# Lecture 7b: Flow Cytometry **Channel:** Annelise Snyder **Source:** https://www.youtube.com/watch?v=R7dsIt0p19M **Transcript page:** https://www.withtranscript.ai/video/R7dsIt0p19M ## Chapters - 0:02 — Flow Cytometry Principles and Setup - 2:50 — Reading Single-Color and Sorted Samples - 11:34 — Designing Multi-Parameter Flow Panels - 20:30 — Clinical Diagnosis with Flow Cytometry - 27:07 — Functional Defects and Course Wrap-Up ## Transcript **[0:02]:** Welcome back to week seven of applied immunology. This is lecture 7B, the second and final lecture for this week's discussion of immunological methods that can be used for clinical diagnostic testing of patient samples. Today's lecture will focus entirely on flow cytometry, which we've introduced briefly a couple of times before this lecture. This is a very powerful tool that is used to analyze both basic research and clinical patient samples. Today, we will discuss the experimental principles of flow cytometry so that you all have a solid understanding of how these assays work. And then at the end of lecture, we will go over a few examples of flow data used to diagnose different types of diseases. Let's start with this slide from lecture 6B, where we first were introduced to flow cytometry. Again, this is a antibody-based method that uses primary antibodies targeted to specific antigens. And these antibodies are also linked to fluorophores that when hit with an excitation wavelength of light, give off a particular wavelength that is used as a readout for the presence of antigen. **[1:06]:** Flow is used to determine the composition and phenotypes of individual cells that are present in a sample that contains a mixture of cells. A common example of this type of sample used in research would be a mouse spleen that has been dissociated into a single cell suspension. Though, we will see later on that patient blood or bone marrow samples are also common samples that are used in clinical diagnostics. No matter the source of your sample, the premise is the same. You take your single cell suspension and incubate it with fluorophore conjugated antibodies to generate a stained sample. The stained sample is then run on a machine called a flow cytometer, which has a fluidics chamber that pulls cells through the cytometer one by one. As each individual cell passes through certain points, they are hit with a laser beam that has the excitation wavelength of a given fluorophore. **[1:53]:** And in turn, there's also a signal detector that picks up the emission wavelength for that corresponding fluorophore. Therefore, the presence or absence of a given emission wavelength can be used to determine whether that individual cell expresses that target antigen. This is pretty straightforward to understand when you're only looking at a single antigen single fluorophore combination. But most flow cytometry experiments involve running large multi-parameter panels containing a dozen or more antibody fluorophore combinations. This simplified diagram shows just one combination of excitation laser and emission detectors. But keep in mind that many standard flow cytometers use up to four different excitation lasers and many other types of emission detectors. Let's start to go through what the actual data from a flow cytometer look like in order to understand how a scientist or clinician would put together information from many different antibody fluorophore combinations at once. **[2:50]:** The simplest way to start is with single color flow cytometry. Let's say that we have a red cell A which expresses this circular red surface antigen. If you incubate a sample containing a pure population of cell A with a single flow antibody that recognizes this red surface antigen, then each cell in the sample will be labeled with those antibodies conjugated to this purple fluorophore. You then place a tube containing this sample onto the spigot for a flow cytometer which again uses a series of fluidics lines to run the sample through the machine and interrogate each individual cell one by one. In this example with just one antibody fluorophore conjugate, the data can be read out as a flow histogram. This is a very simple graph that plots the count or number of individual cells on the Y axis and the relative fluorescent signal on the X axis. **[3:39]:** And this is a read out of the fluorophore's emission wavelength that was picked up by the detector for that wavelength. Again, this is the read out for antigen presence on an individual cell. This is called a single parameter or univariate histogram because you're only looking at the detection of a single parameter, the emission wavelength of the purple fluorophore in this sample. Note that because in this example we are analyzing a sample that is made up entirely of cell A, there is only one peak on this graph that shows up around the 10 to the fourth mark on the x-axis. This means that all cells analyzed in the cytometer are positive for this red surface antigen. Let's next look at a slightly more complicated example where you start out with a mixture of two different cell types in the