Introduction to FlowJo v10 March 2022

0:01all right good evening everyone um

0:05my name is jack panopoulos i'm an

0:07application scientist for bd

0:09specifically with uh flojo

0:12and today we're going to be doing an

0:14intro to flojo

0:16so i'm going to be talking about you

0:19know setting up your flow cytometry

0:21experiments basically how to use flowjo

0:24and try to teach you how to use flojo in

0:26a

0:27you know the most efficient uh manner

0:30possible and i'm going to cover some of

0:32the basic concepts too in flow cytometry

0:34especially when it comes to topics like

0:37compensation um you know and how to you

0:41know some of the statistics that we have

0:42to add and report on

0:44why we choose particular statistics

0:48and that sort of thing we have a fairly

0:50light crowd today it looks like there's

0:52about i don't know seven of us or so

0:54and um

0:56you know the likelihood of people coming

0:58in later is usually pretty high so um in

1:01any case what i would like to do

1:04is

1:05i would like for you guys if you have to

1:08uh if you want to ask a question during

1:10today's session you can use the chat box

1:13and i'm actually going to send a message

1:16to you in the chat box right now i'll

1:18just say hello everyone

1:21and what should happen on your end is

1:23you should see

1:24the zoom panel kind of light up in

1:26orange and you will be able to see the

1:29uh the chat box so sometimes

1:31when you are the attendee

1:33or the panelist sometimes the chat box

1:36is not um

1:37super easy to find

1:39there's also a q a box so if you would

1:42rather you know ask your question there

1:44by all means you can do that and i will

1:46try to go over those questions

1:49you know in between various modules

1:52uh in flojo as we as we go through the

1:55data

1:57okay so

1:58um i'm going to close this little window

2:01or the series of windows here and we'll

2:03go ahead and get started i'm not really

2:05going to use the powerpoint too much

2:07today there is one slide that we will

2:09cover in a moment um but

2:12what i do want you guys to get from this

2:14is if you have any questions with your

2:15flow data

2:16uh you know if you're running into

2:18problems with flojo or there's something

2:19you can't find or whatever but feel free

2:21to send me an email my email is simply

2:23jack.flojo

2:26bd.com okay so we'll go ahead and get

2:29started here

2:30i'm going to be talking to you guys

2:32today about flojo v10 and how we do our

2:34analysis in that particular software

2:36there are older versions of software

2:38that are out there like version 9 and

2:39version 8 etc and they have kind of a

2:42slightly different interface but the

2:43general idea

2:45behind

2:47all the versions of flojo is fairly you

2:50know fairly similar in how you interact

2:52with uh the files and so forth so

2:56let's get started and uh generally

2:58speaking we're gonna probably want to

3:02bring our data files directly into the

3:04flow joe interface so it's worth taking

3:07a second here and mentioning what types

3:08of files

3:10flowcho will actually work with

3:13and in general most flow cytometers will

3:16generate a file that has this particular

3:19extension here the dot fcs so that

3:22stands for

3:24flow cytometry standard

3:26and this is the most widely used and

3:29easiest for flowjo to interpret so if

3:32you have an opportunity

3:34some machines for example for example

3:37beckmann coulter machines a lot of times

3:39will export

3:41an lmd file uh lmd file

3:44uh max will export

3:47mqd files

3:49but you have the option on those

3:50machines to make sure to export as fcs

3:53and that's just my recommendation so

3:55that you minimize your headaches

3:58you know trying to analyze the data

4:01okay so in flow joe you can simply drag

4:04and drop the folder that contains those

4:06fcs files even if that folder contains

4:09other

4:10files in it flo joe will just ignore all

4:13those other files and it'll just pay

4:15attention to

4:16the flow cytometry standard files okay

4:19so when you drag that folder in

4:21the files will populate down here and

4:23then in the ledger kind of in the middle

4:25of your workspace

4:27uh you will see a new line so in the

4:30beginning you saw there's this all

4:32samples and then you saw compensation

4:35but then after that you can see now

4:37they have a ledger that's labeled

4:39immunophenotype data basic okay so this

4:42is just the folder that i had dragged in

4:44from

4:45uh from my workspace right this guy

4:48right here

4:50okay

4:51so

4:52that's one way to bring in your data if

4:53you would rather navigate your file

4:55system you can so

4:57at the very top of your workspace

4:59there's a series of

5:00tools

5:02undo and redo buttons and of course

5:05a button here to add your samples

5:07there's a bit of redundancy because also

5:09underneath the flow joe tab here in this

5:11navigate band you see this add samples

5:13button they do the same thing if you

5:15click the button it's going to ask you

5:17to navigate your file system right to

5:20then go ahead and find the

5:23fcs files that you want to to bring in

5:26so again you can choose the folder or

5:27the individual file click choose and

5:29then it will populate to

5:31uh the workspace

5:33okay now the rest of the workspace here

5:35at the top since we're discussing it

5:38i'll kind of mention it

5:39this button creates extra ledgers in

5:41here so we're going to create different

5:43groups

5:44and uh it's a good idea to group your

5:46samples so that when you go to create

5:49figures for a paper

5:50uh

5:51you know you only get the figures that

5:53are relevant to the the

5:55experiment that you're doing rather than

5:57you know getting stuff that's uh

5:59associated let's say with your single

6:01stain controls only or maybe uh some

6:03other type of control that you don't

6:05need to include

6:07for the figure

6:08okay so it's a good idea to group these

6:11group your files and i'll show you how

6:12to do that momentarily then of course

6:15there's a compensation wizard so this is

6:17what we would use if we're in the

6:19compensation group to generate a

6:21compensation matrix and i'll talk about

6:23how to do that a little bit later

6:26and then we have something called the

6:27table editor here this is where we go to

6:29export our statistics from flojo and put

6:32them into a format that either you know

6:35excel or

6:36graphpad prism or something similar can

6:39use so a table editor will export csv or

6:42excel files for you it's very a handy

6:44little tool

6:46the big l right here this stands for

6:48layout editor

6:50this will um be the place where we go to

6:52create all of our graphical figures so a

6:55lot of the comparisons that we're going

6:56to make if we want to look at you know

6:58how does one time point compare to

7:01another the layout editor is generally

7:03where we go to do that kind of uh

7:05analysis

7:06okay and the latter two buttons really

7:08not super useful at you know unless your

7:10workspace is constantly getting stuck

7:12you can recalculate the statistics here

7:14and this is a button for

7:16accessing

7:17bd research cloud which isn't quite yet

7:19available probably be available sometime

7:21this summer

7:22um

7:24and then i should mention the big

7:26black ball here on the left hand side

7:27with the gray pyramid inside

7:29hey you know it's just like a you know

7:32file tab or whatever has the ability to

7:34open previous workspaces as you can see

7:36here create new ones and save and stuff

7:38like that a lot of general functionality

7:40there okay and i'll talk about all the

7:42different tabs here as we

7:44uh navigate through the program but in

7:46general you click on the tab there are a

7:48lot of different functions that are

7:49contained within each

7:52and i'm going to try and keep it you

7:54know to a clicking minimum if you will

7:57throughout the program

7:59okay so when you bring your data files

8:01in you'll notice that there are three

8:02little icons to the left of your files

8:04right the first icon that's the most

8:07different looking in this scenario that

8:08we're looking at right now is

8:10this one right here it's either a

8:12presents itself as an open square or as

8:14a gray grid okay if your files come in

8:18as open squares like this it means that

8:20the files do not have any compensation

8:23associated with them

8:24and if you're running a multi-color

8:26experiment let's say two or more colors

8:28where you have

8:30spectral overlap between those colors

8:32you need to run compensation first

8:35and apply that compensation matrix to

8:37your samples before you begin the

8:39analysis okay so in my case here all my

8:42single stain controls do not have a

8:44compensation matrix associated with them

8:47okay but all of my test files and my

8:50fluorescence minus one controls all have

8:53this gray grid and when i roll my mouse

8:55over it it says compensation matrix

8:57colon and then it tells you acquisition

8:59defined it means that

9:01you have a compensation matrix that you

9:04generated while you were on

9:06uh the machine okay so that's what

9:08basically acquisition defined

9:10uh

9:12means in this case

9:13okay

9:15all right so

9:16if your files

9:18come in with this gray grid

9:21i generally like to do a little bit of

9:23quality control before my analysis

9:25starts

9:26so the first thing i do is i look at a

9:28fully stained file and i just inspect

9:32the compensation matrix on that file

9:35okay and so in this case i'm going to

9:37choose the first file that is fully

9:39stained

9:40and it has a gray grid next to it i

9:42double click on that and it opens up

9:46a window that's called the matrix editor

9:48okay this window is very important

9:50especially when you are well it's always

9:52important but

9:54when you're beginning it's a wise idea

9:56to always take a look at this window

9:58before you begin your analysis simply

10:00because you need to check your comps to

10:02make sure the compensation was done well

10:03in order to you know uh go forward with

10:06your analysis

10:08okay so what happens is you get this

10:11table at the top um that has a

10:14you know all of the spillover values for

10:16the various fluorochromes which are here

10:18on the left into the various detectors

10:19that you have on the machine

10:21now normally i don't pay too much

10:23attention to these numbers okay the

10:25numbers are not so important what really

10:27is important

10:29are the plots that you see down below

10:31okay so i squish that little table

10:35and then in the plots down below

10:38you're gonna see every two by two color

10:41combination that's possible in your

10:43experiment okay so in this case here

10:45we're looking at the combination in this

10:47first plot right we're looking at alexa

10:49700 versus pack blue

10:52okay and in the next plot over this is

10:54going to be alexa 700 versus pe texas

10:57red so on and so forth okay now there's

11:01two colors two sets of colored dots that

11:03you see by default the first

11:06color that you kind of will immediately

11:08notice i think is the blue

11:10the blue dots represent the raw data or

11:13the data that do not have any

11:15compensation so this is what your data

11:17would look like if you didn't do any

11:18compensation on the machine wherever the

11:21blue dots fall

11:22you would see uh you know some uh

11:26that that would be what your raw data

11:28you know uncompensated data look like

11:31now in the case of these plots that sort

11:33of have the warmer colored background

11:35the warmer colored background is just an

11:37indication that these two dies in this

11:40case alexa 700 and apc87 they have a lot

11:43of interference with one another one

11:45spills over into the others detector

11:48quite heavily and so the color in the

11:50background here goes becomes more uh

11:53like a

11:54more warm right more like uh yellow or

11:57orange or red

11:59okay the higher the value the more red

12:01it becomes

12:03now this is just a tip

12:05so that you know which plots to kind of

12:07zoom in on um in the you know in the

12:09very beginning so

12:12obviously you can see here if you didn't

12:14do any compensation you get this sort of

12:16banana shaped population going through

12:18the middle of your plot and then if you

12:19look at the the bottom of the you know

12:22the bottom blue dots here you can follow

12:24them along the x-axis they suddenly get

