Here, we present a flow cytometric protocol to identify CD4+ and CD8+ T cells, γδ T cells, B cells, NK cells and monocytes in human peripheral blood by using only two fluorochromes instead of seven. With this approach, five additional markers can be recorded on most flow cytometers.
Immune cell characterization heavily relies on multicolor flow cytometry to identify subpopulations based on differential expression of surface markers. Setup of a classic multicolor panel requires high-end instruments, custom labeled antibodies, and careful study design to minimize spectral overlap. We developed a multiparametric analysis to identify major human immune populations (CD4+ and CD8+ T cells, γδ T cells, B cells, NK cells and monocytes) in peripheral blood by combining seven lineage markers using only two fluorochromes. Our strategy is based on the observation that lineage markers are constantly expressed in a unique combination by each cell population. Combining this information with a careful titration of the antibodies allows investigators to record five additional markers, expanding the optical limit of most flow cytometers. Head-to-head comparison demonstrated that the vast majority of immune cell populations in peripheral blood can be characterized with comparable accuracy between our method and the classic "one fluorochrome-one marker approach", although the latter is still more precise for identifying populations such as NKT cells and γδ T cells. Combining seven markers using two fluorochromes allows for the analysis of complex immune cell populations and clinical samples on affordable 6-10 fluorochrome flow cytometers, and even on 2-3 fluorochrome field instruments in areas with limited resources. High-end instruments can also benefit from this approach by using extra fluorochromes to accomplish deeper flow cytometry analysis. This approach is also very well suited for screening several cell populations in the case of clinical samples with limited number of cells.
Flow cytometry is a technique that was developed to analyze multiple parameters on single particles at a rate of several thousand of events per second1. Examples of specimens analyzed by flow cytometry include, but are not limited to, cells, beads, bacteria, vesicles and chromosomes. A fluidic system directs particles at the interrogation point where each particle intersects its path with one or more lasers, and multiple parameters are recorded for further analysis. Forward and side scatters, generated by scattering of the pure laser light, are used to identify the target population and retrieve information about the relative size and internal complexity/granularity of particles, respectively. All the other parameters, that account for most of data in a flow cytometric analysis, are derived by fluorochrome-labeled probes that recognize and bind to specific targets on the particles of interest.
Flow cytometry is a primary tool for immunological studies to identify and characterize cell populations. To dissect the complexity of the immune system, multicolor panels are constantly evolving to expand the number of markers simultaneously recorded for deep immunophenotyping of cell populations1. This is leading to the development of more capable instruments and fluorochromes, with recent high-end flow cytometers exceeding 20 fluorescent parameters. This results in complex study design due to fluorochrome spectral overlap and in higher costs associated with custom antibody labelling and skilled operators. In several instances, complexity and costs are reduced by using separate panels of markers for different cell populations. This approach, however, is error prone, reduces the information in each panel, and can be difficult to apply to samples with limited number of cells. Moreover, increasing the number of markers precludes deep immunophenotyping on instruments with fewer fluorescent parameters. We previously developed a staining protocol to identify major human immune populations (CD4+ and CD8+ T cells, γδ T cells, B cells, NK cells and monocytes) in peripheral blood mononuclear cells (PBMCs) by combining seven lineage markers using only two fluorochromes instead of the seven required using the traditional "one fluorochrome-one marker" approach (www.hcdm.org)2,3. Our initial report explored and validated the notion of combining seven markers in two fluorochromes for deep immunophenotyping. In this report, we present a step-by step protocol to isolate and stain peripheral blood cells, focusing on the practical aspect and troubleshooting steps to achieve a successful staining.