sample that you want to analyze. So, again we have cell A in red, which expresses the red dots that we will call antigen X. **[4:31]:** And then we now have cell B in blue, which expresses the blue dots that we will call antigen Y. Here, your goal would be to probe for multiple surface markers by incubating the sample with a mixture or cocktail of flow antibodies. Specific flow antibodies used for this are selected based on what you know or expect are antigens that are specifically expressed only on cell type A or cell type B. And by using antibodies against those antigens that are conjugated to distinct fluorophores, you can distinguish between these on a cytometer. In this example, we've selected the top antibody, which is anti-antigen X linked to the purple fluorophore, and the bottom antibody, which is anti-antigen Y linked to the green fluorophore. Once incubated, these antibodies are bound to each target cell type and can be analyzed using a flow cytometer. This example uses multiple parameters, as in two or more different fluorophore emission spectra that can be detected by the cytometer. **[5:30]:** These data are represented by a two-parameter or bivariate dot plot, where the y-axis is the intensity of emission wavelength detected for one fluorophore, and the y-axis corresponds to the purple fluorophore that is associated with antigen X, while the x-axis represents the intensity of emission wavelength detection for the second fluorophore, and this is the green one that is associated with antigen Y. Each dot on the plot represents a single cell, and denser areas of the plot with many overlapping cells come up as red and orange colors, while less dense regions with fewer cells come up in blue and green. Again, the detection of a fluorophore's emission wavelength is used as a readout for the presence of a target antigen that you know should be expressed on a certain cell subset. So, the purple fluorophore positive cells are those belonging to red cell type A, while the green fluorophore positive cells are those belonging to the blue cell type B. **[6:25]:** As you can see, each cell type clusters together nicely in these density plots, which means that each of these individual cells is likely a member of the same population. So, we draw something called a gate around each population to quantify the number of cells within that population, and the gate is just these rectangles around uh cell type A or cell type B. **[6:47]:** Let's add some more detail to this specific example and make sure that it still makes sense. This was actually a experimental sample that I had run from a mouse experiment where I stained peripheral blood mononuclear cells or PBMCs from a mouse, and I wanted to look at populations of circulating CD4 positive T cells as well as CD8 positive T cells following the application of a certain experimental treatment. In this example, the red cell type A is actually CD4 positive helper T cells, which obviously express the surface antigen CD4. And these were stained using an anti-CD4 antibody conjugated to a purple fluorophore called BV711, which stands for brilliant violet 711. This refers to the fluorophore's emission wavelength of around 710 nm, so this is a purple color. The blue cell type B is actually CD8 positive cytotoxic T cells, which obviously express the surface antigen CD8. **[7:41]:** These were stained using an anti-CD8 antibody conjugated to a green fluorophore called FITC, which stands for fluorescein. And this fluorophore has an emission wavelength around 520 nm, which is a green color. So, if you plot cells in this sample according to their BV711 emission detection on the Y axis versus their FITC emission detection on the X axis, this is how I was able to separate out uh CD4 versus CD8 positive T cells in my sample. You can also plot this data as a histogram if you want to compare each cell population according to only one parameter or one marker. So, this is going to sound a little weird, but imagine that you take the x-axis of the dot plot in the center and swing it out 90° towards you so that you're only looking at the graph according to the FITC or CD8 axis. Again, this histogram plots cell counts by the fluorescence intensity of FITC. **[8:35]:** Although, now that we have a mixed sample, there's a clear negative population that represents the CD4 T cells, and there's a clear positive population representing the CD8 T cells. This parameter along the x-axis is also called the mean fluorescence intensity or MFI. And this is a measure of the concentration of fluorophores on any given single cell. Obviously, more fluorophores bound to a single cell translates to a higher intensity of emission wavelength. So, this essentially means that uh increase in MFI indicates an increase in antigen density on that individual cell. We will get into more types of multi-parameter analysis uh flow cytometry shortly, but I first want to touch on the fact that flow cytometry can be used to actually sort out certain cell populations from a mixed sample. And this is a technique called fluorescence-associated cell sorting or