Guidelines for the use of flow cytometry and cell sorting in immunological studies (third edition)

Abstract

The third edition of Flow Cytometry Guidelines provides the key aspects to consider when performing flow cytometry experiments and includes comprehensive sections describing phenotypes and functional assays of all major human and murine immune cell subsets. Notably, the Guidelines contain helpful tables highlighting phenotypes and key differences between human and murine cells. Another useful feature of this edition is the flow cytometry analysis of clinical samples with examples of flow cytometry applications in the context of autoimmune diseases, cancers as well as acute and chronic infectious diseases. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid. All sections are written and peer-reviewed by leading flow cytometry experts and immunologists, making this edition an essential and state-of-the-art handbook for basic and clinical researchers.

Figure 1.

Uni-, bi- and multi-parameter and incorrect presentation of flow data. Comparison of two gender and age matched patients: a healthy one (67 years) and a patient with B-CLL (64 years). (A) 1D-histogram presentation of CD3 expression on lymphocytes (red: B-CLL, grey: healthy), (B) 2D-dot-plot presentation of CD3 expression on x-axis vs. CD16/56 expression on y-axis, (C) multivariate presentation of expression of 13 different Abs on 10 colors (OMIP-023 [50]) for nine different leukocyte subsets in a radar-plot. Abbreviations used: B-CLL (B cell chronic lymphocytic leukemia), Th (CD4⁺ T-helper cell), Tc (CD8⁺ cytotoxic T cell), NK (natural killer cell). (D) Incorrect and correct data presentation example (Data analysis: (A and B) FlowJo, V10.2; (C) Kaluza, Beckman-Coulter, V 1.1, (D) FCS Express V.6, De Novo Software).

Figure 2.

Semi-automated clustering and analysis of flow cytometric data by SPADE [32] and hierarchical clustering. (A) SPADE tree display and CD3 expression on blood cells from two male patients. Dot-plot analysis reveals groups of clusters (circles) belonging to the same cell type. (B) Color codes correlate with expression level from low (blue) to high (red) and size of the nodes correlate with cell frequencies (see also scale bar). Data of A and B are from a healthy (B1 and 3; 67 years) and a B-CLL patient (B.2 and 4; 64 years). (C) Hierarchical clustering of flow-cytometry data to visualize and distinguish immune response of pediatric patients (columns) who underwent elective cardiovascular surgery with (left of the yellow line) or without synthetic steroid administration (right) before surgery. PBL was immunophenotyped at day 1 after surgery. Flow cytometry parameters (MFI (mean fluorescence intensity) and cell counts) are displayed horizontally. Red indicates relative upregulation and green relative down-regulation of the respective parameter (see also scale bar). (Data and legend from [57]; reproduction with permission.) (SPADE analysis by Cytoscape, V 3.4.0, Nolan Lab; hierarchical clustering by free software Genes@Work).

Figure 3.

Semi-automated analysis of flow cytometry data by tSNE. (A) Sixteen-part differential of 10 individuals (5 smokers, 5 non-smokers) by OMIP-23 (10 colors, 13 Abs; [50]) showing the location of regular T-helper (Th) and cytotoxic T cells (Tc) with high side scatter (Th hiSSC, Tc hiSSC), T-regulatory cells (Treg), natural killer (NK) and NK-T cells on the tSNE map. Bottom center box contains information for calculating the tSNE plot. The image on the right shows the same figure in a inverted way with less red-green compound and better distinguishable for individuals with Deuteranomaly. (B) Heat map display of expression level of 5 activation markers in non-smokers and smokers and distribution of cell count (All). Scale bars right of each tSNE plot show color coding of fluorescence intensity or cell count levels. (Data of individuals from the LIFE study [58]; data analysis by FCS Express V.6, De Novo Software. Exemplary data and gating examples can be found in the supplementary materials and FlowRepository link of reference [50]).

Figure 4.

Spreading error and fluorochrome brightness in panel design and common compensation artifacts in quality control. (A) A typical example of spreading error is illustrated: BV786 shows significant spectral overlap in the U-780 detector (excitation by UV laser), which manifests as visible spread of the positive population. The relative loss of resolution on this population compared to the negatives is indicated by black bars on the left plot. Right plot shows how spreading error is proportional to signal intensity, and decreases with lower titers of the respective Ab. (B) The absolute compensation value and spreading error are not directly related, as exemplified for BV650+ events in different detectors (spreading error and compensation values for each combination are displayed above the plot). (C) Examples of staining intensities for different fluorochromes: FITC (dim), BV421 and APC (medium), and PE-CF594 (bright). Note that fluorochrome brightness can be instrument-specific. (D) Overview on the critical considerations for fluorochrome assignment for co-expressed markers. Highly expressed targets should preferably be paired with dim fluorochromes generating little spreading error. Dimly expressed (or unknown) targets should be paired with bright fluorochromes and utilize detectors that receive little spreading error. Numbers 1–3 indicate the relevance of the considerations. (E) and (F) show erroneous patterns that usually indicate incorrectly compensated data: (E) example of a correctly compensated plot, and respective over- and undercompensation of marker CD X into detector for CD Y. (F) Example of an incorrectly compensated population CD X (right plot) appearing as “super-negative” population if displayed against an unrelated detector measuring CD Z (left plot). The erroneous pattern is only visible if displayed against the detector measuring CD Y.

Figure 5.

Detection of human circulating endothelial cells and their precursors. Gating stategy used to identify circulating endothelial cells (CEC) and their precursors (EPC) among peripheral blood leucocytes. Debris and aggregates were eliminated using FSC-Area vs FSC-Hight, while possible clogs were removed using the parameter Time vs SSC. Then, a DUMP channel was used to remove from the analysis CD45⁺ cells and dead cells. In the remaining population, nucleated cells were identified by positivity for Syto16. Stem cells were identified according to CD34 positivity, and among this population, EPC (CD133⁺,CD31⁺) and CEC (CD133⁻,CD31⁺) were identified. The expression of CD276, also named B7-H3, and CD309, also named VEGFR-2 or KDR, was evaluated in each subpopulation. In this example, more than ten million events were initially acquired.

Figure 6.

Detection of SARS-CoV-2 T cell-specific response. Gating strategy used to identify SARS-CoV-2 T cells among CD4⁺ and CD8⁺ T cells. Perturbancies during acquisition have been removed and PBMC have been identified according to physical parameters, doublets have been removed as well as dead cells. In this population CD3⁺ T cells have been selected and in this, CD4⁺ and CD8⁺ T cells have been recognized. Representative dot plots showing the percentages of human CD4⁺ T cells producing TNF and IFN-γ after 16 hours of in vitro stimulation with 1μg/ml SARS-CoV-2 Prot S PepTivator (Miltenyi). Unstimulated and stimulated conditions are shown. Upper panel: healthy donor; lower panel: COVID-19 patient.

Figure 7.

Gating strategy to study human CD4⁺ and CD8⁺ T cells in the peripheral blood. Lymphocytes are identified based on the FSC and SSC. Single cells are discriminated from doublets by plotting the pulse area and height against each other for the FSC. CD19⁺ cells (B cells), CD14⁺ cells (monocytes) and CD56⁺ cells (NK and NKT-like cells) are excluded as cell populations other than T lymphocytes. Within the CD14⁻CD19⁻CD56⁻ cells, αβTCR⁺ and γδTCR⁺ T cell subsets can be identified. αβTCR⁺ T lymphocytes include predominantly CD4⁺ and CD8⁺ T cells.

Figure 8.

Gating strategy to identify the differentiation stages and memory subsets of human CD4⁺ T cells in the peripheral blood. (A) Conventional CD4⁺ T cells and Treg cells are identified based on the differential expression of the surface markers CD127 and CD25. Conventional CD4⁺ T cell population (Tconv CD127⁺CD25^low) can be divided in naïve and memory T cell subsets (Tscm, Tcm, Tem and cTfh) based on the surface markers CCR7, CD45RA, CXCR5 and CD95. (B) Co-expression of the chemokine receptor CXCR5 and the activation marker ICOS among CD4⁺ T cells in human tonsils identifies Tfh cells. (C) At least 5 different memory T helper subsets can be detected in the CD4⁺ T cell memory population based on their differential expression of the chemokine receptors CCR6, CXCR3, CCR4 and CCR10. (D) CD4⁺ CTL expressing GzmB can be identified among CD25⁻ Tconv based on the absence of CD27 and CD28 expression.

Figure 9.

Human T-cell subsets as identified by intracellular cytokine staining. (A) Expression of IFN-γ and IL-17A with or without 5 h of PMA/Ionomycin stimulation in the presence of BFA (for the last 2.5 h). (B) Expression of IFN-γ and/or IL-4, IL-17A and/or IL-22, and of IL-10 and/or GM-CSF by total CD4⁺ memory T cells. (C) T cell populations are enriched by flow cytometry according to the gating strategy indicated in Figure 2C and then stimulated in vitro for 5 h with PMA and Iono in the presence of BFA (for the last 2.5 h). Shown is the expression of IFN-γ, IL-4, and IL-17A by Th1, Th2 and Th17 cell subsets sorted ex vivo from the blood.

Figure 10.

Human T-cell subsets analyzed by intranuclear staining for transcription factor expression. (A) Ex vivo GATA3 expression in CCR4⁺CCR6⁻ Th2 memory cells according to CRTh2 expression (B) Naïve, Th1, Th1* and Th17 cells were sorted from PBMC as indicated in Figure 2C and stained for their expression of T-BET and RORC2/RORγT either ex-vivo or after 24 h α-CD3/α-CD28 stimulation (stim.). (C) Tonsillar CXCR5⁺ICOS⁺ Tfh cells and non-Tfh cells gated as in Figure 2B were analyzed for BCL6 expression. (D) Ex vivo expression of the transcription factors Foxp3 and Helios in CD25^hiCD127^lo Treg cells from peripheral blood.

Figure 11.

Clinical relevance: analysis of chemokine receptors expression in CD4⁺ T cells from patients. (A) T helper subsets distribution analysis based on chemokine receptors expression in the blood of a healthy donor (upper panels) and a patient (lower panels) suffering from K. pneumoniae bloodstream infection. (B) Reported frequencies of Th1, Th1* and Th17 cells in healthy donors (HD) and RR-MS patients (RR) with mild (MS score <1) or more severe (MS score >1) disease.

Figure 12.

Murine CD4 and CD8 T cells. Sample gating tree for the identification of murine CD4 and CD8 T cell subsets from the spleen. Conventional CD4 and CD8 T cells can be identified by gating on time, lymphocytes according to FSC and SSC (R1, R2), exclusion of doublets (R3) and dead cells (R4) and gating on CD3ε⁺ or TCRβ⁺ cells (R4) and CD4⁺CD8α⁻ cells (R5) or CD4-CD8α⁺ cells (R6). Naïve, effector and memory T cell populations can then be defined within CD4 T cells using CD44 and CD62L expression to identify CD44loCD62Lhi naïve cells, CD44hiCD62Lhi central memory cells, and CD44^hiCD62L^lo effector memory and effector cells.

Figure 13.

Schematic of murine CD4 T cell differentiation. An array of CD4 helper T cell subsets differentiate from CD4 Tn cells, including Th1, Th2, Th9, Th17, Th22, Tfh, Treg, and cytotoxic CD4 T cells. Molecules under each CD4 helper T cell subsets heading indicate the key effector cytokine/molecules, transcription factor/s, and chemokine receptors.

Figure 14.

Chemokine receptors and transcription factors for identification of murine Th1 and Th17 CD4 cells. Subsets of CD4 T cells can be identified based on the expression of chemokine receptors. CD4 T cells were gated on lymphocytes according to scatter parameters, live cells and CD3⁺/CD4⁺ (see Figure 12) on murine splenocytes ex vivo or after expansion under polarizing conditions for the detection of the indicated chemokine receptors or transcription factors.

Figure 15.

Assessment of Tfh cells. From murine splenocytes A) Tfh cells were identified by gating on lymphocytes (R1), single cells (R2), live, non-B, CD3⁺ cells (R3) and CD4⁺ cells (R4) before identifying Tfh cells through co-expression of high levels of CXCR5 and PD1. B) Higher expression of the transcription factor Bcl6 is detected in Tfh (CD44high/CXCR5high) compared to naïve (TN CD44low/CXCR5low) CD4 T cells.

Figure 16.

Gating strategy of human CD8⁺ T cell subsets in the peripheral blood. After doublet exclusion, lymphocytes are selected on the basis of physical parameters. Gating on live CD3⁺ T cells is followed by discrimination of CD8⁺ and CD4⁺ T lymphocytes. Within CD8⁺CD4⁻ T cells, the CD161^high MAIT population is excluded. CCR7⁺CD95⁻ cells Tn CD8 cells can be subdivided on the basis of CXCR3 expression. CCR7⁺CD95⁺ early memory T cells can be classified as TIGIT⁺PD-1⁺ (T_PEX) or TIGIT⁻PD-1⁻ T_SCM/CM. The CCR7⁻CD95⁺ compartment includes a population of CD28⁺CD127⁺ T_TM, while the CD28⁻ cells can be divided into CD45RA⁻ Tem and CD45RA⁺ T_TE.

Figure 17.

Discriminating murine CD8 T cell subsets. The expression of CD44, CD62L, and CD69 can be used to identify CD8 T cell populations in the different phases of the immune response. CD8 T cells from the spleen displayed in the top row were gated as shown in Figure 12. Naïve mice mainly contain naïve CD8 T cells. Pathogen-specific T cells can be identified using tetramer staining, here GP33-specific CD8 T cells after LCMV infection. During the effector phase (d8 post infection), the majority of LCMV-specific CD8 T cells upregulate CD44 and downregulate CD62L. In the memory phase (day 30+ post infection), T cells retain high expression of CD44 and can be divided in Tcm (CD62L⁺/ CD69⁻), Tem (CD62L⁻/ CD69⁻), and Trm (CD62L⁻/ CD69⁺).

Figure 18.