This protocol is based on the observation that lineage markers have a constant expression on the cell surface and that each cell population has an exclusive combination of lineage markers. In PBMCs, CD3 expression subdivides immune cells into two main categories: CD3-positive T lymphocytes and CD3-negative cells. Within the CD3 positive subgroup, CD4+, CD8+ and γδ T cells can be separated using antibodies that solely target CD4, CD8 and the γδ receptor. In a comparable way, within the CD3 negative subgroup, B cells, NK cells and monocytes can be uniquely identified using antibodies against CD19, CD56 and CD14, respectively. In a standard one fluorochrome-one marker approach, anti-CD3, -CD4, -CD8, -CD14, -CD19, -CD56 and -TCR γδ antibodies are detected with seven different fluorochromes. Our approach combines anti-CD3, -CD56, and -TCR γδ antibodies in one fluorochrome (labeled for convenience fluorochrome A) and anti-CD4, -CD8, -CD14 and -CD19 antibodies in a different fluorochrome (fluorochrome B). This is possible by a combination of antibody titration and differential antigen expression. Both CD4+ and CD8+ T cells are positive for the anti-CD3 antibody in fluorochrome A, but they can be separated in fluorochrome B maximizing the expression of the CD8 signal while placing, with an ad hoc titration, the CD4 signal in between the CD8 and the CD3 positive-CD4/CD8 double negative cells. γδ T cells expresses higher level of CD3 than CD4 and CD8, and therefore they can be identified as CD3 high4. This signal is further boosted by labeling γδ T cells in fluorochrome A with an anti-TCR γδ antibodies, thus improving separation between CD3 low T cells and CD3 high γδ T cells. B cells can be identified as CD3– in fluorochrome A and CD19+ in fluorochrome B. To separate CD3 negative NK cells from B cells, an anti-CD56 antibody was used in fluorochrome A as the anti-CD3. This is possible because CD56 expressed on NK cell at a much lower level than CD3 on T cells5. Finally, monocytes can be identified via a combination of forward-side scatter properties and expression of CD14 in fluorochrome B.
The idea of combining up to four markers using two fluorochromes has been already successfully attempted before6,7,8, and has been used in a clinical protocol to identify malignant lymphocytic populations9. A previous report also combined seven markers (with different specificity from the markers than we used in our protocol) using two fluorochromes, but this approach relied on a complex labelling of each antibody with varying amount of fluorochrome10. This is in contrast to our method which uses commercially available antibodies and can be adapted to the instrument configuration and can take advantage of the new generation of polymer fluorochromes.
The overall goal of this methodology is to expand the optical limits of most flow cytometers allowing for the recording of five additional markers to interrogate complex cell populations. As a consequence, advanced immunological analysis can be performed on affordable 6-10 fluorochrome flow cytometers, and 2-3 fluorochrome field instruments can achieve remarkable results in areas with limited resources. High-end instruments can also benefit from this approach by using extra fluorochromes to accomplish deeper flow cytometry analysis and to create modular flow cytometric panels targeting several lineages at the same time11. This can potentially reduce the number of panels used in modular immunophenotyping flow cytometry and reduce costs, errors and handling time. This approach is also very well suited in the case of clinical samples with limited number of cells.
All studies of human materials were approved by the Johns Hopkins Institutional Review Board under the Health Insurance Portability and Accountability Act. Patient and control samples were de-identified. PBMCs and blood from healthy controls were obtained by informed consent.
NOTE: This protocol has been tested on freshly or frozen isolated peripheral blood cells and whole blood.
1. Cell Preparation
2. Cell Staining
NOTE: Choosing pairs of fluorochromes with virtually no spectral overlap is important to reduce spread of data due to high spillover of a fluorochrome in the other fluorochrome detector. To achieve an optimal identification of al the cell subsets, fluorochromes with a high quantum yield should be used such as antibody pairs PE-BV421 and PE-APC.
3. Antibody Titration
NOTE: Antibody titration is the most critical step for obtaining high-quality, reproducible data. Titration of anti-CD3, -CD8, -CD14, -CD19 and -TCR γδ follows the standard procedure by which the concentration of antibody to optimally separate positive and negative peaks is derived by maximum staining index13,14. Dilutions at the peak or closer to the peak on the rising side of the stain index curve should be selected (Figure 1A-C). The anti-CD4 antibody is titrated to place the peak of the CD4 positive population between CD3 single positive populations and CD3+/CD8+ T cells, closer to the CD3 single positive signal to better discriminate the CD8dim populations (CD8+ γδ T cells and NK T cells). Along the same line, CD56 titration aims to position NK CD56+ cells between the CD3+ and the CD3– population.