FACS, and it uses a specialized type of cytometer called a cell sorter where you can take a heterogeneous mixture of cells and then physically move a portion or a **[9:36]:** subset of those cells into a new container based on their uh flow plots. So, let's say that in our previous sample of mixed T cells isolated from mouse blood, a researcher would like to isolate just the CD8 positive T cells for some type of future analysis or experiment. The mixed sample would be stained as we just described, then run on a cytometer that again interrogates single cells for the presence of either the purple fluorophore for CD4 T cells, or the green fluorophore for uh CD8 T cells. You would run a small amount of your sample to set up a dot plot on the machine, which again allows for separation of CD4 versus CD8 T cells. You would then draw a box around the population that you want to sort, and these are the CD8 T cells that are FITC positive, and then enter this information into a program on the machine that tells the cell **[10:23]:** sorter to isolate FITC positive cells as they pass through the fluidic system. The cell sorter does this by physically redirecting the stream of single cell flow into a separate container whenever it detects a FITC positive cell. This is done using magnets and is so finely controlled that it actually only flicks out a single droplet containing a single FITC positive cell into the sorted cell container. The non-FITC positive cells, or the CD4 T cells, continue flowing through the cytometer and are eventually discarded. At the end of the cell sort, you then have a relatively pure population of FITC positive cells, in this example CD8 T cells, and these are reserved in a separate tube that you can then use for additional experiments or diagnostic readouts. This is a little bit of a silly example because there are some simpler methods for isolating CD8 T cells from a mixed sample, but since we've been using CD4s versus CD8s so far, I hope that this at least **[11:18]:** demonstrates how FACS can be used to isolate pure cell populations. This can get a lot more complicated as we add more colors or flow antibodies into the flow sample, and this allows researchers to isolate much more specialized subsets of immune cells. **[11:34]:** The examples we've covered so far have established principles of flow cytometry using one or two colors at a time, but the real power of this method is in the its ability to look at many different parameters at the same time in one sample. Traditional flow cytometers can examine up to about 15 fluorophores and therefore antigens at a time, while some newer technologies can go up to many more colors. This cocktail of antibody fluorophore conjugates that are used to analyze the sample are called a flow panel for a given experiment, and these can be used to distinguish between multiple cell subsets as well as phenotypes within a given sample that consists of a mixture of cell types. This panel on the left is a relatively straightforward flow panel that I used for an experiment in graduate school where I wanted to analyze immune cells that were found in a mouse tumor. **[12:22]:** Target antigens were selected based on which cell types I wanted to look at and the fluorophores paired with these antigens also had to be taken into consideration, although we're not going to get into that today. Lots of immune cell surface markers start with the abbreviation CD, which stands for cluster of differentiation. So, whenever you see a CD prefix followed by a number, you're usually looking at some sort of cell marker. We won't get into this anymore in this class, but antigen fluorophore pairings need to be chosen pretty strategically based on the relative intensity or brightness of a fluorophore, as well as the relative density of a target antigen. And furthermore, once you start running multi-color flow panels, there's a fair amount of overlap over the edges of emission spectra for each fluorophore, and these need to be unmixed using a process called compensation. You don't need to know the specifics of these processes, but just know that fluorophore selection is not done entirely randomly when designing flow panels. **[13:18]:** Let's look at sample flow plots from this experiment. Whenever you have multiple markers in a flow panel, you need to define a gating strategy for that panel, which is essentially just the sequence of cell populations that you carry through your analysis. I'm not showing it here, but you usually start out by gating cells based on their size and granularity first using measures called forward scatter and side scatter. This helps you get rid of small debris from your analysis. You then gate cells on single cells to get rid of cell clumps, as well as gating on live cells using something called a viability dye. And if you want to look specifically at immune cells, then you include this marker called CD45, which is only expressed on leukocytes. Again, these are all upstream gates that I'm not showing here just for the sake of time, but understand that there are a few upstream steps that you first