12:26you know very bright and then they sort

12:28of curve upward they have this kind of

12:29strange double positive characteristic

12:32when the cells are extremely bright

12:34okay

12:36there are some other plots here that are

12:38characteristic telltale signs of

12:39uncompensated data right when you see

12:41these diagonals going through the middle

12:44you have something that looks like this

12:45okay that's an indication that no

12:47compensation was applied

12:49now in our case we do have a comp matrix

12:51so we're going to go ahead and untick

12:53this box

12:54and what i want to do now these black

12:56dots represent

12:59what your data look like with the

13:01current compensation matrix okay so the

13:03current compensation matrix is listed in

13:06uh the first line here on the left

13:09okay i happen to have an extra file that

13:12i brought in that i exported and brought

13:14in and that's why i have two of them

13:15there but generally speaking

13:17when you first bring in your data you're

13:19probably only going to have one

13:22okay so going through this now what i'm

13:24looking for when i look at these plots

13:26is i'm looking to see if i see any of

13:28those diagonal populations going through

13:30the middle of my

13:32any of these plots okay i also want to

13:34see whether these single positive

13:36populations do they

13:38you know bend and crash in towards the

13:41x-axis

13:43or vice versa do they bend and crash

13:45into the y-axis right or do they form

13:49those banana-shaped populations and kind

13:51of you know drift towards uh towards the

13:53middle

13:55so i'm looking for those three things

13:57okay

13:59the

14:01single positive populations generally

14:03speaking we what we want in an ideal

14:05world is these single positive

14:07populations the middle of these single

14:09positive populations are going to match

14:12the middle of the double negative

14:14population on the left hand side okay

14:17same thing here if i look at my

14:19single positive population that falls

14:21along the y axis i see the middle of

14:23this population looks like it aligns

14:25pretty well with the double negative

14:27population here

14:29okay so this is basically what we want

14:31to see these nice kind of right angles

14:33between the populations

14:36but primarily we don't want to see

14:38bending of these populations down

14:40towards the ax either axis

14:43okay or towards the center of the plot

14:45now on some of these plots we have a

14:47little bit we have some cells that are

14:48starting to form like these little

14:50diagonals

14:51um you know uh keep in mind we're

14:54looking at a fully undated file so we've

14:56got some dead cells debris and doublets

14:58in here so if it's a few cells i'm not

15:00too worried right we have a little bit

15:02of bending over here and a little bit of

15:04bending over here but nothing major

15:06and i assume that this is probably going

15:08to vanish the minute we get rid of our

15:10dead cells because dead cells tend to be

15:12really really bright

15:13for everything okay

15:16in general these populations look pretty

15:19pretty good okay so this is an example

15:22of a pretty darn good compensation

15:25uh matrix

15:27now let me give you an example of a poor

15:29compensation matrix so you have a good

15:32idea of what poor compensation looks

15:35like in case you've never seen it

15:37okay

15:38so this is a different experiment done

15:40by different folks at a different

15:41location

15:43i'm going to go ahead and double click

15:44on their comp matrix editor

15:48all right zoom in here

15:51and i'll squish the table

15:54and again i'll untick the box that shows

15:57the uncompensated data okay

16:00so in this case if we start looking at

16:02these plots

16:04a few of things pop out immediately

16:06right you have a population here along

16:09the you know the y-axis that is

16:11definitely crashing in towards the

16:13y-axis here

16:15bending significantly away from the

16:17negative population right so the middle

16:19of this guy doesn't look like it's

16:21aligned with any of these populations

16:23here

16:24okay if we look at this plot over here

16:26we can see this nice round single

16:28positive population along the y

16:31this population over here looks nice and

16:34round along the x

16:35and then the middle of this population

16:37seems to align really well with this

16:39population here same thing with the one

16:41on the y

16:43uh but the problem then becomes well if

16:45this is the double negative population

16:48then you know what the heck is this guy

16:50over here

16:51okay so

16:52obvious problems when things are not

16:55lining up appropriately right we have

16:57definitely have

16:59uh

17:00offset

17:01medians in this case okay

17:03situation gets worse

17:05when we look at this plot you can see

17:07that

17:07you know these cells are starting to

17:09reach into that

17:11that y-axis over there

17:14all right same problem with the double

17:16negative you you don't know where the

17:18true double negative happens to be

17:21everything seems a little bit off right

17:23definitely some hooking over here

17:25more cells reaching back

17:28in this plot same thing over here and

17:30same thing over here right it's really

17:34this is a really poor compensation uh

17:37matrix and now the question is well why

17:39the heck did this compensation fail

17:42right how does it happen

17:44so

17:45and before i show you what went wrong

17:48with this particular experiment it's

17:50very important that you guys you know

17:52understand the

17:54rules for good compensation in the first

17:57place so i'm just going to show you this

17:58particular slide

18:00mario rotor wrote these rules in a paper

18:03very long time ago

18:05but those pa those rules still are

18:07relevant today

18:09okay regardless of the flow cytometer

18:11that you're using so the first rule is

18:13making sure that your compensation

18:15controls need to be at least as bright

18:17or brighter

18:19than any of the cells in the sample that

18:21you're willing that you're going to test

18:24right so if you know that when you

18:26simulate your samples the cells begin to

18:29express some sort of antigen and that

18:31expression gets really really high

18:34you need to make sure that your single

18:35stain control is at least as bright as

18:38your stimulated cells

18:40right or brighter it's best if it's

18:43brighter because you know you won't have

18:44to worry about any uh you know any

18:47problems there but

18:48just make sure that your comp control

18:51is at least as bright or brighter than

18:53anything that you intend to test that's

18:55extremely important and i have to say

18:56that when i look at people's data and

18:58they ask me what went wrong with their

19:00flow experiment

19:0190 percent of the time i would say that

19:03it's it's because of the violation of

19:05this rule

19:06okay now rule number two is making sure

19:08that the background fluorescence

19:10should be the same for the positive as

19:13well as the negative for your single

19:15stain control so all this really means

19:18is that if you are using beads to

19:20compensate for a particular color

19:23right let's say you have apc in the

19:25experiment

19:26you need to have unstained beads

19:29right and beads that have been stained

19:31with apc h7

19:33in order to

19:35have the background fluorescences match

19:38up right because

19:39beads and cells have different

19:41autofluorescences and we'll see this

19:43when we start to compensate today's

19:45experiment

19:46but again you want to make sure that for

19:48any given color you're using the same

19:50thing so if you're you know for apc if

19:53you're using beads and let's say for

19:54alexa 488 you're using cells just make

19:57sure that you have unstained cells and

19:59positively stained cells don't use

20:02you know unstained beads and positively

20:04stained cells for the same color

20:08okay and then the third rule here is

20:10making sure that your comp controls

20:12exactly match the you know the the floor

20:16chrome that you use in the experiment so

20:18it's very tempting a lot of times like

20:21uh you know if you've got i don't know

20:23cells let's say you've got transgenic

20:24mice and they already express gfp you

20:27don't want to have to go around looking

20:28for a gfp cell expressing cell line or

20:31transfex themselves with gfp

20:33you just decide oh i'm going to go into

20:35the fridge and grab fitsy or alexa 488

20:38and use that as a comp control for the

20:40gfp that's in my mouse

20:42you know endogenous sort of in my mouse

20:44cells and that's a no-no even though

20:47alexa 488 and fitzy are you know have

20:50very similar excitation uh

20:54emission spectra like gfp

20:57they're not the same okay in fact each

21:00one of those fluorochromes is slightly

21:01different than the other and that slight

21:03difference is huge when we talk about

21:06quantum mechanics and that's what flow

21:08psychometry unfortunately

21:11is all about right quantum mechanics

21:13photons etc so

21:15make sure that if you have a gfp mouse

21:19that you're using gfp

21:21uh transfected cells

21:23as your uh single stain control right is

21:26as the positive and you need to have

21:28unstained cells that have no gfp in them

21:30to be your your negative okay do not

21:33substitute fitsy for gfp or alexa 488

21:36for

21:37fitzy or vice versa

21:39okay so that's

21:41those are the good rules the fourth rule

21:43here that i would you know recommend to

21:45you guys it's not written but it's good

21:47panel design take the time

21:49to design your panel well because if you

21:52design a panel that looks you know the

21:54light is well staggered but it doesn't

21:57match

21:58the you know the setup of your machine

22:01like you know what your detector setup

22:03is it doesn't matter how great your

22:05panel is so you always have to build

22:06your panel around your machine and

22:08understanding as much as you possibly

22:10can about the biological system that

22:12you're looking at so that and of itself

22:14is a is a talk

22:15and we just don't don't have the time to

22:18you know to go over uh panel design

22:21but in general

22:23those are the

22:25the good rules for good compensation

22:26okay so let's go ahead and see what

22:28these guys did

22:30uh to violate this and and cause

22:32problems here

22:33so

22:34what i normally do to troubleshoot my

22:36compensation if it doesn't work or

22:38doesn't look right

22:40the first thing that i do is i take my

22:43single stain controls

22:45right that are in your comp group or

22:46somewhere in your workspace and i

22:49overlay them

22:50with the most egregious file so if

22:54there's a particular file in your setup

22:55that just has really wonky looking

22:58populations i take that one and i

23:00overlay it with each of my single stain

23:02controls

23:04and then i look at it across each color

23:06on the uncompensated

23:09parameter so i've already done this for

23:11you guys i'll show you how to create an

23:13overlay later on but in general this is

23:15a troubleshooting step that i would

23:17recommend to you if you have problems

23:19with compensation

23:21okay so let's pay attention i'm going to

23:22shrink this just so we pay attention to

23:24the first graph and then i'll

23:26you know slide along

23:27to the right

23:29okay so here is bb421 uh but this is a

23:32single stain control we have for bb 421

23:35and then the fully stained file is in

23:37blue okay so the test file is in blue my

23:40single stain control is in red and all

23:43i'm looking to see here is how bright

23:45is my sample compared to

23:48my single stain control so in the case

23:50of the the blue here i can see that

23:53falls between the goal posts if you will

23:55of the single stain control so as far as

23:58compensation is concerned

24:00bv 421's comp is pretty bright and it

24:03passes the cells are not brighter than

24:06my compensation control so this one is

24:08good that gets a green check mark okay

24:11if i go to the next

24:13compensation control in this case we're

24:15looking at buv395

24:19again the compensation control is in red

24:22the sample is in blue and i can see the

24:24sample is really block really bright but

24:27it overlaps with my single stain control

24:30so in this case this compensation

24:32control is sufficient for

24:35this particular experiment so get a

24:36green check mark for buv

24:39395.