4. Gating Strategy
Setup and analysis of a flow cytometry experiment of human peripheral blood cells stained with seven lineage markers (anti-CD3, -CD4, -CD8, -CD14, -CD19, -CD56 and -TCR γδ antibodies) using only two fluorochromes are presented.
Representative results are described for anti-CD8 and -CD56 antibody titration. For each antibody (in this example, anti-CD8), data from ten successive 2-fold dilutions were recorded to calculate a stain index curve (Figure 1A-C). Optimal antibody concentration was determined by the maximum stain index signal13,14. Dilutions at the peak or closer to the peak on the rising side of the stain index curve should be selected. Anti-CD4 and -CD56 antibodies were titrated by staining peripheral blood cells together with the other markers in the two-fluorochrome panel (previously titrated). For the anti-CD4 antibody, the titration should aim at placing the anti-CD4 signal between the double CD8+/CD3+ signal and the CD3 single positive population (Figure 1D). Special care should be done to clearly separate CD4+ T cells from CD8+ dim populations. PBMCs were stained with optimal concentration of the indicated markers and different concentrations of anti-CD4. Color code of the concentrations: green indicates concentrations that result in an optimal separation of CD4+ T cells from the other CD3+populations; orange indicates concentrations that result in an acceptable but not ideal separation; red indicates concentrations that result in poor separation of CD4+ T cells from dim CD8 cells or CD4/CD8 double negative populations. The anti-CD56 titration was done in a similar way as the anti-CD4 antibody, by placing NK cells between the CD3–negative and the CD3-positive populations.
Representative gating strategy shows how to identify lymphocytic and monocytic cell populations and remove from the analysis dead cells and most of the residual red blood cells (Figure 2A). All the subsequent analysis was based on this gating strategy. Representative gating strategy is used to identify CD4+ and CD8+ T cells, γδ T cells, B cells, NK cells and monocytes in the two fluorochrome-seven marker staining (Figure 2B-C).
Representative negative results are deriving from improper sample preparation and titration of anti-CD4 and -CD56 antibodies. Failing to proper titrate CD4 results in poor separation of CD4+ T cells from CD8+ T cells (Figure 3A), while a poor CD56 titration can lead to a poor separation of NK from B cells and cells negative for all the markers in the staining panel (Figure 3B). Poor RBC lysis can occur with whole blood staining. If the primary goal of the protocol is to calculate the percentage of different cell population (e.g., % of CD4+ T cells), contamination with RBC double negative cells, that will appear in the dot plot as double negative population, should be excluded from the analysis (Figure 3C). Based on our experience with this protocol, we have noticed that accurate separation between B cells and NK CD8+ cells can be verified by using other markers. As an example, NK cells are double negative for HLA-DR and CCR6, while B cells are double positive (Figure 3D).