Delineating murine SLEC and MPEC populations. The expression of KLRG1 and CD127 can be used to differentiate SLECs (KLRG1⁺CD127⁻) from MPECs (KLRG1⁻CD127⁺). Plots are gated on CD8α⁺ T cells as in Figure 12 (total CD8 T cells, top two rows) and additionally on tetramer+ cells as in Figure 17 (bottom row). Cells are derived from peripheral blood at the peak (day 27, left) or memory timepoint (day 230, right) post-vaccination with recombinant adenoviral vector (serotype 5) expressing SIV-Gag as a target Ag.

Figure 19.

Gating strategy for analyzing CD4⁺ and CD8⁺ human Trm from bone marrow. Similar gating strategies also apply for Trm from other tissues. The Time gate is used in relation to a scatter parameter like SSC-A to identify and remove potential bubbles, clogs, or air. SSC and FSC are used to gate on lymphocytes expressing CD45, followed by gating out doublets using SSC and FSC (both width vs height). Live/dead marker is used to exclude dead cells and CD3 to gate on T cells. CD4 and CD8 are used to gate on CD4⁺ and CD8⁺ T cells. CD69⁺ Trm can be further gated in relation to CD45RO.

Figure 20.

Identifying murine Trm cells from small intestine, liver, spleen, kidney, and lungs using surface markers. Sample gating tree for the identification of pathogen-specific CD8 Trm cells from the intraepithelial lymphocyte fraction (SI IEL) and lamina propria fraction (SI LPL) of the small intestine, liver, spleen, and kidney of LCMV-infected mice as well as lungs of Influenza-infected mouse >d30 post infection. LCMV-specific memory CD8 T cells can be identified by gating on lymphocytes according to FSC and SSC (G1), exclusion of doublets (G2) and dead cells (G3) and gating CD4-CD8α+ cells (G4), GP33-tetramer (LCMV) or NP366-tetramer (Influenza) positive CD44 high cells (G5). Trm can be identified by gating on CD69+/ CD62L− cells in contrast to CD69− circulating T cells. Additionally, CXCR6 is highly expressed on many Trm populations and CD103 is expressed on a subpopulation of epithelial Trm.

Figure 21.

Identification of murine Trm cells in liver using Hobit reporter expression. LCMV-specific CD8 T cells were identified using tetramers as described above (G5) in liver preparations of LCMV-infected Hobit reporter mice (>d30 post infection). Coexpression of tdTomato, reporting the Trm master transcription factor Hobit, and CD69, was used to identify Trm cells.

Figure 22.

Identification of unconventional and conventional murine T cells. Unconventional and conventional murine T cells can have overlapping phenotypes. Splenocytes were gated on scatter parameters (see Figure 20), live cells, and CD3⁺/CD4⁺ T cells. Staining with CD1d PBS-57 tetramers (obtained through the NIH Tetramer Core Facility) was used to identify NKT cells that mainly express CD69.

Figure 23.

Identification and further analysis of intraepithelial lymphocytes in the human duodenum. A.- Total human intraepithelial lymphocytes (ilELs) from the duodenum were identified within singlet (97,5%) viable cells (76,7%) as CD45⁺ (34,2%), and further divided into classical T-cells (70,6%), TCRγδ T cells (27,2%) or NK-like cells (1,9%) based on the expression of CD3 and TCRγδ. This gating strategy is representative of a patient with active celiac disease, as noted by the high proportion of TCRγδ T cells coupled with the low proportion of NK-like cells. (B) The human iIEL subpopulations were further analyzed: NKG2D expression (98,6%) was measured for TCRγδ T cells, NK-like cells were divided into CD7⁺ (31,3%) and CD7⁻ cells (64,7%), and the proportion of CD8⁺ cells (74,65%), CD4⁺ cells (4,39%), double positive cells (CD4⁺CD8⁺) (2,85%) and double negative cells (CD4⁻CD8⁻) (23,8%) was assessed for T-cells.

Figure 24.

Representative gating strategy for TCRγδ⁺ population analysis of (A) murine small intestine intraepithelial lymphocytes (IEL) and (B) lamina propria (LPL). After isolation, lymphocytes were stained with Zombie (Live/Dead -Biolegend), CD45 (104 -Biolegend), TCRβ (REA318 - Miltenyi), TCRγ/δ (GL3 -Biolegend), CD8α (53-6.7 -Biolegend), Vγ7 (F2.67 - provided by P. Pereira: Institut Pasteur, Paris, France), Vδ6.3 (C504.17C -eBioscience), Vγ4 (GL2 -Biolegend) for the IEL cell suspension A and Vγ1 (2.11 -Biolegend) and Vγ4 (UC3-10A6 -Biolegend) for the LPL cell suspension.

Figure 25.

Representative gating strategy and analysis of A TCRαβ⁺ murine small intestine intraepithelial lymphocytes (IEL) and B lamina propria lymphocytes (LPL). After isolation, lymphocytes were stained with Zombie (Live/Dead - Biolegend), CD45 (104- Biolegend), TCRβ (REA318 - Miltenyi), TCRγ/δ (GL3 - Biolegend), CD8α (53-6.7- Biolegend), CD8β (YTS156.7.7 - Biolegend) and CD4 (GK1.5 - Biolegend).

Figure 26.

Gating strategy used to analyze markers related to differentiation, activation status, senescence, and exhaustion within human CD4⁺ T cells from a 45 y.o. healthy donor. Gating strategy is identical to that used in [126). Gate on Promokine-840 negative cells is used to exclude dead cells. Doublets are excluded based on FSC-H and FSC-W. T cells are identified based on physical parameters, and as CD45⁺CD3⁺ cells. Among CD3⁺CD4⁺ cells, naïve T cells are identified as CCR7⁺CD45RA⁺CD28⁺CD27⁺ cells; T_SCM are CCR7⁺CD45RA⁺CD28⁺CD27⁺CD95⁺; central memory (CM) are CCR7⁺CD45RA⁻CD28⁺CD27^+/−; effector memory (EM) are CCR7⁻CD45RA⁻CD28^+/−CD27^+/−; terminal effector memory (Tem) are CCR7⁻CD45RA⁺CD28⁻CD27^+/−. Activated cells are CD38⁺HLA⁻DR⁺; Treg are CD127⁻CD25⁺; exhausted/senescent are PD1⁺CD57⁺.

Figure 27.

Gating strategy used to define Tn, TVM, Tcm, and Tem CD8 T cell subsets in naive mice, using splenocytes from naive SPF 3 month old and 18 month old C57BL/6J mice. (A) Gating strategy, where cells are gated on singlets, lymphocytes, live, dump-, CD8⁺ T cells and then (B) CD44 vs CD62L then CD49d or (C) CD44 vs CD49d to define the populations indicated in the key. Frequencies indicate the frequency of indicated subsets within the CD8 T cell population.

Figure 28.

Gating strategy used to define naïve, memory, and TTDE CD8 T cell subsets in aged chronically infected mice (applies also to Figure 29 and 30). Flow cytometry analysis of the peripheral blood of 8 month old C57BL/6J mouse experimentally infected for 6 months with 10⁶ PFU of a chronically persistent β-herpesvirus, murine cytomegalovirus (MCMV).

Figure 29.

Flow cytometry analysis of KLRG1 and CD27 expression on total CD44^hiCD11a^hi CD8 T cells (pre-gated according to the gating strategy shown in Figure 28) in the peripheral blood of 15 month old (BALB/c×DBA/2) F1 mice experimentally infected for 9 months with 10⁶ PFU of a non-persistent virus, Western Reserve vaccinia virus (VACV) or 10⁵ PFU of a chronically persistent β-herpesvirus, murine cytomegalovirus (MCMV) compared to uninfected littermate mice (MOCK).

Figure 30.

Flow cytometry analysis of CD122 and CD62L expression in naïve, memory, and TTDE CD8 T cell subsets (pre-gated according to the gating strategy shown in Figure 28) in the peripheral blood of 8 month old C57BL/6J mouse experimentally infected for 6 months with 10⁶ PFU of MCMV.

Figure 31.

Gating strategy to quantify human CD25^hiCD127^loFOXP3⁺ Tregs using whole blood and DuraClone tubes. (A-C) From total events, single cells were selected and CD45⁺ lymphocytes were gated based on SSC properties and CD45 expression. (D) From CD3⁺CD4⁺ T cells the CD25^hiCD127^lo gate was identified. If the CD25 resolution is adequate then typically there is a clear separation of this population on a diagonal axis indicated by the grey dashed line. (E and F) show the expression of FOXP3⁺ within the indicated CD25^hiCD127^lo or Tconv cells gates. (G) Identification of CD25^hiFOXP3⁺ Tregs from total CD3⁺CD4⁺ T cells (panel C). Data were collected on a BD Fortessa X20 cytometer (Table 34).

Figure 32.

Phenotyping human CD25^hiCD127^loFOXP3⁺ Tregs in whole blood. Representative staining of healthy adult peripheral whole blood with the Ab panel listed in Table 23. (A) Gating strategy and representative data for CD25^hiFOXP3⁺ staining following fixation and permeabilization with either BD or eBioscience FOXP3 buffer kits. Gates were set on the basis of an isotype control (for comparison the lack of utility of an FMO control for setting the FOXP3 gate is shown). (B) Representative data for CD25^hiCD127^lo staining and FOXP3 MFI with the indicated gated populations of CD25^hiCD127^lo or Tconv cells. Right graph shows the FOXP3 MFI if samples are processed with BD or eBioscience buffers. (C) CD25^hiFOXP3⁺ frequencies and FOXP3 MFI in CD25^hiCD127^lo cells if staining is performed with the 236A/E7 or 259D anti-FOXP3 mAbs. All graphs show data from 6 healthy adults. Wilcoxon signed-rank tests were performed on paired samples. Data were collected on a BD Fortessa X20 cytometer (see Table 34).

Figure 33.

Quantification of human CD25^hhCD127^lo Tregs using whole blood. (A) Count beads were gated based on SSC properties and CD3 expression. (B-E) After the exclusion of the beads, CD45⁺ whole blood cells were selected, doublet cells were excluded, and total lymphocytes were gated based on SSC and FSC properties. (F-H) From CD3⁺ T cells, CD4⁺CD8⁻ T cells were selected. Within the latter gate, CD25⁺CD127^lo Tregs and T conventional cells were identified. The Trucount tubes contain a number of beads that is used to calculate the absolute counts of the Tregs per μL based on the equation: (Number of positive Treg events/Number of bead events) *(Number of beads per tube/Test blood volume). Data were collected on a BD Fortessa X20 cytometer (see Table 34).

Figure 34.

Identification of human Treg subsets in PBMCs. (A-C) Lymphocytes were gated according to their size and granularity, doublets excluded, and live CD4⁺ T cells gated. (D) Regulatory T cells (Treg) were identified as CD4⁺CD25^hiCD127^lo (red gate) and the remaining cells were identified as “non-Treg” cells (blue gate). (E & F) If the cells are fixed and permeabilized, FOXP3 staining can be performed. In (E), Dashed lines show how CD25 negative, low and high expression are defined. In (F), FOXP3 expression in the CD4⁺CD25^hiCD127^lo Tregs (red line) and non-Tregs (blue line) is shown, relative to a Treg FOXP3 fluorescence minus one (FMO) control (solid grey). Mean fluorescence intensity (MFI) values are provided. (G-H) Memory Tregs and non-Tregs were selected as CD45RA⁻CD45RO⁺. (I-N) Treg and non-Treg Th subsets were defined according to their expression of CXCR3, CCR4, and CCR6 as follows: Th17 (CXCR3⁻ CCR4⁺CCR6⁺), Th1 (CXCR3⁺CCR4⁻CCR6⁻), Th17.1 (CXCR3⁺CCR4⁺CCR6⁺), and Th2 (CXCR3⁻CCR4⁺CCR6⁻). Data were collected on a BD Fortessa X20 cytometer (see Table 34).

Figure 35.

Gating strategy to sort CD4⁺CD25^hiCD127^lo Tregs from human peripheral blood. Lymphocytes were gated according to their size and granularity (A), doublets excluded (B) and live cells were gated (C). (D) CD4⁺ T cells from an enriched population of cells were gated during the cell sort. CD4 staining of total lymphocytes is shown for reference. (E) CD25^hiCD127^lo Tregs were gated from the CD4⁺ gate. In this representative plot, cells were enriched using the STEMCELL Technologies CD25-enrichment kit and subsequently stained with the CD25 (clone 2A3) PE Ab. CD25 and CD127 staining in CD25-depleted cells and non-enriched total lymphocytes are shown for reference. Data for panels A-E were collected on an LSR Fortessa X20. (F) CD25-enriched cells (Miltenyi kit) were stained with CD25 (clone 4E3) PE and one of four different CD127 Abs. Data were collected on a Beckman Coulter Moflo Astrios cell sorter (see Table 35).

Figure 36.

Gating strategy to identify CD25⁺FOXP3⁺ Tregs in human thymus. (A) Representative Treg staining from total thymocytes and purified CD25⁺CD8⁻ thymus-derived Tregs ex vivo, and (B) from thymus-derived Tregs and peripheral blood-derived naïve Tregs after 7 days of in vitro expansion. From total events, doublets were excluded based on FSC-H and FSC-A, then live cells were selected based on negative expression of FVD. CD25⁺ cells were gated from live cells, then CD4⁺CD8⁻ T cells were gated from the CD25⁺ gate. From the CD4⁺CD8⁻ T cell gate, the expression of FOXP3 and Helios are shown. Data were collected on a BD Fortessa X20 cytometer (see Table 34).

Figure 37.

Gating strategy to identify CD25^hiFOXP3⁺ Tregs in human intestinal biopsies. (A) Representative Tregs staining from PBMCs and (B) LPMCs. From total events, doublets were excluded based on FSC-H and FSC-A. Live cells were selected based on negative expression of FVD and CD4⁺ T cells were gated based on CD3 and CD4 expression. From CD4⁺ T cells, Tregs were gated as CD25^hiFOXP3⁺ cells. From the Treg gate, the expression of CD161 and Helios are shown. Dashed lines show how CD25 negative, and high expression are defined. Data were collected on a BD Fortessa X20 cytometer (see Table 34).

