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. Author manuscript; available in PMC: 2021 Feb 1.
Published in final edited form as: Methods Mol Biol. 2021;2194:255–290. doi: 10.1007/978-1-0716-0849-4_14

High-Dimensional Flow Cytometry Analysis of Regulatory Receptors on Human T Cells, NK Cells, and NKT Cells

Ryosuke Nakagawa, Jason Brayer, Nicole Restrepo, James J Mulé, Adam W Mailloux
PMCID: PMC7744271  NIHMSID: NIHMS1650769  PMID: 32926371

Abstract

The field of flow cytometry has witnessed rapid technological advancements in the last few decades. While the founding principles of fluorescent detection on cells (or particles) within a uniform fluid stream remains largely unchanged, the availability more sensitive cytometers with the ability to multiplex more and more florescent signals has resulted in very complex high-order assays. This results in the co-use of fluorophores with increased levels of emission overlap and/or spillover spreading than in years past and thus requires careful and well thought out planning for flow cytometry assay development. As an example, we present the development of a large 18-color (20 parameter) flow cytometry assay designed to take an in depth analysis of effector lymphocyte phenotypes, with careful attention to assay controls and panel design.

Keywords: T cell, NK cell, NKT cell, Immune checkpoint, NK receptor, Memory markers, Innate immune cells, Adaptive immune cells

1. Introduction

Cytotoxic lymphocytes can be found among effector populations in both adaptive and innate arms of immunity. This includes the classically defined cytotoxic T cells (TC) which express the CD8 coreceptor and recognize foreign antigens presented in the context of major histocompatibility complex class I (MHC-I; human leukocyte antigen, HLA, in humans) [1], and natural killer (NK) cells which are regulated by a balance of positive and negative signals through activating and inhibitory NK receptors [2, 3]. In addition to these archetypes, another major population called natural killer T (NKT) cells has been described that shares numerous phenotypic markers with both T cells and NK cells [4], but which is derived from its own unique thymic selection [5] and recognizes lipid antigens presented by the molecule CD1d [6, 7]. Investigating regulatory receptors that govern these populations has led to the development of potent immunotherapy drugs, including those that block checkpoint receptors [8]. However, the study of checkpoint receptors is often limited to conventional T cell populations and the study of NK cell receptors is often limited to conventional NK cells, despite the expression of both groups of receptors on T, NK, and NKT cells. This 18-color, 20-parameter flow cytometry panel not only allows for the phenotypic characterization of major T cell, NK cell, and NKT cell subsets, but combines several well-known activating NK cell receptors with four well-studied checkpoint receptors to be analyzed therein. It was developed using human peripheral blood mononuclear cells (PBMCs), but could in theory be applied to any human cell source containing effector lymphocyte populations.

Among T cells, cytotoxic activity is largely restricted to the CD8+ compartment in which TC begin as CD45RA+ naïve T cells and circulate between secondary lymphoid tissues after emigration from the thymus after TCR gene rearrangement. These naïve TC can then become activated through engagement with MHC-I if the presented peptide matches the rearranged TCR specificity. This engagement induces the downregulation of central markers such as CCR7, CD62L, and CD27 and the acquisition of effector functionality, including cytokine and cytotoxic granule production. Following activation, TC downregulate CD45RA expression and become memory T cells, of which two classically defined subtypes exist, longer-lived central memory (CM) T cells which reacquire the expression of central markers, or shorter lived, but more responsive effector memory (EM) T cells which lack central markers [9]. Because T cell clones undergo a process of negative selection in the thymus during development, highly self-reactive clones are deleted as a part of central tolerance [10]. However, subsequent regulatory mechanisms that protect against autoimmune reactivity are also needed, not the least of which are immune checkpoint receptors, such as lymphocyte-activation gene 3 (LAG-3), T-cell immunoglobulin and mucin-domain containing-3 (TIM-3), cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), and programmed cell death protein 1 (PD-1) [11–16]. Importantly, immune checkpoint receptors are transiently upregulated following activation, and are not limited to conventional T cells, but can also be expressed on NK or NKT populations [17–22].

