20.109(S16):Flow cytometry and paper discussion (Day7)

Introduction

We hope that you’ll leave lab today with a sense of accomplishment, after inspecting your raw flow cytometry data and then calculating NHEJ repair values. Unfortunately or excitingly – depending on your perspective – it turns out in scientific research that the hard work is just beginning once the data is quantified! Interpreting the data and drawing (sometimes tentative) conclusions requires deep reading and thinking – a process that shouldn’t be rushed.

Now is the time to clearly understand the nature of the flow cytometry controls that you will examine. For each cell population, you prepared two DNA mixtures: (A) intact pMax_mCherry plus intact pMax_EGFP_MCS, and (B) intact pMax_RFP plus damaged pMax_EGFP_MCS. The re-circularization of pMax_EGFP_MCS (minus the nonsense insert) will be our most direct readout of how much repair occurred. In this simplest view, broken DNA = no fluorescent signal, and repaired DNA = green fluorescent signal. However, it is important to consider the possibility that one cell population simply took up more plasmid DNA than another? In more technical terms, what if the transfection efficiency is higher for one cell type than for the other, and therefore the repair rate artificially appears higher? To control for this we co-transfected with intact pMax_mCherry, which serves as a transfection control. Using the transfection control data, we will normalize for differences in DNA uptake. It is also important to think about the uptake of pMAX_mCherry compared to the uptake of pMAX_EGFP_MCS. What if the plasmids are taken up at different frequencies, or successfully expressed at different frequencies and/or signal intensities? Here is where the dual intact control is useful. It shows us the typical ratio of mCherry:EGFP uptake and expression, which we can use as a secondary normalization. Note that we use pMax_EGFP as a control rather than pMax_EGFP_MCS, because the latter will have very low expression that is not representative of the repaired construct: the nonsense insert separates the promoter and gene by too great a distance for robust expression.

Putting all of the above information together, the NHEJ repair frequency equation can be determined in three steps:

(1) Raw F reporter expression = % cells positive for F x MFI(F) = "RAW"

• F can be "mCherry" or "EGFP" in our case
• MFI is mean fluorescence intensity or median fluorescence intensity

(2) Normalized BFP expression $ \qquad ={RAW_{BFP} \over RAW_{GFP}}\qquad $ = "NORM"

(3) Reporter expression percent $ \qquad ={NORM_{BFP.damaged} \over NORM_{BFP.intact}}\qquad $ = NHEJ repair value

First, reporter expression for mCherry and EGFP alike will be calculated by multiplying percentage of positive cells by fluorescence intensity (FI). We have a choice of whether to use mean, geometric mean, or median fluorescence intensity. Median fluorescence is least susceptible to being influenced by a few outliers, while geometric mean is generally more appropriate for log scale data than arithmetic mean. For normally distributed populations, all three values should be pretty similar. In practice, we have found that while mean and median FI are very different values, after normalization the ultimate NHEJ repair values are quite similar, so we will use the mean value.

The second step is to calculate the ratio of EGFP to mCherry reporter expression for each sample. The final step is divide the damaged-EGFP:mCherry ratio by the maximal possible “repair,” namely the intact-EGFP:mCherry ratio. Convince yourself that this parameter essentially provides the fraction of pMAX_EGFP_MCS repaired.

Most of your time today will be spent at the computer, quantifying your flow cytometry data. Use this time to get a strong start on the data analysis for your Systems engineering research article. On M2D9, we will use statistics to further analyze our data.

Protocols

Part 1: Paper discussion

We will start today with a discussion of the Dietlein et al. research article. In their research, the authors completed a screen to examine 1319 cancer-associated genes from 67 cell lines to identify cancer-cell specific mutations that are associated with DNA-PKcs dependence or addiction. A paradox exists in cancer as whole genome sequencing has revealed that the cells of many tumor types have mutations in genes necessary for DNA repair. These mutations are responsible for cells becoming cancerous, but are also detrimental because, just like normal cells, cancer cells must divide to survive. Thus, a cancer cell will develop an ‘addiction’ to a DNA repair pathway – specifically, a pathway different from the one with the mutation that caused the cell to generate a tumor. Recent cancer therapies seek to exploit this addiction by targeting the intact pathway used by the tumor cells to repair DNA damage due to intrinsic breaks that occur during replication. In addition, the effectiveness DNA damage induced by chemotherapy treatment may be enhanced by also disrupting the functional repair pathways of tumor cells. The review article by Shaheen et al. provides further information on repair pathway addiction as a target in cancer treatment.