Figure 38.

Gating strategy to identify CD25^hiFOXP3⁺ Tregs in human skin and fat tissue. (A) Human blood-derived CD3⁺CD4⁺CD8⁻CD25⁻CD127⁺CD45RA⁺ naive Tconv, blood-derived CD3⁺CD4⁺CD8⁺CD25⁺CD127⁻CD45RA⁺ naive Treg, blood-derived CD3⁺CD4⁺CD8⁻CD25⁺CD127⁻CD45RA⁻CCR8⁻ and CCR8⁺ memory Treg were sorted, fixed, and stained intracellularly, followed by re-acquisition of fixed cells. Contribution of cell subtypes in the respective gates based on color code. (B) Expression of FOXP3 (left) and BATF (right), with BATF control staining (middle, no primary Ab). (C) Example gating strategy for human tissue Treg cells using skin tissue. (D) Identification of CD25⁺CD127⁻CD45RA⁻CCR8⁺ Treg cells in human skin tissue. (E) Identification of CD25⁺CD127⁻ CD45RA⁻CCR8⁺ Treg cells in human fat tissue. (F) Identification of CD25⁺CD127⁻CD45RA⁻CCR8⁺ Treg cells in human blood. Data were collected on a BD Symphony cytometer (see Table 36).

Figure 39.

Phenotyping of Treg cells from the murine thymus. Gating strategy to identify Treg cells in the thymus. From all events, lymphocytes can be distinguished by their FSC/SSC properties (gate G0). After lymphocyte gating, doublets are excluded twice (gates G1 and G2), followed by exclusion of dead or autofluorescent cells (gate G3). From G3, CD4SP thymocytes (gate G4) are gated, from which two Treg cell precursors (G5 and G6) and thymic Treg cells (G7) can be identified. Thymic Treg cells (G7) and CD4SP thymocytes (G4) can be subdivided into two subsets of CD24^highCD69⁺ immature (G8 and G10) and CD24^dim/lowCD69⁻ (G9 and G11) mature cells. Numbers indicate frequencies of cells within respective gates. Figures are based on thymocyte isolations from Foxp3^EGFPCreERT2ROSA26^YFP mice.

Figure 40.

Phenotyping of Treg cells from murine spleen and lymph nodes. (A) Gating strategy to identify Treg cells in the spleen. From all events, lymphocytes can be distinguished by their FSC/SSC properties (gate G0). Based on G0, doublets are excluded twice (gates G1 and G2) followed by exclusion of dead or autofluorescent cells (gate G3). From G3, CD4⁺CD3ε⁺ T cells (gate G4) are gated, from which Foxp3⁺ Treg cells (gate G6) and Foxp3⁻ Tconv cells (gate G5) can be further identified. From G6, Helios⁺ tTreg (gate G7) and Helios⁻ pTreg cells (gate G8) are gated. Finally, a staining for CD62L and CD44 on Treg cells (gate G6, blue) and Tconv cells (gate G5, orange) are shown together, with CD62L⁻CD44⁺ effector/memory cells being gated (gate G9). (B and C) Gating strategy to identify Treg cells in skin-draining lymph nodes (B) and mesenteric lymph nodes (C). Gates as described in panel A. Numbers indicate frequencies of cells within respective gates. Figures are based on spleen and lymph node isolations from wild-type mice.

Figure 41.

Isolation and analysis of Treg cells from murine liver and spleen. (A) Image of liver tissue pre-cut (left) and after cutting (right) in a metal sieve. After cutting, a syringe plunger can be used to disseminate the tissue. (B) Image of the preparation of a liver suspension in the Percoll gradient (left). The bottom phase consists of 80 % Percoll-PBS, the top phase of 40% Percoll-PBS and the digested liver cells. On the right, a representative image of a sample after centrifugation is shown. Three layers can be discriminated: a top layer consisting mainly of hepatocytes, the middle layer with target cells, and a bottom layer with unwanted cells. (C) Gating strategy to identify tisTregST2 cells in liver. From all events, a CD4-gate to identify T cells can be drawn (gate G0). In the plot, the smaller color-coded plots indicate expression of CD3ε in the same SSC-A versus CD4 plot. Presence of CD3ε⁺ cells in the G0 gate can be appreciated. Based on G0, lymphocytes can be identified by their FSC/SSC properties (gate G1). Next, doublets are excluded (gate G2) as well as unwanted, dead or autofluorescent cells (gate G3). From G3, CD4⁺CD3ε⁺ T cells (gate G4) are gated, from which Treg cells (gate G6) and Tconv cells (gate G5) can be identified. Finally, Klrg1⁺ST2⁺ tisTregST2 (gate G7) are gated from Treg cells (gate G6). A staining of Gata-3, shown in the histogram, exemplifies the expression of this marker in liver Tconv cells (gate G5, orange, dotted line), liver Klrg1⁺ST2⁺ tisTregST2 cells (gate G7, red), and liver Klrg1⁻ST2⁻ Treg cells (gate G8, blue), numbers indicate geometric mean fluorescence intensity of Gata-3. In (D), the same gating strategy as described for liver is applied to a spleen sample. In both tissues, CD4⁺Foxp3⁺Klrg1⁺ST2⁺Gata-3^high tisTregST2 cells can be identified with the proposed gating strategy. CD3ε or TCRβ Abs can be used. Numbers indicate frequencies of cells within respective gates. Figures are based on liver digestions and spleen isolations from Foxp3^{DTR, GFP} animals.

Figure 42.

Isolation and analysis of Treg cells from murine skin. (A) Representative image of skin tissue in digestion buffer after cutting with scissors. Cutting can be performed directly in the GentleMACS^® C tube. (B) Image of the skin tissue after digestion. The sample is poured onto a metal mesh and can be dissociated manually using a syringe plunger. (C) Sequential filtration workflow for skin samples. (D) Gating strategy to identify tisTregST2 cells in skin tissue. From all events, a CD4-gate to identify T cells can be drawn (gate G0). In the plot, the smaller color-coded plots indicate expression of Foxp3 in the same SSC-A vs CD4 plot. Presence of Foxp3⁺ cells in the G0 gate can be appreciated. Based on G0, lymphocytes can be identified by their FSC/SSC properties (gate G1). Smaller plot shows FCS/SSC of all events without CD4 pre-gating. Next, doublets are excluded (gate G2) as well as unwanted, dead, or autofluorescent cells (gate G3). From G3, CD4⁺TCRβ⁺ T cells (gate G4) are gated, from which Treg cells (gate G6) and Tconv cells (gate G5) can be identified. Finally, Klrg1⁺ST2⁺ tisTregST2 (gate G7) are gated from Treg cells (gate G6). A staining of Gata-3, where numbers indicate geometric mean fluorescence intensity, exemplifies the expression of this marker in skin Tconv cells (gate G5, orange, dotted line) and skin Klrg1⁺ST2⁺ tisTregST2 cells (gate G7, red). Numbers indicate frequencies of cells within respective gates. Figures are based on skin digestions from Foxp3^{DTR, GFP} animals.

Figure 43.

Isolation and analysis of T cells from the murine fat and lung tissue. Gating strategy to identify Treg cells in fat (A) and lung (B) tissue. From all events, a CD4-gate to identify T cells can be drawn (gate G0). Based on G0, lymphocytes can be identified by their FSC/SSC properties (gate G1). Next, doublets are excluded (gate G2) as well as unwanted, dead, or autofluorescent cells (gate G3). From G3, CD4⁺TCRβ⁺ T cells (gate G4) are gated, from which Treg cells (gate G6) and Tconv cells (gate G5) can be identified. Finally, Klrg1⁺ST2⁺ tisTregST2 (gate G7) are gated from Treg cells (gate G6). A staining of Gata-3, where numbers indicate geometric mean fluorescence intensity, exemplifies the expression of this marker in Tconv cells (gate G5, orange, dotted line), Klrg1⁺ST2⁺ tisTregST2 cells (gate G7, red), and Klrg1⁻ST2⁻ Treg cells (gate G8, blue). Numbers indicate frequencies of cells within respective gates. Figures are based on lung and fat digestions from Foxp3^{DTR, GFP} animals.

Figure 44.

Isolation and analysis of Treg cells from the murine colon tissue. (A) Image of colon tissue after excision. The appendix is still attached (left image) and should be removed (right image). (B) Image of the colon tissue after cleanup (left). Feces have been removed and the colon has been cut longitudinally. The colon is then cut into 1 cm pieces (right) and can be washed. (C) Gating strategy to identify Treg cells in colon tissue. From all events, a CD4-gate to identify T cells can be drawn (gate G0). Based on G0, lymphocytes can be identified by their FSC/SSC properties (gate G1). Next, doublets are excluded (gate G2) as well as unwanted, dead, or autofluorescent cells (gate G3). From G3, CD4⁺TCRβ⁺ T cells (gate G4) are gated, from which Treg cells (gate G6) and Tconv cells (gate G5) can be identified. Finally, Klrg1⁺ST2⁺ tisTregST2 (gate G7) are gated from Treg cells (gate G6). A staining of Gata-3, where numbers indicate geometric mean fluorescence intensity, exemplifies the expression of this marker in Tconv cells (gate G5, orange, dotted line), Klrg1⁺ST2⁺ tisTregST2 cells (gate G7, red), and Klrg1⁻ST2⁻ Treg cells (gate G8, blue). Numbers indicate frequencies of cells within respective gates. Figures are based on colon digestions from Foxp3^{DTR, GFP} animals.

Figure 45.

Flow cytometric analysis of IL-10 production by human CD4⁺ T-cell subsets. (A) Conventional human CD4⁺ T-cells (Complete gating strategy see Chapter human CD4⁺T-cells) were isolated according to IL-7R and CCR6 expression and stimulated for 4 or 30 hours with anti-CD3 Abs in the absence or presence of anti-CD28 Abs or 100 U/ml IL-2. The production of IL-2 and IL-10 is shown. The frequencies of IL-10⁺ cells are reported (Statistics: paired student’s t-test, n=5). (B and C) Representative intracellular IL-10 and IFN-γ stainings of FACS-purified CD4⁺CXCR5⁺ICOS⁺ tonsillar TFH-cells (B) or of human blood CD4⁺IL-7R^lowCD25⁻T-cells (C) stimulated with PMA and Ionomycin for 4 h. (D) Same as in C, but IL- 10 was analyzed with a secretion assay and combined with CD40L surface staining. (E) IL-10 secretion of human CD4⁺T-cells following overnight stimulation of total PBMC with SEB gated on CD4⁺LAG3⁺CD49b⁺Tr1- cells. 2010 Häringer et al. Originally published in J. Exp. Med. https://doi.org/10.1084/jem.20091021 [163] (Fig. 45A) and https://doi.org/10.1084/jem.20082238 [165] (Fig. 45C).

Figure 46.

Expression of phenotypic markers associated with human Tr1-cells. (A) LAG3 and CD49b surface stainings of human peripheral blood of a healthy donor, in an inflamed tonsil and in the intestinal lamina propria ex vivo. (B) Ex vivo LAG3 and CD49b surface staining with a polyclonal or a monoclonal anti-LAG3 Ab on total CD4⁺ T-cells, or on gated IL-7R⁻CCR5⁺ Tr1-like cells in human peripheral blood of a healthy donor. C CCR5 and PD1 co-expression among gated CD4⁺IL-7R^lowCD25⁻T-cells allows to enrich for Tr1-like cells in different tissues. Shown is peripheral blood of a representative healthy donor. 2010 Rivino et al.

Figure 47.

Human Tr1-like cells tracked by intracellular staining for transcription factors. A. Intracellular Eomes versus IL-10 or IFN-γ expression in FACS-purified human blood CD4⁺IL-7R^lowCD25⁻ CCR5⁺CD27⁺Tr1-like cells after PMA and Ionomycin stimulation for 4 h. (B) Ex vivo Eomes versus T-betexpression in PBMC gated on conventional CD4⁺T-cells according to IL-7R expression. Among IL-7R^lowCD25⁻ CD4⁺ T-cells, Tr1-like cells can be identified as Eomes^hiT-bet^lo, while CTL are T-bet^hiEomes^lo.

Figure 48.

Human Tr1-like cells tracked by intracellular staining for cytotoxic molecules. (A) Ex vivo GzmK and GzmB expression in gated conventional CD4⁺T-cells and FOXP3⁺Tregs. (B) GzmK and IL- 10 co-expression in FACS-purified CD4⁺IL-7R^lowCD25⁻T-cells after 4 hours of stimulation with PMA and Ionomycin. (C) Co-expression of GzmK and Eomes in FACS-purified CD4⁺IL-7R^lowCD25⁻T-cells ex vivo D: Ex vivo IL-7R and CCR6 expression patterns in among conventional CD4⁺ T-cells gated as GzmK⁺ or GzmB⁺ (see A).

Figure 49.

Gating strategy to identify murine Tr1 cells in Plasmodium berghei ANKA-infected mice. A representative gating strategy is shown (A). The dump channel allows exclusion of the majority of myeloid cells (CD11c, CD11b, MHCII), NK cells (NK1.1), gd T cells (gd TCR), CD8⁺ T cells (CD8), B cells (CD19) and – importantly - platelets (CD41), which might stick to CD4⁺ T cells. Especially in the liver, additional gating for exclusion of NKT cells is recommended. NKT cells are TCRb dim and bright for CXCR6. Stringent gating for CD4⁺ T cells is crucial, since LAG-3 and CD49b not only identify Tr1 cells within the CD4⁺ T cell subset, but also enrich for IL-10-expressing cells within other cell types. Tr1 cells co-expressing LAG-3, CD49b, and IL-10 can be gated according to the strategy shown or alternatively, cells can be first gated for LAG-3⁺ CD49b⁺ followed by gating for IL-10⁺ cells. Staining for LAG3/CD49b and IL-10 GFP reporter is shown for cells from liver (B), spleen (C) and blood (D), both during steady state (left panel) and malaria (right panel).