establish to basically clean up your data to make sure that **[14:07]:** you're only analyzing live single cells. So, once that's done, the first plot I'm showing here is looking at lymphocytes in a tumor sample. So, I plotted the B cell marker CD19 by the T cell marker CD3 in order to separate out CD19 expressing B cells along the Y axis um from CD3 expressing T cells along the X axis. Because I was interested in phenotyping T cells for this experiment, I gated on the CD3 positive T cells and then further subgated this population based on CD8 versus CD4 expression. I then looked at one additional marker per cell subset to further characterize my CD4s and my CD8s. So, first I took the CD8 positive T cells and gated them based on CD44 expression. This is shown in the middle plot here on the bottom. CD44 is just a surface marker whose expression is increased in certain types of activated T cells. **[15:01]:** So, CD44 positive CD8s are typically defined as activated cytotoxic T cells. Second, I took the CD4 T cells and stained them intracellularly for the transcription factor FoxP3. This plot is shown in the bottom right hand panel here. As we learned back in week four, FoxP3 is the master transcription factor for regulatory CD4 T cells. So, I defined the CD4 FoxP3 double positive population as Tregs in this example. For this experiment, I wanted to characterize the quality of T cells infiltrating a type of mouse tumor. So, being able to compare the relative proportions of activated cytotoxic T cells and comparing that to regulatory T cells was a helpful experimental readout that I was able to characterize using flow cytometry. **[15:51]:** We've learned about a lot of different immune cell subsets so far in this course, each of which differ with respect to the surface markers that they express at baseline, as well as the molecules that they produce when they become functionally activated. This table sums up several common antigens that can be used to both define the identity of immune cells such as B and T lymphocytes, dendritic cells, NK cells, and macrophages, as well as some of the markers of functional activation of each cell type. And these functions range from surface marker expression to transcription factors that are associated with certain cell lineages. You can also look at the production of effector molecules, as well as cytokine and chemokine signaling. Now, this isn't an exhaustive table of phenotyping markers for each cell type, and I'm not going to go over every single one of these markers. So, please don't worry about memorizing this entire table for exam questions. **[16:41]:** My point here in presenting all of this information is more to drive home the fact that in flow cytometry, there are almost limitless combinations of antigens that you can include in a flow panel. And this can get a bit overwhelming kind of quickly. So, marker selection for flow panel design really needs to be based on the specific cell subsets and functions that you want to interrogate within a given tissue sample, whether this is from an experiment using mice or PBMCs that have been isolated from a patient that you're trying to diagnose. So, although this is a powerful tool that can be used um to some extent for exploratory research, you really need to approach flow cytometry with a specific hypothesis in mind, or at least be able to define a manageable group of immune cells and their functions that you would like to characterize in a given flow panel. **[17:30]:** Before we move into applications of flow for diagnosing clinical samples, I want to touch on two specific types of flow cytometry markers that are particularly helpful for characterizing lymphocytes, so T cells and B cells. And the first are flow antibodies that recognize different isotypes of surface immunoglobulin. This sample from the textbook shows flow plots analyzing B cells from a mouse spleen. And these were gated on live singlets and B cells using the marker CD19 before then plotting IGD isotype on the Y axis by IGM isotype on the X axis. Looking at this color density plot in the lower right-hand corner, you can see clearly that the majority of the B cells in the spleen are double positive for both IGD and IGM with a small proportion of IGM single positive B cells. As we know from our week five lectures on B cell biology, this double positive population reflects mature naive B cells, which make up the predominant population in the spleen. **[18:26]:** Note that the other panels of this figure all represent the same set of flow data from this experiment. As a standard dot plot on the upper right or as histograms along either IGM or IGD axes in the left-hand column. The other specialized flow reagent that I want to cover today is something called a tetramer, which comes in both B cell and T cell forms. This example here outlines the structure of a T cell tetramer, which consists of a molecule of streptavidin complexed with a fluorophore, which is also connected to several recombinant MHC class 1 molecules loaded with a single defined antigenic epitope. This epitope is selected based on the