NK cells, which can be identified in humans as CD56+, CD16high or CD16low, and CD3− [23], are large granular lymphocytes that do not express T cell receptor. Instead, NK activation and cytotoxic function is regulated through a net sum of positive and negative signals given by the engagement of activating and inhibitory NK receptors [2, 3]. A common ligand for inhibitory NK receptors are MHC-I molecules, which can be downregulated on malignant or infected cells, whereas common ligands for activating NK receptors are often upregulated on infected or malignant cells [24–26]. Hence NK cells serve a complementary, nonredundant role with TC cells. In addition to NK receptors, NK cells can also express immune checkpoint receptors under certain conditions [17–22], although by comparison the role of immune checkpoint receptors in NK cell function is far less studied than in conventional T cells.

NKT cells recognize lipid antigen presented in the context of the CD1d molecule [6, 7], and in this way reflect the adaptive nature of conventional T cells but also display innate-like properties regarding their regulation through NK cells receptors under specific conditions [27, 28]. Despite overlapping cell surface marker expression, NKT cells are derived from common lymphoid progenitors via thymic selection similar to conventional T cells, but through a TCR restricted to CD1 isoforms [4]. While lipid antigen recognition makes NKT cells important for recognizing organisms such as mycobacterium [29], it can also lead NKT cells to play a complex and important role in regulating autoimmune disease in which pathologic or commensal bacteria result in the presentation of lipid antigens [30]. There are two major subsets of NKT cells that have been defined: type I or invariant NKT cells (iNKT) that express a single TCR specific (Va24Ja18) for alpha-galactosylceramide (aGalCer) presented by CD1d, and type II or variant NKT cells (vNKT) with diverse TCR clonality specific for a range of glycolipids or phospholipids presented by CD1 molecules [31].

2. Materials

  1. Flow Buffer: 1% FBS, 1% BSA, 200 nM EDTA in PBS.

  2. 12 × 75 mm test tubes.

  3. Antibody: anti-human CD19 BV510 (Table 1).

  4. Antibody: anti-human CD3 BV786 (Table 1).

  5. Antibody: anti-human CD4 BUV805 (Table 1).

  6. Antibody: anti-human CD8 AF700 (Table 1).

  7. Antibody: anti-human CD16 BUV496 (Table 1).

  8. Antibody: anti-human CD56 APC-Fire750 (Table 1).

  9. Antibody: anti-human vα24jα18 PE-Cy7 (Table 1).

  10. Antibody: anti-human CD45RA BV6350 (Table 1).

  11. Antibody: anti-human CD27 BUV737 (Table 1).

  12. Antibody: anti-human CD62L BUV395 (Table 1).

  13. Antibody: anti-human CCR7 BB515 (Table 1).

  14. Antibody: anti-human NKG2D PerCP-Cy5.5 (Table 1).

  15. Antibody: anti-human NKp30 BV421 (Table 1).

  16. Antibody: anti-human NKp46 PE-Dazzle594 (Table 1).

  17. Antibody: anti-human PD-1 BV605 (Table 1).

  18. Antibody: anti-human CTLA4 APC (Table 1).

  19. Antibody: anti-human TIM-3 BV711 (Table 1).

  20. Antibody: anti-human LAG-3 PE (Table 1).

  21. Fixable Live/Dead™ Aqua.

  22. FcR Blocking reagent.

  23. Ficoll-Paque PLUS.

  24. RBC lysis buffer: 0.15 M NH4Cl, 10.0 mM KHCO3, 0.1 mM EDTA in ddH2O pH 7.2–7.4, 0.2-μm filtered.

  25. Brilliant Stain Buffer (BD Biosciences).

  26. Cell Sample (here human peripheral blood mononuclear cells; PBMC).

Table 1.