Figure 50.

Gating strategy to identify murine Tr1 cells in the small intestine. A representative gating strategy is shown (A). The dump channel allows exclusion of the majority of myeloid cells (CD11c, CD11b, MHCII), NK cells (NK1.1), gd T cells (gd TCR), CD8⁺ T cells (CD8), B cells (CD19), and – importantly - platelets (CD41), which might stick to CD4⁺ T cells. Stringent gating for CD4⁺ T cells is crucial, since LAG-3 and CD49b not only identify Tr1 cells within the CD4⁺ T cell subset, but also enrich for IL-10-expressing cells within other cell types. Tr1 cells co-expressing LAG-3, CD49b, and IL-10 can be gated according to the strategy shown or alternatively, cells can be first gated for LAG-3⁺ CD49b⁺ followed by gating for IL-10⁺ cells.

Figure 51.

Analysis of an in vitro suppression assay testing the function of murine Tr1 cells. First, responding CD4⁺ T cells are identified by gating for physical parameters (lymphocyte and single cell gate), living cells, CD4⁺ T cells, which are positive for the CellTrace. Within the Responder population, the number of cell divisions can be tracked according to the fluorescence intensity of the CellTrace, since the fluorescence intensity is reduced by half after every division. In order to calculate the number of cells that underwent division out of the population of precursor cells, the number of cells (#) is adjusted to the number of divisions of each cell. For example, the number of cells that have divided once is divided by two (#/2), since two cells originate from one precursor cell. Based on the frequency of total precursor cells and divided precursor cells, the frequency of division can be calculated. To calculate the suppressive capacity of the experimental group (=Tr1 cells), the frequency of division in the negative control is set to 100%.

Figure 52.

Human γδ T cells found in the peripheral blood. Each population is divided based on their Vδ chain usage, primarily due to the availability of TCR Vδ1 and TCR Vδ2 mAbs. Each subset is displayed alongside a set of cell surface markers that accurately define them in the steady state. Vγ9⁻/Vδ2⁺ and Vδ2⁻ T cells seem to undergo postnatal selection in the periphery from a naïve γδ T cell pool. Vγ9⁺/Vδ2⁺ T cells are established in the perinatal period and are rapidly matured after birth, resulting in a uniform responsiveness to pAgs. Vδ1⁻Vδ2⁻ T cells express a Vδ3-8 TCR chain pairing and are rare in the peripheral blood but enriched in the tissues such as the liver. The markers that define them and if they form further subsets are unclear.

Figure 53.

Gating strategy to define human cells γδ T cells in the peripheral blood. The gating strategy used to define human γδ T cells involves manual gating of Lymphocytes > Single Cells > Live cells > CD3⁺ cells > TCRγδ⁺. The use of γδTCR vs αβTCR mAbs provides the consistent ability to accurately discriminate γδ T cells in even the most challenging samples, i.e., where γδ T cell numbers are very low or viability is poor. γδ T cell subsets are then defined based on expression of TCRγδ⁺ > Vδ1⁺, Vδ2⁺, Vδ1/2⁻. Vδ2⁺ T cells can be further sub-divided in cells that are Vγ9⁺ or Vγ9⁻ (rare in peripheral blood).

Figure 54.

Functional sub-populations of human Vγ9⁺/Vδ2⁺ T cells. After using the gating strategy to define human γδ T cells described in Figure 53, human circulating Vγ9⁺/Vδ2⁺ T cells can be further split into effector subsets based on CD27, CD28, and CD16 expression. These populations are highly variable between individuals and it is unclear how these populations are derived.

Figure 55.

Identifying human naïve and effector sub-groups of adaptive Vγ9⁻/Vδ2⁺ and Vδ2⁻ γδ T cells in the circulation. After application of the gating strategy described in Figure 53, the distribution of clonally diverse naïve human γδ T cells can be identified by the expression of CD27 and CD45RA (CD27^hi; marked in red) and clonally expanded effector γδ T cells (CD27^lo; marked in blue), see Davey et al. [531]. These naïve and effector subsets display very distinct phenotypes and can be further defined by the expression of CX₃CR1, Granzyme A/B or IL7Rα. The data shown here is an example of the expression of these markers in human Vδ1⁺ γδ T cells.

Figure 56.

Murine pLNγδ T cells. Representative gating Lymphocytes were defined in FSC-A vs SSC-A plot. Subsequently doublets were excluded by FSC-H vs FSC-W and SSC-H vs SSC-W gating followed by exclusion of dead (Zombie-Aqua positive) and non-hematopoietic cells (CD45 negative) cells. (A) Representative contour plot for direct gating of γδ T cells defined as CD3⁺ γδ TCR⁺. (B) Representative contour plots for exclusion of αβ T cells before gating γδ T cells.

Figure 57.

γδ T cells in Tcrd-H2BeGFP reporter mice. Representative gating strategy for the identification of genuine γδ T cells in pLN. Lymphocytes were defined in FSC-A vs SSC-A plot. Subsequently, doublets were excluded by FSC-H vs FSC-W and SSC-H vs SSC-W gating followed by exclusion of dead (Zombie-Aqua positive) and non-hematopoietic cells (CD45 negative) cells. Genuine γδ T cells were defined based on the H2BeGFP fluorescence in Tcrd-H2BeGFP mice and counterstaining with anti-TCRβ.

Figure 58.

Murine IL-17- versus IFN-γ-producing γδ T cells. γδ T cells from pLN of Tcrd-H2BeGFP mice were gated as in Figure 57 above. (A-B) Intracellular cytokine staining in correlation to CD44 (A) and CD27 (B) surface marker expression. (C-D) Representative analyses of γδ T cells from pLN of Tcrd-H2BeGFP mice correlate CD27, CD44, and Ly6C surface staining to expression of Vγ4 and Vγ6.

Figure 59.

Dissection of mouse ear skin. Scheme depicting dissection of ear skin for subsequent isolation of lymphocytes.

Figure 60.

Murine ear skin γδ T cells. Representative gating strategy of murine ear skin cells stained with DAPI, CD45, αβ TCR, γδ TCR (GL3) and CD3 to detect dermal γδ T cells (CD3⁺ and γδ TCR⁺) and epidermal γδ T cells (DETC, CD3^hi, and γδ TCR^hi). Backgating was done by gating on lymphocytes in FSC-A vs SSC-A plot. Subsequently, doublets were excluded by FSC-H vs FSC-W and SSC-H vs SSC-W gating followed by exclusion of dead (DAPI positive) and non-hematopoietic cells (CD45 negative) cells. Total γδ T cells were defined as γδ TCR⁺ and αβ TCR⁻. Among total γδ T cells dermal and epidermal γδ T cells were discriminated based on the expression level of CD3 and γδ TCR.

Figure 61.

Murineγδ T cell subpopulations according to TCR expression. Representative gating strategies of murine Vγ4⁺ (red) and Vγ6⁺ (green) _γδ T cells in pLN (A) as well as epidermal murine Vγ5⁺ γδ T cells (DETCs, red) and dermal murine Vγ4⁺ and Vγ6⁺ γδ T cells (blue) in ear skin (B). Backgating was done by gating first on lymphocytes in FSC-A vs SSC-A plot. Subsequently doublets were excluded by FSC-H vs FSC-W and SSC-H vs SSC-W gating followed by exclusion of dead (DAPI positive or Zombie-Aqua+) cells. Among TCRß⁻ cells total γδ TCR ⁺CD3⁺ γδ T cells were gated and finally separated into different γδ subsets by staining with anti-Vγ4 as well as 17D1 followed by conjugated anti-IgM to detect Vγ6⁺/Vγ5⁺ γδ T cells.

Figure 62.

Gating on human blood NKT cells. (A) Lymphocytes are distinguished amongst PBMCs based on their relative FSC-A and SSC-A intensities. Single cells are then isolated by their relationship between FSC-H versus FSC-A, and SSC-W versus SSC-A. To remove any non-specific or TCR-independent CD1d-tetramer staining, dead cells are removed from analysis based on their uptake of LIVE/DEAD^™ Fixable Near-IR viability dye. Monocytes and B cells are also excluded based on their CD14 and CD19 expression respectively. (B) The frequency of circulating Type I NKT cells, as determined by co-staining for CD3ε and α-GalCer (PBS-44)-loaded CD1d-tetramer (left) in relation to a vehicle control CD1d-tetramer (right). (C) The frequency of iNKT cells was assessed by co-staining with 6B11 and anti-Vβ11. (D) Co-staining with anti-Vα24 and anti-Vβ11, which non-exclusively enriches for iNKT cells.

Figure 63.

Basic gating strategy for murine thymic iNKT cells. (A) Basic gating strategy for non-enriched murine thymic iNKT cells. (B) Basic gating strategy for thymic iNKT cells following magnetic-bead enrichment. Sample was additionally stained with Zombie Aqua viability dye and Abs against lineage markers. Numbers adjacent to gates indicate frequency of parent population.

Figure 64.

Murine thymic iNKT cell populations. (A) Magnetic-bead enriched iNKT cells from C57BL/6 mice were additionally stained with Abs against CD44, NK1.1, and CD24. The upstream gating strategy is shown in NKT Figure 63. (B) Magnetic-bead enriched iNKT cells from C57BL/6 mice were additionally stained intracellulary with Abs against PLZF, T-bet and RORγt. The upstream gating strategy is shown in NKT Figure 63. (C) Magnetic-bead enriched iNKT cells from C57BL/6 mice were additionally stained with Abs against CD122 and CD4. Numbers adjacent to gates indicate frequency of parent population. The upstream gating strategy is shown in NKT Figure 63. Boldface S0, S1, S0/1, S2, S3 adjacent to gates indicate developmental stages. Boldface p, 1, 2, and 17 adjacent to gates indicate NKTp, NKT1, NKT2, and NKT17 subsets, respectively.

Figure 65.

Murine peripheral iNKT cell populations. (A) Magnetic-bead enriched iNKT cells from C57BL/6 mice were additionally stained intracellulary with Abs against PLZF, T-bet, and RORγt. The upstream gating strategy is analogous to that shown NKT Figure 63. (B) Magnetic-bead enriched iNKT cells from C57BL/6 mice were additionally stained with Abs against CXCR3 and CD4. Numbers adjacent to gates indicate frequency of parent population. The upstream gating strategy is analogous to that shown NKT Figure 63. Boldface 1, 2, and 17 adjacent to gates indicate NKT1, NKT2, and NKT17 subsets, respectively.

Figure 66.

Flow cytometry detection of human peripheral blood MAIT cells. (A) Gating strategy. Lymphocytes are distinguished from PBMC preparations based on their FSC-A and SSC-A. Single cells are identified by their linear relationship between FSC-H versus FSC-A, enabling doublets to be excluded. To remove any non-specific or TCR-independent MR1-5-OP-RU tetramer staining, dead cells are excluded with the use of a viability dye (7-AAD), and monocytes and B cells are excluded based on the expression of CD14 and CD19 respectively. MAIT cell frequencies can be presented as a percentage of CD19⁻ lymphocytes, or as a percentage of T cells. (B) MAIT cells can be divided into subsets based on expression of CD4 and CD8 co-receptors and, relative to non-MAIT T cells, are typically enriched for CD8⁺ and CD4⁻CD8⁻ double negative (DN) subsets, with only minor populations of CD4⁺ or CD4⁺CD8⁺ double-positive (DP) cells.

Figure 67.

MR1-tetramer staining controls. Representative plots depict MR1-5-OP-RU tetramer staining among CD19⁻ lymphocytes from human PBMCs in comparison to a MR1-Ac-6-FP tetramer control and a fluorescence minus one (FMO) control. Refer to Figure 66A for gating strategy.

Figure 68.

Identifying human peripheral blood MAIT cells using surrogate markers. Gating strategy utilized identical to Figure 66A. Plots depict the identification of human MAIT cells among CD19⁻, CD3⁺ lymphocytes via their expression of TRAV1-2 and CD161 and how this relates to MR1-5-OP-RU tetramer staining from a normal donor (top) and an abnormal donor (bottom).

Figure 69.

Human MAIT cell enrichment. Gating strategy utilized identical to Figure 66A. Top panel depict plots with the percentages of MAIT cells among CD19⁻, CD3⁺ lymphocytes from PBMCs either prior to (first panel) or following MR1-5-OP-RU tetramer enrichment (second panel). Bottom panel depict plots with the percentages of MAIT cells among among CD19⁻, CD3⁺ thymocytes either prior to (first panel) or following TRAV1-2 Ab enrichment (second panel). Further phenotypic analysis of MAIT cells reveal heterogeneous subpopulations based on CD4, CD8, CD27 and CD161 (third and fourth panel).

Figure 70.

Basic gating strategy for murine thymic MAIT cells. (A) Basic gating strategy for non-enriched murine thymic MAIT cells. (B) Basic gating strategy for thymic MAIT cells following magnetic-bead enrichment. Numbers adjacent to gates indicate frequency of parent population. Stainings with control tetramer MR1-6-FP-APC are displayed as well.

Figure 71.

Murine thymic MAIT cell populations. (A) Magnetic-bead enriched MAIT cells from C57BL/6 mice were additionally stained with Abs against CD44 and CD24. Upstream gating was performed as shown in MAIT Figure 70B. (B) Magnetic-bead enriched iNKT cells from C57BL/6 mice were additionally stained intracellulary with Abs against PLZF, T-bet, and RORγt. Numbers adjacent to gates indicate frequency of parent population. Upstream gating was performed as shown in MAIT Figure 70B. Boldface S1, S2, S3 adjacent to gates indicate developmental stages. Boldface 1 and 17 adjacent to gates indicate MAIT1 and MAIT17 subsets, respectively.

Figure 72.

