Difference between revisions of "20.109(S16):Flow cytometry and paper discussion (Day7)"

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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.
 
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.
  
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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.  
+
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, and (B) intact pMAX_mCherry 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:
 
Putting all of the above information together, the NHEJ repair frequency equation can be determined in three steps:
Line 15: Line 15:
 
::*MFI is mean fluorescence intensity ''or'' median fluorescence intensity
 
::*MFI is mean fluorescence intensity ''or'' median fluorescence intensity
 
<br>
 
<br>
−
:(2) Normalized BFP expression <math>\qquad ={RAW_{BFP} \over RAW_{GFP}}\qquad</math> = "NORM"
+
:(2) Normalized EGFP expression <math>\qquad ={RAW_{EGFP} \over RAW_{mCherry}}\qquad</math> = "NORM"
 
<br>
 
<br>
−
:(3) Reporter expression percent <math>\qquad ={NORM_{BFP.damaged} \over NORM_{BFP.intact}}\qquad</math> = NHEJ repair value
+
:(3) Reporter expression percent <math>\qquad ={NORM_{EGFP.damaged} \over NORM_{EGFP.intact}}\qquad</math> = NHEJ repair value
 
<br>
 
<br>
  
−
==Protocols==
+
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.
  
−
===Part 1: Paper discussion===
+
The second step is to calculate the ratio of EGFP to mCherry reporter expression for each sample. The final step is to 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.
  
−
As described in the Day 5 homework, we will be discussing the[[Media:M2D6paperS15.pdf | Goglia et al.]] paper in class today.  
+
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.
  
−
====Technical Background====
+
==Protocols==
−
 
+
−
The paper by Goglia et al. utilizes a fluorescence based DNA repair sensor similar to the one that you are employing in Module 2. There are, however, some important differences in the construction and function of the EJ-RFP (end joining-red fluorescent protein) sensor versus the pMAX-BFP-MCS sensor that was constructed for 20.109. Another paper from the same lab, [http://nar.oxfordjournals.org/content/41/11/e115.long published in 2013], details the development of the EJ-RFP sensor. You do not need to read the entire paper, but make sure that you understand how the sensor works so that you can fully grasp the high throughput screen that was completed in the Goglia et al. manuscript.
+
−
 
+
−
In particular, please read the Introduction and the first two Results sections of the 2013 paper. You will find this [[Media:BackgroundInformation_S15.pdf | background presentation]] to be helpful for understanding the DNA repair sensors in the Goglia et al. paper.
+
−
 
+
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====Discussion Topics====
+
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+
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+
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+
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=====Content=====
+
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+
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The following questions will guide our in-class discussion; consider them as a starting point rather than a check-list.
+
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+
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'''(A) DNA repair background'''
+
−
 
+
−
#What is the difference between canonical NHEJ and (what the authors term) mutagenic NHEJ? What type of NHEJ does your sensor measure?
+
−
#How did the authors develop a specific screening tool for mNHEJ versus cNHEJ?
+
  
 +
===Part 1: Paper discussion===
  
−
'''(B) Drug screening background'''
+
We will start today with a discussion of the Dietlein ''et al.'' [[Media:M2D7 Cancer Discovery-2014-Dietlein-592-605.pdf |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 [[Media:M2D7 DNA repair addiction.pdf |review article]] by Shaheen ''et al.'' provides further information on repair pathway addiction as a target in cancer treatment.
  
−
#What is the difference between a "reverse chemical genetic" screen and a "forward chemical genetic" screen? Why would you use one versus the other?
+
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. '''During the paper discussion, everyone is expected to participate - either by volunteering or by being called upon!'''
−
#What type of equipment did the authors use to perform the drug screen? Think about a couple reasons why this would be not only convenient, but also important for the study.
+
−
#Why was DMSO added at 1% in wells not containing drug?
+
−
#What was the purpose of adding Shield1 and TA? The authors state that the "absence" of these ligands is a positive control. What do they mean?
+
−
#What is an orthogonal assay? What is the purpose of performing these types of assays?
+
−
#What orthogonal assays were performed to confirm the original hits?
+
  
