31. Hence, we examined our method in human renal epithelial (HK2) cells as a popular and reliable cell line utilized in the field of nephrology. Our data also supported the feasibility of the live cell imaging system to measure LDL influx in HK2 cells. As shown in Figure 2B, HK2 cells took up LDL cholesterol linearly throughout the duration of the influx study (4 hours).
Due to the importance of the cholesterol metabolism in the development and progression of atherosclerosis32,33,41, the leading cause of cardiovascular disease, which in turn is the number-one killer worldwide42, we aimed to validate our method in an atherosclerosis-relevant cell type. We used Human Coronary Arterial Endothelial Cells (HCAECs), as these are one of the first cell types to be exposed to a cholesterol insult in the coronary artery lumen of an atherosclerosis patient. Our data shown in Figure 2C indicates that this LDL influx method also works effectively with HCAECs. The resultant graph is a sigmoid-like curve similar to that of HepG2 cells.
To test the validity and sensitivity of this improved LDL influx assay for screening compounds affecting LDL-cholesterol uptake, we used three controls, LDL uptake-lowering agents Dynasore and rPCSK9, and LDL influx activator Simvastatin. Here, we treated the above mentioned cell lines (HepG2, HK2 and HCAECs) with optimized concentrations of Dynasore or rPCSK9 prior to the influx assay. Our results showed that all three tested cell lines responded to the treatments with significant reductions in LDL influx over a 4-hour time course (Figure 2). For instance, at 4.33 h as the final time point, treatment with Dynasore at 40 &#181;M significantly reduced LDL influx in HepG2 cells, HK2 cells and HCAECs by 53%, 68% and 54%, respectively (p&#60;0.0001; Figure 2A-C and Table 2A-C). In addition, rPCSK9 at 10 &#181;g/mL caused a marked 55% reduction in LDL influx in HK2 cells (p&#60;0.0001; Figure 2B and Table 2B). Moreover, our findings showed that treating HepG2 cells with Simvastatin resulted in a marked increase in LDL uptake (Figure 3), supporting the sensitivity of this method to detect significant alterations in the LDL influx. Studies of treatment with rPCSK9 in HepG2 and HCAEC cells are not included in this protocol as rPCSK9 is used as an additional control treatment with well-documented results, but is costly to buy in small quantities. Therefore rPCSK9 was only used to validate this protocol in HK2 cells.
Live cell imaging analysis, along with the functional and timely measurement of LDL influx, allowed for continuous monitoring of the health and morphology of the cells. This advantage can efficiently detect potential cytotoxicity of the applied compounds, making this method an ideal technique for simultaneously monitoring pharmacological activity and cytotoxicity. Figures 5-7 illustrate representative images of the three tested cell lines at the final endpoint (4.33 h) as a visual reference for the effect of the treatments on net LDL influx and also shows the healthy morphology of the cells following the tested treatments. We recommend visual inspection of all the images from each well to assure the heathy morphology of the cells for the duration of the study. For example, in data not shown, when images of HepG2 cells treated with 80 &#956;M Dynasore were inspected, we observed indications of cell detachment as the edges of the cells appeared to be lifting off the plate, suggesting cell detachment at higher concentrations of Dynasore. Furthermore, high concentrations of simvastatin (3-10 &#956;M) also led to altered morphology indicating induced apoptosis as reported for high dosages of statins45. This protocol was used to perform a titration of the Dynasore treatment at 20-80 &#956;M, and Simvastatin at 0.5-10 &#956;M concentration, following which the cell images were used to analyze the health of the cells and determine potential ctotoxicity of the treatments at various concentrations. Results suggested the use of 40 &#956;M for Dyansore and 1 &#956;M for simvastatin as optimal concentrations.
Lastly, we suggest performing a cell density titration study if another cell line is to be tested with this method in order to identify the optimal cell number per well to obtain consistent results. Our cell density optimization study showed that 10,000 cells/well in a 24-well plate lead to consistent LDL influx outcomes for HK2 and HCAE cells. It is important to note that for this LDL influx assay using live cell imaging system, a monolayer of non-confluent cells distributed evenly on the wells is desirable as clumps of cells may give rise to errors in the final normalized LDL influx values. The reason for this is that for the normalization of the influx data, the phase object area is used as a measure of cell density and this parameter can be adversely affected when cell clumps are formed. We observed that HepG2 cells have a tendency to form clumps when seeded at densities higher than 5,000 cells/well causing inconsistent influx results; therefore, we used 5,000 cells per well in a 24-well plate as optimal density for HepG2 cells.
Collectively, our method provides a medium-to-high throughput platform for screening the pharmacological activity and cytotoxicity of compounds regulating LDL influx concurrently. This method can be readily adapted for use with other fluorescently-labeled ligands that enter the lysosomal compartment to evaluate ligand uptake in real time. While this protocol offers specifications for InCucyte live imaging and analysis system, the protocol can be adapted for alternative imaging systems such as Cellomics.