Principal of Ag-specific stimulation assays. (A) Peripheral blood mononuclear cells (PBMC) or single cell suspensions from tissues are incubated with the Ag of interest or without Ag as negative control to determine background levels of the assay. If whole proteins are used for stimulation, the Ag has to be taken up by the autologous Antigen-presenting cells of the cell source, processed and presented on MHC molecules. Peptides of a certain length can bind externally to MHC molecules. (B) The Ag-specific T-cells will start to secrete cytokines and/ or cytotoxic molecules (5-12 hours), express activation markers (5-16 hours) and at later time points start to proliferate (3-5 days). For the different functions of T-cells, such as cytokine release, cytotoxicity, expression of activation markers and proliferation single-cell flow-cytometric assays are available and for most technologies also selection markers on the cell surface are available allowing additional isolation of the specific cells.

Figure 73.

Enrichment of human Ag-specific T-cells increases sensitivity for the detection of rare cells. (A) CD154 and TNF-α expression was analyzed on human CD4⁺ T-cells without addition of an Ag and following stimulation with the neo-Ag keyhole limpet hemocyanin (KLH). Cells are gated on CD4⁺ T-cells and percentage and absolute numbers of CD154⁺ cells after acquiring 5x10e5 PBMCs (upper plots) or obtained from 1×10e8 PBMCs after enrichment of CD154⁺ cells (lower plots). (B) Phenotypic characterization of the enriched CD154⁺CD4⁺ T-cells to discriminated between CD45RO⁺ memory cells and CD45RO-CCR7⁺ naive T-cells, following stimulation with a peptide pool of C. albicans MP65 as recall Ag or KLH as neoantigen. (C) Parallel detection of Ag-specific Tconvs (CD154⁺) and Tregs (CD137+) following stimulation with birch pollen lysate and magnetic enrichment for CD154⁺ and CD137⁺ cells from 2x10e7 stimulated PBMC. Upper plots: cells are gated on CD4⁺ T-cells and absolute cell counts of CD154⁺ and CD137⁺ cells with and without stimulation are indicated. Lower plots: Overlayed flow-cytometric analysis of birch-specific CD154⁺ and CD137⁺ cells. Numbers indicate percentages among CD137+CD154-CD4⁺ T-cells and absolute numbers of CD137+CD25+FOXP3⁺ Treg. (D) To describe the precision of flow cytometry data, the coefficient of variance (CV) can be calculated from the variance and the standard deviation (SD). For rare cell analysis, the approximations SD = √r and CV [%] = 100/√r can be used, where r is the number of positive events (53). From CV [%] = 100/√r follows r = [100/CV]². Using this approximation, the number of total required events is illustrated depending on the frequency of target cells for different CVs.

Figure 74.

Schematic overview of the combinatorial staircase encoding 75 unique pMHC complexes with 75 unique dual fluorochrome combinations, allowing the detection of 75 different T cell responses in parallel.

Figure 75.

Representative gating strategy used to identify human SARS-CoV-2-specific CD8 T cell responses. Data used in this figure is from [776], which is licensed under CC BY 4.0. Step 1: Gating strategy used to identify single and live CD8⁺ cells. Step 2: Gating strategy used to identify pMHC⁺ CD8⁺ cells required for the Boolean gating. Step 3: Representative overview of all 75 pMHC dual color code combinations after Boolean gating. Antigen-specific CD8 T cells (double-positive pMHC⁺ CD8⁺ cells) are shown in green and bulk CD8 T cells (pMHC⁻ CD8⁺ cells) in grey. Percentage of Ag-specific CD8 T cells of total CD8 T cells is indicated if applicable.

Figure 76.

Production and usage of pMHC multimers (Originally published in The Journal of Immunology [790]). (A) pMHC monomer generation through folding and functionalization; (B) Epitope exchange technologies enable high-throughput generation of pMHC complexes for different Ag-specificities; (C) Different usage of nonreversible, reversible, and dye-conjugated reversible pMHC multimers; (D) The steric structure of multimers in a three dimensional space forms tetrahedron like structures limiting the binding layer to three pMHC molecules at once.

Figure 77.

Versatile analysis of a murine H2-k^b/SIINFEKL-specific T cell population. Double staining with nonreversible pMHC multimerized with streptavidin-PE (“Tetramer”) and reversible pMHC multimerized with streptactin-APC (“Streptamer”) before (red) and after (blue) addition of D-biotin; dissociation of Alexa488-conjugated monomeric SIINFEKL-pMHC molecules over time after addition of D-biotin (outside red box); pregating on lymphocytes, singlets, living CD19⁻, CD8⁺ CD45.2 and CD90.2-congenic coded T cells; gate of SIINFEKL-MHC-A488 additionally pregated on streptavidin-PE⁺ T cells.

Figure 78.

Gating strategy to identify Ag specific human CD4⁺ T cells. Human lymphocytes were gated based on physical parameters (FSC-SSC), then doublets were excluded using FSC-A and FSC-A parameters. Dead cells were excluded using viability stain 780. T cells were identified as CD3⁺ and among these CD4⁺ T cells were selected. Antigen-specific CD4⁺T cells were identified as cells expressing CD154; CD4⁺CD154⁺ T cells were then evaluated for IFN-γ, TNF-α, and IL-2 expression. Representative plots of PBMCs unstimulated (medium, negative control) stimulated with SEB (positive control) or Spike peptide pools, from a COVID-19 recovered subject evaluated at 7 days from SARS-CoV-2 vaccination, are shown.

Figure 79.

Use of congenic markers in adoptive transfer experiments in mice. (A) Gating strategy for the identification of CTV-labeled, Thy1.1⁺ OT-II cells by flow cytometry. Wildtype C57BL/6 mice were injected i.v. with 5x10⁵ naïve OT-II TCRtg CD4⁺ T cells. In this setting, such high numbers of naïve OT-II TCRtg CD4⁺ T cells (in contrast to classical adoptive transfer experiments with typically less than 1-5 × 10⁴ naïve OT-II cells per mouse) are required for recovering enough events for proper cell division analyses. One day later, recipient mice were immunized with 5 μg OVA and 2 μg LPS in the hock. Three and a half days later, draining popliteal lymph nodes were dissected, single-cell suspensions were prepared and the cell surface was stained with appropriate combinations of fluorescently labeled monoclonal Abs. Thereafter, samples were fixed and stained with the Foxp3 transcription factor staining set and samples were then acquired on a BD LSRFortessa. Single lymphocytes were first gated based on FSC/SSC characteristics. CD4⁺ T cells were further gated to exclude dead cells and B cells, and finally with the congenic marker Thy1.1 and CTV to differentiate transferred OT-II cells from endogenous (Thy1.1⁻) T cells of the recipient. The CTV profile of the identified OT-II cells is shown in the histogram. To reduce the overall size of the acquisition data file, 50,000 lymphocytes were acquired first and then only TCRtg Thy1.1⁺ CD4⁺ T cells were appended to the file. (B) Competitive co-transfer of CD45.1⁺ and CD45.1/2 double-positive CD4⁺ T cells into wild-type C57BL/6 recipients. Equal numbers of CD45.1⁺ and CD45.1/2 double-positive naïve OT-II TCRtg CD4⁺ T cells of two different genotypes (1×10⁴ cells each) were injected i.v. into wild-type C57BL/6 recipient mice. It is recommended to check for the correct ratio of transferred cells by flow cytometry, e.g., by analyzing a left-over aliquot of the injected cell suspension on a flow cytometer. One day after adoptive transfer, recipient mice were immunized i.p. with 100μg NP-OVA in alum. Seven days later, spleens were dissected, single-cell suspensions were prepared and stained with appropriate combinations of fluorescently labeled mAbs. The samples were acquired on a BD LSRFortessa and gated on live CD4⁺ T-cells. Staining for CD45.2 versus CD45.1 allows distinguishing the two transferred TCRtg cell populations from the CD45.2⁺CD45.1⁻ host T cells.

Figure 80.

Experimental procedure for a 2-color “in vivo Multiplexed Antigen-Specific Cytotoxicity Assay” (iMASCA) in the mouse with nine target cell populations. Seven days after LCMV infection of CD45.2⁺ mice (1), target cells are isolated from the spleen of CD45.1⁺ donor mice and labeled with high or low concentrations of CFSE or Cell Tracker Violet (CTV) or combinations of both (2). Subsequently, nine differentially labeled target cell populations are loaded with distinct concentrations of the respective peptide, washed and mixed prior to injection into LCMV-infected recipient mice (3). Three hours later, recipient splenocytes are analyzed by flow cytometry (4). Relative target cell frequencies are determined, and peptide-specific lysis is calculated for each target cell population (5).

Figure 81.

Three-color iMASCA in the mouse with 26 target cell populations (For experimental details see protocol). Responder C57BL6/J mice were infected with LCMV and 7 days later cytotoxic CD8⁺ T cell activity was quantified in uiuo against six different concentrations of 4 different epitopes each to calculate the respective KC₅₀. (A) Table summarizing the 26 targets used for cytotoxicity assay. (B) Flow cytometric gating strategy and deconvolution to identify the different CTL targets recovered from spleen of responder mice 3h after adoptive transfer. Numbers on the dot plot indicate target populations as listed in (A). (C) Quantification of in vivo CTL activity for the indicated peptide complexed to the respective MHC-I molecule. KC₅₀ was calculated by using regression analysis in the linear range. Data represents mean ± SD (n = 10 mice). CFSE, Carboxyfluorescein succinimidyl ester; CTV, CellTrace violet; CPD, Cell proliferation dye eFluor 670.

Figure 82.

pS6ribo evaluation on PBMNC following TCR stimulation. Lymphocytes were gated based on physical parameters, then T cells were identified as CD3⁺CD14−. T helper cells were gated as CD4⁺. pS6ribo was evaluated either on unstimulated cells or upon anti-CD3 and anti-CD28 stimulation for 10, 20 or 30 minutes. As a positive control of the procedure PBMNC were stimulated with PMA and Ionomycin for 20 minutes. In this experiment anti-CD3 and anti-CD28 capping was performed by the addition of PE-conjugated anti-isotype mAbs (anti-mouse IgG1 and anti-mouse IgG2a). Thus, cells with an efficient crosslink can be detected as PE-positive.

Figure 83.

Cytokine secretion assay performed on PBMNC for the detection of IFN-γ and IL-17 producing human T helper cells. Cells were stimulated with PMA/Ionomycin. Lymphocytes were gated based on physical parameters, then doublets removed using FSC Height and Area (FSC-H and FSC-A, respectively). Dead cells were excluded as PerCP-positive and PE-positive following Propidium Iodide addition. T helper cells were then identified as CD3 positive, CD4 positive, CD8 negative. IFN-γ, and IL-17 expression were subsequently analyzed.

Figure 84.

Flow Cytometer setup for multiplex-beads based array. A. FSC-SSC plot for the identification of beads based on their physical parameters. Histogram plots of APC-Cy7 (B) and ACP (C) channels showing that PMT voltages are optimally set to the highest visible MFI. By this way, it is possible to properly distinguish the different types of beads used. Panel (D) represents histogram plot of PE channel (the fluorochrome bound to the secondary Ab) measured on unstained beads.

Figure 85.

Quantification of human soluble cytokines with cytometric bead array (CBA). (A) Representative flow cytometry analysis of an experimental setting for evaluation of four different cytokines from culture supernatants of polyclonally stimulated human CD4⁺ T cells. The FSC/SSC plot allows identification of the total beads population; the APC-APC-Cy7 plot allows the identification of each bead corresponding to a specific analyte. Single beads are clustered based on the conjugation with different quantities of two different fluorochromes. (B) Representative flow cytometric plots of an experiment for evaluation of six different cytokines from culture supernatants of polyclonally stimulated human CD4⁺ T cells. The FSC/SSC plot allows identification of the total bead population; the APC-APCCy7 plot allows identification of each bead corresponding to a specific analyte. Single beads are clustered based on their fluorescence intensity; in this case each bead population is conjugated with the same quantity of the two different fluorochromes used for its identification. (C and D) Representative flow cytometric plots of a standard curve from an experiment for the evaluation of six different cytokines, as reported in panel B: the “zero” tube in panel C (0 pg/ml) and the “top” tube in panel D (2500 pg/ml). Beads clusters are identified in APC (or APC-Cy7) channel and the different quantities of each analyte are defined by PE MFI.

Figure 86.

Sorting strategies for human and murine Treg. (A) Gating strategy of CD4 pre-magnetically enriched human PBMC CD4⁺CD25⁺CD127^loFOXP3⁺ cells and further sub gating into fractions I (Naïve Tregs), II (effector Tregs) and III (Non-Tregs/Tfr). In this example,CD4 APC-Cy7 was used to avoid clash with CXCR5 BV421 but we would recommend CD4 V500 and IR live/dead when this is not the case. (B) Gating example of murine CD3⁺CD4⁺B220⁻CD25⁺Foxp3-GFP⁺ Treg cells from lymph nodes. (C) Alternative gating strategy of murine CD3⁺CD4⁺B220⁻CD25⁺Foxp3-GFP Treg cells from lymph nodes if Foxp3 reporter is available.

Figure 87.

Flow chart illustrating steps of cell subsets isolation from human PBMCs. Flow chart illustrating steps of cell subsets isolation. A portion of PBMCs is used for enrichment of CD8⁺ T cells, another portion is used for enrichment of Tregs. The enriched CD8⁺ T cells fraction (untouched) is used for isolation of Naïve (negative fraction) and EM+EMRA (positive fraction) CD8⁺ T cells, with Naïve CD8⁺ T cell isolation kit.

Figure 88.