Antibodies and reagents

Specificity Clone Fluorochrome Vendor Purpose Optimal titera SIb
CD19 HIB19 BV510 BioLegend Utility 2.5 μl 22
Live/Dead Aqua ThermoFisher Utility
CD3 UCHT1 BV786 BD Biosciences Lymph. Lin. 2.5 μl 15
CD4 L3T4 BUV805 BD Biosciences Lymph. Lin. 2.5 μl 69
CD8 HIT8a AF700 BioLegend Lymph. Lin. 0.1 μl 42
CD16 3G8 BUV496 BD Biosciences Lymph. Lin. 5 μl 26
CD56 5.1H11 APC-Fire750 BioLegend Lymph. Lin. 0.6 μl 10
va24ja18 6B11 PE-Cy7 BioLegend Lymph. Lin. 0.3 μl 5
CD45RA HI100 BV650 BioLegend Memory Markers 5 μl 53
CD27 L128 BUV737 BD Biosciences Memory Markers 5 μl 21
CD62L SK11 BUV395 BD Biosciences Memory Markers 0.3 μl 71
CCR7 3D12 BB515 BD Biosciences Memory Markers 5 μl 17
NKG2D 1D11 PerCP-Cy5.5 BD Biosciences Reg. Receptors 5 μl 2.6
NKp30 P30–15 BV421 BD Biosciences Reg. Receptors 10 μl 3.5
NKp46 9E2 PE-Dazzle594 BioLegend Reg. Receptors 1.2 μl 11
PD-1 EH12.2H BV605 BioLegend Reg. Receptors 5 μl 3.6
CTLA-4 L3D10 APC BioLegend Reg. Receptors 5 μl 3.2
TIM-3 7D3 BV711 BD Biosciences Reg. Receptors 0.3 μl 4.5
LAG-3 T47–530 PE BD Biosciences Reg. Receptors 5 μl 3.3
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AF Alexa Fluor, BUV Brilliant Ultra Violet™, PE R-phycoerythrin, BB Brilliant Blue™, BV Brilliant Violet™, Cy cyanine, PerCP-Cy5.5 Peridinin-chlorophyll Cy5.5, APC allophycocyanin, MFI median fluorescent intensity, Lymph. Lin. lymphocyte lineage, Reg. regulatory

a

Volume of commercially available antibody used per 1.0 × 106 cells in 100 μl staining volume

b

SI, stain index as calculated using the listed optimal titer volume using the formula: SI = [(MFI of positive cells) – (MFI of negative cells)]/(2 × SD of negative cells)

3. Methods

3.1. Staining Protocol

  1. PBMC were prepared from fresh blood using density gradient centrifugation with Ficoll-Paque PLUS or RBC lysis buffer as per the manufacturer’s instructions.

  2. Some aliquots of PBMC were treated with 3000 IU/ml recombinant human IL-2 and/or 10 μg/ml of alpha-galactosylceramide (aGalCer) for 2 days.

  3. Single-color compensation and fluorescence-minus-one (FMO) controls are performed using PBMC.

  4. Wash cells in flow buffer by adding 2–3× volume, spinning down in the centrifuge at 300–400 × g. for 5 min and then carefully pour off supernatant.

  5. Resuspend the cells at 1.0 × 107 cells/ml in flow buffer.

  6. Add 100 μl into each 12 × 75 mm test tube (1.0 × 106 cells).

  7. Spin cells in a centrifuge at 1300–1400 RPM for 5 min and then carefully pour off supernatant.

  8. Vortex briefly to break up cell pellet, and add 1 ml of PBS.

  9. Add 1 μl of Fixable Live/Dead™ Aqua to each appropriate tube, vortex briefly, and incubate at RT for 20 min in the dark.

  10. Cells were washed as in step 3 twice.

  11. Brilliant Stain Buffer (optional; see Note 1) can be added at this point to the flow buffer to minimize interaction between brilliant violet and brilliant ultraviolet conjugates.

  12. Vortex briefly to break up cell pellet, and add the appropriate amount of anti-CD16 BUV496.

  13. Incubate for 30 min at 4 °C in the dark.

  14. 2 μl FcR blocking reagent was added to each tube for 15 min.

  15. Vortex briefly (do not wash), and add the appropriate volume of each remaining antibody to the appropriate tubes in the residual volume (approximately 100 μl) of buffer left in the tube (Table 1).

  16. Incubate for 30 min at 4 °C in the dark.

  17. Cells were washed as in step 3 twice.

  18. Resuspend in 250 μl of flow buffer.

  19. Analyze the cells using a BD LSRII with the optical configurations listed in Table 2, or on a similarly configured cytometer.

Table 2.