24:41okay but now what happens right i go to

24:44bv605 okay same scenario cell's sample

24:48is in blue

24:50control is in red and you can clearly

24:53see here that the

24:55sample the test sample has a group of

24:57cells that are much brighter

24:59than

25:00the compensation control right they

25:03escape it they exceed the brightness and

25:05so these guys i usually refer to these

25:07as my little escapees

25:10these guys are the ones that cause all

25:12these crazy problems in your flow

25:14experiment because they will appear

25:17either over compensated or under

25:19compensated

25:21and in weird locations throughout your

25:24uh throughout your analysis okay so

25:26bb605 gets a red x going through it

25:29that's a terrible

25:31uh terrible control or at least it's a

25:33terrible setup there

25:35if we look at p e next same thing right

25:38the cells are brighter than the

25:40compensation control so that's another

25:42one that gets a red x so now you got two

25:44colors that are blown

25:45we go over to alexa 647 in this case

25:49uh the comp control is sufficient okay

25:53so

25:54two of the five colors are problematic

25:56here and one thing that i would mention

25:59is this compensation control just you

26:01know from having experienced you know

26:03comp control that's out to 10 to the

26:05fourth or more is pretty dang bright

26:07okay but

26:09the sample what's interesting here is

26:11the sample's background is really really

26:13high right if you have a thousand

26:15fluorescence units as your baseline

26:18you're doing something wrong generally

26:20unless your cells are just naturally

26:22autofluorescent which in this case they

26:24are not

26:25it's just the fact that these guys did

26:27not titrate the antibodies first okay so

26:30don't run a flow cytometry experiment if

26:33you've never titrated the antibodies

26:35this is the type of stuff that you're

26:36going to run across and you're going to

26:38have

26:39you know headaches

26:40these guys could probably titrate this

26:42antibody down about 10 fold and it you

26:45know this would be just fine the comp

26:47control would be okay because the cells

26:50would you know cells would

26:52move down a notch or two

26:55in the case of pe though i don't know pe

26:57is a really bright dye

26:59thus

27:00antibody seems titrated well enough

27:02because the the background here is

27:04matching up pretty well with the you

27:06know with the baseline where it ought to

27:07be

27:08but for whatever reason their their

27:10whatever antigen they used here is

27:12pretty weakly expressed if it was beads

27:14that they were using

27:16i'm not exactly sure why the bead is so

27:18inefficient at binding that particular

27:20antibody but in any case that that's

27:22that's the problem okay so two colors

27:24blown

27:25not bright enough and that leads to this

27:28absolute mess of a compensation matrix

27:30and the bad news is you can't correct

27:32this

27:33you know a lot of people try to come in

27:35here and change these values around and

27:36it's like playing whack-a-mole you

27:38change this value over here it might

27:41move this you know these populations in

27:44a little bit but then you'll notice that

27:46in other plots those cells look you know

27:48other cells look really weird so it

27:50becomes this

27:52constant you know manipulation of the

27:54data to get it to look the way you want

27:56it to and that's not good for

27:57reproducibility

27:59and a lot of reviewers nowadays are

28:01gonna you know they ask you for your

28:02compensation uh files when uh

28:06when reviewing uh

28:07you know your your manuscripts so

28:11do the compensation right in the

28:12beginning and then you should be okay

28:15you know down downstream in your

28:17analysis

28:18all right so i'm going to close that

28:20workspace

28:22and that's

28:23uh concludes sort of quality control

28:25step number one inspecting the

28:27compensation matrix okay the next thing

28:29that i always recommend people do is to

28:32take a look at the time axis

28:35okay because when you look at the data

28:39a lot of times you know when we go to

28:41the

28:43flow cytometer we don't filter our

28:45samples and sometimes we clog the

28:46machine

28:47and clogs can cause problems with the

28:49data as well so you want to be aware of

28:51those problems

28:53before you begin your analysis and what

28:55i would do here is i would just like

28:57you know change the x-axis over to time

29:00generally speaking if you put uh i don't

29:02know the y-axis to

29:04kind of like a far red emitter the far

29:06red emitters are a little bit more

29:07sensitive usually

29:09um to perturbations in the you know the

29:12flow rate if you have a clog you'll see

29:14shifts

29:15generally a lot uh

29:17a lot more pronounced in the red channel

29:19than you will in the others okay but

29:21it's not

29:22not the hard and fast rule that way but

29:24generally what i'm looking for here is

29:26just to make sure that everything is

29:27sort of evenly distributed from left to

29:29right across the screen right from left

29:31to right

29:32everything looks pretty

29:34pretty nice and even there doesn't

29:36appear to be any problems here okay so

29:38i'll show you again another

29:40uh i'll show you another workspace here

29:43i'm going to go ahead and delete my

29:44gates

29:46and i'll show you this guy that has some

29:48difficulty okay so

29:50let's look at this

29:52right if i just look at time versus side

29:55scatter you can clearly see

29:57right there are some time points there

29:59are some cells here that have been

30:00shifted downward with respect to scatter

30:03and then after about the 20 second time

30:05point or so everything looks pretty even

30:07and smooth without this

30:09extreme adjustment of these cells in a

30:12downward uh direction

30:14okay so

30:16this is a perfect example of a clog

30:19you have a lot of like start stop you

30:21see a lot of lines here there's a lot of

30:23cells kind of bumping around shifting

30:25their fluorescence intensities

30:27and it's not their fault it's just that

30:29this is what happens when we clog the

30:31machine there's a lot of

30:33um you know there's a lot of uh

30:36stuff where the cells get stuck you know

30:38in the laser etc and uh you get a lot of

30:42a lot of odd

30:43looking populations okay so you want to

30:46be aware that this is there and um

30:51what i would recommend that you do is

30:52just a couple of routes you can go here

30:54is you can either clip out

30:57you know the bad data by creating a gate

30:59on the good stuff right extending this

31:02all the way out you could just i don't

31:03know call it time or something like that

31:06you know and then work with this

31:07population going uh going downstream

31:12uh the other thing that you could do

31:13here is you could use something called

31:15flow ai this is a automated tool that we

31:18make available to you guys

31:20and basically what this does is when you

31:22apply it to your sample it goes through

31:24and tries to clean up

31:26the data automatically

31:28and so i've run this algorithm already

31:31but i'll just show you what the data

31:33here look like when

31:36you run this particular tool okay so

31:38here are the bad

31:40you know the bad cells and then on the

31:42right hand side is the good events that

31:44it that it gives you okay so it does a

31:46pretty good job of eliminating those

31:48um cells that are in the in the clog

31:51gets rid of it has some other checks so

31:53it it gets rid of other cells

31:55um that spike throughout the system so

31:58not all the cells will be centered

31:59around that that particular clog

32:02but in any case um you know it you can

32:05you can modify what you want the

32:07algorithm to check so you can tell it

32:09that you want to only look at

32:11violators say of the flow rate alone or

32:13the signal acquisition right or you want

32:16to just uh you know look at stuff that's

32:19within the particular dynamic range okay

32:22there's a lot of manipulations you can

32:23make to the algorithm but if you guys

32:25want that particular plug-in try to use

32:27it to clean up your data

32:29go to the workspace tab here at the top

32:31so you'll notice that flowjo has a bunch

32:32of these tabs you click on the one that

32:35says workspace there's this populations

32:37band

32:38and the plugins drop down menu will be

32:40there now in your case you probably

32:42don't have any plugins in this list like

32:44i do

32:46but all you have to do to get that

32:47plug-in is go to the first option here

32:50where it says flow joe exchange

32:52this is going to go ahead and take you

32:54to our website

32:57right where we have all of these plugins

32:59so you just scroll down and you look for

33:01where you can find flow ai you can go

33:03ahead and download it here

33:06and then when you open up the file

33:08there's a how-to instruction manual and

33:10it tells you how to install it so that

33:13flow joe can work with it okay it's free

33:15it doesn't cost anything you just have

33:17to download and install it

33:20all right so in any case that is how

33:22that is the second quality control step

33:25looking at that clogs you know looking

33:27at the time axis across all your files

33:29just flip through them all

33:30you know and see if you don't see any

33:32anomalies and just be aware of them you

33:34don't have to place the gate on the

33:37axis right away you can do it after you

33:39do your analysis

33:41but

33:42you know just be aware that it's there

33:44okay i'm flipping through all my files

33:46here and i can see everything is pretty

33:48nice and clean so that's

33:50kind of the last part of the qc okay now

33:52essentially the data have been qc'd the

33:55next step for me generally is to

33:58annotate the data or make sure that the

34:00data has annotations in it so that i

34:03know what the heck i'm looking at

34:05and in case i have to talk to the pi or

34:07i have to create a figure or something

34:09like that

34:10i have the pertinent information to to

34:12generate

34:13uh the right figure

34:16okay so the first thing that i usually

34:18inspect in my data files

34:21after i do the quality control this i'll

34:23open up a graph window and then i just

34:25look at the names of my parameters right

34:28i have this parameter list

34:30and i can see here that i have you know

34:33forward and side scatter great and then

34:35i have a whole bunch of fluorochromes

34:36and now what i normally do is i look to

34:38see that those fluorochromes have

34:40some sort of antigen listed after them

34:43okay now in the case of ard it's the

34:45only one that does not have a

34:48designation this is my live dead die

34:51okay so my viability die

34:54all the others are labeled which is fine

34:57what i want to do here though is i want

34:58to label my aard i want to find out you

35:02know uh i want to label this as live

35:04dead or something um you know something

35:06along those lines so

35:08if i want to create a label there for

35:11this guy

35:12i have to make a little manipulation in