Gating strategy of cell subsets from human PBMCs. (A) Representative flow cytometry analysis of the gating strategy applied for the identification of CD8⁺ and CD4⁺ T cells from human PBMCs. Briefly, lymphocytes were first gated by the physical parameter Forward and Side scatter area (FSC-A and SSC-A) and doublets and debris were eliminated by plotting the width against the area of FSC and SSC parameters (FSC-W and SSC-W). Dead cells were excluded using Viability Dye (VD), and gating into live cells we identified CD8⁺ and CD4⁺ T cells. B) Representative FC analysis of pre- and post-enrichment of naïve (N) or effector memory plus effector memory RA⁺ (EM+EMRA) CD8⁺ T cells, gated on (dextramer⁺)-CD8⁺ T cells (upper) or (dextramer⁻)-CD8⁺ T cells (lower). (C) Representative FC analysis of pre- and post-enrichment of Treg cells with magnetic beads.

Figure 89.

Human suppression assay of Ag-specific T cells. Representative histograms of purified CFSE-stained CD8⁺ T (N) cells (A) or effector memory plus effector memory RA⁺ (EM+EMRA) CD8⁺ T cells (B), from human PBMCs, stimulated with autologous APCs (aAPCs) pulsed (or not) with 20 μg/ml of peptides (aAPCs + peptides) and co-cultured (or not) with Treg cells at a CD8:Treg ratio of 10:1 for 7 days. Histograms indicate the percentage of cell proliferation (as detected by CFSE dilution) and differentiation (as detected by CD45RA downregulation) in (dextramer⁺)-CD8⁺ T cells. C) Mean values of four independent suppression assays at different CD8:Treg ratios. %Treg suppression = (MFI CFSE-stained dextramer⁺ CD8⁺ T cells with Treg cells – MFI CFSE-stained dextramer⁺ CD8⁺ T cells without Treg cells) / (MFI CFSE-stained dextramer⁺ CD8⁺ T cells unstimulated − MFI CFSE-stained dextramer⁺ CD8⁺ T cells without Treg cells) x 100. *p < 0.05 one-way ANOVA with Tukey’s multiple comparison test.

Figure 90.

Human killing assay of Treg cells by Ag-specific CD8⁺ T effector cells. Representative FC analysis of dead Tregs (isolated from human PBMCs), as detected by the percentage of VD+ cells in Tregs, alone (0:1) or co-cultured with purified CD8⁺ Tem + EMRA cells (10:1) and aAPCs stimulated or not with peptides in the presence or absence of iNKG2D. aAPCs: autologous APCs.

Figure 91.

Analysis of human polyclonal suppression assay. (A) Gating strategy for identification of responder cells in human polyclonal suppression assay. (B) Proliferation histograms of human Tconv cells cultured with various ratios of Treg cells, irradiated CD4⁻ splenocytes, and anti-CD3 for three days. C) Summary data comparing % divided and division index of Tconv cells performed in duplicate. Division index is the average number of divisions by each cell as calculated in FlowJo Software.

Figure 92.

Analysis of proliferation in human suppression assays of Ag-specific T cells. Comparison between different methods to analyze proliferation in suppression assay of Ag-specific T cells, from human PBMCs, (as described in Figure 4); mean values of four independent experiments are reported. A) Left panel shows % divided of N or EM+EMRA (dextramer⁺)-CD8⁺ T cells, at different CD8:Treg ratio. Right panel shows %Treg suppression calculated using % divided T cells (see formula reported in Figure 4C). B) Left panel shows MFI of CFSE of N or EM+EMRA (dextramer⁺)-CD8⁺ T cells, at different CD8:Treg ratio. Right panel shows %Treg suppression calculated using MFI of CFSE (see formula reported in Figure 4C). *p < 0.05 one-way ANOVA with Tukey’s multiple comparison test.

Figure 93.

T_DS assay for analysis of human blood CD8 T cells. (A) Gating strategy. Example of viable single CD8 T cell gating in 6 steps: 1) Single cells. Single cells having 2n≤ DNA content ≤4n were selected on the DNA-Area (A) versus (vs) DNA-Width (W) plot; 2) Time. Stable acquisition over time (seconds) was monitored on the time vs DNA-A plot and any events collected in case of pressure fluctuations were excluded; 3) Live cells, no “dump”. CD4⁺, CD14⁺, CD19⁺ cells were excluded on the “dump” channel, and live cells selected using the L/D eF780 dye; 4) “relaxed” FSC/SSC gate. A “relaxed” gate was used on the FSC-A vs SSC-A plot, to include highly activated and cycling lymphocytes [877, 878]; 5) CD8 T cells. CD8 T cells were gated on the CD3 vs CD8 plot. 6) No “shadow” doublets. A few remaining doublets composed by one cell sitting on top of another (so called “shadow” doublets) were excluded as Ki-67^int/⁻ events having > 2n DNA content [878]. Numbers indicate cell percentages in the corresponding gate. This gating strategy was used as a base for CD8 T cell analysis. EBV-specific CD8 T cells in Infectious Mononucleosis (B) and Asymptomatic EBV-Carrier (C). Example of CD8/ EBV-tetr plot showing EBV-tetr⁺ cell gate (top left) and tetr FMO control (bottom left). Cell cycle phases of EBV-tetr⁺ cells were defined on DNA-A vs Ki-67 plot as follows (top center): cells in G₀ were identified as DNA 2n/ Ki67⁻ (bottom left quadrant); cells in G₁ as DNA 2n/ Ki-67⁺ (upper left quadrant); cells in S-G₂/M as DNA>2n/ Ki-67⁺ (T_DS cells, top right quadrant). Ki-67 FMO controls are shown (bottom center). Note that the “No shadow doublet” gate (Step 6 in A) cannot be applied to Ki-67 FMO samples. FSC-A/-SSC-A plot (top right), showing T_DS cells (in red) overlaid on total EBV-tetr⁺ cells (in grey). Gating strategy and mAb panel indicated in A. Unpublished data in relation to [878]. Naïve/memory CD8 T cells in COVID-19 patients (D) and Healthy donors (E). The following naïve/memory subsets of CD8 T cells were identified in the CCR7 vs CD45RA plot (left): CD45RA⁺ CCR7⁺ naïve, CD45RA⁻ CCR7⁺ (CM), CD45RA⁻ CCR7⁻ (EM), and CD45RA⁺ CCR7⁻ TEMRA. Cell cycle phases of each subset were analyzed as in B and C (right). Gating strategy and mAb panel as in A, except for using CD4 BV711 only in “dump” channel (step 3), and CD3 FITC and CD8 PerCp-Cy5.5 (step 5). Unpublished data in relation to [880].

Figure 94.

T_DS assay for analysis of mouse blood CD8 T cells. BALB/c mice were vaccinated against HIV-1 gag used as a model Ag by prime with ChAd3-gag and boost with MVA-gag, as described [877]. T_DS assay was performed on blood collected at day 3 and day 44 after boost. Blood from 3 mice was pooled to obtain enough cells for analysis. (A) Gating strategy. Example of gating of viable single CD8 T cells in 6 steps: 1) Single cells. Single cells having 2n≤ DNA content ≤4n were selected on the DNA-Area (A) versus (vs) DNA-Width (W) plot; 2) Time. Stable acquisition over time (seconds) was monitored on the time vs DNA-A plot and any events collected in case of pressure fluctuations were excluded; 3) Live cells. Live cells were selected on the FSC-A vs L/D eF780 plot; 4) “relaxed” FSC/SSC gate. A “relaxed” gate was used on the FSC-A vs SSC-A plot, to include highly activated and cycling lymphocytes [877]; 5) CD8 T cells. CD8 T cells were gated on the CD3 vs CD8 plot. 6) No “shadow” doublets. A few remaining doublets composed by one cell sitting on top of another (so called “shadow” doublets) were excluded as Ki-67^int/⁻ events having > 2n DNA content [878]. Numbers indicate cell percentages in the corresponding gate. This gating strategy was used as a base for CD8 T cell analysis. (B) gag-specific CD8 T cell frequency at day 3 and day 44 post-boost. Example of gag-tetr/ gag-pent plots, showing “gag-specific” and “not gag-specific” cell gates in untreated (left) and vaccinated (right) mice at day 3 (top) and day 44 (bottom) post-boost, as indicated. (C) Cell cycle of gag-specific and not gag-specific CD8 T cells from vaccinated mice at day 3 and day 44 post-boost. Cell cycle phases of “gag-specific” (left) and “not gag-specific” (right) cells from vaccinated mice at day 3 (top) and day 44 (bottom) post-boost were defined on DNA-A vs Ki-67 plot as follows: cells in G₀ were identified as DNA 2n/ Ki-67⁻ (bottom left quadrant); cells in G₁ as DNA 2n/ Ki-67⁺ (upper left quadrant); cells in S-G₂/M as DNA>2n/ Ki-67⁺ (T_DS cells, top right quadrant). Ki-67 FMO controls are shown. Note that the “No shadow doublet” gate (Step 6 in A) cannot be applied to Ki-67 FMO samples. Unpublished data in relation to [877].

Figure 95.

Gating strategy for the identification of human B cells. (A-E) Gating example for peripheral blood: (A) Lymphocytes are identified by their light scattering properties. (B) Exclusion of doublets. (C) Cells positive for CD3 and CD14 and DAPI stained dead cells are excluded. (D) B cells are identified by their expression of CD19 and CD20 including CD20^low plasmablasts. (E) B cell subsets, CD19⁺CD20⁺ gated, are discriminated by CD27 and IgD: CD27⁻IgD⁺ naïve B cells, CD27⁺IgD⁺ pre-switch memory B cells, CD27⁺IgD⁻ switched memory B cells, CD27⁻IgD⁻ B cells containing switched memory B cells. (F) B cell subsets, gated as CD19⁺CD20+/− can also be discriminated by CD27 and CD20 in peripheral blood, spleen, tonsil and bone marrow: conventional naïve B cells are CD27⁻ CD20⁺ (containing CD27⁻ memory B cells) memory B cells CD27⁺ CD20⁺ and plasmablasts CD27⁺⁺ CD20^low. Cell subsets defined by CD27 and CD20 expression were color-coded and depicted in a CD27 vs CD38 plot (pink: CD27⁻CD20⁺ B cells, dark blue: CD27⁺CD20⁺ B cells, green (only in tonsil): CD27^intCD20^high, turquois (only in bone marrow): CD27⁻CD20⁻).

Figure 96.

Ig isotype expression of human B cell subsets in different tissues. B cell subsets discriminated by CD27 and CD20 in peripheral blood, spleen, tonsil and bone marrow: conventional naive B cells are CD27⁻ CD20⁺ (containing CD27⁻ memory B cells) memory B cells CD27⁺ CD20⁺ and plasmablasts CD27⁺⁺ and CD20^low. Cell subsets defined by CD27 and CD20 expression were color-coded and depicted in a IgD vs IgM and IgA vs IgG plot to show Ig surface expression of each subset (pink: CD27⁻CD20⁺ B cells, dark blue: CD27⁺CD20⁺ B cells, green (only in tonsil): CD27^intCD20^high, turquois (only in bone marrow): CD27⁻CD20⁻).

Figure 97.

Identification of TT specific human memory B cells (CD27+CD20+) and plasmablasts (CD27++CD20low), gated as in section 1 - Human B cells and their subsets, before (day 0) and after TT vaccination (day 7 and day 14) in peripheral blood. Staining and block with unlabelled TT are shown.

Figure 98.

Determining optimal concentrations of multimerized Ag-tetramers for staining in HEK 293T cells. (A) Titration of CCP2-SA-APC, CArgP2-SA-APC and of ‘empty’ streptavidin APC tetramers on ACPA-expressing HEK 293T (HEK-ACPA^™) and wild-type HEK 293T (HEK^WT) cells. Gates are based on unstained controls. The red square marks the optimal concentration of CCP2-SA-APC. (B) Staining of HEK-ACPA^™ and HEK^WT cells with combinatorial CCP2 and CArgP2 tetramers. Gates are based on unstained controls.

Figure 99.

Gating strategy to identify ACPA-expressing human B cells in peripheral blood of patients with rheumatoid arthritis. (A) Setting up a “B cell store gate” which will be used during sample measurement to store data in order to obtain a manageable size of data to be analyzed. Freshly isolated, patient-derived PBMC are used. (B) Gating strategy to identify ACPA-expressing human B cell subsets in peripheral blood. Single live lymphocytes are identified based on FSC/SSC followed by identification of CD3/CD14/DAPI⁻CD19⁺ B cells (first row). Subsequently, ACPA-expressing B cells are identified within the CD19⁺ lymphocyte population as cells staining positive for both CCP2-SA tetramers (BV605- and APC-labeled). The double-positive fraction is then evaluated for reactivity with the arginine controlvariant of the peptide (CArgP2-EA-PE). Using the CCP2-SA negative, total B cell population as reference, gates are placed for differential expression of CD20 and CD27 by CD19⁺ B cells. These reference gates are copied to allow for the phenotypic assessment of the Ag-specific, CCP2-SA-BV605/CCP2-SA-APC^+/+CArgP2-EA-PE⁻ B cell population. (C) Back-gating of ACPA-expressing B cells as additional measure of control to verify cell size and granularity within the large pool of PBMC-derived B cells.

Figure 100.

Identification of B cells expressing different Immunoglobulin heavy chain isotypes in a human PBMC sample (healthy individual age 47, male). (A) Lymphocytes were identified based on their FSC and SSC, Doublet exclusion was performed on FSC-H vs FSC-A, and B cells were gated as CD19⁺ and zombie yellow (viability dye) negative. (B) Non-switched B cells (IgD⁺) and class-switched (IgM-IgD−) were gated. (C) Within the IgM-IgD− population, IgA⁺ B cells, IgA2⁺ and IgA⁻ cells can be distinguished. IgA1⁺ B cells were defined as IgA⁺IgA2⁻. (D and E) IgA− B cells were further differentiated based on expression of IgG1, IgG2 (D), IgG3 and IgG4 (E).

Figure 101.

Fluorescence minus one (FMO) controls for IgG subclasses in B cells from a human PBMC sample from (Figure100C). (A) FMO for IgG1-Dylight-405. (B) FMO for IgG2-PE-Cy5.5. (C) FMO for IgG4-APC. (D) FMO for IgG3-PC7.

Figure 102.

