I am studying anticancer effect of plant extract on A549 cells using PI staining and BD LSR Fortessa flow cytometer.
attached below is the image
Dear Ms. Makhija,
Greetings!!
Yeah, PI histogram plot output looks fine. Just carefully adjust the areas (percentages) falling under G0/G1, S and G2/M phases properly.
This link will be helpful for you to understand: https://www.bdbiosciences.com/sg/instruments/accuri/applications/cell_cancer_bio.jsp
Best Regards,
Samrat
Hi,
First, your control should look like DCM 25 µg/ml in a way we can see a normale distribution of cells upon the three phases: G1, S, G2/M.
For control, DCM 150 and 200 µg/ml I guess that you don't have enough events.
Second, you can use ModFit LT software to have a more accurate distribution of cells. You can find the attached file an example of what I am talking about.
Abdelhafid Nani , Pooja Makhija
I hope in the control group the A549 cells are without any treatment. So may be due to considerable presence of Sub-G1 population there is a occurrence of a broad peak.
Samrat Das thank you very much for responding.
Yes control are untreated ones.
I have run this experiment three times
other two time the control looked like this (image attached)
Abdelhafid Nani thanks for responding
I tried adjusting control few times everytime i repeated but somehow it stayed the same. yes the events in 150 and 200 are very low coz apoptosis is 70-98% in these ranges.
I have not been trained on this software but i can try to figure out how this should be fitted.
Samrat Das
" Just carefully adjust the areas (percentages) falling under G0/G1, S and G2/M phases properly. "
Please help me to understand what could be the ideal way of doing it.
I have tried to relate this with data from annexin as well but I am not sure what is the best way to adjust areas under each phase.
Pooja Makhija,
I think you can move the vertical software plot lines (i mean those ones P3, P4 etc) by clicking on them and dragging the cursor (Hope your software allows that). Ok, now if it is possible clearly define your sub-G1 from your G0/G1 in your control samples. Probably, If you see minutely there is a kind of two small peaks in your G0/G1. Probably, if you run some more control samples you might observe a less heighted population of cells (i.e, Sub-G1) towards the left of G0/G1. You have to also clearly define the areas of cells falling under other phases (i.e, S, G2/M) in the control samples.
Go through the below link, and click on (Fig.1), and look at (B), it will help you to understand:
http://www.bdbiosciences.com/br/instruments/accuri/articles/archive/2014_04/index.jsp
Best Regards
Please note that there are several ways to control the quality of DNA cell cycle analysis. 1. CV of G0/G1 For cell culture samples it should be
1. Try to read some basics to know more about the method, the expected results, and how to interpret the histogram plots.
2. Try to start with preparing a known number (that is desirable for your future experiments, considering the size of well and time of run) of healthy untreated cells. Optimize the amount of PI added, RNase amount added and time allowed for staining/digestion, so that you can obtain a beautiful histogram in which you can clearly see the G0/G1 and G2/M peaks with around 2-fold difference in the DNA amount.
3. Your untreated control cells plots are problematic. Probably you prepared too many cells and added inadequate PI that is not sufficient to saturate all the cells. This is the major reason for doing point 2. If you cannot obtain good results with control cells, all other results are meaningless.
4. You need to know your aim for drawing the regions and how to execute and achieve that. From your plots, it seems like you are drawing random regions with no clues, possibly because you don't know what's going on looking at the plots.
5. As you are looking at anti-cancer effect, most probably your drug would inhibit proliferation and lead to cell death. It would be better to plan the experiments more carefully so that you can have similar number of cells in different groups (control, and different drug concentrations). Avoid exceeding the optimal cell number that you determined in point 2. Big difference in cell numbers in different samples may lead to results that have the peaks moving around and generates problem during analysis.
I also suggest you to read Michael Ormerod's Cytometry book which is available on internet free of charge.
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