The Graph Window, Part 2

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I'm gonna move into this CD3 positive population, and I'm gonna go ahead and take a look at the T cell marker CD4 to CD8 on the Y, CD4 on the X. And this is a nice distribution where you see the different populations of T cells in the peripheral globe. This is a great distribution for a quadrant tool. Quadrant will subdivide into four distinct populations that are all linked together. They are Straight Quads, that I'm showing you here with the blue lines. Then there are Curly Quads, which bend with the spillover spreading ... So you can take that into account; with a Bendy Quad. Or even a Spider Quad, where you set the center, and then you can angle the arms of the quadrant gates just the way you like them, if you don't have the best separation between those different populations.

And so I'm gonna go ahead and stick with those Spider Quads, and focus on the CD8 cells ... And we'll take a look and three more markers. I'm gonna go Phospho-ERK here; this is the phos-4 laded form of the extracellular signal regulated kinases. I'm gonna use a rectangle gate. I'm gonna call this PR positive ... There's my gated population. I'm gonna change then, the parameter on the Y axis to look at Interferon Gamma. Remember this is a, "no stem" sample so far, so there's not much Interferon Gamma going on. Now we'll cause IFNG positive, and then we'll do one more ... Which, is the perforin marker in cytotoxic T cells. This is the granule cytotoxic, granule associated protein. I'm gonna call that; per Perf+.

I made single marker gates ... And you know, I don't like how much extra space I'm wasting in this profile, with that much extra negative decade space. And so what I'm gonna do is go to the T button here, customize access, and remove the negative decade space by using that extra negative decade "slider" ... Taking it to the left. And I'm gonna apply that change; not just to perforin, but the other two markers that I just looked at ... The Phospho-ERK and the interferon gamma markers. Click "apply" and it spreads that data out. I'm using the graphical space better to show the distribution, instead of wasting a bunch of space on the top of the bap.

So far, I have these populations that we've created ... And you'll notice that the statistics here are the frequency appearance; and then the number of cells go down. So I start with 250,000 and go down to 34,000. So Ebony asked, "Is there anywhere else to manipulate Bi-exponential transformation in FlowJo?" You can set for any instrument in your preferences, which is available from this "heart" icon in the top right-hand corner. If you go to "preferences" and use the "Cytometer's" preferences pane, you'll have a list of instruments that your installation has received files from. So in this case I've just got a couple, 'cause I reset my "prefs" recently. But here's my LSR2 instrument under the "dollar sign" site; keyword says, "LSR2."

What you can do is, enabled transforms and set the default width bases, that files from that instrument should get when they come into FlowJo. And so, oftentimes you may load data from an LSR2 and it'll get by default, a width basis of -100. That seems to compressed for me. I prefer to use a -10 width bases to start, and to see how that looks. I just find that spreading that data out a little more with a -10 as default, is better for most of my data files that come off of the LSR2's in Fortessa. But that'll be unique to every instrument and really every panel, depending on your panel. So that is the way you can get a good starting point. By finding a width bases compression that you like, Going to your preferences; "cytometer's" preferences, and then selecting that instrument and setting the width bases here. When I Press "OK" the new workspace I create will now have that as the default width basis. Does that make sense Ebony?

There are also transform types, of course within the graph window "T" button. Customize axis ... We have different scaling for different instrumentation. So there's Arcsine for CyTOF data, Hyper-logs Logicle Miltenyi transforms, or just regular linear or logarithmic. But most Fluorescence Cytometry data is best gonna be displayed on This Bi-exponential transform. And then you really just have the ability to scale the top end, the bottom end, or the compression with that Bi-ex transform.  Okay so right now, I have these data ... I can look at this data distribution, but I can not scroll through the other samples because this "gating tree" only lives on a single sample.

What I'm gonna need to do, is applied these gates to a group of samples here in my "gating tree" or in my groups pane, in order to apply them. And the simplest mechanism here, is to highlight the group you want, highlight the gating population; the gates that you just created ... And then what I do is "right click" and copy my analysis to the group I have selected here. "Copy analysis to group" is a "right click" mechanism.  You can also grab the gates and hold down the "mouse" button, and "drag-and-drop" them onto the group, and that will apply them in the same way. But I like to get use to this, "copy analysis to group" mechanism in FlowJo version 10. For some other reason, if you have gates that are intermittent, it's easier to get them to the right population apparent.

So I'm gonna "copy this analysis to group" ... Boom! And it goes to my master gates group. The gates you notice, turn red, which means that there now "group owned." I've Group applied to gates ... Every red gate at any given level in the gating hierarchy, is identical to every other red gate on all of the samples in that group. So that's what I call a unified master gating structure. Now when I open this up ... I have to open the CD8 population; I look at a gating marker like let's say, Phospho-ERK. So if I start with a "no-stem" sample; no stem, not much going on right? 5% positive ... There's a few that have phosphorylated. I can now use my navigation buttons in the graph window, which, are in the top right hand corner. The left and right arrows, just scroll through the different samples.

And I'm gonna use the next sample button here, and it'll shape me to the 20 minute stem. And you can see the shift in phenotype here, where all of those cells have now phosphorylated the ERK protein, and we are detecting the phosphorylated form. If I go to a 2 hour, or a 2 hour 20 minute, it does the same thing. And I can see that happen over and over again. "No stem" 2 hours, 2 hours and 20 minutes. If we look at a different marker, like Interferon Gamma; then you see the "no stem" doesn't have much going on, there's not many events that are positive. If you give it a "20 minute stem," that's not sufficient for Interferon Gamma to become detectable ... It takes at least 30 minutes to really see it. And then if I go to the 2 hours, then you get a nice response with Interferon Gamma profile.

2 hours 20 minutes ... Same thing second patient, no stem, 20 minutes is not sufficient for gamma to come up, 2 hours is. And in so you can scroll through in the graph window, and review the gating at any level in the gating hierarchy, using your navigation errors here. But that would be very inefficient if you have lots of markers to look at. And so what I recommend people do, is go to the next step, which is; once you've created your gates, let's go ahead and make a overall gating hierarchy view using your "layout editor" ... Where we can see plots at every level in the gating hierarchy and review all the gates at the same time.