Identification of regulatory B cell subsets from human CpG-stimulated PBMC. PBMC from healthy female adult subject cultured for 72 h with media alone or media containing 1μM CpG-ODN 2006. Before staining, cells stimulated for 5 h with 25 ng/ml phorbol 12-myristate 13-acetate, PMA and 1 μg/ml iono and for the last 2h with 10 μg/ml BFA. Cells harvested and surface and intracellular Ab stainings performed. Total viable B cells gated from lymphocytes after doublet discrimination (A). Breg subsets gated from viable single CD19⁺ B cells (B-E). IL-10⁺ B cells gated from (B) CD19⁺ CD24^high CD38^high B cells, (C) B10/pro-B10 cells (CD19⁺ CD24^high CD27⁺), (D) suppressive plasmablasts (CD19⁺ CD27^int CD38⁺), and (E) CD19⁺ CD73⁻CD25⁺CD71⁺ B cells. Breg subsets gated from IL-10⁺ CD19⁺ B cells based on surface markers showing enrichment of IL-10⁺ B cells (F).

Figure 103.

IL-10 staining and control stainings of human PBMC. PBMC cultured for 72 h with media alone or media containing 1μM CpG-ODN 2006. The last 5 h before staining, PBMC additionally stimulated with 25 ng/ml phorbol 12-myristate 13-acetate, PMA and 1 μg/ml iono and for the last 2h with 10 μg/ml BFA (A, C) or medium control (B). Viable B cells were gated as shown in Figure 102A. IL-10⁺ B cells were gated from total viable CD19⁺ B cells. Anti-IL-10 Ab staining is shown for B cells after stimulation with PMA, iono and BFA (A) or medium control (B). Isotype control staining is shown in (C).

Figure 104.

Discrimination of murine B cell progenitors in BM. Single cell suspensions from BM were stained for B220, CD43, IgM and IgD. (A) Left plot: Gating strategy to exclude debris. Middle plot: Gating strategy to exclude doublets. Right plot: Pre-B cells (gate I), pro-B cells (gate II) and pre- pro-B cells (gate III) are identified by their distinct B220/CD43 phenotypes. (B) Cells were gated through the gates I, II or III as indicated. Exclusion of IgD^pos and IgM^pos cells eliminates contaminating immature and mature B cells.

Figure 105.

Discrimination of murine immature and mature B cells in BM. Single cell suspensions from BM were stained for CD19, B220, IgM and IgD, and doublets and debris were excluded by gating (see Figure 104). (A) B220^high/CD19^neg cells (gate I) include pre-pro B cells, while all other B cell subtypes (except plasma cells) are included in the B220 ^high/CD19^pos population (gate II). (B) Cells were gated through gate II. Immature (gate III) and mature B cells (gate IV) were identified according to their IgM/IgD phenotypes. Gate V includes a mixture of pre- and pro B cells.

Figure 106.

Analysis of murine follicular and MZ B cells. Single cell suspensions from spleen were stained for B220, CD21, CD23, IgM and IgD. (A) Gating strategy to exclude doublets and debris. (B) B cells are gated according to B220 expression and follicular and MZ B cells were further discriminated by their CD21 ^intmed/CD23^high and CD21^high/CD23^low/neg phenotype, respectively. (C) Gated follicular and MZ B cells exhibit distinct IgD/IgM expression characteristics.

Figure 107.

Analysis of murine B-1 cells. Single cell suspensions from the peritoneal cavity were stained for CD19, CD5, CD23 and IgM. (A) Gating strategy to exclude doublets and debris. (B) B cells were identified by CD19. (C) IgM^pos B-1a and B-1b cells are distinguished according to CD5 expression, as indicated.

Figure 108.

Analysis of murine Bregs. Following 5 hours stimulation with PMA/ionomycin/LPS, single cell suspensions from the spleen were stained for B220, CD1d, CD5, and IgM. Cells were then fixed and stained for cytoplasmic IL-10 expression. Doublets and debris were excluded from the analysis as described above (upper plots). B220⁺B cells were further analyzed by gating on CD1d^high/CD5^intermed B-10 cells (upper row, left plot), CD1d^neg/CD5^neg B-2 cells (middle row, left plot) and CD1d^neg/CD5^pos B-1 cells (lower row, left plot). Intracellular IL-10 expression of B10 cells, B-2 cells and B-1 cells are shown, respectively (right plots).

Figure 109.

Representative gating strategy and analysis of human peripheral blood PB/PC in a patient with active systemic lupus erythematosus (SLE). Patients with flaring SLE show increased numbers and frequencies of peripheral blood PB/PC [942, 1088]. We thus chose an SLE blood sample for illustration, containing approx. 15% PB/PC among total B cells. Note that in steady-state, i. e. in the absence of in intentional immune activation or symptomatic disease, PB/PC are found at frequencies of commonly approx. 1% among total B cells. (A) Gating strategy. Data were analyzed for changes of scatter or fluorescence parameters over the time of data acquisition, and optionally gated to remove parts of the acquisition that show irregular or discontinuous cytometric patterns. Then, a large light scatter parameter gate was used to identify lymphocytes and monocytes. FSC^high cells represent doublets and were excluded. SSC^high cells correspond to remaining granulocytes, likely low density granulocytes described before in SLE [1102] that were co-enriched along with PBMC. Next, cell aggregates were removed by gating on cells showing closely correlating area and height values of the FSC signal. Most cell doublets are characterized by a relatively increased FSC-area vs. FSC-height ratio. Live B cells were detected by staining for CD19, and exclusion of T cells, monocytes and dead cells according to CD3, CD14, and DAPI staining. Note that the B cell gate captures CD19^dim cells, which can be strongly enriched for PB/PC. CD19 expression itself is subject to regulation in e. g. autoimmune conditions [1103, 1104], so that boundaries of the B cell gate should be carefully validated. CD19⁺CD3⁻CD14⁻DAPI⁻ B cells were then analyzed for CD20 and CD27 expression, revealing CD20⁺ subsets of naive and memory B cells besides PB/PC with a CD27^highCD20^low/− phenotype. In this (SLE) sample, PB/PC are detectable at increased frequencies; normal donors show commonly less than 2% PB/PC among CD19⁺ B cells. (B) PB/PC were then analyzed for expression of CD38 and CD138. Virtually all CD27^highCD20^low/− gated PB/PC expressed high levels of CD38, and two thirds expressed CD138. T cells and monocytes not expressing CD138 and containing very few CD38^high cells are shown for comparison. (C) As an alternative to the PB/PC gating shown in (A-B), total PB/PC, or CD138⁺ PB/PC can be gated in various combinations of the markers CD20, CD38, CD27 and CD138, with consistent results. (D) PB/PC show a unique FSC and SSC profile distinct from that of total lymphocytes, B lymphocytes, and monocytes. (E) Backgating confirms the validity of the gating strategy. In particular, it shows that the entire PB/PC subsets was included during light scatter gating, some PB/PC events were excluded as doublets, and that significant amounts of T cells and/or monocytes share the CD27^highCD20^−/low phenotype of PB/PC and may contaminate this population unless careful CD19 gating and DUMP channel exclusion is employed.

Figure 110.

Detection of human Ag-specific PB/PC in peripheral blood by flow and mass cytometry. (A) Gating strategy for the detection of SARS-CoV-2 receptor-binding domain (RBD)-specific PB/PC by flow cytometry. CD38^high/CD27^high PB/PC were identified in PBMC by serial gating comprising a large FSC/SSC gate, followed by the exclusion of residual CD45-negative cells, CD3⁺ T cells, CD14⁺ monocytes and dead cells.Within the remaining cells, CD19⁺ B cells, including CD19^low/CD38^high PB/PC were gated and cell aggregates/doublets were removed by gating in a FSC-A vs. FSC-H plot. Finally PB/PC were gated according to their high CD38 and CD27 expression. RBD-specific PB/PC were detected by a co-staining with two RBD tetramers (APC and PE/Dazzle594 signals). RBD-specific PB were detectable in a blood sample 7 days after booster immunization with BNT162b2, and in an acutely infected COVID-19 patient. No or background amounts of such cells could be detected in PBMC of a SARS-CoV-2 naive and unvaccinated donor, or in a control staining omitting RBD-protein incubation (SA only) in a blood sample 7 days after booster immunization with BNT162b2, confirming the specificity of the staining. (B) Detection of tetanus-specific PB/PC after secondary tetanus re-vaccination by mass cytometry. PB/PC were identified within total B cells after opt-SNE dimension reduction. Two CD38^high expressing PB/PC populations were gated corresponding to IgA⁺ or IgM⁺ PB/PC and IgG⁺ PB/PC. Identification of tetanus toxoid (TT) - specific PB/PC was facilitated by a co-staining with ¹⁵⁹Tb- and ¹⁶⁷Er-labeled tetanus toxoid. Before and three weeks after immunization few or no TT-specific PB/PC were detected in both individuals, while at day 7 up to 16% of the PB/PC were TT-specific.

Figure 111.

Representative gating strategy and analysis of human bone marrow PC. (A) Analytical gating strategy. Time/CD45 visualization confirms the stability of the cytometric measurement over time. Time frames showing discontinuous data were excluded if applicable. As PC exhibit particular light scatter and background fluorescence properties, the CD138⁺CD38^high PC population was gated first, followed by cell aggregate exclusion and gating on CD3⁻, CD16⁻, CD10⁻, CD14⁻ and DAPI⁻ cells for exclusion of dead cells and potentially contaminating cell types. Then, the FSC-A/SSC-A plot reveals that PC show a broader light scatter signal distribution compared to other lymphocytes due to their increased size and ellipsoidal shape. Should the FSC-A/SSC-A plot reveal remaining FSC^low and/or SSC^low cell debris or electronic artifacts, these should be excluded by gating at this step. (B) Human BM PC consistently display distinct populations with either high or low to no expression of CD19 [901, 1105]. The absence of HLA-DR expression confirms at large the absence of PB [941, 1084], and remaining HLA-DR⁺ PB are excluded. (C) Backgating analyses of the procedure shown in (A). (D) Comparison of Ab staining and light scatter properties of total CD138⁺CD38⁺ BM PC vs total BM mononuclear cells. PC exhibit increased background fluorescence signals compared to other cells (possibly integrating cell size effects, autofluorescence, and non-specific binding of labeled Abs) stressing that gating should be adjusted at the level of PC rather than at global levels. Consistent with their increased size, non-spherical shape, and high organelle content, BM PC show a FSC / SSC pattern distinct from that of other BM cells.

Figure 112.

Detection of human peripheral blood PB/PC in a high-dimensional mass cytometry dataset of peripheral blood leukocytes. Data represent leukocytes of a pool of ten cryopreserved whole blood samples. Samples were thawed and RBC were lysed. Leukocytes underwent barcoding, were pooled and stained with cocktail of isotope-conjugated Abs including the markers depicted, and acquired on a Helios mass cytometer (Fluidigm). Detailed information is provided in the publication of the full dataset [1101]. After curation, data of up to 200.000 cells from each sample were subjected to opt-SNE (plotted: 1.937 x 10⁶ cells). (A) Major cell populations and CD38^high PB/PC were gated according to their characteristic marker expression profile in the tSNE dimensions and mapped to the t-SNE plot. Note that PB/PC form distinct and condensed subpopulations. (B) Expression of major leukocyte lineage markers and CD38. (C) Gated PB/PC (1697 cells) were subjected to a second opt-SNE run, revealing subsets of IgA⁺ and IgA⁻ PB/PC, and differential expression of HLA-DR.

Figure 113.

Identification of aberrant plasma cells in human multiple myeloma bone marrow. (A-C) Plasma cells are defined as the CD38- and CD138-positive population (blue gate shown in Ccorresponds to the purple plasma cell population in D-E) among leukocytes (black) after exclusion of debris (A) and doublets (B). No live/dead staining is performed. Aberrant plasma cells (purple) in this sample are partially CD56-positive, homogeneously negative for CD19 and CD45-low (D-E). Moreover, aberrant plasma cells do show immunoglobulin light chain restriction (in this case lambda, indicated in red, F), which ultimately characterizes them as abnormal plasma cells. As an internal comparison, B cells (gate shown in D) present characteristic CD19 and immunoglobulin kappa and lambda light chain expression. The immunoglobulin light chain expression should be evaluated along a diagonal. B cells typically show lower expression levels of immunoglobulin light chains compared to plasma cells. (F). The hierarchy of defined populations as well as absolute and relative numbers of events are shown in (G). Open circles indicate population centers. Gating was performed with Infinicyt^™ Flow Cytometry Software.

Figure 114.

Identification of non-malignant plasma cells in human bone marrow. An example of a normal plasma cell population is shown. The gating strategy for identification of single nucleated cells, plasma cells, and B cells as well as color coding are identical to Figure 113. Plasma cells are defined as the CD38− and CD138-positive population (purple, A) among leukocytes (black). Normal plasma cells usually express CD19 and CD45 (B) in combination with heterogeneous kappa/lambda light chain expression (C). The hierarchy of defined populations as well as absolute and relative numbers of events are shown in (D). Open circles indicate population centers. Gating was performed with Infinicyt^™ Flow Cytometry Software.

Figure 115.

Comparison of common two-color flow cytometric analyses of murine plasma cell populations. (A) Exemplary gating strategy for single extended lymphocytes in the spleen. Viable cells were defined using FSc/SSc characteristics. (B) Single-cell suspensions from bone marrow (BM), spleen and mesenteric lymph nodes (mLN) of BLIMP1:GFP-reporter mice were isolated and stained as described with Abs against CD138 and one additional surface marker indicated on the y-axis. The input gate for all dot plots in (B) was set to the gate in the rightmost panel described in (A). The BLIMP1:GFP^hi/CD138^hi gate was used as the reference gate for the plasmablast/plasma cell populations, and events in this gate are highlighted in green in the following plots.

Figure 116.