The Low Density Lipoprotein Receptor (LDLR)-mediated LDL endocytosis is an important area of study since circulating LDL cholesterol levels are at the core of cardiovascular disease1, kidney disease2 as well as a variety of inflammatory diseases3 and genetic disorders with mutations in cholesterol transport genes4,5,6,7. Studies in LDLR-mediated cholesterol influx have led to identification of multiple research tools, such as Dynamin inhibitors including the chemical Dynasore8,9,10, as well as LDL-regulating proteins such as Proprotein Convertase Subtilisin/Kexin type 9 (PCSK9)11,12.
The LDL-LDLR endocytosis pathway begins with sequestering the LDL-LDLR complex on the cell surface into clathrin-coated pits13. Vesicles are then formed by invagination of the cell surface membrane internalizing the LDL-LDLR complex in vacuoles for transport inside the cell. As the formed vesicle matures into early and then late endosomes, the pH drops inside the late endosome, causing disassociation of the LDL from its receptor14. In the past, the methods of quantification of LDL influx depended on radio-labeled 125I-LDL co-incubation with cells and subsequent extraction of the radio-labelled protein from cells for quantification15. This was then replaced by the use of fluorescently labelled LDL proteins such as DiI-LDL, and subsequent immunostaining or extraction of protein for fluorescent readings using a spectrophotometer or plate reader15,16. Fluorescently labelled LDL has also been used in Fluorescence-activated cell sorting (FACS) for analysis of internalization of LDL and cell surface LDL binding17. While these methods allow for collection of data after treatment, monitoring the viability of the cells during treatment is not possible.
The acidic pH in the late endosome allows the use of a pH-activated fluorescent LDL probe such as pHrodo Red LDL that fluoresces after internalization18,19. This property allows for a continuous time course of LDL uptake assessment in live cells. Therefore, this protocol utilizes pHrodo Red-LDL fluorescence imaging in a live cell analysis to serially measure LDL uptake with concurrent monitoring for the cell health. The results indicate the reliability of this novel technique as tested over a four-hour time course in three different human cell lines, human hepatic carcinoma (HepG2) cells, human renal epithelial (HK2) cells and human coronary arterial endothelial cells (HCAEC). These cell lines are clinically significant to LDL clearance20,21,22,23,24,25,26,27, kidney disease28,29,30,31, and heart disease32,33, respectively. In addition to monitoring the LDL influx, this protocol incorporates treatment with two well-known LDL uptake inhibitors, Dynasore Hydrate and recombinant PCSK9 protein as well as a statin inducer of LDLR expression and LDL uptake, simvastatin. Dynasore and recombinant PCSK9 each work through different pathways to reduce LDL uptake.
Dynasore is a small molecule inhibitor of Dynamins10 and reduces LDL uptake by blocking clathrin-dependent endocytosis of LDL-LDLR complex10,34. Recombinant PCSK9, on the other hand, is a member of peptidase S8 family that binds to LDLR and inhibits its recycling to the cell surface after releasing LDL from the internalized complex by blocking required conformational changes35,36. Decreased cell surface LDLR density eventually leads to reduced LDL uptake by the cell. Statins, while directly blocking the 3-hydroxy-3-methylglutaryl-coenzyme (HMG-CoA) reductase enzyme and thus cholesterol biosynthesis, are also known to upregulate the expression of LDLR25,38 leading to increased LDL uptake. The sensitivity of this protocol is validated by detecting significant reductions in LDL influx in three clinically relevant human cell lines, HK2, HepG2 and HCAECs, by Dynasore and/or recombinant PCSK9, and a marked increase in LDL uptake in HepG2 cells by Simvastatin in a four-hour time course with monitoring for cell morphology/health. Taken together, this method provides a medium-to-high throughput platform for concurrently screening the pharmacological activity and cytotoxicity of compounds regulating LDL uptake in live cells.