The protocol presented in this manuscript is meant to be part of a multicolor staining panel to interrogate several immune populations in samples with limited number of cells. By using this approach, we investigated dynamics in immune populations of longitudinal samples from donors with multiple myeloma receiving a stem cell transplant (Clinicaltrials.gov NCT0056609816). Frozen PBMC were collected and analyzed by flow cytometry at day 0, 14, 28, 60, 180, 360 after transplantation. By using this approach, we were able to interrogate several lymphocyte populations focusing on their naïve/memory profile (CD45RA, CCR7), activation and cell exhaustion status (HLA-DR, CD57, CD45RA+ effector memory, CD16), and T effector phenotype (CCR4, CCR6, CXCR3) in a single staining panel17,18,19,20,21,22,23,24,25,26. This has been particularly useful considering that the number of collected cells for some of the patients and time points was barely sufficient for only a single staining panel. Representative gating strategy (Figure 4) and dynamics of selected cell populations (Figure 5) of a relapsing patient over time. In multiple myeloma B cells can express the NK marker CD56. To exclude this possibility, we used HLA-DR and CCR6 to further differentiate B cells from NK cells (Figure 4B). CD8+ memory and naïve T cells were identified by the expression of CD45RA and CCR7: naïve (CD45RA+/CCR7+), central memory (CM, CD45RA–/CCR7+), effector memory (EM, CD45RA–/CCR7–) and effector memory CD45RA+ (EMRA, CD45RA+/CCR7+) (Figure 4C). Expression of HLA-DR and CD57 in CD8+ naïve, total memory T cells (which comprise CM, EM and EMRA), CM, EM and EMRA (Figure 4D). CD4+ memory and naïve T cells were identified by the expression of CD45RA and CCR7: naïve (CD45RA+/CCR7+), central memory (CM, CD45RA–/CCR7+), effector memory (EM, CD45RA–/CCR7–) and effector memory CD45RA+ (EMRA, CD45RA+/CCR7+) (Figure 4E). HLA-DR and CD57 expression in CD4+ naïve and memory population (which comprise CM, EM and EMRA), CM, EM and EMRA (Figure 4F). CCR4 and CCR6 were used as markers to identify within the memory population Th9 CD4+ T cells (Figure 4G). Th1, Th1/17, Th2 and Th17 CD4+ T helper subpopulations were identified by expression of CCR4, CCR6 and CXCR3 (Figure 4H). CD16 and CD57 expression in NK cells (Figure 4I). Stem cells transplantation resulted in a sustained CD4+ and CD8+ T cell activation as shown by increased expression of HLA-DR and CD57, and in a skew of T helper to a Th1 phenotype. At day 60 the percentage of B cells dramatically augmented predicting the patient relapse (Figure 5).
Figure 1: Representative antibody titration. (A) Dot plot shows CD8 expression on fresh PBMC stained with the indicated concentration of the antibody. (B) Table represent the median and standard deviation of fluorescent intensity of the CD8+, median fluorescent intensity CD8– population, and the derived stain index for each concentration tested. (C) The graph shown how to derivate the optimal concentration of the antibody as a function of stain index. (D) Representative titration of CD4 antibody. Panel D has been modified from Boin et al. 20172. Please click here to view a larger version of this figure.
Figure 2: Representative gating strategy and results of subpopulation discrimination. (A) Schematic representation of doublet exclusion, live cells discrimination and size-based gating of lymphocytes and monocytes. (B) Lymphocyte subpopulations identified with the two fluorochrome approach. (C) Monocytes identified with the two fluorochrome approach. The figure has been modified from Boin et al. 20172. Please click here to view a larger version of this figure.
Figure 3: Representative results obtained from improper sample separation and wrong antibody titration. (A) Incorrect titration of CD4 antibody results in poor resolution between CD4+ and CD8+ populations. (B) Poor CD56 titration can lead to a bad separation of NK from B cells. (C) Effect of RBC incomplete lysis on subpopulations discrimination. (D) Example of usage of other markers to verify accurate separation between B cells and NK cells: B cells are HLA-DR and CCR6 double positive, whereas NK cells are double negative. Panel D has been modified from Boin et al. 20172. Please click here to view a larger version of this figure.
Figure 4: Gating strategy to analyze samples from a patient with multiple myeloma. (A) Lymphocytes were gated on the basis of their FSC-A and SSC-Area and their flow cytometric profile with the two-fluorochrome immune-cell staining is shown. (B) Separation of NK and B cells using CCR6 and HLA-DR. (C) Gating strategy to identify CD8+ memory and naïve T cells. (D) Expression of HLA-DR and CD57 in CD8+ naïve and memory T cells. (E) Gating strategy to identify CD4+ memory and naïve T cells. (F) HLA-DR and CD57 expression in CD4+ naïve and memory T cells. (G) Identification of Th9 CD4+ T cells (H) Identification of Thelper CD4+ T cell subpopulations (I) CD16 and CD57 expression in NK cells. The figure has been adapted from Boin et al. 20172. Please click here to view a larger version of this figure.