fact that for most pathogens, there is one antigen epitope that is most easily recognized by lymphocyte antigen receptors, and we refer to this uh as a immunodominant epitope for a given pathogen. This is true for both B cells as well as CD4 and CD8 T cells. **[19:21]:** So, tetramer reagents are designed using the immunodominant epitope for a pathogen that has been previously characterized through phenotyping of endogenous T or B cell responses to infection. Now, tetramers are incubated with a sample along with other flow antibodies. And if any T cells in the sample recognize the recombinant peptide MHC complex on the tetramer, then they will bind to it, and this can be detected using flow cytometry. Since this example is using a MHC class 1 tetramer, you can see that out of the CD8 positive T cells in this sample, there's a small percentage that are also specific for tetramer, and which in this case is made using an epitope expressed by cytomegalovirus or CMV. These CMV specific CTLs identified by flow are the same ones that recognize this immunodominant epitope presented on MHC class 1 by infected cells during natural infection, and this is shown on the diagram on the right. **[20:15]:** Tetramers can be used to phenotype virus specific or also tumor antigen specific lymphocyte responses. And these can also be used for sorting antigen-specific cells using facts for further analysis if necessary. **[20:30]:** I want to end this lecture by going over a few examples of flow cytometry data used to diagnose clinical patient samples. And we'll start with a group of pediatric blood cancers called leukemias. So, acute leukemias consisting of malignant leukocytes are the most common type of cancer diagnosed in children. And one of the most common of these is acute lymphoblastic leukemia from B cell lineages. This is abbreviated as B cell ALL. These patients usually come into the doctor with a history of symptoms like recurring infections, complaints of fatigue, abnormal amounts of bruising, or swollen lymph nodes. And all of these symptoms result from uncontrolled proliferation of cancerous B cell precursors, which can crowd out other cell populations in the bone marrow. This leads to infection susceptibility, as well as insufficient platelet formation. Bone marrow samples may then be taken to examine the presence of B cell blasts, which is a cellular morphology associated with immature B cells that are present in much higher percentages in these leukemia patients. **[21:32]:** Flow cytometry phenotyping of lymphocytes in these patients following initial diagnosis helps to definitively establish a diagnosis of B cell ALL because it can provide additional information on the patient's B cells beyond basic morphological readouts such as blast morphology. This information can then be compared to marker expression phenotypes that are considered uh characteristic of this form of B cell malignancy. And these are based on the abnormal expression of several surface markers that are not usually co-expressed in healthy B cells subjected to properly regulated B cell development pathways. **[22:10]:** This set of sample flow plots show phenotypic comparisons between a leukemia patient in red and a healthy control patient in light blue. Now, these populations are gated on CD19 positive B cells taken from bone marrow samples, and then stained with five other markers that are used to distinguish between leukemic versus normal B cell precursors. So, healthy B cells in light blue have a surface phenotype that is defined as CD10 intermediate, CD34 negative, CD38 positive, and CD58 intermediate. And then lastly, they are also CD45 positive. The red B cell samples from ALL patients differ from healthy controls as they are characterized by increased CD10 expression, shown here in the upper left-hand panel. They have absent CD38 expression in the upper right and lower left-hand panels, increased expression of CD58 in the lower left-hand panel, and then both increased CD34 expression and decreased CD45 expression as are both shown in the lower right-hand **[23:11]:** panel. So, I know that this is a lot of information on B cell markers, but don't get bogged down in the details here. The main take-home point is that because flow cytometry allows you to simultaneously measure several different parameters from the same patient sample, it can be used to establish some really informative phenotyping information on cells that are suspected of being cancerous. In the case of B cell ALL, this type of diagnostic testing can allow a clinician to specifically classify a patient's leukemia according to one of several subtypes. And this is important because it affects decisions about treatment planning and patient prognosis. **[23:50]:** Flow cytometry analysis of blood samples can also be used to definitively diagnose certain types of rare pathologies called severe combined immunodeficiencies or SCID. If you completed the week four discussion prompts on T cell forms of SCID, then some of this information may sound familiar to you. Now, SCID is a type of primary immunodeficiency, which