<table><tbody><tr><td>pHrodo Red-LDL</td><td>ThermoFisher Scientific</td><td>L34356</td><td></td></tr><tr><td>HepG2 cells</td><td>E. Thomas Lab, U. Miami</td><td>HB-8065</td><td></td></tr><tr><td>MEM</td><td>Sigma</td><td>M0325</td><td></td></tr><tr><td>Sodium Pyruvate</td><td>Sigma</td><td>P5280</td><td></td></tr><tr><td>L-Glutamine 200 mM solution</td><td>Sigma</td><td>G7513</td><td></td></tr><tr><td>FBS</td><td>Atlas Biologicals</td><td>FP-0500-A</td><td></td></tr><tr><td>HK2 cells</td><td>ATCC</td><td>CRL-2190</td><td></td></tr><tr><td>Keratinocyte SFM media kit</td><td>Gibco</td><td>17005-042</td><td></td></tr><tr><td>Primary Coronary Artery Endothelial Cells</td><td>ATCC</td><td>PCS-100-020</td><td></td></tr><tr><td>Vascular Cell Basal Medium</td><td>ATCC</td><td>PCS-100-030</td><td></td></tr><tr><td>Endothelial Cell Growth Kit-VEGF</td><td>ATCC</td><td>PCS-100-041</td><td></td></tr><tr><td>Human Lipoprotein Deficient Serum</td><td>Millipore</td><td>LP4</td><td></td></tr><tr><td>PBS</td><td>Sigma</td><td>D8537</td><td></td></tr><tr><td>Trypsin-EDTA (0.25%)</td><td>Gibco</td><td>25200056</td><td></td></tr><tr><td>Trypsin-EDTA (0.05%)</td><td>Gemini Bio-Products</td><td>400-150</td><td></td></tr><tr><td>Trypsin Neutralizing Solution</td><td>ATCC</td><td>PCS-999-004</td><td></td></tr><tr><td>24 well plate</td><td>Falcon</td><td>353226</td><td></td></tr><tr><td>40 &amp;mu;M mesh cell strainer</td><td>VWR</td><td>10199-654</td><td></td></tr><tr><td>15 mL conical tubes</td><td>VWR</td><td>89039-666</td><td></td></tr><tr><td>50 mL conical tubes</td><td>VWR</td><td>89039-658</td><td></td></tr><tr><td>Trypan Blue Staining (0.4%)</td><td>Life Technologies</td><td>T10282</td><td></td></tr><tr><td>Counting Slides</td><td>Bio-Rad</td><td>145-0011</td><td></td></tr><tr><td>Incucyte Zoom</td><td>Sartorius</td><td>Zoom</td><td>Imaging and analysis platform</td></tr><tr><td>Dynasore Hydrate</td><td>Sigma</td><td>D7693</td><td></td></tr><tr><td>PCSK9 Recombinant Protein</td><td>Cayman Chemicals</td><td>20631</td><td></td></tr></tbody></table>

ldl cholesterol uptake assay using live cell imaging analysis with cell health monitoring

The regulation of LDL cholesterol uptake through LDLR-mediated endocytosis is an important area of study in various major pathologies including metabolic disorder, cardiovascular disease, and kidney disease. Currently, there is no available method to assess LDL uptake while simultaneously monitoring for health of the cells. The current study presents a protocol, using a live cell imaging analysis system, to acquire serial measurements of LDL influx with concurrent monitoring for cell health. This novel technique is tested in three human cell lines (hepatic, renal tubular epithelial, and coronary artery endothelial cells) over a four-hour time course. Moreover, the sensitivity of this technique is validated with well-known LDL uptake inhibitors, Dynasore and recombinant PCSK9 protein, as well as by an LDL uptake promoter, Simvastatin. Taken together, this method provides a medium-to-high throughput platform for simultaneously screening pharmacological activity as well as monitoring of cell morphology, hence cytotoxicity of compounds regulating LDL influx. The analysis can be used with different imaging systems and analytical software.