Figure 5: Dynamics of cell population in patient with multiple myeloma. (A) Dynamic of major lymphocyte populations in PBMC isolated and cryopreserved at the indicated day after stem cell transplant (SCT). (B) Characterization of CD8 subpopulations over time. (C) Characterization of CD4 subpopulations over time. (D) Analysis of NK subsets. Data have been plotted with GraphPad Prism. The figure has been adapted from Boin et al. 20172. Please click here to view a larger version of this figure.
Target | Clone | Fluorochrome | Vendor | Concentration | Purpose |
CD3 | UCHT1 | BV421 | BD | 1/20 | Lineage |
CD56 | NCAM16.2 | BV421 | BD | 1/900 | |
TCRγδ | B1 | BV421 | Bio | 1/30 | |
CD4 | RPA-T4 | PE | BD | 1/450 | |
CD8 | RPA-T8 | PE | BD | 1/20 | |
CD14 | M5E2 | PE | BD | 1/15 | |
CD19 | HIB19 | PE | BD | 1/300 | |
Dead cells | L/D Blue | LT | 1/300 | Live/Dead discrimination | |
BD = BD Biosciences, Bio = BioLegend, LT = Life Technologies |
Table 1: Antibody panel used for the two-fluorochrome immune-cell staining of PBMC (BV421-PE combination).
Target | Clone | Fluorochrome | Vendor | Concentration | Purpose |
CD3 | UCHT1 | APC | BD | 1/20 | Lineage |
CD56 | NCAM16.2 | APC | BD | 1/60 | |
TCRγδ | B1 | APC | Bio | 1/30 | |
CD4 | RPA-T4 | PE | BD | 1/450 | |
CD8 | RPA-T8 | PE | BD | 1/20 | |
CD14 | M5E2 | PE | BD | 1/15 | |
CD19 | HIB19 | PE | BD | 1/300 | |
Dead cells | L/D Blue | LT | 1/300 | Live/Dead discrimination | |
BD = BD Biosciences, Bio = BioLegend, LT = Life Technologies |
Table 2: Antibody panel used for the two-fluorochrome immune-cell staining of PBMC (APC-PE combination).
Target | Clone | Fluorochrome | Catalog | Vendor | Concentration | Purpose |
CD3 | UCHT1 | BV421 | 562426 | BD | 1/20 | Lineage |
CD56 | NCAM16.2 | BV421 | 562751 | BD | 1/900 | |
TCRγδ | B1 | BV421 | 331217 | Bio | 1/30 | |
CD4 | RPA-T4 | PE | 555347 | BD | 1/450 | |
CD8 | RPA-T8 | PE | 555367 | BD | 1/20 | |
CD14 | M5E2 | PE | 555398 | BD | 1/15 | |
CD19 | HIB19 | PE | 555413 | BD | 1/300 | |
CCR7 | G043H7 | AF647 | 353217 | Bio | 1/30 | Differentiation |
CD45RA | HI100 | APC-H7 | 560674 | BD | 1/60 | |
CCR4 | 1G1 | PE-Cy7 | 561034 | BD | 1/60 | Th subsets |
CCR6 | G034-E3 | BV605 | 353419 | Bio | 1/30 | |
CXCR3 | 1C6/CXCR3 | AF488 | 561730 | BD | 1/30 | |
CD57 | NK-1 | PE-CF594 | 562488 | BD | 1/900 | Activation/Exhaustion |
HLA-DR | G46-6 | BV510 | 563083 | BD | 1/30 | |
CD16 | 3G8 | BUV395 | 563784 | BD | 1/30 | NK, Monocyte activation |
Dead cells | L/D Blue | L-23105 | LT | 1/300 | Live/Dead discrimination | |
BD = BD Biosciences, Bio = BioLegend, LT = Life Technologies |
Table 3: Antibody panel used to stain frozen PBMC from a patient with multiple myeloma.
