Purpose and Appropriate Samples

  1. Top of page
  2. Purpose and Appropriate Samples
  3. Background
  4. Similarity to Published OMIP
  5. Literature Cited
  6. Supporting Information

This panel was developed in order to simultaneously quantify both conventional peptide-MHC-restricted, and innate-like T-cell compartments in human peripheral blood samples. The panel can assess the dynamics of naïve through to terminally differentiated effector memory T-cell subsets, as well as enumerating natural killer T (NKT) cells, mucosal-associated invariant T (MAIT) cells, γδ T-cells, and subsets thereof. The panel is suitable for use on both, freshly isolated or cryopreserved peripheral blood mononuclear cells (PBMC). Staining may be performed in a 96-well plate to increase throughput.


  1. Top of page
  2. Purpose and Appropriate Samples
  3. Background
  4. Similarity to Published OMIP
  5. Literature Cited
  6. Supporting Information

Investigating T-cells based on their differentiation and activation status allows broad tracking of the conventional T-cell dynamics by longitudinal data analysis, and is widely used in clinical research. This allows researchers to define changes in T cell responses to specific insults such as infectious disease or cancer and to describe aberrations in T cell homeostasis [1].

Currently, there is growing interest in a group of T-cells collectively known as “innate-like T-cells”. These include CD1d-restricted, lipid antigen-reactive, NKT cells. Two broad classes of these cells exist [2]: Type-1 NKT cells are the most widely studied, and express an invariant TCR-α chain consisting of TRAV10 (Vα24) joined to TRAJ18 (Jα18), paired with a limited array of TCR-β chains that utilize the TRBV11 (Vβ11) gene with a range of TRDB (Dβ) and TRBJ (Jβ) genes. Type-1 NKT cells are also defined by their ability to recognize the prototypic glycolipid antigen, α-Galactosylceramide (αGalCer). Type-2 NKT cells are also CD1d restricted, but use a broad range of TCR-α and TCR-β chains and recognize other lipid-based antigens but not αGalCer. Another intriguing population of innate-like T cell is the MHC-related protein 1 (MR1)-restricted Mucosal-Associated Invariant T (MAIT) cells. These cells also express an invariant TCR-α chain, TRAV1-2 (Vα7.2) joined to TRAJ33 (Jα33), paired predominantly to TCR-β chains using the TRBV6 (Vβ13) and TRBV20 (Vβ2) family genes [3], and recognize Vitamin B metabolite antigens derived from microbial Riboflavin metabolism [4]. And finally, γδ T-cells represent an entirely distinct lineage of T cell that utilize TCR-γ and TCR-δ genes to form their CD3 associated TCR heterodimer [5]. γδ T cells appear to recognize a broad range of antigens, including lipid antigens, phosphoantigens as well as MHC and MHC-like molecules [6].

Innate-like T cells behave in a fundamentally different manner from conventional T-cells in that they circulate with an effector memory phenotype, poised to rapidly expand and surmount a response upon stimulation, thus acting as peripheral sentinels of the immune system. Furthermore, these cells display broad antigen reactivity to both endogenous and exogenous antigens, typically although not always, via restriction to nonpolymorphic MHC class I-like molecules. Innate-like T-cells play roles in microbial immunity, tumor immunity, and tissue homeostasis [7-9]. Importantly, the innate-like T-cells can make up to 20% of the human peripheral blood T-cell pool, although they are highly variable in frequency and number [10]. In addition, fluctuations in their peripheral blood frequencies have been linked to several disease states [11-20].

In depth analysis of multiple T-cell subsets from clinical samples can be difficult because of limited sample availability and limited cell numbers per sample. For example, Type-1 NKT cells (from here on referred to NKT cells for the sake of simplicity) are relatively rare in human peripheral blood (typically between 0.01% and 0.5% of total T-cells) [21]. Therefore in order to collect sufficient events for their phenotypic analysis, between 100,000 and 1,000,000 PBMCs must be analyzed. Furthermore, analysis of conventional T-cells and subsets thereof can be confounded by large fluctuations in the innate-like T-cell populations, which often go unnoticed because of a lack of specific markers in monoclonal antibody (mAb)/tetramer cocktails [8].