BD LSR II SORP configuration

Laser Detector Optics Channel
Wavelength (nm) Power (mW) Array Position Mirror Filter Name OMIP Use
488 50 Trigon A 685 LP 710/50 BP Blue A PerCP-Cy5.5
B 505 LP 525/50 BP Blue B BB515
C – 488/10 BP Blue C SSC
405 50 Octagon A 760 LP 785/50 BP Violet A BV 786
B 685 LP 710/50 BP Violet B BV 711
C 635 LP 670/30 BP Violet C BV 650
D 595 LP 610/20 BP Violet D BV 605
E 505 LP 525/50 BP Violet E BV 510 and Fixable L/D™ Aqua
F – 450/50 BP Violet F BV 421
640 40 Trigon A 755 LP 780/60 BP Red A APC-Cy7
B 685 LP 730/45 BP Red B Alexa Fluor 700
C – 670/30 BP Red C APC
561 50 Octagon A 755 LP 780/60 BP YG A PE-Cy7
B 635 LP – –
C 600 LP 610/20 BP YG C PE-Dazzle
D – 582/15 BP YG D PE
355 60 Octagon A 770 LP 820/60 BP UVA BUV 805
B 690 LP 740/35 BP UV B BUV 737
C 410 LP 515/30 BP UV C BUV 496
D - 379/28 BP UV D BUV 395
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SSC side scatter, AF Alexa Fluor, BUV Brilliant Ultra Violet™, PE R-phycoerythrin, BB Brilliant Blue™, BV Brilliant Violet™, Cy cyanine, PerCP-Cy5.5 Peridinin-chlorophyll Cy5.5, APC allophycocyanin, LP long pass, BP band pass

3.2. Development Strategy

This panel can differentiate a broad spectrum of T, NK, and NKT cell subsets, and analyze the expression of activating NK cell receptors and checkpoint receptors therein. It was optimized using human peripheral blood mononuclear cells (PBMC), but could be performed on any source of human cells containing T, NK, or NKT cells. To build the panel, dyes and markers of interest were first assigned categories based on their usefulness to exclude dead or unwanted cell populations (utility), identify cellular subsets (lymphocyte lineage markers), identify memory phenotypes (memory markers), or measure activating NK or checkpoint receptors (regulatory receptors) (Table 1). Fluorescent conjugates were then selected for each marker considering fluorochrome brightness, expected density of each antigen, commercial availability, and spillover spreading (Fig. 1) that occurs on the BD LSRII detailed in Table 2. Regarding the latter, spillover spreading was largely limited to spillover into the UV C channel (in which the BUV496 fluor is assayed). Therefore, we chose to analyze CD16 using BUV496 as this marker typically stains brightly with good signal to noise ratios.

Fig. 1.

Fig. 1

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Spillover spreading matrix (SSM) calculated from single color controls. The matrix is color-coded to reflect SS values with red being higher SS values

All antibody–fluorochrome conjugates were titrated individually using PBMC freshly prepared from human blood as a function of stock volume used in residual buffer. (approximately 100 μl) on 1.0 × 106 cells. Blood was prepared using density gradient centrifugation (Ficoll-Paque PLUS; GE Healthcare Life Sciences). Staining index was then calculated for each dilution using the formula: SI = [(MFI of positive cells) – (MFI of negative cells)]/(2 × SD of negative cells). Optimal antibody volumes were selected based on calculated SI (Fig. 2). The frequency of type I NKT cells in human PBMC is generally low and highly variable ranging from undetectable to 1%. Therefore, three blood samples were used to titrate the antibody detecting the iNKT va24ja18 TCR (clone 6B11), and the sample displaying the highest frequency was used to calculate the SI. Resting lymphocytes express little or no checkpoint receptors, but can be induced under certain activating conditions. To titrate antibodies against these antigens, we stimulated PBMC using PHA, LPS, or anti-TCR (OKT3) overnight, and selected the best induction condition to calculate SI (Fig. 3).

Fig. 2.

Fig. 2

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Titrations of antibodies used in this OMIP performed on PBMC. Dilutions are μl of stock antibody concentration provided from the vendor. Staining index was calculated for each dilution using the formula: SI = [(MFI of positive cells) – (MFI of negative cells)]/(2 × SD of negative cells). Selected dilutions are demarcated by a red box

Fig. 3.