Target | Clone | Fluorochrome | Vendor | Concentration | Purpose |
CD3 | UCHT1 | BV421 | BD | 1/80 | Lineage |
CD56 | NCAM16.2 | BV421 | BD | 1/400 | |
TCRγδ | B1 | BV421 | Bio | 1/200 | |
CD4 | RPA-T4 | PE | BD | 1/1200 | |
CD8 | RPA-T8 | PE | BD | 1/100 | |
CD14 | M5E2 | PE | BD | 1/80 | |
CD19 | HIB19 | PE | BD | 1/300 | |
Dead cells | L/D Blue | LT | 1/300 | Live/Dead discrimination | |
BD = BD Biosciences, Bio = BioLegend, LT = Life Technologies |
Table 4: Antibody panel used for the two-fluorochrome immune-cell staining of whole blood (BV421-PE combination).
The protocol presented here has been shown to be quite flexible and insensitive to changes in staining buffer, temperature and peripheral blood cell preparation due to the high expression of lineage markers on the cell surface. The most critical step for obtaining high-quality, reproducible data is antibody titration. Of note, since the titration of antibodies should always be performed during the setup of a flow cytometric panel, this step does not add extra bench-time to our two-fluorochrome approach. Titration of anti-CD3, -CD8, -CD14, -CD19 and -TCR γδ follows the standard procedure by which the concentration of antibody to optimally separate positive and negative peaks is derived by maximum staining index13,14. Dilutions at the peak or closer to the peak on the rising side of the stain index curve should be selected (Figure 1A-C). On the other hand, an ad hoc titration of anti-CD4 and anti-CD56 antibodies needs to be performed. The anti-CD4 antibody is titrated to place the peak of the CD4 positive population between CD3 single positive populations and CD3+/CD8+ T cells, closer to the CD3 single positive signal to better discriminate the CD8dim populations (CD8+ γδ T cells and NK T cells). Along the same line, CD56 titration aims to position NK CD56+ cells between the CD3+ and the CD3 population. The naturally lower expression of CD56 makes the titration of this antibodies easier with the concentration to use close to the value obtained in a saturation curve. Using high quantum yield fluorochromes is another critical factor for an optimal separation of multiple markers/populations on the same detector. We obtained successful results with APC, BV421 and PE, but other fluorochromes, such as the new generation of polymer dye, should give comparable results. To decrease the possibility of artifacts due to compensation, it is also important to choose a pair of fluorochromes with little, if any, spectral overlap, such as PE and APC, or PE and BV421. Choosing pairs of fluorochromes with virtually no compensation is important to reduce spread of data due to high spillover of a fluorochrome in the other fluorochrome detector. Spreading reduction facilitates gating immune subpopulations by minimizing signal distortion and allows to use this methodology, if limited to two fluorochromes, without need of compensation controls.
The combination of markers that we proposed is highly customizable based on the investigator requirements. Indeed, some of the markers can be excluded from the analysis if they do not refer to a population of interest. For example, it is possible to remove the anti-CD19 antibody to exclude B cells, or the anti-CD4 antibody to focus only on the CD8+ T cells. Of note, anti-CD8 antibody is important to identify CD8+ NK cells and γδ T cells, and therefore should not be removed from the panel. To improve the separation of rare cell populations, other fluorochromes/detectors can be used for some of the markers of the two-fluorochrome staining. As an example, CD56 can be moved to a different detector to detect NKT cells, which is not possible with the two-fluorochrome panel. While it is possible to reduce the number of markers from the panel, caution should be exerted in adding, changing or switching markers.