Live Cell Imaging Allows for Reliable Monitoring of Cell Health During Cholesterol Influx Studies in Three Human Cell Lines 
We validated our assay in three human cell lines in which cholesterol homeostasis regulation plays a major pathophysiological role, including human hepatic carcinoma (HepG2) cells, human renal epithelial (HK2) cells, and human coronary artery endothelial cells (HCAECs). We used a live cell imaging system to perform the LDL uptake assay in a 4 h time course with serial measurements at 1 h intervals. Our results indicate that all the cell types tested were compatible with this new technique and result in curves indicating continuous LDL uptake for the duration of the influx study with 4.33 h as the final endpoint (Figure 2). The influx data shown in Figure 2 were obtained by normalizing the total pHrodo Red-labeled LDL fluorescence (total red object integrated intensity per image in RCU x &#181;m2/image) to the total cell area in each image of each well (phase object area per image in &#181;m2/image) to eliminate the variability in the cell density across the wells. Furthermore, to validate the sensitivity of the LDL influx assay for the purpose of screening compounds affecting LDL cholesterol uptake, we used two positive controls known to inhibit LDL influx, Dynasore and rPCSK9, and one positive control known to induce LDL uptake, Simvastatin. As validated by our results, following treatment with Dynasore and rPCSK9, all three tested human cell lines (HepG2, HK2 and HCAEC) showed significant reductions in LDL influx over a 4 h time course (Figure 2A-C). For example, Figure 2A shows that the LDL influx is reduced in HepG2 cells with treatment of Dynasore over the time course, compared to the cells treated with DMSO; while the DMSO as the vehicle control for Dynasore control had no significant effect on LDL influx compared to the untreated control group. Furthermore, our findings showed a marked increase in LDL uptake by HepG2 cells following treatment with Simvastatin (Figure 3), supporting the sensitivity of this method to detect significant alterations in the LDL influx. At about the 4.5 h time point, which is a typical time point in LDL uptake studies, the LDL influx is significantly reduced with either Dynasore or rPCSK9 treatment, and increased by Simvastatin treatment (Figure 4).
A major advantage to using live cell imaging for LDL uptake studies is that this system provides real time images of the cells in each well that could be used to monitor potential cytotoxicity of the examined compounds. Figures 5-7 illustrate representative images of the three cell lines investigated at the initial time point (0.33 h) and final endpoint (4.33 h) as a visual reference for the net LDL influx. The images confirm the normal morphology of the cells following Dynasore or rPCSK9 treatments, indicating both effectiveness and safety of these compounds.
Live Cell Analysis Gives Reliable Serial Quantitative Cholesterol Influx Measurements with Various Treatments
A major advantage of using this protocol with a live cell imaging system is the ability to collect data throughout the time course and compare LDL influx at multiple time points rather than just one final time point as traditionally done.Using this protocol, we are able to calculate the percent reductionin LDL influx at the terminal time point as well as at 1 h intervals throughout the time course. Table 2 summarizes the reduction in LDL influx in three tested human cell lines following 10 min pre-treatment with 40 &#181;M Dynasore or 1 h pre-treatment with 10 &#181;g/mL rPCSK9. At 4.33 h as the final study end point, treatment with Dynasore at 40 &#181;M significantly reduced LDL influx in HepG2 cells, HK2 cells and HCAECs by 53%, 68% and 54%, respectively (Table 2A-C) and treatment with rPCSK9 at 10 &#181;g/mL resulted in 55% reduction in LDL influx in HK2 cells (Table 2B). In addition to quantifying the terminal time point as in traditional assays, we are able to perform a quantitative analysis in the reduction of LDL influx due to treatment at each time point of the experiment. For example, Table 2B shows that treatment with rPCSK9 in HK2 cells resulted in a reduction of LDL uptake by 79% at 1.33 h, 67% at 2.33 h, 59% at 3.33 h post treatment compared to untreated cells. This protocol provides a reliable method for quantitative analysis of LDL influx after treatment.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/58564/58564fig1.jpg" /> 
Figure 1: Processing Definition Mask. Representative images of HK2 cells are depicted following the application of the appropriate processing definition (detailed in Table 1). Shown areHK2 cells without masking (A), with Phase Maskapplied (B), with Red Object Mask applied (C), or with both Phase and Red Object masks applied (D). Scale bar = 100 &#181;m. <a href="https://www.jove.com/files/ftp_upload/58564/58564fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/58564/58564fig2.jpg" /> 
Figure 2: Reduction in LDL uptake using live cell imaging system over a 4.33 h time course. Live cell analysis system is used to measure LDL influx in human hepatic carcinoma (HEPG2) cells (A), human renal tubular epithelial (HK2) cells (B), and human coronary artery endothelial (HCAE) cells (C). The cells were treated with Dynasore (10 min before the run) or rPCSK9 (1 h before the run) as positive controls. DMSO was used as a vehicle for Dynasore treatments. Positive controls significantly decreased the LDL influx in all 3 cell lines. LDL influx values were obtained by normalizing the total red object integrated intensity (RCUx&#181;m2/image) to the total phase object area (&#181;m2/image). Data are mean&#177;SEM. N = 6 wells/group. Data are representative of 2 or 3 independent experiments. ****p &#60; 0.0001 vs blank, and ####p &#60; 0.0001 vs DMSO, using two-way ANOVA. <a href="https://www.jove.com/files/ftp_upload/58564/58564fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/58564/58564fig3.jpg" /> 
Figure 3. Increase in LDL uptake using live cell imaging system over a 4.33h time course. Live cell analysis system is used to measure LDL influx in human hepatic carcinoma (HEPG2) cells. LDL uptake is significantly increased following treatment with Simvastatin for 12 h (A), 18 h (B), or 24 h (C) using media containing 2% FBS. The 24 h time point was also performed with media containing 5% LPDS (without FBS). DMSO was used as negative control. LDL influx values were obtained by normalizing the total red object integrated intensity (RCU x &#181;m2/image) to the total phase object area (&#181;m2/image). Data are mean&#177;SEM. N = 6 wells/group. Data are representative of one independent experiment. ****p &#60; 0.0001 vs DMSO, using Student&#39;s t-test. <a href="https://www.jove.com/files/ftp_upload/58564/58564fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/58564/58564fig4.jpg" /> 
Figure 4. Significant LDL influx reduction by LDL uptake-lowering agents at 4.3 h time point. LDL influx is significantly reduced in human hepatic carcinoma (HepG2) cells (A), human renal tubular epithelial (HK2) cells (B), and human coronary artery endothelial (HCAE) cells (C) following treatment with LDL uptake inhibitors Dynasore for 10 min or rPCSK9 for 1 hour. LDL influx is markedly increased by Simvastatin in HepG2 cells after 12, 18 or 24 h treatments (D). Data are mean&#177;SEM. N = 6 wells/group. Data are representative of 2 or 3 independent experiments. ****p &#60; 0.0001 using two-way ANOVA. <a href="https://www.jove.com/files/ftp_upload/58564/58564fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/58564/58564fig5.jpg" /> 
Figure 5. Reduced LDL influx in hepatocellular carcinoma (HepG2) cells by LDL uptake-lowering agent, Dynasore. Representative images of the phase object and red object for HepG2 cells are depicted at 0.33 h (left panels) and the 4.33 h endpoint (right panels) show healthy status of cells. 40 &#181;M Dynasore, known to reduce LDL-cholesterol uptake, was used as positive control (C). Images were taken at 10X magnification. Scale bar = 100 &#181;m. <a href="https://www.jove.com/files/ftp_upload/58564/58564fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/58564/58564fig6.jpg" /> 
Figure 6. Reduced LDL influx in human renal tubular epithelial (HK2) cells by LDL uptake-lowering agents, Dynasore and rPCSK9. Representative images of the phase object and red object for HK2 cells depicted at 0.33 h (left panels) and the 4.33 h endpoint (right panels) show healthy status of the cells. 40 &#181;M Dynasore (C), or 10 &#181;g/mL rPCSK9 (D), known to reduce LDL cholesterol uptake, were used as positive control. Images are taken at 10X magnification. Scale bar = 100 &#181;m. <a href="https://www.jove.com/files/ftp_upload/58564/58564fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/58564/58564fig7.jpg" /> 
Figure 7. Reduced LDL influx in human coronary artery endothelial cells (HCAECs) by LDL uptake-lowering agent, Dynasore. Representative images of the phase object and red object for HCAECs depicted at 0.33 h (left panels) and the 4.33 h endpoint (right panels) show healthy status of the cells. 40 &#181;M Dynasore, known to reduce LDL-cholesterol uptake, was used as positive control (C). Images were taken at 10X magnification. Scale bar = 100 &#181;m. Data are mean&#177;SEM. N = 6 wells/group. Data are representative of 2 or 3 independent experiments. <a href="https://www.jove.com/files/ftp_upload/58564/58564fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Table 1" src="/files/ftp_upload/58564/58564table1.jpg" /> 
Table 1: Processing Definition Parameters. These parameters are specific for the analysis system used in this protocol. Parameters should be set up to analyze red area in the red channel and the area of the cell in the phase channel. Parameter settings for HepG2, HK2, HCAE cell lines are presented.
<img alt="Table 2" src="/files/ftp_upload/58564/58564table2.jpg" /> 
Table 2: Percent Change in LDL influx in HepG2, HK2 and HCAE cells treated with Dynasore, rPCSK9, or Simvastatin at 4.3 h. (A)&#160;HepG2 cells, (B)&#160;HK2 cells, (C)&#160;HCAE cells, and (D)&#160;HepG2 cells.