Our paper discussion will be guided by all that you have learned about how to write a cohesive story that clearly reports the data and provides strong support for the conclusions made about the data.

Introduction

Remember the key components of an introduction:

• What is the big picture?
• Is the importance of this research clear?
• Are you provided with the information you need to understand the research?
• Do the authors include a preview of the key results?

Results

Carefully examine the figures. First, read the captions and use the information to 'interpret' the data presented within the image. Second, read the text within the results section that describes the figure.

• Do you agree with the conclusion(s) reached by the authors?
• What controls are included and are they appropriate for the experiment performed?
• Are you convinced that the data are accurate and/or representative?

Discussion

Part 2: Flow cytometry analysis

Overview:

• You will begin by looking at images from the instructor samples to learn how to read the flow cytometry plots and summary statistics.
• Next you will peek at your own images and form preliminary expectations about your data set.
• Finally, you will work in Excel to precisely calculate the NHEJ repair value for each of your three conditions (two replicates each).

Protocol:

1. The pdf files with your data are posted on the M2D6 Talk page.
2. The instructor samples are listed in the table below. From this table, and from the T/R and W/F image sets, try to address the questions below.
   • Background. The scatter data is used – in three steps – to make gate P3, which should consist primarily single cells. Next a gate that we called Live cells was set based upon the addition ToPro3 to the media. ToPro3 can only pass through the membranes of dead cells, staining them with a dye that fluoresces when exited with 647 nm wavelength light. The cytometer we used calls this channel the APC channel (because the dye APC also fluoresces at that wavelength). So, using the plot that shows FSC vs APC we can gate-in the live cells (exclude the dead ToPro3+ cells).
3. From the cells gated in Live Cells, two sub-gates are made that capture all GFP-positive cells ("Green cells" gate) and all BFP-positive cells ("Blue cells" gate). Both singly and doubly positive cells are included in each gate. It is important to read the "% Parent" statistics: these indicate XFP-positive cells as a percentage of all the cells in Live Cells. The "% Total" statistics include debris, aggregates, and clearly dead cells!
   • What percent Green cells are in the mock sample on each day? What about Blue cells?
   • What percent of singly-transfected cells express GFP? Do cross-day replicates agree well or not?
   • What percent of singly-transfected cells express BFP? Do cross-day replicates agree well or not?
4. Now, start to look at your K1-intact conditions (and possibly those of your classmates).
   • What percent of co-transfected cells express GFP? Express BFP? How many express both?
   • How is within-day and cross-day replicate agreement for the co-transfected samples?
5. Answer the following by looking at a team that used DMNB (hint: there is a spreadsheet on the Talk page that will help you locate those samples).
   • Does DMSO appear to affect scatter profiles? What about affecting co-expression?
   • Hint: you'll need to compare the K1 intact samples from a DMNB group versus a different group.
6. After you understand the instructor data, skim over your 12 sample plots. Can you see apparent differences between K1, K1+inhibitor, and xrs6?
7. Now that you have a good conceptual understanding of the data, it's time to crunch some numbers. Open the .csv file and save it as a newly named .xlsx file.
8. Begin by deleting all of the rows except the twelve containing your own dataset.
9. Next delete all of the columns except the few that interest you. Keep in mind that you need to know Green cell and Blue cell gating as a % of the parent gate, Live Cells. Class-wide, you are only required to do your calculations based on mean fluorescence intensity (MFI), but you should also keep the median data in case others want to use it.
10. We recommend that you prepare a new Excel file with your NHEJ equations, and just copy-paste in the appropriate % and MFI data; this approach is a versatile one. Your final worksheet might look similar to the screenshot below.
11. Remember that for each of the twelve wells you should calculate raw reporter expressions and a BFP/GFP normalized value. Then, for each intact/cut pair you can calculate an NHEJ value. In this way, we should have quadruplicate NHEJ values for most repair topology/cell population conditions, which will allow us to do statistical comparisons.

Reference information:

Day	Tube #	Condition
T/R	1	Mock transfection
T/R	2	GFP Intact Only
T/R	3	BFP Intact Only
W/F	1	Mock transfection
W/F	2	GFP Intact Only
W/F	3	BFP Intact Only

Sample NHEJ calculator screenshot.

You must email your Excel sheet to Shannon (T/R) or Leslie (W/F) before leaving lab today. We instructors will post a summary file for ease of class-wide data analysis by Wednesday evening or Thursday morning.

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