35:14the workspace first okay so the way to

35:16do that is to go to your configure tab

35:19at the very top

35:21and configure and then you go over here

35:23to this edit columns

35:26and this will allow you to annotate the

35:30stain name okay so if you scroll down

35:32this little list you'll see a whole

35:34bunch of stuff labeled reagent here

35:36find the one

35:39that has aard in my case right this is

35:41my viability you say add column make

35:43sure it shows up here on the right and

35:45then click ok and it will go ahead and

35:46add this column for you

35:48okay and now i can add this and say i

35:51don't know viability or something like

35:53that

35:54okay now i'm going to make use of

35:55another tool it's maybe a little bit

35:57more advanced but

35:58i'm going to make use of a little tool

36:00here that's going to copy this

36:01information across uh to my the rest of

36:04my samples so that i don't have to type

36:06this a hundred times right so i have

36:08viability written i go to the workspace

36:10tab

36:11and there's this nice little keywords

36:13band that has a function in it that says

36:16copy value to group

36:18and that will go ahead and copy with

36:20viability across

36:21now when i go back to my little graph

36:23window you can see here that it's

36:25labeled viability

36:27okay if i happen to make a mistake on

36:29any of the other antigens here you can

36:31rename them just the same way that i

36:33showed you to bring in the viability

36:35okay so in the case of i don't know

36:37apc87 if it wasn't dr maybe it was

36:40something else

36:41you can just

36:43go to your configure right go to the

36:45edit columns and bring in your apc h7

36:48and just overwrite whatever it has

36:50written there

36:51okay now i'm going to remove this guy so

36:53i'm going to select it and say remove

36:55column we don't need it anymore

36:57so that's the first form of metadata or

37:00data about the file that i'm really

37:02interested in in annotating

37:04the next thing that i want to annotate

37:05is my experiment files right i can see

37:08here all of these ones that are labeled

37:10ld this to me stood for local donor

37:13right local donor number one ns is kind

37:16of the stimulation that's applied and

37:18then a1 is just the well id that it has

37:20associated with it because it was run in

37:22a plate

37:23okay but what happened here is i have a

37:26time course i have a zero a 20 minute a

37:29one hour and a two hour stem and i would

37:31like to

37:33apply that information to my to my data

37:36files okay and the way i'm going to do

37:38that is

37:39uh similar

37:41to what we just did but in this case

37:43what i'm going to do is i'm going to

37:45right click here and i'm going to say

37:48add

37:48keyword okay now in this case i'm going

37:51to say

37:52the keyword title is going to be my time

37:55point that's what i'm going to designate

37:58okay and then gives me a nice column

38:00here

38:01to input that information

38:04now since i only want to add a time

38:06point for

38:07all the files that are labeled ld

38:09through ld1 through ld14 what i'm going

38:12to do is i'm going to block highlight

38:13these guys and i'm going to put them

38:14into a group

38:16all by themselves first

38:18then i'm going to go ahead and label

38:20them okay so i have them all highlighted

38:22here i go over to the workspace tab

38:25there's this very handy little groups

38:27band with this button that says

38:29group selected samples okay we do that

38:32and we can call this test

38:34and that'll go ahead and create this

38:36group that has the 20 files that we're

38:38in now i want to assign the keyword

38:40right

38:41the time point

38:42so in this case we're going to go ahead

38:43and say

38:46i want to create something called a

38:47keyword value series because the numbers

38:50change from sample to sample okay i go

38:53to the keywords tab here and i basically

38:55say okay for the first value i want it

38:57to be 0

38:58second value i want it to be 20 the next

39:01one i want it to be 60

39:04and the one after that we'll keep it all

39:05in minutes

39:07so it's easy to sort okay so

39:100 20 60 120

39:13and now

39:14below that you'll see this little rules

39:16box this is a very powerful little thing

39:19but this allows you to change the

39:20pattern okay so as you can see here the

39:23little

39:23generated series is just telling you

39:25what it would look like if you click the

39:27ok button right now so i would have 0 20

39:2960 120 and then it would go 5 6 7 8 9

39:33which isn't very helpful okay we want

39:35more than just the first four samples

39:37labeled we want this to you know repeat

39:40that pattern essentially across all the

39:41other samples

39:43so i have to come in here make some

39:44manipulations i just say okay start over

39:47every fourth sample

39:49and give show me what the series is okay

39:51so 0 20 60 120 then it goes 0 20 60 120

39:54blah blah looks good click ok

39:58it applies those numbers for me okay and

40:00now i can actually sort by the zero time

40:02point if i want to

40:04if i want to go and sort by the patient

40:06name i can right it's going to go

40:08alphabetically so ld1 ld2 blah blah blah

40:11right but now i have those two that i

40:14can use any other annotations that you

40:16want to add you can always just add

40:17keyword okay and then

40:20add it to the uh

40:21add it to the set of keywords that you

40:24have

40:25all right now

40:26that's pretty much the annotation that i

40:28want to talk about the last group of

40:30files that i want to put sort of

40:32separate from everything else are these

40:34that are labeled fmo so fmo stands for

40:36fluorescence minus one

40:38these are gating controls

40:40and what they do is they help you to

40:42identify where the positive boundary is

40:44for expression of any given marker right

40:47so they include all of the stains except

40:49the one of interest

40:51so this one that says fmo no cd4 means

40:54that it has all colors

40:56except the color that

40:59cd4 is using so i would gate on

41:02this fmo up to the cd4 level and then it

41:05would show me you know where i should

41:07place the gate essentially for that cd4

41:10where do the cd4 positive cells

41:13fall

41:14okay so anyway

41:16i've got the fmos here i block highlight

41:19them and i'm going to group them again

41:20all by themselves so i'm going to call

41:22this fmo

41:24and we go ahead and add that and i'm

41:26just going to change the color of the

41:28fml group so it's a little bit easier

41:29for you guys to see

41:32into like a purple color or whatever

41:34okay

41:36so there we go we got fmo we've got test

41:40i'm going to start my gating now on my

41:42fluorescence minus one controls okay

41:44again these are gating controls they

41:46show me where the bound between positive

41:48and negative ought to be

41:49so i'm going to start with these guys

41:51okay now everybody's gating is going to

41:54be different than mine for the most part

41:56so i'm only going to sort of speak in

41:57generalities here as i create gates i'm

42:00going to start with a tissue

42:02tissue specific gait here so i'm going

42:04to lock on to our lymphocytes since this

42:07experiment looks at the activation of t

42:09cells

42:10so look at the lymphocytic population i

42:12draw a gate around there

42:15once you draw a gate on a population

42:17just double click on that anywhere

42:20inside the gate and it'll take you to

42:22the

42:22the child population right that only

42:24contains

42:26uh the lymphocytic cells

42:28and then you can go on to the next uh

42:31stage

42:32right you could either do like a

42:33viability or in my case since i'm only

42:35looking at one population of cells here

42:38uh i'm going to go ahead and create a

42:39gate for the

42:41single cells right doublet

42:43discrimination

42:44so i normally pick the area and height

42:46parameters for site or sorry for forward

42:49scatter or side scatter i'll do them

42:50both if i happen to have them

42:52the cleaner the data the better

42:54in the end

42:56so we go ahead and create a diagonal

42:59date there that sort of

43:01follows the majority of the population

43:03for our singlets

43:05and i double click on that gate and then

43:07we'll look at our viability right so

43:10put viability here maybe look along the

43:12side scatter

43:14right and then i'm going to go ahead and

43:15isolate the cells that are living versus

43:19the cells

43:20that are dead

43:24okay and then from here we'll go ahead

43:27and start breaking down our lineages

43:29okay so dr we'll say versus uh

43:35cd3

43:37let's actually transpose these axes so

43:39let's put cd3 on the x and dr on the y

43:44because i'm going to be after the cd3s

43:46over here and i want to show you the

43:49separation between this guy and this guy

43:52as clearly as possible

43:54okay so

43:57if you want to rescale the data right

43:59it's

44:00we can clearly see that we have two

44:02populations here positive and negative

44:04but

44:05you know we've got a lot of wasted space

44:07over here a lot of wasted space over

44:09here and over here that we can you know

44:12we can kind of take advantage of so

44:13let's drop in and

44:15you know

44:16expand this out a little bit cut cut

44:18some of this useless stuff out so we'll

44:20say customize axis

44:23right and i'm just going to click the

44:25little minus button over here to kind of

44:26stretch

44:28right this guy out maybe maybe not be so

44:31aggressive but

44:33enough and then

44:35to handle what's going on on the left

44:37hand side of the scale you can use this

44:38with basis controller and the extra

44:40negative decades

44:42all right you stretch that all the way

44:44out you're kind of zooming in around

44:45zero and then we use this negative

44:47decade slider

44:48to pull the stuff that ends up piling up

44:50on the axis okay and then you click

44:52apply

44:54we did a good job to stretch this out

44:56probably could stretch it out even

44:58further

44:59right

45:01but we'll go ahead and do the

45:03y-axis next so say customize axis again

45:07i'll

45:08take a couple of ticks off

45:10go ahead and stretch that out and pull

45:12the cells off

45:13click apply

45:15boom look at that right nice and pretty

45:18we can clearly see our dr positive uh

45:21cells right

45:22cd3 positive cells away from the

45:24negatives there's even a nice little gap

45:26here in between the two that's

45:28relatively easy to gate on right so i'm

45:31just going to use the quads here

45:33okay and then we'll isolate the cd3

45:36positives

45:37okay so from cd3

45:39now i'm going to split these up going

45:41along cd4 cd8 okay so here's the fml

45:44again

45:45right for cd4 that's why we don't really

45:47have much going on there's probably a

45:49little bit of carryover from the tube

45:50before

45:52right but you can clearly see

45:54that the extent

45:55of spillover that comes from all of the

45:57other dies is about you know i don't

45:59know yay high somewhere around here

46:02so we can come in with our