Fig. 3

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Activation conditions needed to titrate checkpoint receptor antibodies. Titrations of antibodies used in this OMIP performed on PBMC. Dilutions are μl of stock antibody concentration provided from the vendor. Staining index was calculated for each dilution using the formula: SI = [(MFI of positive cells) – (MFI of negative cells)]/(2 × SD of negative cells). Selected dilutions are demarcated by a red box

To build the panel, we progressively added each category set of dyes/antibodies listed in Table 1, beginning with the utility markers using Fixable Live/Dead™ Aqua as per the manufacturer’s instructions and with the optimal dilution of staining antibodies following the staining protocol below. We then performed the flow staining using the utility markers and markers in the lymphocyte lineage category. The staining performance of utility markers was then compared to those stained with utility markers alone, and no significant interference was observed. Flow staining was then performed a third and fourth time adding in cumulative succession the markers in the “memory markers” and “regulatory receptors” categories, each time comparing the staining performance of the previously added groups with no significant interferences observed. Compensation was applied using single color controls and a compensation matrix calculated using FlowJo™ software (version X; BD Biosciences). Gates were drawn at fluorescent intensities based on fluorescence minus one (FMO) controls, or along natural breaks in staining patterns that are at least as bright as the FMO(s). Pairwise dot plots of FMO controls are displayed in Fig. 4a–r.

Fig. 4.

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Fig. 4

Fig. 4

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(a–r) Fluorescence minus one (FMO) controls for the staining parameters in this OMIP. Displayed are N × N plots showing every possible dot plot combination, and plots relevant to each FMO are outlined in red. Prior to displaying these plots, doublet events were gated out using forward and side scatter height and width characteristics and then cells were selected for based on FSC versus SSC characteristics similar to the main figure

3.3. Gate-Based Analysis

This flow cytometry panel includes a basic framework that allows for the differentiation between conventional NK and T cell subsets and for the identification of invariant and variant NKT cells. This panel was made by first titrating the staining dilution of each antibody and calculating the optimal staining index (Figs. 2 and 3), and then in a step-wise manner building the panel based on category sets of parameters (Table 1), adding each category in turn. One possible gate-based analysis exemplified here, first excludes doublet events and aggregates using forward-scatter (FSC) height and width properties, and then by side-scatter (SSC) height and width properties. This panel includes as a utility anti-CD19 conjugated to brilliant violet® 510 (BV510), which can be excluded from analysis in the same detector as the viability marker Live/Dead™ Aqua. Viable CD19− cells are then selected based on low or negative median fluorescent intensity (MFI) using this “dump” channel, and then leukocytes are gated on based on FSC and SSC area parameters (Fig. 5a).

Fig. 5.

Fig. 5

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Gating schema for the analysis of regulatory receptors on T cell, NK cell, and NKT cell subsets. (a) Single cells are gated on using FSC and then SSC height and width parameters, and then CD19− viable cells are gated on based on low MFI on the BV510 channel. Lymphocytes are then selected for by gating on FSC versus SSC parameters. (b) CD3+ are then further gated into CD4+, CD8+, and CD4−CD8− populations for further analysis. (c–e) Subsets of CD3+ (c) or CD3− cells (d) can then be subdivided based on CD56 expression or by the expression of va24ja18, before the expression of NKG2D, NKp30, NKp46, CTLA-4, LAG-3, TIM-3, and PD-1 is analyzed and compared between fresh and IL-2/aGalCer-treated PBMC and defined subpopulations (e). (f–g) Naïve and memory subsets can be defined on TH (f) and TC cells (g) using CD45RA expression versus CD27, CCR7, or CD62L expression. NKG2D, NKp30, NKp46, CTLA-4, LAG-3, TIM-3, and PD-1 were then analyzed and compared on fresh and IL-2/aGalCer-treated PBMCs and between N, CM, EM, or Act TH (f) and TC cells (g). Lastly, CD3+CD4−CD8− cells were analyzed for the expression of LAG-3, NKp30, NKp46, va24ja18, CD45RA, CD27, and CD62L (h). Unstimulated PBMC are depicted or overlaid in blue density plots or histograms, while PBMC stimulated with IL-2 and α-GalCer for 48 h are depicted in red density plots or histograms