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LDL Cholesterol Uptake Assay Using Live Cell Imaging Analysis with Cell Health Monitoring

This protocol provides an efficient approach to measuring LDL cholesterol uptake with real time influx rates using a live cell imaging system in various cell types. This technique provides a platform to screen the pharmacological activity of compounds affecting LDL influx while monitoring for cell morphology and hence potential cytotoxicity.

The regulation of LDL cholesterol uptake through LDLR-mediated endocytosis is an important area of study in various major pathologies including metabolic ...

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Research

JoVE Journal

Biology

13.3K Views.  University of Miami Leonard M. Miller School of Medicine. We presenteren een methode voor live beeldanalyse van LDL cholesterol opname in verschillende menselijke celtypes. Dit maakt het mogelijk om te screenen op verbindingen, die het transport van LDL-cholesterol wijzigen. Het belangrijkste voordeel van het gebruik van live celbeeldvorming voor LDL influx kwantificering, is dat het mogelijk maakt monitoring celmorfologie gedurende de test duur, dus potentieel de giftige verbindingen kunnen worden gedetecteerd.Om te beginnen, aspirate de med...

Video: LDL Cholesterol Uptake Assay Using Live Cell Imaging Analysis with Cell Health Monitoring

Long-term Live-cell Imaging to Assess Cell Fate in Response to Paclitaxel

Live-cell imaging is a powerful technique that can be used to directly visualize biological phenomena in single cells over extended periods of time. Over the past decade, new and innovative technologies have greatly enhanced the practicality of live-cell imaging. Cells can now be kept in focus and continuously imaged over several days while maintained under 37 &#176;C and 5% CO2 cell culture conditions. Moreover, multiple fields of view representing different experimental conditions can be acquired simultaneously, thus providing high-throughput experimental data. Live-cell imaging provides a significant advantage over fixed-cell imaging by allowing for the direct visualization and temporal quantitation of dynamic cellular events. Live-cell imaging can also identify variation in the behavior of single cells that would otherwise have been missed using population-based assays. Here, we describe live-cell imaging protocols to assess cell fate decisions following treatment with the anti-mitotic drug paclitaxel. We demonstrate methods to visualize whether mitotically arrested cells die directly from mitosis or slip back into interphase. We also describe how the fluorescent ubiquitination-based cell cycle indicator (FUCCI) system can be used to assess the fraction of interphase cells born from mitotic slippage that are capable of re-entering the cell cycle. Finally, we describe a live-cell imaging method to identify nuclear envelope rupture events.