is a heterogeneous collection of pathologies caused by inherited genetic mutations to pathways related to either immune cell development or signaling. In SCID, these mutations usually result in drastically reduced or absent numbers of T cells and sometimes B and NK cells as well. This renders these patients extremely susceptible to infections. So, they're usually brought into the hospital at a young age due to recurring severe infections. One of the first tests used to diagnose potential skid cases is done by running flow on a PBMC sample from a patient and staining it using markers associated with the three main populations of lymphocytes, which again are T, **[24:49]:** B, and NK cells. This panel usually includes CD3 for T cells, CD19 for B cells, and CD56 for NK cells. The top panel in this figure shows flow plots for a panel for a patient that lacks T cells, but still has both NK cells and B cells. So, this patient is listed in the panel title as being T negative, B positive, and NK positive. You can see in the left panel that there are no CD3 expressing T cells in the PBMC sample, but plenty of CD56 positive NK cells. In the right panel, again, there are no CD3 expressing T cells, but plenty of CD19 expressing B cells. So, this patient would still have functional cytotoxic NK cells and partially functional humoral immunity through B cells, but due to the lack of T cells, the patient would lack cytotoxic T lymphocytes, as well as helper T cells, including **[25:41]:** T follicular helper T cells that are required for affinity maturation and isotype switching of B cells. So, this would be a purely T and B cell driven form of skid. The bottom panel in this figure shows flow plots for a patient that lacks both T cells and NK cells. So, again, there are no CD3 expressing lymphocytes in the PBMC fraction of this blood sample, but you can see from both the left and right panels that there are no CD56 positive NK cells. They do have CD19 expressing B cells, however. So, this patient might have a similar set of issues as the first patient in the top row of panels, but additionally, they might be even more susceptible to viral infection and certain types of tumors due to their complete lack of NK cells. This initial diagnosis of the major lymphocyte population deficiencies observed in a patient is a very important first step in confirming that the patient has some form of skid that's defined by some combination of missing lymphocytes. **[26:38]:** In either of these cases depicted above, a clinician might next order additional genetic testing to try and determine the underlying mutation of each type of skid or they might also run flow on additional surface phenotyping markers or intracellular signaling proteins. Determining the likely genetic defect that is responsible for driving a patient's skid pathology is an important next step before deciding which types of treatments are both available as well as which ones are worth pursuing. **[27:07]:** The last examples of clinical diagnostics using flow cytometry that we will go over today have to do with looking at markers associated with the function of certain immune cell subsets. The first one here on the left is a form of primary immunodeficiency called perforin deficiency. So, we've talked about perforin before in our discussions of T cell and NK cell effector functions. It's a small molecule that's released by both cytotoxic cell types and is responsible for causing apoptosis in target cells. Certain genetic mutations can reduce perforin expression in one or both of these immune cell subsets and this leaves patients susceptible to infections that require cell-mediated cytotoxicity for the control of infection. In this plot, the top row shows upstream gating of NK cells and T cell subsets. Again, identifying NK cells through CD56 expression and then subgating CD3 positive T cells based on CD4 expression. **[28:00]:** Because CD4 positive helper T cells, abbreviated here as TH, do not express perforin, they are used as a negative control for perforin staining, which the NK cells are then compared to in these bottom histograms. The bottom row shows histograms of perforin expression in both CD4 T cells in red and NK cells in blue. This compares a healthy control patient on the left to a patient with perforin deficiency on the right. You can see that in the healthy control sample, NK cells express perforin as shown through this increased MFI of the blue peak, while helper T cells in the red curve do not. However, in the perforin deficiency patient, the histograms for perforin staining overlap between NK cells and helper T cells, and both are on the negative side of the histogram. Therefore, staining cytotoxic immune cell subsets such as NK cells for perforin is a important diagnostic tool for this form of primary immunodeficiency. **[28:56]:** The last example that we will talk about today is something called chronic granulomatous disease or CGD, which we've introduced before in lecture 7A when talking about a type of diagnostic test called the NBT test. This is also a primary immunodeficiency that you