46:04gate right and we can place it something

46:06like this this is going to be our

46:09right cd4 positive

46:12gate

46:14okay now i want to you know just a lot

46:17of times when you know once i start

46:18creating populations i kind of want to

46:21see how this looks on the other samples

46:24right that have cd4 present

46:27so in order to do that i need to copy

46:29the whole hierarchy of cells or you know

46:31the whole hierarchy of populations that

46:33i've created so far

46:34to the rest of the samples and this is

46:37where i want you guys to pay attention

46:38because this is the trick that will save

46:40you the most amount of time

46:42okay i recommend that you you do this

46:44you highlight the samples or sorry you

46:47highlight the populations that you've

46:50dated

46:51and then you right click and use this

46:53function here where it says copy

46:54analysis to group okay and then that

46:56will copy all of your gates that you

46:59created to the fmo group and then all of

47:01the samples in the fmo group

47:05pick up that hierarchy

47:07okay now the reason i tell you to do

47:09this

47:10is because

47:12if we go back into our analysis right

47:15let's say that we want to

47:17i don't know include the granulocytic

47:19cells so i go back to the ungated

47:21population and just create a gate that

47:23looks something like this

47:25and then i decide you know i don't

47:27really need an fmo for cd8s because

47:29they're pretty

47:30you know it's a pretty easy population

47:32to sort of

47:33identify it's kind of on off

47:36okay oops did i give it the same name i

47:39did didn't i shame on me and i'm

47:41surprised that flojo took it in any case

47:44this is cd8 positive not cd4

47:50okay

47:52now

47:53in this scenario you have two new gates

47:56that you added to your hierarchy okay so

47:59if you want to do it the drag and drop

48:01way which is kind of the old school

48:03method you'll notice that the minute you

48:05try to bring the granule acidic gate and

48:07the cd8 gate at the same time and apply

48:10it to the uh the hierarchy here

48:14the cd8 gate will get put in the wrong

48:16location and the granulocytic gate will

48:18be all right

48:20okay so this forces you if you do the

48:23drag and drop you have to do one at a

48:25time you can't do both at the same time

48:27if they occupy different levels in the

48:29hierarchy so that for me is highly

48:30inefficient

48:32i'm going to go ahead and dump the

48:35uh cd8 gate here i might as well get rid

48:37of the granule size so we'll create it

48:39again so i can show you

48:42the benefit right so in this case i'll

48:44just go ahead and draw like a

48:46ellipse

48:50okay so i've got the granulocytic gate i

48:52have the new cd8 gate and instead of

48:55doing the drag and drop i do the copy

48:56analysis to group look at that

48:59right granular sites get put in the

49:01right location as do the cd8s

49:04fantastic right and anytime we want to

49:06make a change

49:08right if i say that oh i want to move

49:10the lymphocytic gate in this location

49:11now granted that's not the right spot

49:13but just to show you that that's what i

49:14want to do i come back here i say copy

49:16analysis to group

49:18right it gives me a little warning here

49:20you already have that gate but now you

49:22can see okay it updated for this sample

49:24and all the other samples in the list

49:27right and now it doesn't matter which

49:28sample you're on if you want to move it

49:30back to

49:31a more reasonable location you go ahead

49:33and do that

49:34see the little blue diamond on the left

49:36hand side here and you'll find your

49:38changed gate right we just go ahead and

49:40say copy analysis to group

49:42click ok and now it will be updated

49:45right for all of the other samples

49:48okay

49:49so we'll scroll on back to the cd4s

49:52which we were working on

49:54okay now

49:57let's go uh take a look at our cd8 since

50:00these are the ones that we want some

50:02sort of a readout for in the end so i'm

50:04going to go ahead and take a look at the

50:06expression of some terminal markers like

50:08perforin

50:09phosphoerc and interferon these are good

50:12markers for activation of

50:14the cells

50:16so

50:17here we have peripheral expression on

50:20our cd8 positive t cells okay now in

50:23order to determine the boundary again

50:25between where does perforin expression

50:27begin and end

50:29i'm not going to rely on my eye here i'm

50:31going to rely on the empirical evidence

50:33provided by

50:35my fmo so i'm going to scroll here until

50:37i find the sample that says no perforin

50:41right so no interferon there's no perf

50:44look at that okay so we have no perf i'm

50:47going to go ahead and create a gate

50:48that's sort of the same length as uh

50:52you know uh

50:53cd8s and i'll just label this as perform

50:56or perform positive something like that

51:00okay now the next marker that i want to

51:03look at is going to be phospho erc

51:06notice that i'm not going to double

51:07click on the peripheral

51:09population i'm just going to leave it

51:11here and then i'm going to shift over to

51:13the phosphoerc because we want to remain

51:16looking at our cd8

51:18t cells we don't want to be looking at

51:21perforin positive and

51:23phosphor positive okay we just want to

51:26look at cd8 positive and whether or not

51:28they express phospho-erc

51:31so in this case here we can see that you

51:33know again we've got

51:35probably too easy just relatively easy

51:37to see populations and granted if i had

51:39transformed the axis here probably be

51:41easier to see but again i'm just going

51:42to rely on my

51:45uh no phospho irk control in order to

51:49make my gate so that i know where does

51:52possible arc expression begin okay so

51:55i'll just label this

51:57p dash

51:59arc

52:00and move that just a little bit off okay

52:03and then the last readout marker that

52:05we'll take a look at is interferon okay

52:08this is pretty good good on off marker

52:10in a way there is some smeariness though

52:13so again i'm going to use right the

52:17fmo

52:18for interferon so i know exactly where

52:22my expression begins and ends right so

52:26here is ifn

52:28and we'll just move it there okay

52:31something like that

52:33all right so we have our three terminal

52:35gates now we just have to copy them to

52:37the group here's my interferon you can

52:39see it's just under the blue diamond

52:41blue diamond just marks which graph

52:43window is in the forefront okay so we

52:46say copy analysis to group

52:48find our perforin that one is positive

52:50two so we say copy analysis to group

52:54and then find our phospho irk

52:56same thing

52:58copy analysis to group okay so we have

53:00our three terminal readout populations

53:02to determine whether or not the cells

53:03have been activated or maybe they're

53:05activated with respect to these markers

53:07in different ways at different time

53:09points

53:10so downstream we want to you know we

53:12want to measure this

53:13so we're going to evaluate these gates a

53:16little bit a little bit later

53:18okay normally when you create readout

53:21populations or any population for that

53:23matter you want to have some

53:27measure of the fluorescence intensity

53:29and primarily to give you an idea of

53:32what you know

53:34sort of the baseline expression versus

53:36the you know when the marker is actually

53:39being expressed what is that you know

53:41what does that look like how do we know

53:43whether it's on or off etc

53:45well usually the statistic that we use

53:47for that

53:48is something called the median

53:50fluorescent intensity sometimes it's

53:52printed

53:53mean fluorescent intensity but in

53:56general the statistic that we want here

53:59is the median so we right click here and

54:01we say add statistic

54:03and you'll see in the upper left

54:07there's a statistic box here

54:10right with a list of all different types

54:12of statistics that you can add

54:14to the workspace now i haven't described

54:17this previous

54:18but maybe i should do that now what are

54:20the default statistics that are being

54:22shown to you okay in the beginning when

54:24you start creating your gates you will

54:26see in the stats column there's some

54:27numbers

54:28okay

54:29when it's next to a population the

54:31population is kind of indicated by this

54:33little you see the little three green

54:35dots in the white dish it's supposed to

54:37be a petri dish but i think of it as a

54:39as a yoshi egg you know

54:42and anyway

54:43you ever play you know nintendo when

54:45you're a kid

54:46it just reminds me blast of the past but

54:49anyway

54:50what we want to do here right this

54:52statistic that's in the statistic column

54:54this is just telling you the frequency

54:56of the parent so in this case

54:58cd3s right cd3 positive dr negative

55:01these are my cd3t cells they are 82

55:04of the live cell population okay and all

55:07the other quads make up

55:09the remainder to 100 percent

55:12okay my cd4s

55:14uh are in this case because it's an fmo

55:17right my cd4s are

55:20a very tiny fraction of a percent here

55:23right 0.02 percent

55:25of

55:26the cd3s okay but the cd8s are roughly

55:2924

55:30of the cd3s

55:32so on and so forth so it's always

55:34referencing the statistic or the

55:36population that's above it

55:38and indented slightly to the left so

55:40lymphocytes

55:42are 88

55:44of the ungated population

55:47okay

55:48so now we want to add some other

55:49statistics i should say that in the

55:51cells column here that's telling you how

55:52many cells are in that gate or in that

55:55population right so here you have

55:58cd8 t cells that are 24

56:01of the live you are capturing uh 38

56:04366

56:06cd8 positive t cells okay now so

56:10uh other statistics though that you can

56:12add as i mentioned are here so when it

56:14comes to the mfi we either want to use

56:16the median or the geome it's really up

56:19to you which one you want to use they're

56:20calculated differently

56:22just whatever you do avoid the mean okay

56:25we don't want to use the mean because

56:28our data right the flow cytometry data

56:31is plotted along a logarithmic scale so

56:35a single outlier in the positive or in

56:37the negative is going to very heavily

56:39skew that statistic and generally

56:41speaking the mfi what we're trying to do

56:43with that

56:44is we're trying to say that those cells

56:46the vast majority of the cells are at

56:48this location

56:50right they have this much fluorescence

56:52intensity

56:54uh

56:54you know when you don't stimulate the

56:56cells and then it shifts to the right or

56:58to the left when you add something to

57:00the cells right they shift to the to the

57:02right or to the left

57:03so this

57:04it's a way of tracking where the middle

57:07of this population is

57:09on or along either axis okay

57:13so in this case here we'll keep some

57:14simple statistics

57:16i'll include both the median and the

57:18geomean so you can see that they're

57:19calculated differently but again it's up