We therefore developed this flow cytometric panel (Tables 1–2, and Online Methods) of reagents in order to assess the conventional T-cell compartment in parallel with the innate-like T-cell subsets. We have included a mAb directed against all γδ T-cell receptors (TCR) as these cells are often excluded or not separately analyzed. CD1d tetramers loaded with αGalCer specifically bind the NKT TCR [22, 23], thus identifying NKT cells, and MAIT cells are identified by co-staining with a mAb directed against the TRAV1-2 (Vα7.2) variable region of the MAIT TCRα chain in combination with anti-CD161 [24]. Figure 1c illustrates this gating strategy.


Figure 1. Multiparameter Gating strategy. (A) Lymphocyte gating strategy involves exclusion of dead cells followed by gating on the bulk lymphocyte population based on FSC-A vs SSC-A. Doublets are then removed (as shown?) and then (b) lymphoid cells are subjected to boolean gates to remove dye aggregates. (c) NKT-cells are gated using CD3 vs αGalCer-loaded CD1d tetramers. CD3 vs TCRγδ is then used to discriminate between γδ and αβ T-cells. αβ T-cells are subsequently gated for MAIT cells using TRAV1-2 vs CD161.

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Most CD8+ T-cells express CD8 on their surface as a heterodimer, comprising both the CD8α and CD8β subunits. However some T-cells, including γδ T-cells, αβ T-cells, NKT cells, and MAIT cells, express CD8α as a homodimer [25] as depicted in Figure 1c, and these cells display innate-like characteristics. Indeed NKT cells, MAIT cells, and γδ T-cells are often CD8αα+ [26]. We have therefore included both CD8α and CD8β to distinguish between these sub-populations.

Finally, conventional peptide-MHC restricted T-cells generally follow adaptive-like dynamics, progressing from a naïve state through several stages of memory, each with specified function [27]. For example, CD45RO/RA isoforms delineate between antigen experienced and naïve, respectively, while CCR7 and CD45RA expression allow discrimination between naïve (CCR7+, CD45RA+), central memory (CCR7+, CD45RA), effector memory (CCR7, CD45RA) and terminally differentiated effector memory (CCR7, CD45RA+) subsets as exemplified in two healthy donors in Figure 1c. CD27 and CD28 markers allow further subsetting of these cells. Importantly, distinguishing between these subsets has provided insight into several disease processes [28, 29]. We have thus included in our panel CD27, CD28, CD45RA, CD45RO, and CCR7 to define the differentiation status of conventional T-cells in parallel to fluctuations within the innate-like T-cell compartment [1].

Similarity to Published OMIP

  1. Top of page
  2. Purpose and Appropriate Samples
  3. Background
  4. Similarity to Published OMIP
  5. Literature Cited
  6. Supporting Information

OMIP-019: Quantification of Human γδT-Cells, iNKT-Cells, and Hematopoietic Precursors [30].

Table 1. Summary table for application of OMIP-021
PurposeNKT cells, γδ T-cells, MAIT cells, Memory T-cell subsets
Cell TypesPBMC
Cross ReferencesOMIP-019
Table 2. Reagents used for OMIP-021
Dead cellsViViDDump
CD27BV711O323Memory T-cell Subsets
hCD1d-PBS44BV421n/aNKT cells
TCRγδFITC11F2γδ T-cells
TRAV1-2PE3C10MAIT cells

Literature Cited

  1. Top of page
  2. Purpose and Appropriate Samples
  3. Background
  4. Similarity to Published OMIP
  5. Literature Cited
  6. Supporting Information
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Supporting Information

  1. Top of page
  2. Purpose and Appropriate Samples
  3. Background
  4. Similarity to Published OMIP
  5. Literature Cited
  6. Supporting Information

Additional Supporting Information may be found in the online version of this article.

cytoa22475-sup-0001-suppfig1.tif3175KSupporting Information Figure 1.
cytoa22475-sup-0002-suppfig2.tif3175KSupporting Information Figure 2.
cytoa22475-sup-0003-suppfig3.tif3175KSupporting Information Figure 3.
cytoa22475-sup-0004-suppfiglegends.docx14KSupporting Information Figure Legends
cytoa22475-sup-0005-suppinfo01.doc681KSupporting Information
cytoa22475-sup-0006-supptables.docx31KSupporting Information Tables

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