may have selected for your week two discussion prompt since this was an option for summarizing innate primary immunodeficiencies. CGD is a pathology where patients typically have mutations in enzyme subunits needed for the respiratory burst that generates ROS. This is required to kill phagocytosed pathogens. Neutrophils that cannot generate ROS are much worse at killing phagocytosed pathogens, and so these CGD patients are highly susceptible to bacterial and fungal infections as a result. In addition to other types of diagnostic tests like the NBT test, flow cytometry analysis can also be performed on neutrophils isolated from a patient's blood sample in order to distinguish between two underlying genotypes that commonly drive CGD. **[29:55]:** And this is done by staining for expression of a few different subunits of the NADPH oxidase complex that is responsible for generating superoxide, and superoxide is a major component of neutrophil ROS. So, NADPH oxidase is made up of one heterodimer between the proteins gp91phox and p22phox. Then there's also a heterotrimer made up of the proteins p40, p47, and p67. So, mutations underlying CGD typically either lead to p47phox deficiency or gp91phox deficiency. And either of these mutations can lead to non-functional NADPH oxidase, and therefore you end up with defective neutrophils. The top row of this figure shows histogram plots for patient neutrophils that were stained for a subunit of NADPH oxidase and this uses an antibody clone 7D5, which recognizes an epitope on the GP91 subunit of NADPH. In these histograms looking at 7D5 uh staining, you can see that the neutrophils in the control patient in the middle panel shown by the blue curve stain positive for this NADPH oxidase subunit while a control cell population in **[31:07]:** red is negative. However, in a CGD patient in the right panel, NADPH subunit expression is lost in neutrophils. So, now this curve overlaps with the negative control population. You can therefore conclude that this CGD patient has an underlying mutation that affects the expression of the GP91 phox p22 phox subunit of NADPH oxidase. This is important genotyping information that can inform a clinician's decisions about which treatment steps to pursue next. In summary, today we've learned about the principles behind flow cytometry and how this powerful tool can be used to analyze experimental and clinical samples in order to define the phenotype of immune cells by evaluating many different parameters at the same time. We've established that this tool can be used in basic research to characterize a lot of information about the identity and activation status of different immune cells in a sample and that we can also use cell sorting to isolate **[32:03]:** target populations if we want to conduct further experiments or analyses on these cells. We've also learned about using flow to phenotype immune cells in clinical settings in order to learn more information about a patient's immune cells that can help inform their diagnosis. This provides important information about immune cells that can go far beyond the standard cell counts and proportions that you might learn about after performing something like a CBC panel as a part of more standard blood work. We ended with a discussion of several diseases where flow provides a necessary level of detail about immune cells. This ranges from settings of leukemia to a variety of rare primary immunodeficiencies that affect either the presence or the function of immune cells. I also want to end by making the point that flow cytometry analysis is also a very important tool that can be used in clinical research settings as well. **[32:53]:** And in this case, a researcher might want to establish patient immunophenotyping in response to drug treatments or their long-term memory T cell and B cell populations following something like infection or vaccination. That's all for lectures this week since you have a shorter lecture schedule for week seven. Again, this is a good opportunity to also get caught up on the lecturing and reading materials from week six if you were not able to finish those while working on your midterm last week. Don't forget that you also have an extra credit quiz this week whose questions will incorporate information from both week six and week seven lectures. Lastly, you have a discussion board prompt if you want to use this week for one of your graded discussion responses. So, this one is a bit of a choose your own adventure style response where you can choose to either design a basic research experiment based on some preliminary observations that were made in a research lab or you can **[33:47]:** propose a series of diagnostic tests to confirm a disease diagnosis in a patient that comes into the clinic with a specific set of symptoms. So, discussion is a little bit different this week and might be a little bit more challenging, but I hope that this might sound interesting to any of you that are working towards a career in either research or medical diagnostics. Good luck with all of your assignments and see you next week.