One possible gating schema to analyze this panel, as demonstrated in Figs. 5, 6, and 7, begins by gating out doublet events using forward scatter height and width properties followed by side scatter height and width properties. Viable, CD19− events are then selected based on negative or low median fluorescent intensity (MFI) on the Violet E channel before lymphocytes are gated on using side scatter and forward scatter characteristics. T cells and NKT cells are then separated from CD3− cells using CD3 staining. Convectional NK cell subsets can then be analyzed in the CD3− fraction using CD56, and CD16, whereas NKT populations can be analyzed using CD56, CD16 and the iNKT TCR. Importantly, some iNKT staining is observed in the CD3− fraction in cells treated with IL-2 and aGalCer, although this is a minor number of cells given that the CD3− fraction represents less than 2% of expanded lymphocytes (Fig. 5) and was not observed in IL-2 or aGalCer-only controls (Figs. 6 and 7). Conventional T cells can then be divided into TH and TC populations using CD4 and CD8 staining, and naïve, activated, central memory and effector memory can then be defined using the naïve marker CD45RA along with CD27, CCR7, or CD62L. A smaller population of CD3+CD4−CD8− cells can be seen, and may have some overlap with NKT populations. Once subdivided, the expression of activating NK receptors and checkpoint receptors can be assess on each subpopulation.

Fig. 6.

Fig. 6

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IL-2 treatment control. (a) Single cells are gated on using FSC and then SSC height and width parameters, and then CD19− viable cells are gated on based on low MFI on the BV510 channel. Lymphocytes are then selected for by gating on FSC versus SSC parameters. (b) CD3+ are then further gated into CD4+, CD8+, and CD4−CD8− populations for further analysis. (c–e) Subsets of CD3+ (c) or CD3− cells (d) can then be subdivided based on CD56 expression or by the expression of va24ja18, before the expression of NKG2D, NKp30, NKp46, CTLA-4, LAG-3, TIM-3, and PD-1 is analyzed on defined subpopulations (e). (f–g) Naïve and memory subsets can be defined on TH (f) and TC cells (g) using CD45RA expression versus CD27, CCR7, or CD62L expression. NKG2D, NKp30, NKp46, CTLA-4, LAG-3, TIM-3, and PD-1 were then analyzed on N, CM, EM, or Act TH (f) and TC cells (g). Lastly, CD3+CD4−CD8− cells were analyzed for the expression of LAG-3, NKp30, NKp46, va24ja18, CD45RA, CD27, and CD62L (h)

Fig. 7.

Fig. 7

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aGalCer treatment control. (a) Single cells are gated on using FSC and then SSC height and width parameters, and then CD19− viable cells are gated on based on low MFI on the BV510 channel. Lymphocytes are then selected for by gating on FSC versus SSC parameters. (b) CD3+ are then further gated into CD4+, CD8+, and CD4−CD8− populations for further analysis. (c–e) Subsets of CD3+ (c) or CD3− cells (d) can then be subdivided based on CD56 expression or by the expression of va24ja18, before the expression of NKG2D, NKp30, NKp46, CTLA-4, LAG-3, TIM-3, and PD-1 is analyzed on defined subpopulations (e). (f–g) Naïve and memory subsets can be defined on TH (f) and TC cells (g) using CD45RA expression versus CD27, CCR7, or CD62L expression. NKG2D, NKp30, NKp46, CTLA-4, LAG-3, TIM-3, and PD-1 were then analyzed on N, CM, EM, or Act TH (f) and TC cells (g). Lastly, CD3+CD4−CD8− cells were analyzed for the expression of LAG-3, NKp30, NKp46, va24ja18, CD45RA, CD27, and CD62L (h)