57:21to you which one you want to use just

57:23pick one and stick to it

57:25so i'm going to use uh we're going to

57:27look at cd8 right or no sorry interferon

57:30so

57:31we'll go ahead and select median and

57:33geomean and then we just align it with

57:35the color or the antigen that we want to

57:37know that for okay so the population

57:39here is interferon that's the one we

57:41started with we click add oops it's

57:43because i selected the cd8 to show you

57:45guys here that it added under that

57:47particular population so my bad

57:53but we'll go back to that

57:54uh let's go back to our ad statistic

57:58menu

57:59and while we're here if we don't fiddle

58:01around and go back to our graphs we can

58:04just quickly swap to the next population

58:06that we want the mfi for so again we'll

58:09keep median and geo mean in this case

58:11we'll look at phospho work we go ahead

58:13and add that

58:14then swap over to perforin and we can do

58:18the same thing right click add and it'll

58:19add it there

58:21okay now we have our statistics

58:24right

58:25and we want to

58:28copy this to the group so same thing

58:30copy analysis to group like we did gates

58:32so that all the samples get those

58:34statistic nodes

58:36okay

58:38now we have our gates we have our

58:40statistics on the fmos but what about

58:43our test files okay test files still do

58:45not have the gates so we use the gates

58:47that we placed on our fmos we need to

58:51transfer them over to

58:53the test group so for this we do have to

58:56do the drag and drop so we come over

58:57here we drag

58:59drop

59:00there we go we get our

59:02gates okay and the nice thing about

59:04doing it this way is that once we

59:06transfer those gates we can have high

59:07confidence in their placement because we

59:09use the fmos to determine the gate

59:11bounds especially with respect

59:14to these terminal populations or the

59:16populations that are a little bit

59:17questionable

59:19okay so we have all this information

59:21here now we want to start doing some

59:23comparisons

59:24between our samples right in the uh in

59:27the workspace what happened in our

59:29experiment right we have a different

59:31time course i want to see whether or not

59:34the patients are actually you know are

59:36they responding to the stimulus or not

59:39okay so in order to do that type of

59:41analysis or to make it easier anyway

59:44rather than flipping be true between

59:46graph windows

59:47go ahead and open up the layout editor

59:49okay that's a big l over here

59:52and we open this guy up

59:54and it basically presents you with a big

59:56sheet

59:58okay and then you can bring in any

1:00:00population that you want now in general

1:00:03most of the time our first figure is

1:00:04going to look something like

1:00:06you know i don't know something like

1:00:08this let me hide my stat nodes real

1:00:10quick so they don't get in the way but a

1:00:12lot of times our first figure is going

1:00:14to look something like this where we

1:00:16drag in all of our data and then

1:00:19uh you know we move these

1:00:21uh graphs around with arrows and stuff

1:00:23and we say this population leads to that

1:00:25and that leads to something else blah

1:00:26blah it takes a little while for us to

1:00:29you know set up all this stuff

1:00:33that is usually you know a good idea we

1:00:35like to have a review of what the what

1:00:38the data you know look like etc and i

1:00:40would just recommend that instead of

1:00:42sort of just dragging and dropping them

1:00:44directly into the layout editor

1:00:46go ahead and use this tool right here

1:00:48this is the grid tool

1:00:50this will just make it a little bit

1:00:51nicer and neater for you guys to look

1:00:53over your samples okay so i'm going to

1:00:56right click here and i'm going to add

1:00:57another

1:00:58uh i'm going to add another additional

1:01:01row

1:01:02so that i can squeeze all of my

1:01:04populations in here i wasn't sure

1:01:06exactly how many i had but i started

1:01:08with a 4x3 grid so adding that extra row

1:01:12makes sure that i get in right and now i

1:01:14can see all of my gates and stuff really

1:01:18you know really nicely if i don't like

1:01:20the

1:01:21grid i can get rid of the grid and have

1:01:23my nice little plots all kind of

1:01:25arranged

1:01:26in one way and then you can put them all

1:01:28on one page now from here if i want to

1:01:30kind of review this one by one

1:01:32across

1:01:34all of my samples

1:01:36i can change the iteration options here

1:01:38over to sample

1:01:40and then i can use the little arrow to

1:01:42kind of move through each of my

1:01:46each of my samples i can select from the

1:01:48drop down menu and i could say oh i want

1:01:50to see patient number 12 blah blah blah

1:01:52right and see what you know happens

1:01:55to this guy this is a good way to sort

1:01:57of review one by one your samples

1:01:59without eating up too much ram okay but

1:02:03if you want the full report right you

1:02:04want to show the boss or you need it for

1:02:06a figure or something like that

1:02:08what you can do is you can quickly just

1:02:10press this button that says create batch

1:02:12report and this is going to create a

1:02:15report that shows you all of the graphs

1:02:18right for each one of the samples that

1:02:20you have in the group so i have 20

1:02:23uh 20 files so you're going to see

1:02:25essentially 20 panels here until we get

1:02:28to the very um to the very end

1:02:31okay

1:02:32so that's a couple

1:02:34things you can do

1:02:36um

1:02:38i really like that grid tool for that

1:02:40purpose

1:02:41let's talk about overlays so because

1:02:44ultimately what you guys want to do is

1:02:45you want to compare one sample to the

1:02:47next okay so i'll show you a couple

1:02:49different ways to do this

1:02:51let's say we're only interested in the

1:02:52terminal populations our interferon our

1:02:55frostbolt work and our perforin

1:02:58so we can go ahead and drag those guys

1:03:00in

1:03:02okay if we want to do the

1:03:05overlay you can simply drag and drop

1:03:09right

1:03:10interferon population this is sort of

1:03:12the long way of doing it but you go to

1:03:13the next sample you drag and drop that

1:03:16guy you know if you're lucky enough and

1:03:18actually

1:03:19see that population right away you can

1:03:21drop it

1:03:23you can drop it on top without having to

1:03:25drop it first in the layout editor and

1:03:27then drag it over but in my case it was

1:03:28hidden

1:03:31okay so you finally get your little

1:03:33overlay like this

1:03:35if you have a lot of samples that you

1:03:36need to overlay fairly quickly so in my

1:03:38case i need to do four at a time

1:03:41i think it's faster to do it this way

1:03:44i just right click on the population i

1:03:47want and then i say so select equivalent

1:03:49nodes

1:03:50this is really cool

1:03:52that function will highlight all the

1:03:54phospho irks for me and then all i have

1:03:57to do is just simply drag and drop that

1:03:58into the layout editor okay and i

1:04:01dragged it on top of the graph in this

1:04:02case

1:04:04now that does create kind of a mess

1:04:06right i have this huge

1:04:08overlay that i don't want but in my

1:04:09opinion it's really easy to clean up the

1:04:11legend right i can say okay i want to

1:04:13see everything except

1:04:16patient number one i want to read i'll

1:04:17just look at patient number one's time

1:04:19course

1:04:20so you right click here say remove layer

1:04:22and it'll trim that massive overlay okay

1:04:26do the same thing for the little extra

1:04:28one that's added

1:04:29and then for perforin i'll show you that

1:04:30trick again right you highlight

1:04:33select equivalent

1:04:35and then drag it over and dump it on top

1:04:39and you have this massive overlay you

1:04:40click inside the legend

1:04:43you highlight

1:04:45what you don't want

1:04:46and then you just right click here

1:04:48remove layer

1:04:51and you can put your legend here etc and

1:04:54we can remove it

1:04:56okay now i probably want to include in

1:04:58the legend here uh

1:05:00you know some information about the time

1:05:02course

1:05:04so i'm going to go ahead and double

1:05:05click on the legend it'll open up this

1:05:08little window and i'm going to click on

1:05:10this button that has little key

1:05:12that stands for keywords

1:05:14pretty crafty there i think by the

1:05:16developers

1:05:18little keyword

1:05:20keyword using a picture

1:05:22okay scroll down to the bottom and

1:05:24you'll find your little time point

1:05:26keyword that we created and we go ahead

1:05:28and say okay

1:05:30it'll add it there and i click ok again

1:05:32and it'll expand my little legend so

1:05:34that now i can see

1:05:36my 0 20 60 120 okay

1:05:39now we probably on top of this we

1:05:41probably want to make these

1:05:43instead of dot plots we probably want to

1:05:45compare them as histograms so i'll go

1:05:47ahead and select all of them double

1:05:50click

1:05:51and i go to the specify tab here and i

1:05:53just tell it okay i want you as a

1:05:55histogram

1:05:57and then i will tell it to go ahead and

1:06:00scale this modally so that all the peaks

1:06:02are comparable to one another right so

1:06:05we can see whether or not any shifts are

1:06:08occurring

1:06:09okay now the last problem that we have

1:06:11associated here is the fact that these

1:06:13graphs are smashed on top of one another

1:06:16and we need to sort of separate them

1:06:18right so

1:06:20let's offset the histogram so

1:06:22unfortunately we have to do this kind of

1:06:24one by one so you select the first graph

1:06:26you right click and go all the way down

1:06:28to the bottom and you'll find this

1:06:29histograms

1:06:31okay and then from here you can say oh i

1:06:33want to do a full offset

1:06:35okay so now i can see all the different

1:06:37peaks

1:06:38right that 120 time point the two hours

1:06:41at the top my zeros at the bottom etc

1:06:44i'm going to come over here i'll show

1:06:46you a different offset so maybe here we

1:06:48can do half right you can see how it

1:06:50overlaps

1:06:51the peaks overlap one another

1:06:54and then over here we can do mr cool

1:06:57the stagger offset right so if you want

1:07:00to you know kind of trick out your boss

1:07:03just be like hey check this out you know

1:07:05i could stretch i can make it go like

1:07:07this whatever

1:07:09whoa

1:07:09anyway

1:07:11probably not as academically useful

1:07:12right but definitely looks cool so maybe

1:07:15maybe something for a web page or you

1:07:17know if you're

1:07:18creating a figure you need a title slide

1:07:21or something like that you could

1:07:22probably use that guy

1:07:25anyway

1:07:26now we've offset these histograms

1:07:30and

1:07:30we're ready now to look and see what

1:07:33does this look like not only for patient

1:07:36number one but what does it look like

1:07:37now for all of the other patients that i

1:07:39have right so if i go back to the

1:07:41workspace here you can see that i have

1:07:43patient 2

1:07:44patient 4 12 and 14.