Live-cell imaging provides a wealth of information on single cells or whole populations that is unattainable by fixed cell imaging alone. Here, live-cell imaging protocols to assess cell fate decisions following treatment with the anti-mitotic drug paclitaxel are described.

Live-cell imaging is a powerful technique that can be used to directly visualize biological phenomena in single cells over extended periods of time. Over ...

Cancer Research

A Microfluidic Chip for the Versatile Chemical Analysis of Single Cells

We present a microfluidic device that enables the quantitative determination of intracellular biomolecules in multiple single cells in parallel. For this purpose, the cells are passively trapped in the middle of a microchamber. Upon activation of the control layer, the cell is isolated from the surrounding volume in a small chamber. The surrounding volume can then be exchanged without affecting the isolated cell. However, upon short opening and closing of the chamber, the solution in the chamber can be replaced within a few hundred milliseconds. Due to the reversibility of the chambers, the cells can be exposed to different solutions sequentially in a highly controllable fashion, e.g. for incubation, washing, and finally, cell lysis. The tightly sealed microchambers enable the retention of the lysate, minimize and control the dilution after cell lysis. Since lysis and analysis occur at the same location, high sensitivity is retained because no further dilution or loss of the analytes occurs during transport. The microchamber design therefore enables the reliable and reproducible analysis of very small copy numbers of intracellular molecules (attomoles, zeptomoles) released from individual cells. Furthermore, many microchambers can be arranged in an array format, allowing the analysis of many cells at once, given that suitable optical instruments are used for monitoring. We have already used the platform for proof-of-concept studies to analyze intracellular proteins, enzymes, cofactors and second messengers in either relative or absolute quantifiable manner.

In this article we present a microfluidic chip for single cell analysis. It allows the quantification of intracellular proteins, enzymes, cofactors, and second messengers by means of fluorescent assays or immunoassays.&#160;

We present a microfluidic device that enables the quantitative determination of intracellular biomolecules in multiple single cells in parallel. For this ...

Bioengineering

Studying Membrane Biogenesis with a Luciferase-Based Reporter Gene Assay

To study the coordination of different lipid synthesis pathways during membrane biogenesis it is useful to work with an experimental system where membrane biogenesis occurs rapidly and in an inducible manner. We have found that phagocytosis of latex beads is practical for these purposes as cells rapidly synthesize membrane lipids to replenish membrane pools lost as  wrapping material  during particle engulfment. Here, we describe procedures for studying changes in phagocytosis-induced gene expression with a luciferase-based reporter gene approach using the Dual-Glo  Luciferase Assay System from Promega.

Here, we describe procedures for studying changes in phagocytosis-induced gene expression with a luciferase-based reporter gene approach using the Dual-GloTM Luciferase Assay System from Promega.

To study the coordination of different lipid synthesis pathways during membrane biogenesis it is useful to work with an experimental system where membrane ...

Live-3D-Cell Immunocytochemistry Assays of Pediatric Diffuse Midline Glioma

Cell migration and invasion are specific hallmarks of Diffuse Midline Glioma (DMG) H3K27M-mutant tumors. We have already modeled these features using three-dimensional (3D) cell-based invasion and migration assays. In this study, we have optimized these 3D assays for live-cell immunocytochemistry. An Antibody Labeling Reagent was used to detect in real-time the expression of the adhesion molecule CD44, on the plasma membrane of migrating and invading cells of a DMG H3K27M primary patient-derived cell line. CD44 is associated with cancer stem cell phenotype and tumor cell migration and invasion and is involved in the direct interactions with the central nervous system (CNS) extracellular matrix. Neurospheres (NS) from the DMG H3K27M cell line were embedded into the basal membrane matrix (BMM) or placed onto a thin coating layer of BMM, in the presence of an anti-CD44 antibody in conjunction with the antibody labeling reagent (ALR). The live-3D-cell immunocytochemistry image analysis was performed on a live-cell analysis instrument to quantitatively measure the overall CD44 expression, specifically on the migrating and invading cells. The method also allows visualizing in real-time the intermittent expression of CD44 on the plasma membrane of migrating and invading cells. Moreover, the assay also provided new insights into the potential role of CD44 in the mesenchymal to amoeboid transition in DMG H3K27M cells.

This study presents a protocol of live-3D-cell immunocytochemistry applied to a pediatric diffuse midline glioma cell line, useful to study in real-time the expression of proteins on the plasma membrane during dynamic processes like 3D cell invasion and migration.

Cell migration and invasion are specific hallmarks of Diffuse Midline Glioma (DMG) H3K27M-mutant tumors. We have already modeled these features using ...