−
=====Figures & Results=====
+
====Introduction====
−
We will not discuss all of the figures in this paper. Concentrate on the Results sub-sections and Figures outlined below. Be prepared to discuss all of the figures listed below. It is completely fine to have questions about the paper and to not fully grasp all of the material, but it is expected that you will have put forth a good faith effort to do so.
+
  
−
#'''Figure 1'''
+
Remember the key components of an introduction:
−
#*Figures 1A and 1B are typical schematic diagrams that you find within journal papers. Why are these helpful and what specific information do you obtain from these sub-panels?
+
*What is the big picture?  
−
#*Figure 1C provides an example of the type of output data obtained. What factors do the authors report are important to achieve adequate cell segmentation?
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*Is the importance of this research clear?
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#*The remainder of Figure 1 shows important control experiments. Answer the following questions while you read:
+
*Are you provided with the information you need to understand the research?
−
#**What controls are shown and why is it important to show these controls in a figure?
+
*Do the authors include a preview of the key results?
−
#**What is a Z'-factor and why do the authors use it?  
+
−
#**Do you feel convinced that the data obtained in the large inhibitor screen will be believable after reading about and examining Figure 1?
+
−
#'''Figure 2'''
+
−
#*What is a RADaR plot and how do you read it? (Part of ''selling'' your science is coming up with memorable acronyms.)
+
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#*Explain the significance of Figures 2B and 2C.
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#*Why did the authors start with the LOPAC 1280 library (and what is it)?
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#'''Figure 3'''
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#*This figure shows a graphical representation of the 20,000 compound screen. How many times was this replicated? Where did the 20,000 compound library come from?
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−
#'''Figure 4'''
+
−
#*This is another great example of an useful schematic diagram in this paper. Consider all of the information that is contained in this small flowchart and, as you read, keep track of how many times you reference it.
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−
#*As you put together your Mod2 report, think about how you might use schematic diagrams to help the reader understand your study.
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#'''Figure 5'''
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#*What is RU-0084411 and why is it an interesting ''hit'' in the screen? Why might the authors (and pharmaceutical companies) be especially interested in following up on this type of hit?
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#*The curves shown under Figure 5A-2&3 are generally referred to as 'dose-response' or 'inhibition' curves. Many dose-response curves have a sigmoidal shape. How does one estimate the IC50 from analyzing this type of data?
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#*Do any of the plots shown in Figure 5A give you pause with respect to future use of Mibrefradil as a clinical NHEJ inhibitor?
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#'''Figure 6'''
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#*What is an orthogonal assay in the context of this paper? Why is it important to do them?
+
  
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=====Discussion & Conclusions=====
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====Results====
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Please read the entirety of the Discussion section.
+
−
*List three reasons why the authors state that their study is novel. What type of evidence do they use to convince you?
+
−
*List a couple limitations of the paper (that the authors address in this section).
+
  
−
The purpose of a Discussion section is (at least) four-fold:
+
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.
−
#'''Provide a summary and explanation of the data in the paper.''' This is the place to do all of your interpretation. For example, in the third paragraph of the Discussion section that starts "We identified several novel molecules...", the authors admit to being surprised at some of their findings. They then postulate why these findings might be real and suggest further studies that would be required to further tease apart the current data.
+
*Do you agree with the conclusion(s) reached by the authors?
−
#'''Convince the audience that your study makes a contribution to the field.''' The Discussion section is the place to compare and contrast your current results with those that have already been published. Why are your results interesting and important? Re-visit your Introduction -- what was your 'big picture' motivation? How did your study impact that?
+
*What controls are included and are they appropriate for the experiment performed?
−
#'''Admit your limitations.''' No study is perfect, don't let anyone tell you that it is! Perhaps your data doesn't quite get to the answer and there is a technical limitation -- tell the audience. Perhaps the cell system you are using isn't the optimal one (which may or may not be available) -- tell the audience. Perhaps the data from your DNA repair assay is noisy and you know why -- tell the audience. The Discussion section should admit to limitations and suggest specific ways to address them.
+
*Are you convinced that the data are accurate and/or representative?
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#'''Suggest the next big thing.''' Where does your study leave off? Since you are now the expert -- what is the next most important thing to do?!?
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===Part 2: Flow cytometry analysis===
+
====Discussion====
  
