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11:39 min
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July 21st, 2017
DOI :
July 21st, 2017
•0:05
Title
0:42
Jurkat Cell LC3, LAMP1, and p62 Labeling
2:18
Multispectral Imaging Flow Cytometry (MIFC)
3:43
MIFC Analysis
8:35
Results: Representative MIFC Analyses
10:28
Conclusion
文字起こし
The overall goal of this experiment is to quantify the colocalization of three autophagy markers in conjunction with LC3 puncta quantification for the measurement of autophagy in an objective, quantitative and statistically robust manner. This method allows for the colocalization of three autophagy markers to be measured in a single assay, potentially leading to novel insights into the induction and regulation of autophagy. The main advantage of this technique is that three autophagy markers can be measured simultaneously in an objective, quantitative and statistically robust manner.
Begin by adding two times 10 to the sixth Jurkat cells into individual 15 milliliter centrifuge tubes labeled with the appropriate sample names. Next, add enough wash buffer to each tube to bring the final volume of each sample up to 15 milliliters and pellet the samples by centrifugation. Re-suspend the pellets in 100 microliters of 4%formalin, then transfer the samples to labeled siliconized polypropylene microcentrifuge tubes.
After 10 minutes at room temperature, wash the samples in one milliliter of fresh wash buffer. Re-suspend the pellets in 100 microliters of the first antibody of interest in permeabilization buffer for a 30 minute incubation in the dark at room temperature, protected from the light. Wash the labeled and permeabilized cells in one milliliter of fresh permeabilization buffer and label them with the next antibody of interest, as just demonstrated.
After all of the samples have been labeled with the final antibody, re-suspend the pellets in 1%formalin solution, followed by the addition of 10 microliters of 10x DAPI nuclear stain. Mix the cells with light vortexing, then incubate the samples for 10 minutes at room temperature, protected from light. At the end of the incubation, load the default template on the multispectral imaging flow cytometer and select the appropriate lasers.
Enter the desired laser power in the window next to each laser button, then click the load button to load the 1.5 milliliter tube onto the instrument. Create an area M01 versus aspect ratio M01 dot plot for the all population, and draw a region around the cells. Name this region Cell.
Set the acquisition parameters. Specify the destination folder and file name and change the number of events to 5, 000, then select the cell population and click acquire to acquire the file and run the rest of the samples as just demonstrated under the same laser settings. After collecting the file, click return to retrieve the sample tube.
To analyze the multispectral imaging flow cytometry data, click start analysis to run the open file wizard in the imaging analysis software, then click on the apoptosis wizard icon and the select wizard button to open the apoptosis wizard. After all of the apoptotic cells have been removed from the analysis, make a region about the non-apoptotic cells and label this population cells. Next, click on the building blocks icon.
Select fluorescence positives one color and select cells population and intensity MC channel three. Label the X axis intensity lamp one and draw a region that includes the cells with intensities greater than 10, 000. Label this region lamp one positive.
After gating the P62 and LC3 positive cells in the same way, click the analysis tab and select mask. Click new and then function. Under function, select peak.
Under mask, select M11. Under channel, select channel 11. Set the spot to cell background ratio to four.
Then click okay to close the defined mask function window followed by okay to add the mask to the mask list. Click close to exit the mask manager then click the analysis tab again and select features. Click new.
Under feature type, select spot count and select the peak mask that was just created. Enter spot count LC3 for the name and click okay. Click new again.
Under feature type, select bright detail colocalization three or BDC3. Under mask, select MC.Under image one, select channel two for P62. Under image two, select channel three for lamp one.
Under image three, select channel 11 for LC3. Enter BDC3p62/LAMP1/LC3 for the name and click okay and close to exit the feature manager. Click on the new scatter plot icon and select the LAMP1 positive p62 positive LC3 positive population.
Select BDC3/p62/LAMP1/LC3 for the X-axis and the spot count LC3 feature for the Y-axis and click okay. Under the file menu, select save data analysis file and click save and yes to save the starved plus chloroquine data as a data analysis file and to replace the previous version of the file. Now open the control file and select the appropriate compensation matrix and the starved plus chloroquine data analysis file as the template.
Click okay. To create regions for the autophagosome accumulation and the autolysosome accumulation, open the control sample and draw a rectangular region from the 0.1 to three X-axis coordinates and the 1.5 to 0.3 Y-axis coordinates. Name this region low spots.
Next, draw a region from the 0.1 to one X-axis coordinates and 17.5 to 1.5 Y-axis coordinates and name this region high spot low BDC3 then draw a region from the one to three X-axis coordinates and 17.5 to 1.5 Y-axis coordinates and name this region high spots/high BDC3. Save the control file so it can be used as a template then open the other files including the starved plus chloroquine file using the control file for the template as previously demonstrated. The size, shape and brightness of the LC3 puncta can vary drastically between cells as observed in these spot maskings of LC3 puncta in Jurkat cells using different spot masks.
The mask that worked best for this experiment was peak with a spot to cell background ratio of four. In these representative spot counts of anti-LC3 labeled Jurkat cells, the control and starved mean spot counts are not significantly different. When chloroquine is added, a large difference in the mean of the control plus chloroquine compared to the starved plus chloroquine mean is observed.
In this experiment, there was a shift between the measured control BDC3 colocolization mean to the starved mean as well as between the control plus chloroquine mean and the starved plus chloroquine mean. BDC3 does not consider the number of autophagy organelles that colocolize, resulting in a large degree of variability in the number of autophagozomes for the same BDC3 score. Indeed, in most cases there is overlap between all three of the probes because even at the Beyza levels, p62, LAMP1, and LC3 should, to a certain extent, colocolize in similar regions of the cells.
As a contrast, here's an example of the BDC3 feature on three probes that should not colocalize. P62, LC3 and DAPI. When the spot count and the BDC3 features are combined, the presence of the different subpopulations that improved the ability to distinguish between the various samples and conditions becomes evident.
Once mastered, this technique can be completed in approximately eight hours if it is performed properly. However, if a different spot count feature is required, additional analysis time would be needed. While attempting this procedure, it is important to remember to use the appropriate controls.
For example, use the control sample to set the Beyza level thresholds and the control plus chloroquine sample as a control for the starved plus chloroquine sample. This procedure can also be used for other intracellular applications that require the colocolization of three different markers by simply changing to the appropriate antibodies of interest. After watching this video, you should have a good understanding of how to label cells with atophagy markers, acquire data on a multi-spectral imaging flow cytometer, and analyze the data to assess atophageic flux.
Don't forget that working with formalin can be extremely hazardous and that precautions such as wearing appropriate personal protective equipment should always be taken while performing this procedure.
Here, multispectral imaging flow cytometry with an analytical feature that compares bright detail images of 3 autophagy markers and quantifies their co-localization, along with LC3 spot counting, was used to measure autophagy in an objective, quantitative, and statistically robust manner.
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