Automated Kinetic Imaging for Drug Assessment in Patient-Derived Tumor Organoids

Multiplexed Live-Cell Imaging for Drug Responses in Patient-Derived Organoid Models of Cancer

Patient-derived organoid (PDO) models of cancer are a multifunctional research system that better recapitulates human disease as compared to cancer cell lines. PDO models can be generated by culturing patient tumor cells in extracellular basement membrane extracts (BME) and plating them as three-dimensional domes. However, commercially available reagents that have been optimized for phenotypic assays in monolayer cultures often are not compatible with BME. Herein, we describe a method to plate PDO models and assess drug effects using an automated live-cell imaging system. In addition, we apply fluorescent dyes that are compatible with kinetic measurements to quantify cell health and apoptosis simultaneously. Image capture can be customized to occur at regular time intervals over several days. Users can analyze drug effects in individual Z-plane images or a Z Projection of serial images from multiple focal planes. Using masking, specific parameters of interest are calculated, such as PDO number, area, and fluorescence intensity. We provide proof-of-concept data demonstrating the effect of cytotoxic agents on cell health, apoptosis, and viability. This automated kinetic imaging platform can be expanded to other phenotypic readouts to understand diverse therapeutic effects in PDO models of cancer.

Author Spotlight: Developing Multiplexed Kinetic Assays for Organoid-Based Drug Response Analysis

Patient-derived tumor organoids are a sophisticated model system for basic and translational research. This methods article details the use of multiplexed fluorescent live-cell imaging for simultaneous kinetic assessment of different organoid phenotypes.

Patient-derived organoid (PDO) models of cancer are a multifunctional research system that better recapitulates human disease as compared to cancer cell ...

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.

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 ...

Cell-free Biochemical Fluorometric Enzymatic Assay for High-throughput Measurement of Lipid Peroxidation in High Density Lipoprotein

Low high-density lipoprotein cholesterol (HDL-C) levels are one of the most powerful independent negative predictors of atherosclerotic cardiovascular disease (CVD). The structure and function of HDL rather than HDL-C may more accurately predict atherosclerosis. Several HDL protein and lipid compositional changes that impair HDL function occur in inflammatory states such as atherosclerosis. HDL function is usually determined by cell based assays such as cholesterol efflux assay but these assays have numerous drawbacks lack of standardization. Cell-free assays may give more robust measures of HDL function compared to cell-based assays. HDL oxidation impairs HDL function. HDL has a major role in lipid peroxide transport and high amount of lipid peroxides is related to abnormal HDL function. Lipid-probe interactions should be considered when interpreting the results of non-enzymatic fluorescence assays for measuring the lipid oxidative state. This motivated us to develop a cell-free biochemical enzymatic method to assess HDL lipid peroxide content (HDLox) that contributes to HDL dysfunction. This method is based on the enzyme horseradish peroxidase (HRP) and the fluorochrome Amplex Red that can quantify (without cholesterol oxidase) the lipid peroxide content per mg of HDL-C. Here a protocol is describedfor determination of HDL-lipid peroxidation using the fluorochrome reagent. Assay variability can be reduced by strict standardization of experimental conditions. Higher HDLox values are associated with reduced HDL antioxidant function. The readout of this assay is associated with readouts of validated cell-based assays, surrogate measures of cardiovascular disease, systemic inflammation, immune dysfunction, and associated cardiovascular and metabolic risk phenotypes. This technical approach is a robust method to assess HDL function in human disease where systemic inflammation, oxidative stress and oxidized lipids have a key role (such as atherosclerosis).

We describe here a fluorometric cell-free biochemical assay for determination of HDL-lipid peroxidation. This rapid and reproducible assay can be used to determine HDL function in large scale studies and can contribute to our understanding of HDL function in human disease.

Low high-density lipoprotein cholesterol (HDL-C) levels are one of the most powerful independent negative predictors of atherosclerotic cardiovascular ...

Immunology and Infection

Lipid Exchange Assay in Living Cells

Lipid rafts are dynamic, ordered domains in the plasma membrane often formed during membrane protein clustering and signaling. The lipid identity of the outer leaflet drives the membrane&#39;s propensity to form lipid rafts. The transient nature of lipid rafts makes it difficult to study in living cells. Therefore, methods that add or remove raft-forming lipids at&#160;the outer leaflet of living cells facilitate studying the characteristics of rafts, such as their effects on membrane proteins. Lipid exchange experiments developed in our lab utilize lipid-loaded cyclodextrins to remove and add exogenous phospholipids to change the lipid constitution of the plasma membrane. Substituting the membrane with a raft or non-raft-forming lipid can aid in studying the effects on transmembrane protein activity. Here, we describe a method for lipid exchange on the outer leaflet of the plasma membrane using lipid-loaded cyclodextrin. We demonstrate the preparation of the exchange media and the subsequent treatment of attached mammalian cells. We also showcase how to measure the efficiency of exchange using HP-TLC. This protocol yields a nearly complete replacement of the outer leaflet with exogenous lipids without altering cellular viability, permitting further experimentation on modified intact plasma membranes.

We describe a method for using cyclodextrin to mediate exchange between lipids of the plasma membrane with exogenous lipids. This technique can be paired with experiments studying transmembrane proteins, which behave differently in lipid raft-like environments than they do in non-raft-like environments.

Lipid rafts are dynamic, ordered domains in the plasma membrane often formed during membrane protein clustering and signaling. The lipid identity of the ...