1:07:48okay so if i try to go to the batch

1:07:50though you'll notice that the bash is

1:07:52blocked right all of this stuff is

1:07:54grayed out and like what the heck i want

1:07:56to see the result now for all the other

1:07:58samples

1:07:59okay what you have to do again is go to

1:08:01the iteration options anytime you have a

1:08:03block in batching it's usually due to

1:08:05iteration so if you go to iteration this

1:08:08is set to off you need to change it

1:08:11to panel okay panel is the easiest

1:08:13there's another way to do it via keyword

1:08:15which is cool but it doesn't usually

1:08:17work unless you have the right keywords

1:08:19okay so we say panel

1:08:22and then a little box opens up okay the

1:08:24box is asking you

1:08:27how many samples you have overlaid with

1:08:30one another okay so in our case we have

1:08:32four right

1:08:34four time points we tick this on up to

1:08:36four our graphs get restored and most

1:08:39importantly you can see that the batch

1:08:41report options return right

1:08:44so now if i batch this report

1:08:48check this out

1:08:50right you take

1:08:52cut up off the top

1:08:55right

1:08:57slide on down to the bottom here

1:09:00look at this right here's patient number

1:09:02two

1:09:03you see their time course you can see

1:09:05that interferon turns on around an hour

1:09:07or two hours but not in the first 20

1:09:09minutes

1:09:10okay with uh phosphoerc it's a little

1:09:13bit harder to tell but it looks like the

1:09:1420 minute time point is

1:09:16probably the most shifted relative to

1:09:18one in two hours

1:09:21right if we look at patient number four

1:09:23same thing maybe not as robust as

1:09:25patients one and two but still there

1:09:28patient 12 pretty robust response right

1:09:31patient 14 same thing okay now you have

1:09:35the report for all of all the patients

1:09:38and if you want to save this again for

1:09:39everything

1:09:41you could just go to the file tab here

1:09:43and you could say export right you can

1:09:44export this as a pdf

1:09:47tiff or an svg svg is pretty cool it

1:09:49stands for scalable vector graphic

1:09:52it allows you to expand or contract the

1:09:54image without losing resolution

1:09:57so you can manipulate this pretty pretty

1:09:59nicely

1:10:01in adobe illustrator and similar

1:10:05pdf and tiff will give you 300 dpi

1:10:09so again if you're making a figure you

1:10:10probably want to export it like that

1:10:13the other thing you can do is when

1:10:14you're in this stage here when you have

1:10:16it all set up

1:10:18instead of batching to a new layout one

1:10:20of my favorite options is to batch to

1:10:22printer

1:10:24right so this is always set to default

1:10:26just new layout but you could say

1:10:27printer

1:10:29and then when you click batch report

1:10:32it's going to go ahead and pull up your

1:10:34print spooler right and from here you

1:10:36can see that

1:10:38you can have

1:10:40you can split this so that you can have

1:10:42so many per page or per row

1:10:44right per row per column i should say

1:10:46and you can totally ignore these little

1:10:48dashed lines in the background

1:10:51like your print spooler will do all this

1:10:53work for you and then when you go to

1:10:54print this

1:10:56you have the option to save as a pdf

1:10:59right so you get the hard copy you get

1:11:02the pdf and you retile the data kind of

1:11:04all in one so it's kind of a nice

1:11:07uh

1:11:08i think it's a nice little

1:11:10option um instead of

1:11:13uh you know new layout and then going

1:11:15over here and then you know

1:11:17saving the uh

1:11:19saving the image file as a pdf or

1:11:21whatever

1:11:22okay there are some other choices here

1:11:24but

1:11:25probably not as

1:11:27important or as useful powerpoint is

1:11:29okay but

1:11:31what i hate about powerpoint is when it

1:11:33comes into powerpoint all of this stuff

1:11:35you cannot ungroup any of the objects so

1:11:38what i tell people is if you know you're

1:11:40going to go to powerpoint

1:11:41uh first set up your you know your tiles

1:11:45and then i would duplicate your layout

1:11:47so that you always have like an original

1:11:50so you dupe the layout creates another

1:11:52one for you and then if you want to dump

1:11:55stuff in powerpoint i just strip

1:11:56everything down

1:11:58all right so i get rid of all of this

1:11:59stuff

1:12:00uh double click here and i'll go to

1:12:02annotate and get rid of the axes and all

1:12:04this kind of stuff right i just get rid

1:12:06of everything because really when i get

1:12:09to powerpoint i'm probably going to have

1:12:10to relabel everything anyway

1:12:12so i just you know i just make sure that

1:12:15these plots are

1:12:17uh all

1:12:20you know removed of their extra stuff

1:12:24and then

1:12:25i'll either copy paste

1:12:27to powerpoint or i will just simply

1:12:30batch here to powerpoint now i'll have a

1:12:32slide that has these three guys in them

1:12:35and granted it's a single image but you

1:12:37can kind of crop in between these right

1:12:39but you don't have to

1:12:40worry about the

1:12:42you know the axes ticks and all that

1:12:44kind of stuff

1:12:45okay

1:12:46so hopefully that's uh clear

1:12:51um there's a lot of other things we can

1:12:53do in the layout editor but we just

1:12:55there's just not enough time to talk

1:12:57about all the little nuances and again

1:12:59i'm i'm trying to give you guys an intro

1:13:01to flojo like how to get

1:13:03through your analysis here kind of as

1:13:05fast as possible using all of the major

1:13:08like the major tools and again if you

1:13:11guys have any like

1:13:13questions by all means use the chat box

1:13:15or the q a box and i will stop and and

1:13:18answer them

1:13:20okay so

1:13:21now since our let's say our graphical

1:13:23reports are done let's go ahead and get

1:13:26our statistics out of here as well okay

1:13:28so we have our stat nodes i bring them

1:13:30back and then i'm going to go to the

1:13:33table editor here

1:13:35and

1:13:36this is where i'm going to add my

1:13:38statistics okay so i just take the stat

1:13:40nodes

1:13:42right and then we just

1:13:44you know we put them over into the table

1:13:46editor

1:13:48and if you want to make this look you

1:13:50know

1:13:51really cool like you're doing some 360s

1:13:53in the surf or something like that you

1:13:55just go ahead and

1:13:57highlight the statistics here and go

1:13:59over to the visualize tab

1:14:02click on this button here this handy

1:14:03dandy button right it says heat map

1:14:07now

1:14:09now you're ready

1:14:11okay you go ahead and click create table

1:14:14and it's going to go ahead and give you

1:14:15a nice little heat map table where you

1:14:17can see the trend right of the data

1:14:19what's happening

1:14:20now if you want to see this sort of

1:14:22juxtaposed to your

1:14:24time point keyword go ahead and say edit

1:14:27i want to add a column

1:14:28okay and then you say i want a keyword

1:14:31column

1:14:32go ahead and add your time point here

1:14:35all right let's put this at the front of

1:14:37the list so i'll put it on top and then

1:14:38we rebatch the table

1:14:41now i have my time point next to all of

1:14:43my stuff

1:14:44right and i can see it

1:14:46and i can clearly see that yeah the

1:14:48latter two time points

1:14:50high for interferon

1:14:52right the first time point 20 minutes is

1:14:54high for phosphoerc and then it dampens

1:14:56and then if we look at perforin it's

1:14:58kind of i don't know it's all over the

1:14:59place

1:15:00varies by patient

1:15:02versus

1:15:03treatment

1:15:05okay and now you can take this and you

1:15:06can say file save as right you can save

1:15:09it as csv or excel you could do control

1:15:11c control v if you want

1:15:13um

1:15:16you know the other thing you could do

1:15:17here is you go to table editor

1:15:20change this from display to to file and

1:15:22then you tell it oh i want a csv or i

1:15:24want an excel okay

1:15:26i would recommend that you do csv just

1:15:28because you know excel can interpret

1:15:30csvs just fine if you happen to go to

1:15:32prism or something like that you have

1:15:34the csv handy

1:15:36okay and i'll i will mention that when

1:15:38you bring in statistics or when you

1:15:40bring in population nodes right if i

1:15:42bring in these populations the default

1:15:43statistic is always going to be the

1:15:45frequency of the parent which is this

1:15:46statistic here

1:15:48if you want something other than the

1:15:50frequency of the parent add it to the

1:15:52workspace first as a statistic right you

1:15:55say add statistic

1:15:57so even if it's just like the count

1:15:59right you just say oh i want the count

1:16:01for the grands okay great

1:16:03there's a count add account for lymphs

1:16:05okay great add it for the single cells

1:16:08great

1:16:09okay then you take this count statistic

1:16:11and you drop it here rather than

1:16:14coming into the table editor double

1:16:16clicking going through the list here and

1:16:18changing it out to whatever other

1:16:20statistic you want this double click

1:16:21back and forth for multiple

1:16:24multiple lines multiple populations is

1:16:26going to be really time consuming

1:16:29so do it first by adding the stat node

1:16:32the statistic node first

1:16:34right that way you have it

1:16:37and then you simply drag it into

1:16:39the layout or the table editor right and

1:16:42then now when you go to batch this you

1:16:44have it for everything

1:16:47okay

1:16:54all right

1:16:55so let's talk about

1:16:59uh

1:17:00let's talk about the compensation since

1:17:04that's sort of the last topic on the

1:17:05list here right we went through our

1:17:07analysis

1:17:09we created some layouts we created some

1:17:11tables

1:17:13how do we create a compensation matrix

1:17:16in flow joe if we didn't do it on the

1:17:18machine

1:17:19okay so if you didn't do it on the

1:17:21machine bring your comp controls here

1:17:24to the compensation group so make sure

1:17:26all your compensation controls are here

1:17:29okay

1:17:31if not you can always drag them from

1:17:33some other group right if this sample

1:17:35was supposed to be a comp

1:17:37comp sample i can simply drag and drop

1:17:39it into the comp group

1:17:40okay it's not so i'm going to go ahead

1:17:43and delete it but in any case just to

1:17:44show you that that's all you have to do

1:17:47now once you have all your single stains

1:17:49here go ahead and run the compensation

1:17:52wizard

1:17:55okay and then the wizard has like a peak

1:17:57finder in it and it's going to try and

1:18:00align all of the fluorochromes with the

1:18:03associated single stain controls and

1:18:05it's going to try to auto create a bunch

1:18:07of gates for you to make this process a

1:18:10little bit faster a little bit easier

1:18:13okay

1:18:14so it tries to do some of the work for

1:18:16you

1:18:17and in general i'd say it does a pretty

1:18:19good job

1:18:21okay

1:18:22now

1:18:24in the list on the left right so you get

1:18:26this window that has two panels the top

1:18:29has a panel with all of the

1:18:30fluorochromes and the single stains that

1:18:32are associated to those fluorochromes

1:18:35and then the bottom panel here has a

1:18:37series of graphs

1:18:38kind of showing you the negative

1:18:40population and the positive population

1:18:42for every single stain control that you

1:18:44have

1:18:45so normally i focus on the table at the

1:18:48top first right we have to make sure

1:18:51that the floor chrome here is matched up

1:18:53with the correct sample

1:18:55single stain control so just make sure

1:18:57that this is correct

1:18:59right aard here okay so we're looking at

1:19:02the live dead die

1:19:04we can see here in the actual comp

1:19:07control that's put in its place is the

1:19:09aard comp control so that matches good

1:19:12deal they're in the right spot then you

1:19:14go to the next one you make sure that

1:19:16you know the floor chrome is matching

1:19:19with the appropriate single stain

1:19:20control now in my case here alexa 647

1:19:23was a color

1:19:25a detector that we had left open but we

1:19:27actually didn't have we didn't have

1:19:29alexa 647 in the experiment we didn't

1:19:31have an antibody conjugated to alexa 647

1:19:34so

1:19:36this particular parameter represents a

1:19:38detector that was left open

1:19:40okay we just want to get rid of this

1:19:43for uh this experiment because it

1:19:45doesn't it doesn't have any value for us

1:19:48in terms of the compensation so i go

1:19:50ahead and say remove this parameter and

1:19:52that will dump

1:19:54alexa 647 from the experiment

1:19:58okay and again that was just an empty

1:20:00detector we don't need it for anything

1:20:02okay check all the others they look fine

1:20:05okay now the two columns that we have on

1:20:08the right

1:20:09we have a negative and a positive okay

1:20:11these essentially every single stain

1:20:13control is going to be split into the

1:20:15negative population and the positive

1:20:17population and this information is

1:20:20essentially what gets

1:20:21uh sent down to the lower panel so what

1:20:24you see in blue over here corresponds to

1:20:26what's going on here what you see in

1:20:28green over here corresponds to

1:20:30this guy okay and then the plot in the

1:20:33middle is just an overlay of this

1:20:35population right and this population and

1:20:38then it

1:20:39usually split amongst blue and green

1:20:42right the blue represents the net what

1:20:44is coming from the negative the green

1:20:45comes from the positive

1:20:48okay so now we can smash the upper layer

1:20:51there and i'll zoom in so it's easier

1:20:53for you guys to see here

1:20:57i'm going to try and stretch this across

1:21:00a little bit

1:21:02and then go here

1:21:06okay

1:21:07so we have our negative positive and

1:21:10then the overlay in the middle you can

1:21:11kind of see here that the

1:21:13green and the blue are almost

1:21:15overlapping with one another okay so in

1:21:17the bottom half this this bottom half

1:21:20flojo tries to take the color

1:21:23find some cells or some beads that have

1:21:25that color and then overlay them in the

1:21:28context of you know the negative

1:21:30uh population and the positive

1:21:32population

1:21:34now in flojo's case here

1:21:36for aard