−
'''Overview:'''
+
Consider the following components of a discussion:
−
*You will begin by looking at images from the instructor samples to learn how to read the flow cytometry plots and summary statistics.
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*Are the results summarized?
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*Next you will peek at your own images and form preliminary expectations about your data set.
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*Did the authors 'tie' the data together into a cohesive and well-interpreted story?
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*Finally, you will work in Excel to precisely calculate the NHEJ repair value for each of your three conditions (two replicates each).
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*Do the authors overreach when interpreting the data?
 +
*Are the data linked back to the big picture from the introduction?
  
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'''Protocol:'''
+
===Part 2: Analyze flow cytometry data===
−
#The pdf files with your data are posted on the M2D6 Talk page.
+
−
#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.
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−
#*Background. The scatter data is used &ndash; in three steps &ndash; 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).
+
−
#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!
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#*What percent Green cells are in the mock sample on each day? What about Blue cells?
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#*What percent of singly-transfected cells express GFP? Do cross-day replicates agree well or not?
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#*What percent of singly-transfected cells express BFP? Do cross-day replicates agree well or not?
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#Now, start to look at your K1-intact conditions (and possibly those of your classmates).
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#*What percent of co-transfected cells express GFP? Express BFP? How many express both?
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#*How is within-day and cross-day replicate agreement for the co-transfected samples?
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#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).
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#*Does DMSO appear to affect scatter profiles? What about affecting co-expression?
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#*Hint: you'll need to compare the K1 intact samples from a DMNB group versus a different group.
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#After you understand the instructor data, skim over your 12 sample plots. Can you see apparent differences between K1, K1+inhibitor, and xrs6?
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#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.
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#Begin by deleting all of the rows except the twelve containing your own dataset.
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#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.
+
−
#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.
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#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.
+
  
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'''Reference information:'''
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In the previous laboratory session, you learned how the gates are established in flow cytometry.  This very important step is necessary in enabling you to distinguish and count the specific cell populations within your samples.  Today you will analyze the numerical data, or counts, that were collected for your transfection conditions based on the mCherry and EGFP gates.
  
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<center>
+
Before you calculate the frequency of DNA repair that occurred in your samples, examine  the data obtained in Spring 2015 to acquaint yourself with calculations discussed in prelab.  The spreadsheet below shows a template used to calculate the NHEJ repair efficiency using the EGFP:BFP reporter system employed by students previous to this semester.  Use the data in the spreadsheet to answer the questions below.
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{| border="1"
+
−
!Day
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−
!Tube #
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!Condition
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|-
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|T/R
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|1
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|Mock transfection
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|-
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|T/R
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|2
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|GFP Intact Only
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|-
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|T/R
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|3
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|BFP Intact Only
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|-
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|W/F
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|1
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|Mock transfection
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|-
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|W/F
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|2
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|GFP Intact Only
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|-
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|W/F
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|3
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|BFP Intact Only
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|}
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</center>
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<br style="clear:both;"/>
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[[Image:S14M2_worksheet-example.png|thumb|center|750px|'''Sample NHEJ calculator screenshot with Spring 2015 flow cytometry data.''' Mean (top) and median (bottom) fluorescent values were used to calculate NHEJ efficiency (%) in the wild-type K1 and Ku70 mutant xrs6 cell lines.  "GFP/BFP" indicates cells were co-transfected with pMAX_EGFP (transfection control) and pMAX_BFP (intact reporter) to determine 100% DNA repair.  "GFP/cutB" indicates cells were co-transfected with pMAX_EGFP (transfection control) and damaged pMAX_BFP_MCS (damaged reporter) to determine repair efficiency compared to 100% DNA repair (GFP/BFP value).]]
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[[Image:S14M2_worksheet-example.png|thumb|center|550px|'''Sample NHEJ calculator screenshot.''']]
+
  