Biochemistry

Monitoring Cell-autonomous Circadian Clock Rhythms of Gene Expression Using Luciferase Bioluminescence Reporters

In mammals, many aspects of behavior and physiology such as sleep-wake cycles and liver metabolism are regulated by endogenous circadian clocks (reviewed1,2). The circadian time-keeping system is a hierarchical multi-oscillator network, with the central clock located in the suprachiasmatic nucleus (SCN) synchronizing and coordinating extra-SCN and peripheral clocks elsewhere1,2. Individual cells are the functional units for generation and maintenance of circadian rhythms3,4, and these oscillators of different tissue types in the organism share a remarkably similar biochemical negative feedback mechanism. However, due to interactions at the neuronal network level in the SCN and through rhythmic, systemic cues at the organismal level, circadian rhythms at the organismal level are not necessarily cell-autonomous5-7. Compared to traditional studies of locomotor activity in vivo and SCN explants ex vivo, cell-based in vitro assays allow for discovery of cell-autonomous circadian defects5,8. Strategically, cell-based models are more experimentally tractable for phenotypic characterization and rapid discovery of basic clock mechanisms5,8-13.
Because circadian rhythms are dynamic, longitudinal measurements with high temporal resolution are needed to assess clock function. In recent years, real-time bioluminescence recording using firefly luciferase as a reporter has become a common technique for studying circadian rhythms in mammals14,15, as it allows for examination of the persistence and dynamics of molecular rhythms. To monitor cell-autonomous circadian rhythms of gene expression, luciferase reporters can be introduced into cells via transient transfection13,16,17 or stable transduction5,10,18,19. Here we describe a stable transduction protocol using lentivirus-mediated gene delivery. The lentiviral vector system is superior to traditional methods such as transient transfection and germline transmission because of its efficiency and versatility: it permits efficient delivery and stable integration into the host genome of both dividing and non-dividing cells20. Once a reporter cell line is established, the dynamics of clock function can be examined through bioluminescence recording. We first describe the generation of P(Per2)-dLuc reporter lines, and then present data from this and other circadian reporters. In these assays, 3T3 mouse fibroblasts and U2OS human osteosarcoma cells are used as cellular models. We also discuss various ways of using these clock models in circadian studies. Methods described here can be applied to a great variety of cell types to study the cellular and molecular basis of circadian clocks, and may prove useful in tackling problems in other biological systems.

Circadian clocks function within individual cells, i.e., they are cell-autonomous. Here, we describe methods for generating cell-autonomous clock models using non-invasive, luciferase-based real-time bioluminescence technology. Reporter cells provide tractable, functional model systems for studying circadian biology.

In mammals, many aspects of behavior and physiology such as sleep-wake cycles and liver metabolism are regulated by endogenous circadian clocks ...

Live Cell Imaging of Early Autophagy Events: Omegasomes and Beyond

Autophagy is a cellular response triggered by the lack of nutrients, especially the absence of amino acids. Autophagy is defined by the formation of double membrane structures, called autophagosomes, that sequester cytoplasm, long-lived proteins and protein aggregates, defective organelles, and even viruses or bacteria. Autophagosomes eventually fuse with lysosomes leading to bulk degradation of their content, with the produced nutrients being recycled back to the cytoplasm. Therefore, autophagy is crucial for cell homeostasis, and dysregulation of autophagy can lead to disease, most notably neurodegeneration, ageing and cancer.	
	Autophagosome formation is a very elaborate process, for which cells have allocated a specific group of proteins, called the core autophagy machinery. The core autophagy machinery is functionally complemented by additional proteins involved in diverse cellular processes, e.g. in membrane trafficking, in mitochondrial and lysosomal biology. Coordination of these proteins for the formation and degradation of autophagosomes constitutes the highly dynamic and sophisticated response of autophagy. Live cell imaging allows one to follow the molecular contribution of each autophagy-related protein down to the level of a single autophagosome formation event and in real time, therefore this technique offers a high temporal and spatial resolution.	
	Here we use a cell line stably expressing GFP-DFCP1, to establish a spatial and temporal context for our analysis. DFCP1 marks omegasomes, which are precursor structures leading to autophagosomes formation. A protein of interest (POI) can be marked with either a red or cyan fluorescent tag. Different organelles, like the ER, mitochondria and lysosomes, are all involved in different steps of autophagosome formation, and can be marked using a specific tracker dye. Time-lapse microscopy of autophagy in this experimental set up, allows information to be extracted about the fourth dimension, i.e. time. Hence we can follow the contribution of the POI to autophagy in space and time.

Time-lapse microscopy of fluorescently labeled autophagy markers allows monitoring of the dynamic autophagy response with high temporal resolution. Using specific autophagy and organelle markers in a combination of 3 different colors, we can follow the contribution of a protein to autophagosome formation in a robust spatial and temporal context.

Autophagy is a  cellular response triggered by the lack of nutrients, especially the absence of  amino acids. Autophagy is defined by the formation of ...

LDL Cholesterol Uptake Assay Using Live Cell Imaging Analysis with Cell Health Monitoring

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