1:21:38we it sees that we have some unstained

1:21:40cells so it goes ahead and takes the

1:21:42unstained cells as the negative that's

1:21:44fine

1:21:45and then the positively stained cells it

1:21:47finds the aard sample which is great the

1:21:50only problem that it does here is that

1:21:52it creates a gate on the group of cells

1:21:55that are the most populous right the

1:21:57lymphocytic population here

1:22:00there's nothing uh

1:22:02wrong with the the gate in the sense

1:22:04that you know the shape or anything like

1:22:06that it's just that floyd doesn't really

1:22:09have any biological knowledge of the

1:22:10system so for a live dead dye

1:22:13we don't want to place a gate on

1:22:16lymphocytic cells or whatever cells are

1:22:18most populous what we want to do here is

1:22:21we want to put that gate on some cells

1:22:23that are going to be dead or dying

1:22:25so normally if you're looking at forward

1:22:27and side scatter the dead and dying

1:22:29cells are going to be kind of off to the

1:22:30left maybe a little bit up like in the

1:22:3211 o'clock position here

1:22:35okay the minute i move that gate off of

1:22:37the lymphocytic population you can

1:22:39clearly see now we have some

1:22:41aard-positive cells and that

1:22:44the ranged gate here automatically

1:22:46adjusts

1:22:47okay that's nice

1:22:49we're starting to look good here we have

1:22:51good separation between positive and

1:22:53negative

1:22:54uh the only problem is that the

1:22:55autofluorescence remember

1:22:57autofluorescence is that second rule

1:23:00of good compensation the

1:23:01autofluorescence of your living

1:23:03population or the you know the negative

1:23:06is not matching the autofluorescence of

1:23:08the positive okay there's a little bit

1:23:09of a disparity here

1:23:12so what we need to do in order to match

1:23:14the autofluorescence is we need to come

1:23:16to the negative population and we need

1:23:17to move that gate as well into a similar

1:23:20location

1:23:22notice how that when i move that gate

1:23:24into the dead cell position you see how

1:23:26the shoulder got wider

1:23:29now we have overlap right of the

1:23:31autofluorescence

1:23:33of the positive population right

1:23:35overlapping with the autofluorescence of

1:23:38the negative and that's good because we

1:23:40want it to drop out of the equation

1:23:42okay when the autofluorescences don't

1:23:44match we get into problems in

1:23:46compensation okay

1:23:48so here we go

1:23:50first one looks good now that we've

1:23:52changed the the gate position okay now

1:23:55if we come over to apc h7

1:23:58what do we have good separation between

1:24:00positive and negative but we're using

1:24:02beads for the stain population

1:24:05and for the negative we're using cells

1:24:08okay so this again

1:24:10violates that

1:24:11autofluorescence rule

1:24:13uh that we were talking about in the

1:24:15very um the very beginning

1:24:17right so

1:24:19what do i need to do i need to make sure

1:24:21that apc87

1:24:23since we're using positively stained

1:24:25beads here i need to find unstained

1:24:27beads for the negative

1:24:29so i go to apch7

1:24:31i follow this across and i find for the

1:24:34negative i want to put in unstained

1:24:36beads

1:24:38so there i can find my unstained beads i

1:24:41go ahead and

1:24:43click on that and it goes ahead and

1:24:44swaps it out now we have unstained beads

1:24:48with stained beads autofluorescences are

1:24:50the same

1:24:51we can move on

1:24:54okay alexa 488 same problem beads over

1:24:58here cells over here

1:25:00let's

1:25:02clean that up and tell it okay i want

1:25:04unstained beads for this guy okay

1:25:08good

1:25:09then we go to alexa 700. now alexa 700

1:25:12we're using cells again which is good

1:25:14it's okay there's nothing wrong with

1:25:15that

1:25:16the only problem is we have an

1:25:18autofluorescence problem again we've got

1:25:20a gate on the lymphocytic cells

1:25:23and we have a gate over here on the

1:25:24unstained cells in the dead zone

1:25:27okay what i need to do is i need to

1:25:29transfer this gate that i have here i

1:25:31want it on this population in the

1:25:32negative

1:25:34so that we match the autofluorescences

1:25:37okay so in order for me to do that

1:25:38though i need to go back to the

1:25:40workspace real quick

1:25:42i'm going to move this over

1:25:44and i'm going to find my alexa 700 the

1:25:47positive right

1:25:48and there it is

1:25:51alexa 700

1:25:52and my unstained cells are right

1:25:54underneath that

1:25:56now what i want to do is i want to take

1:25:57this gate the one that's labeled size on

1:26:00my alexa 700 and i want to move it over

1:26:03to the unstained sample

1:26:06okay the problem is if i take it right

1:26:08now and i duplicate it or i i drop it on

1:26:11top of this guy

1:26:12it's going to overwrite this gate that's

1:26:14already labeled size okay so in order to

1:26:18avoid

1:26:19overwriting this

1:26:21this this gate that i have in the

1:26:23unstained sample i'm just going to

1:26:25change the name so i can rename the

1:26:28population instead of size we can call

1:26:30it something else like cells

1:26:33and when i call it cells

1:26:36go over here and then i just simply drag

1:26:38and drop that onto

1:26:41right

1:26:42the unstained sample

1:26:45now i have cells and i have a size gate

1:26:48i go back to the wizard i can see i have

1:26:50both gates represented which is really

1:26:53good

1:26:54but we need to make sure that flojo uses

1:26:56this gate the one that's on the

1:26:58lymphocytic population

1:27:00instead of the one that's on the dead

1:27:02cells for alexa 700 okay so for alexa

1:27:04700 we follow this guy across

1:27:07and we see that it's using the size gate

1:27:10we want to use the cells gate right

1:27:13unstained cells

1:27:15cells

1:27:16okay and that'll swap it out so that's

1:27:18now using the right one and now the

1:27:20autofluorescences

1:27:22are matched

1:27:24okay

1:27:25last thing we need to do is sometimes

1:27:27i'm not sure why but flowjo may

1:27:30encompass the negative population here

1:27:32with this ranged gate so we just move

1:27:34this over

1:27:37okay and then we drift on down to the

1:27:40next one so pe

1:27:42another violation of autofluorescence

1:27:43we're using cells in the negative beads

1:27:45for the positive

1:27:47so we'll correct that by putting

1:27:48unstained beads pe cy5 same thing size 7

1:27:52the same thing texas red the same thing

1:27:55and pack blue

1:27:56the same thing so the last four or five

1:27:59colors

1:28:00all we need to do here is tell it to go

1:28:03ahead and use unstained beads

1:28:06right for these guys

1:28:10rather than unstained cells

1:28:13okay one quick check here to make sure

1:28:15that our beads have

1:28:17you know they're in the right spot

1:28:19and then

1:28:20we've got good separation between

1:28:22positive and negative

1:28:24right everything looks good now we can

1:28:26go ahead and produce our compensation

1:28:28matrix right so we click on the button

1:28:30here that says view matrix

1:28:32this is going to go ahead and create the

1:28:34new compensation matrix for us

1:28:37right and you can see that it's listed

1:28:38here and it has kind of a red color

1:28:40associated with it

1:28:42right and there's your table

1:28:44if you want to compare your compensation

1:28:47matrix the one you made versus the one

1:28:49that you did on the machine

1:28:53just highlight the two matrices and then

1:28:55you will see gray dots and red dots here

1:28:58right and what you're basically looking

1:29:00for maybe i should make this a different

1:29:02color like maybe make it black so it's

1:29:05easy to

1:29:06to contrast with the gray

1:29:08right so black dots and gray dots

1:29:12and basically again what you're looking

1:29:14for here is to see whether or not the

1:29:16black dots are significantly shifted

1:29:18compared to the to the gray dots because

1:29:20we know the gray dots

1:29:22you know they were a pretty good

1:29:23compensation matrix

1:29:25right

1:29:27dude does the compensation that we did

1:29:29in flojo how does it compare to what we

1:29:31did on the machine okay well in as in

1:29:33this case they look pretty much you know

1:29:35all the cells and populations here are

1:29:37superimposable with one another

1:29:40okay so

1:29:43if you're satisfied with that matrix and

1:29:45let's say you i don't know maybe you

1:29:48prefer the the new matrix that we

1:29:50generated on flojo now in order to apply

1:29:53this matrix to all your samples you can

1:29:55click the m button here you want to

1:29:57click and drag it onto the group of

1:29:59choice so maybe you want it to go under

1:30:01your test files

1:30:02the minute you do that you can see the

1:30:04color changes from gray to black

1:30:06so now we're using our new compensation

1:30:09matrix

1:30:10on those cells okay if you want it to go

1:30:12to the fmo group you can do that as well

1:30:15if you want to swap back to the original

1:30:18same thing click and drag the m button

1:30:20onto the group of choice

1:30:22and it will return to the gray matrix

1:30:27okay

1:30:30so i should mention this is probably for

1:30:32an advanced session

1:30:35but there are other ways to compensate

1:30:37in flowjo and in fact we have a recorded

1:30:40webinar that did this last month i

1:30:41believe

1:30:43uh

1:30:44where we talked about auto spill

1:30:46and spectral uh compensation so there

1:30:50are two additional mathematical ways to

1:30:52compensate your files the traditional

1:30:54method is the one that you're probably

1:30:56used to when you're on the machine

1:30:58uh the auto spill is fairly new that

1:31:01came out in 2018 and we've been talking

1:31:03about it since then i think it was

1:31:05implemented in 2018 or 2019 in flojo

1:31:09uh and then spectral compensation has

1:31:11been around for a while but this

1:31:13requires that

1:31:14you have a spectral machine like a

1:31:16symphony a5

1:31:18or a3

1:31:20obviously if you have a scitec aurora

1:31:23or a sony

1:31:25those are spectral

1:31:27uh those are cytometers with spectral

1:31:30capability so

1:31:32in order to use that

1:31:34this capability you need to have you

1:31:36need to have that kind of machine to

1:31:38begin with

1:31:40okay but these other two methods are

1:31:41available to you if you have a standard

1:31:43cytometer and they're pretty powerful

1:31:46the auto spill is really good if you

1:31:48have cells for comps

1:31:50it does a great job and it also can

1:31:52handle autofluorescence so it's a pretty

1:31:54handy

1:31:55tool if you have highly autofluorescent

1:31:57cells and you have trouble with your

1:31:58compensation you might want to try this

1:32:01guy if you want to

1:32:03investigate or see that compensation

1:32:05webinar the way to do it is to go to

1:32:08flojo.com

1:32:10right go to the

1:32:11base website go to learn here and you'll

1:32:14see this webinars

1:32:16tab

1:32:18and then

1:32:19down at the bottom of our webinars sort

1:32:22of list of things you'll see this button

1:32:24that says recorded so today's one is

1:32:27recorded as well

1:32:29uh

1:32:30but you can see here

1:32:31the list of recorded webinars

1:32:34right so we had one i guess the one we

1:32:36had in uh recently was flojo's new

1:32:39plug-and-play bundle

1:32:41uh

1:32:42i'm looking here

1:32:45so all the intro to flojo's on the

1:32:47right-hand side we have high dimensional

1:32:49stuff

1:32:51i knew there was a compensation one that

1:32:53was done on this topic before so in any

1:32:55case there it's got to be here somewhere

1:32:57there it is

1:32:58this compensation in 10-7 that's when we

1:33:02implemented that roka the the auto spill

1:33:04method anyway that's there trying to see

1:33:07if there's anything more recent hard to

1:33:09believe that we did that over a year ago

1:33:15okay

1:33:18bring your questions no no no no none of

1:33:21this stuff is

1:33:24ah okay well i guess that's the most

1:33:26recent one but it we definitely

1:33:27discussed that and then here's one

1:33:29spectral compensation

1:33:31right so this one talks specifically

1:33:33about spectral

1:33:34and then the other one compensation in

1:33:3710 7 probably covers

1:33:39spectral compensation

1:33:41uh

1:33:42auto spill and the traditional method so

1:33:44this one covers all three of them so if

1:33:46you're interested in understanding all

1:33:48three of those methods by all means go

1:33:50to the

1:33:51that page and just click on the link and

1:33:54it will open up and the video will start

1:33:55right away

1:33:57all right last thing that i'm going to

1:33:58mention here is saving your work this is

1:34:00extremely important because we've been

1:34:03running into a lot of

1:34:05problems with people saving their data

1:34:07to a

1:34:09server and so what you want to do is

1:34:11whenever you work in flow joe you want

1:34:13to make sure that your data files right

1:34:15those raw fcs files make sure they're on

1:34:18your hard drive so they're here if

1:34:20you're on a mac

1:34:21it's like going to be in your desktop

1:34:23your documents or applications this is

1:34:25all on your hard drive

1:34:27right

1:34:28cloud related stuff is down here in the

1:34:31locations right this is stuff that is

1:34:33like either my phone which is connected

1:34:35to my computer or

1:34:37right a network

1:34:38uh if you happen to have a thumb drive

1:34:41now the thumb drive isn't a cloud drive

1:34:44per se but the point is you want the fcs

1:34:47files and your workspace while you're

1:34:49working on them

1:34:50to be on your hard drive someplace if

1:34:53you're on a pc

1:34:54this is usually the c drive right c

1:34:57colon blah blah blah

1:34:59don't have it on one drive don't have it

1:35:02on a network map drive like the z drive

1:35:04or the w drive or the f drive whatever

1:35:08right you can store it there but when

1:35:10you do analysis make sure that you bring

1:35:13those files to the hard drive

1:35:16open them up from this location do your

1:35:18analysis and then save it

1:35:21back to the hard drive

1:35:23once you're done saving it on the hard

1:35:25drive then go ahead and transfer them

1:35:28you know

1:35:29through the finder or the explorer

1:35:31window transfer them manually

1:35:33to the server

1:35:35okay if you try to do that with flojo on

1:35:38the server