−
<font color=FF33FF>'''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.'''</font color>
+
#To test yourself, use the numbers provided in the sample spreadsheet to calculate the NHEJ efficiency with the median fluorescent values by hand.
 +
#*Include your math in your notebook.
 +
#First, you will create a template in Excel that will enable to quickly determine the values for your samples.
 +
#*Feel free to use the format of the sample NHEJ calculator.
 +
#*Enter the median values from the sample spreadsheet to test your template.
 +
#[[Image:Sp16 M2D7 plot statistics.png|thumb|right|500px|'''Flow cytometry plot statistics summary.''']]Now let's look at the data collected using your conditions.
 +
#*Open the PDF posted to the M2D7 Discussion page named "Instructor FC plots" and the sample key named "Instructor FC sample key" and find the plot for sample #8 (M059K cells co-transfected with pMAX_mCherry and pMAX_EGFP).  At the bottom of the page you should see a table similar to the one shown at the right.
 +
#*The values in this table are the counts that were measured for this sample.  Consider the following:
 +
#** #Events = the number of cells counted based on the gate established (''i.e.'' the number of red cells is based on the mCherry gate)
 +
#**%Parent = the percentage of cells in a given population (''i.e.'' the number of red cells within the live cell population)
 +
#**FITC-A = filter used to 'see' green cells
 +
#**PE-Cy5-5... = filter used to 'see' red cells
 +
#*Use the numbers in the table shown here to calculate the normalized mCherry:EGFP for these data with your template.
 +
#**Confirm your answer with the teaching faculty to ensure you used the correct numbers in the calculation.
 +
#When you are confident in your template and you are confident you know which numbers to use for your calculations, find the data for your conditions in the "Instructor FC plots" document and visually compare the fluorescent values between the variables tested.
 +
#*Do you notice any apparent trends or differences in the data?
 +
#Finally, it is time to crunch some numbers!
 +
#*Open the xslx file named "Instructor FC spreadsheet" posted to the M2D7 Discussion page and locate the values for your conditions.
 +
#Copy the values into your template spreadsheet.
 +
#*Though you do not need to report both the median and mean values in your Systems engineering research article, you are required to complete both sets of calculations and post the results of both calculations to the M2D7 Discussion page on the wiki so your fellow classmates have ready access to the data.  You may want to add on to your template so both calculations are completed for you simultaneously.
 +
#For each of the twelve wells you should calculate the raw reporter expressions and a mCherry/EGFP normalized value. Then, for each intact/cut pair you can calculate an NHEJ value.
 +
#Compare the calculated values.
 +
#*Do the data support the trends or differences you noted when visually comparing the plots?
 +
#Before you leave today, you should provide the teaching faculty with the information required by the table on the M2D7 Discussion page.
 +
#*Rather than simply entering the data when you complete the calculations, please alert the teaching faculty when you have your values and she will either tell you to enter the information or will do it on your behalf. If more than one group edit at the same time data can be lost!
  
 
==Navigation links==
 
==Navigation links==
 
Next day: [[20.109(S16):Journal Club II (Day8)| Journal Club II]]
 
Next day: [[20.109(S16):Journal Club II (Day8)| Journal Club II]]
 
Previous day: [[20.109(S16):DNA repair assays (Day6)| DNA repair assays]]
 
Previous day: [[20.109(S16):DNA repair assays (Day6)| DNA repair assays]]

Latest revision as of 13:19, 4 April 2016

20.109(S16): Laboratory Fundamentals of Biological Engineering

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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, and (B) intact pMAX_mCherry 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 EGFP expression $ \qquad ={RAW_{EGFP} \over RAW_{mCherry}}\qquad $ = "NORM"


(3) Reporter expression percent $ \qquad ={NORM_{EGFP.damaged} \over NORM_{EGFP.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 to 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. During the paper discussion, everyone is expected to participate - either by volunteering or by being called upon!