In healthy human blood, the frequency of NKT cells, and in particular iNKT cells, is low and highly variable, ranging from 0% to 1% but averaging less than 0.1% of peripheral blood leukocytes [32]. In addition, many of the regulatory receptors examined by this panel are not expressed on resting lymphocytes, but are inducible in response to activation or other stimuli. To best exemplify the potential of this flow cytometry panel, resting PBMC were analyzed alongside PBMC cultured in recombinant human interleukin 2 (IL-2) and aGalCer for 2 weeks to expand iNKT cells and to induce the expression of checkpoint receptors on different lymphocyte populations (Fig. 5b–h). After gating on single viable CD19− leukocytes, conventional TH and TC cells can be gated based on CD3+ expression, and then CD4+ and CD8+ expression respectively (Fig. 5b), while NK, iNKT, and vNKT cells can be gated using CD56 and CD16 expression, or va24ja18 expression respectively (Fig. 5c). While the majority of expanded iNKT cells are CD3+, some expanded va24ja18+ cells can be found in the CD3− gate as well (Fig. 5d). Differential expression of checkpoint receptors or activating NK receptors were then compared between CD3+CD56+ and CD3+CD56− cells in fresh or IL-2/aGalCer-treated PBMC (Fig. 5e).

For conventional T cell populations, naïve (N), central memory (CM), effector memory (EM), and activated (Act) phenotypes can be defined using CD45RA staining versus a central marker such as CD27, CCR7, or CD62L. For conventional TH and TC cells, phenotypes defined by these central markers are largely overlapping. Here, we compared the expression of checkpoint receptors or activating NK receptors on N, CM, EM, and Act T cells in both TH (Fig. 5f) and TC (Fig. 5g) subsets as defined by CD45RA and CD27 expression, although a similar comparison could be made using CD45RA staining along with CCR7 or CD62L expression.

Lastly, the gating schema here explores the phenotypes of CD3+CD4− and CD3+CD8− cells, which are more frequent in IL-2/aGalCer-treated PBMCs (Fig. 5b). A large portion of these treated cells express the activating NK receptors NKp30 and NKp46, are va24ja18+, have up-regulated Lag-3 expression, and downregulated CD45RA, CD62L, and CD27 expression (Fig. 5h) suggesting that they are expanded iNKT cells and overlap with those populations defined in Fig. 5d. In contrast, these CD3+CD4−CD8− cells in fresh PBMC lack expression of NKp30, NKp46, va24ja18, LAG-3, and maintain expression of central markers such as CD62L and CD27 and are largely positive for the naïve marker CD45RA, suggesting that these cells are largely naïve CD4−CD8− T cells.

Beyond the gating schema exemplified here, it is now appreciated that NK, and NKT cells exist along a spectrum of phenotypes, which now encompass six distinct subsets of NKT cells [4], and two distinct populations of NK cells [33] which can be further subdivided based on the expression of CD27, CCR7, CD62L, and NK receptors [34, 35]. This phenotypic spectrum is further complexed by the description of three types of innate lymphoid cells (ILCs) called ILC-1, ILC-2, and ILC-3 [36] which express no antigen receptors, but instead react to environmental queues through activating and inhibitory receptors including checkpoint and NK receptors [37, 38]. ILC 1–3 may serve a supportive or regulatory role analogous to helper T cells (TH) subsets TH1, TH2, and TH17 respectively, which greatly influence the abundance and effectiveness of other cytotoxic populations [39]. In healthy PBMC, ILC make up less than 0.01% of circulating lymphocytes [40], and thus the PBMC samples used here suboptimal for ILC analysis. However, this panel in theory could also be useful to investigators wishing to study ILC in other context. In such case, the addition of CD127 would aid in the differentiation of ILC from other lymphocytes, and the addition of CRTH2 and c-kit would aid in the subdifferentiation of ILC-1, ILC-2, and IL-C3 cell types within the ILC compartment [41].

4. Notes

  1. Brilliant Stain Buffer is a proprietary additive available from BD Biosciences that minimizes the interactions of Sirigen polymer dyes (such as Brilliant Violet or Brilliant Ultraviolet fluorophores). If only one or less Brilliant Violet or Brilliant Ultraviolet fluorophore is used, this additive has no benefit.

Acknowledgments

This work was supported by the Moffitt Cancer Center—Innovative Core Projects (Project number 16060201), NCI–NIH (1 R01 CA148995-01; P30CA076292; P50CA168536), the V Foundation, the Dr. Miriam and Sheldon G. Adelson Medical Research Foundation, and the Chris Sullivan Foundation.

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