Provided the necessary instrumentation, antibodies and skill set, the standard one-fluorochrome-one marker approach is still the most accurate way to identify multiple immune populations and discriminate rare subpopulations, such as NKT or γδ T cells. However, the primary goal of this method is not to substitute for the classic approach, but rather to achieve a deep immunophenotyping when working with instruments with a low number of detectors, or samples with limited numbers of cells, while reducing complexity and cost in setting up the experimental system. We have done extensive screening of clinical samples from patients with multiple myeloma, systemic sclerosis, dermatomyositis and Lyme disease showing that this staining procedure can improve simultaneous interrogation of several populations with limited number of cells. Our results so far have shown that this procedure is insensitive to chronic immune activation or infectious disease, but preliminary testing should be conducted to assess the accuracy of this protocol in different disease states.
Future directions to further strengthen the potential of this protocol include studies to characterize infiltrating lymphocytes in primary tissues from clinical samples. This is relevant for tumor and autoimmune disease immunology where this approach could provide invaluable information for the analysis of specimens with limited material. We are planning to test this panel on permeabilized cells to expand the potentiality to detect cytokine expression and signaling molecules on the same clinical samples. Finally, it should be noted that similar approaches, aimed at expanding the number of recordable markers, could also be developed using different sets of markers and can also be developed fordifferent animal models.
The authors have nothing to disclose.
This study was supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases, <https://www.niams.nih.gov/>, award number P30-AR053503; The Stabler Foundation, www.stablerfoundation.org; National Institute of Allergy and Infectious Disease, www.niaid.nih.gov, T32AI007247; Nina Ireland Program for Lung Health (NIPLH), https://pulmonary.ucsf.edu/ireland/.
CD3 | BD Biosciences | 562426 | Antibody for staining RRID: AB_11152082 |
CD56 | BD Biosciences | 562751 | Antibody for staining RRID: AB_2732054 |
TCRgd | BD Biosciences | 331217 | Antibody for staining RRID: AB_2562316 |
CD4 | BD Biosciences | 555347 | Antibody for staining RRID: AB_395752 |
CD8 | BD Biosciences | 555367 | Antibody for staining RRID: AB_395770 |
CD14 | BD Biosciences | 555398 | Antibody for staining RRID: AB_395799 |
CD19 | BD Biosciences | 555413 | Antibody for staining RRID: AB_395813 |
CD3 | BD Biosciences | 555335 | Antibody for staining RRID: AB_398591 |
CD56 | BD Biosciences | 555518 | Antibody for staining RRID: AB_398601 |
TCRgd | BD Biosciences | 331211 | Antibody for staining RRID: AB_1089215 |
CCR7 | Biolegend | 353217 | Antibody for staining RRID: AB_10913812 |
CD45RA | BD Biosciences | 560674 | Antibody for staining RRID: AB_1727497 |
CCR4 | BD Biosciences | 561034 | Antibody for staining RRID: AB_10563066 |
CCR6 | BD Biosciences | 353419 | Antibody for staining RRID: AB_11124539 |
CXCR3 | BD Biosciences | 561730 | Antibody for staining RRID: AB_10894207 |
CD57 | BD Biosciences | 562488 | Antibody for staining RRID: AB_2737625 |
HLA-DR | BD Biosciences | 563083 | Antibody for staining RRID: AB_2737994 |
CD16 | BD Biosciences | 563784 | Antibody for staining RRID: AB_2744293 |
Dead cells | Life technologies | L-23105 | Live/dead discrimination |
Falcon 5 ml round-bottom polystyrene test tube with cell strainer snap cap | BD Bioscience | 352235 | to filter cell suspension before passing though the flow cytometer |
Falcon 5 ml round-bottom polystyrene test tube | BD Bioscience | 352001 | to stain whole blood |
Recovery Cell Culture Freezing Medium | Thermo fisher | 12648010 | Freezing cells |
96-well V-bottom plate | Thermo fisher | 249570 | plate for staining |
FACSAria IIu Cell Sorter | BD Biosciences | Flow cytometer | |
FCS Express 6 | De Novo Software | FACS analysis | |
Graphpad Prism | GraphPad software | Data analysis |