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

Consider the following components of a discussion:

  • Are the results summarized?
  • Did the authors 'tie' the data together into a cohesive and well-interpreted story?
  • Do the authors overreach when interpreting the data?
  • Are the data linked back to the big picture from the introduction?

Part 2: Analyze flow cytometry data

In the previous laboratory session, you learned how the gates are established in flow cytometry. This very important step is necessary in enabling you to distinguish and count the specific cell populations within your samples. Today you will analyze the numerical data, or counts, that were collected for your transfection conditions based on the mCherry and EGFP gates.

Before you calculate the frequency of DNA repair that occurred in your samples, examine the data obtained in Spring 2015 to acquaint yourself with calculations discussed in prelab. The spreadsheet below shows a template used to calculate the NHEJ repair efficiency using the EGFP:BFP reporter system employed by students previous to this semester. Use the data in the spreadsheet to answer the questions below.

Sample NHEJ calculator screenshot with Spring 2015 flow cytometry data. Mean (top) and median (bottom) fluorescent values were used to calculate NHEJ efficiency (%) in the wild-type K1 and Ku70 mutant xrs6 cell lines. "GFP/BFP" indicates cells were co-transfected with pMAX_EGFP (transfection control) and pMAX_BFP (intact reporter) to determine 100% DNA repair. "GFP/cutB" indicates cells were co-transfected with pMAX_EGFP (transfection control) and damaged pMAX_BFP_MCS (damaged reporter) to determine repair efficiency compared to 100% DNA repair (GFP/BFP value).
  1. To test yourself, use the numbers provided in the sample spreadsheet to calculate the NHEJ efficiency with the median fluorescent values by hand.
    • Include your math in your notebook.
  2. First, you will create a template in Excel that will enable to quickly determine the values for your samples.
    • Feel free to use the format of the sample NHEJ calculator.
    • Enter the median values from the sample spreadsheet to test your template.
  3. Flow cytometry plot statistics summary.
    Now let's look at the data collected using your conditions.
    • Open the PDF posted to the M2D7 Discussion page named "Instructor FC plots" and the sample key named "Instructor FC sample key" and find the plot for sample #8 (M059K cells co-transfected with pMAX_mCherry and pMAX_EGFP). At the bottom of the page you should see a table similar to the one shown at the right.
    • The values in this table are the counts that were measured for this sample. Consider the following:
      • #Events = the number of cells counted based on the gate established (i.e. the number of red cells is based on the mCherry gate)
      • %Parent = the percentage of cells in a given population (i.e. the number of red cells within the live cell population)
      • FITC-A = filter used to 'see' green cells
      • PE-Cy5-5... = filter used to 'see' red cells
    • Use the numbers in the table shown here to calculate the normalized mCherry:EGFP for these data with your template.
      • Confirm your answer with the teaching faculty to ensure you used the correct numbers in the calculation.
  4. When you are confident in your template and you are confident you know which numbers to use for your calculations, find the data for your conditions in the "Instructor FC plots" document and visually compare the fluorescent values between the variables tested.
    • Do you notice any apparent trends or differences in the data?
  5. Finally, it is time to crunch some numbers!
    • Open the xslx file named "Instructor FC spreadsheet" posted to the M2D7 Discussion page and locate the values for your conditions.
  6. Copy the values into your template spreadsheet.
    • Though you do not need to report both the median and mean values in your Systems engineering research article, you are required to complete both sets of calculations and post the results of both calculations to the M2D7 Discussion page on the wiki so your fellow classmates have ready access to the data. You may want to add on to your template so both calculations are completed for you simultaneously.
  7. For each of the twelve wells you should calculate the raw reporter expressions and a mCherry/EGFP normalized value. Then, for each intact/cut pair you can calculate an NHEJ value.
  8. Compare the calculated values.
    • Do the data support the trends or differences you noted when visually comparing the plots?
  9. Before you leave today, you should provide the teaching faculty with the information required by the table on the M2D7 Discussion page.
    • Rather than simply entering the data when you complete the calculations, please alert the teaching faculty when you have your values and she will either tell you to enter the information or will do it on your behalf. If more than one group edit at the same time data can be lost!

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