Flow cytometric distinction between murine plasmablasts, early- and late mature plasma cells. Single-cell suspensions from the bone marrow (femur and tibia) of BLIMP1:GFP-reporter mice were analyzed for their surface expression of CD138, TACI, CD19 and B220. Viable cells were defined using FSc/SSc characteristics. (A) CD138⁺/TACI⁺ cells and subpopulations defined on their B220 and CD19 abundance were analyzed for their BLIMP1:GFP-expression (MdFI: Median fluorescence intensity); MdFI values are indicated in the depicted histograms. CD19 and B220 surface expression was used to further subdivide the CD138⁺TACI⁺ population (P1: CD19⁺/B220⁺ (dividing plasmablasts); P2: CD19⁺/B220^low (early plasma cells); P3: CD19^low/B220^low (mature plasma cells). CD138⁻/TACI⁻ cells were used as a negative control for BLIMP1:GFP-expression. (B) BLIMP1:GFP⁺/CD138⁺ cells were divided based on their fluorescence intensities in high-expressing population (CD138^high/BLIMP1:GFP^high) and low-expressing population (CD138⁺/BLIMP1:GFP⁺). These two subpopulations are further subdivided based on heterogeneous CD19/B220 expression.

Figure 117.

Syk and pSyk (Y352) expression at baseline and after anti-BCR stimulation of CD27-CD20⁺and CD27+CD20⁺human B cells from peripheral blood. (A) Representative histograms of Syk and pSyk (Y352) of peripheral blood of one donor for CD27−CD20⁺naive B cells (pink), CD27+CD20⁺memory B cells (blue) and CD19-CD3-CD14- cells (grey). (B) Histograms of pSyk (Y352) upon anti-BCR stimulation after 5, 8 or 15 min or 5 min incubation with RPMI as control (named 0 min) (C) Kinetic curve showing median FI of pSyk (Y352) over time after anti-BCR stimulation for CD27-CD20⁺naive B cells (pink), CD27+CD20⁺memory B cells (blue) and CD19-CD3-CD14- cells (grey).

Figure 118.

Detection of IL-10 secreting human B cells. CD19⁺ B cells were cultured at 2.5x 10⁶/ml in RPMI1640 medium supplemented with 10% FCS and Pen/Strep for 2 days in the presence of 2 μg/ml anti-IgM/IgG-F(ab)₂ fragments (Jackson ImmunoResearch), 1 μg/ml anti-CD40 (clone 82111 R&D systems), 10 ng/ml IL-4 (Immunotools, Germany). After a restimulation with 10 ng/ml PMA and 1 μM Ionomycin for 3 hours, the cells were labeled with a bivalent IL-10 capture matrix and the IL-10 secretion period was performed at 37°C for 30 min. After washing, the cells were stained and analyzed with a flow cytometer. Cells were first gated according to FSC/SSC (linear scales) to remove debris and dead cells from the analysis (A). Live CD19⁺ B cells were then identified within this FSC/SSC gate according to the lack of propidium iodide labeling and CD19 staining (log scales) (B). Dot plots gated on live CD19⁺ lymphocytes then show CD19 and IL-10 staining (log scales) for stimulated cells with secretion period and IL-10 staining (C). Negative controls include (D) stimulated cells with no secretion period and IL-10 staining, (E) stimulated cells with secretion period and isotype control staining, (F) unstimulated cells with IL-10 staining. Data derived from a representative experiment.

Figure 119.

BCR induced Ca²⁺ mobilization in human B cell subsets from peripheral blood. (A) Setting of Indo-1 AM bound versus Indo-1 AM unbound. The photomultipliers (PMTs) should be adjusted so that unstimulated cells occur on a line about 45° to the y-axis. (B) Gating strategy for the analysis of Ca²⁺ mobilization in human B-cell subsets from peripheral blood after stimulation with anti-IgM. After exclusion of cells that did not bind Indo-1 lymphocytes are determined by FSC/SSC. Gating of CD19⁺ B cells is followed by differentiation of CD21^low B cells and non-CD21^low B cells. IgG/IgA and CD27 is subsequently used to differentiate IgG/IgA⁻/CD27⁻ naïve or naïve and transitional (ti) B cells, IgG/IgA⁻/CD27⁺IgM Memory B cells (IgM Mem) and IgG/IgA⁺/CD27⁺class switched B cells (CD27^pos sw Mem) and IgG/IgA⁺/CD27⁻ switched Mem B cells (CD27^neg sw Mem). IgG/IgA⁻ CD27⁻ non-CD21^low B cells can further be differentiated in ti and naïve B cells. (C) Kinetics (upper panels) and dot plots of time versus the ratio Indo-1 bound/unbound (lower panels) of the subpopulations of non-CD21^low B cells or CD21^low B cells as described above. After baseline acquisition anti-IgM (arrow) was added inducing a shift of Indo-1 AM bound/unbound in IgM-expressing naïve and IgM Memory B cells whereas this ratio is at baseline levels in IgM class switched memory B cells. After addition of iono (arrow) the ratio of Indo-1 AM bound/unbound is rapidly increasing in all subsets. Data were acquired with a BD LSR FortessaTM and analyzed by FlowJoTM.

Figure 120.

Identification of human tonsil ILCs. Representative gating strategy (upper panel, gate numbers reflect population frequencies) and expression of transcription factors (lower panel, gate numbers reflect MFI of indicated population) of human ILCs derived from tonsillectomy. After magnetic depletion of CD3⁺ cells, cells were gated as viable (LDeF780⁻), CD3(APC-eF780)⁻ CD14(APC-eF780)⁻ CD19(APC-eF780)⁻ FcsRIα(APC-Vio770)⁻ CD123(APC-eF780)⁻ CD11c(APC-Vio770)⁻ BDCA3(APC-Vio770)⁻ (Lin⁻) and either CD94^+/lo CD127^−/lo CD56⁺ NK cells; CD94⁻ CD127^hi CD117^+/lo CRTH2⁺ ILC2; CD94⁻ CD127^hi CD117⁻ CRTH2⁻ NKp44⁻ CD56⁻ ILC1; CD94⁻ CD127^hi CD117⁺ CRTH2⁻ NKp44⁺ ILC3; or CD94⁻ CD127^hi CD117⁺ CRTH2⁻ NKp44⁻ ILC.

Figure 121.

Identification of murine SI LP ILCs. Representative gating strategy of ILCs derived from the SI LP of 6-week-old C57BL/6 mice. Mononuclear cells (MCs) were prepared as previously described [1294]. Cells were gated as viable (LD, eF780⁻), B220(APC-Vio770)⁻ CD11c(APC-Cy7)⁻ Gr-1(APC-Fire780)⁻ F4/80(APC-Fire780)⁻ Fc√R1α(APC-Cy7)⁻ (Lin⁻) CD45⁺ TCRβ⁻ TCRγδ⁻ and either as NKp46⁺ (grey gate, A) T-bet⁺ Eomes⁻ ILC1, Eomes⁺ T-bet⁺ NK cells or as CD127⁺ (black gate, B) GATA3⁺ RORγt⁻ ILC2 and RORγt⁺ GATA3^lo ILC3 which can be further separated according to NKp46 and CD4 expression (B). Gate numbers reflect population frequencies.

Figure 122.

Identification of human NK cells and NK cell subsets in the PB of a healthy donor. In this PB samples, lymphocytes are first gated based on their physical parameters (upper left grey dot plot) than Human NK cells can be first gated on the basis of identified for their surface level of CD56 surface expression and lack of CD3. The CD56^bright NK subpopulation (in blue) is positive for NKG2A, negative for KIRs and CD57 while CD16 can be either negative or dimly expressed (as shown). NKG2A and KIR surface expression allows three subpopulations of CD56^dim NK cells (in red), namely “maturing” (NKG2A+KIR−) in dark red), “double positive” (NKG2A+KIR+ in dark red) and “mature” (NKG2A-KIR+ in light red), to be identified. To discriminate among these CD56^dim maturation steps, we used a cocktail of anti-KIR (clones: EB6B, GL183, Z27) that did not include anti-LIR1, for this reason in the dot plot also a double negative population is present. Among the mature population (in light red), CD57 molecule is expressed on the, so called, “terminally differentiated” NK cells. In CMV positive donors, a percentage of this latter population can also express NKG2C representing the so called “memory NK cells”. Recently it has been demonstrated that in some CMV positive individuals a fraction of the NKG2C subset can also express PD1. Percentage of subpopulation are not specified because they are extremely diverse among different individuals and do not give additional information to the gate strategy. Total PBMCs are shown as a light grey population to highlight how they are expressed in each dot plot, while percentages shown for each gate refer to coloured NK cells only.

Figure 123.

Identification of murine NK cells in the blood and spleen of C57B/6 mice. Whole blood (A) was stained in BD Trucount tubes and analyzed after red blood cell lysis. Lymphocytes were gated among CD45⁺ leucocytes based on their morphology and, after exclusion of CD3⁺ T cells and CD19⁺ B cells, NK cells were gated as NK1.1+NKp46+ cells. For the analysis of spleen NK cells (B), due to extraction techniques, doublets and dead cells need to be gated out. CD3⁺ T cells and CD19⁺ B cells were excluded, and NK cells were gated as NK1.1+NKp46+ splenocytes.

Figure 124.

Identification of murine liver NK cells in C57B/6 mice. After Percoll density gradient centrifugation of single cell suspension obtained scratching the liver, lymphocytes were analyzed. As in Figure 123, doublets, dead cells, CD3⁺ T cells and CD19⁺ B cells were sequentially excluded. Moreover, considering that in this district also CD45- cells are present, a further exclusion gate has to be included. Among NK1.1+NKp46+ cells NK cells were gated as CD49b+CD49a- cells, and distinguished from CD49b-CD49a+ ILC1s.

Figure 125.

Identification of murine small intestine lamina propria NK cells in C57B/6 mice. After enzymatic digestion and Percoll density gradient centrifugation, single cell suspension obtained from the small intestine was analyzed. As in figure 124, doublets, dead cells, CD45- and CD19⁺ B cells were sequentially excluded. T cells were gated out based on their expression of TCRβ. Rorγt+ cells represent ILC3s, which can be further distinguished in NCR+ and NCR- ILC3s. Among Rorγt- NKp46+ cells, NK cells are gated as NK1.1+Eomes+ cells.

Figure 126.

Flow cytometric analysis human monocytes and macrophages. In blood (A), the identification of human monocytes subsets as single, live, CD45⁺, CD3⁻, CD19⁻, CD20⁻. HLADR⁺, CD88⁺ cells. Using CD14, CD16 and HLA-DR, classical (cMo), intermediate (iMo) and non-classical (ncMo) monocyte subsets can be identified. A similar gating strategy is applied for the spleen (B), lymph node (C), and lung (D). Of note, CD14⁺ cells have been termed monocytes/macrophages (Mo/Mac). Within the lung (D), alveolar macrophages are identified as SSC-A^hi CD206⁺ cells and interstitial macrophages as SSC-A^lo CD206⁺ CD11b⁺ CD14⁺ LYVE-1^hi cells.

Figure 127.

Flow cytometric analysis of human dendritic cells. In blood (A), the identification of human dendritic cells as single, live, CD45⁺, CD3⁻, CD19⁻, CD20⁻, CD16⁻, HLADR⁺, CD88⁻ cells. These cells can be further sub-divided into the DC subsets using the cell surface markers shown. A similar gating strategy is applied for the lymph node (B) and spleen (C).

Figure 128.

Flow cytometric analysis murine monocytes. (A-F) Example for basic gating strategy from FSC-A/SSC-A, over doublet exclusion and gating on Live, CD45⁺ LIN⁻ cells (defined as CD3/CD19/CD49b/Ly6G⁻) for blood, BM, spleen, lung, intestine, and LN murine samples. (A-D, F) Monocytes are identified as CD115⁺CD11b⁺ cells and can be further divided into Ly6C^lo and Ly6C^hi monocytes (blue and red gates, respectively). (E) Monocytes are identified as CD11c^loCD11b⁺ cells and can be further divided into Ly6C^lo MHCII^hi transitional monocytes (tMono) and Ly6C^hiMHCII^lo monocytes (black and pink gates, respectively).

Figure 129.

Flow cytometric analysis murine macrophages. Example for basic pre-gating strategy from FSC-A/SSC-A, over doublet exclusion and gating on Live, CD45⁺ LIN⁻ cells (defined as CD3/CD19/CD49b/Ly6G⁻) for BM and spleen (A, B) or CD45⁺ cells in lung, intestine, dermis and epidermis murine samples (C-E). Macrophages are gated as CD11b⁺F4/80⁺ CD64⁺MerTK⁺ cells in the BM (green gate; A) or CD11b⁻F4/80⁺/CD64⁺ red pulp macrophages in the spleen (B). In the lung macrophages can be divided into MerTK⁺CD64⁺ SiglecF⁺CD11b⁻ AM or SiglecF⁻CD11b⁺ IM (C). Intestinal macrophages are gated as MHCII⁺CD11c⁺CD11b⁺Ly6C⁻ cells (D), while in the dermis and epidermis Langerhans cells are identified as CD11b⁺EpCAM⁺ (F4/80⁺) cells, with mature LCs also expressing CD24, prior gating on CD11b⁺CD64⁺ macrophages (E).

Figure 130.

Flow cytometric analysis murine dendritic cells. Examples for basic pre-gating strategy from FSC-A/SSC-A, over doublet exclusion and gating on Live, CD45⁺ LIN⁻ cells (defined as CD3/CD19/CD49b/Ly6G⁻) across murine tissues is shown in Figure 128. Here, murine cDCs are gated as CD11c^hiMHCII⁺ cells that can be further divided into cDC1 (CD8/XCR1⁺/CD24⁺CD11b⁻, red gates) and cDC2 (CD8⁻/XCR1⁻CD11b⁺, blue gates) in the blood (A), BM (B) and spleen (C), while pDCs are identified as CD11c^intSiglecH⁺B220⁺mPDCA-1⁺ cells (pink gates; A-C). In the lung, intestine and dermis cDC1 are gated as CD103⁺CD11b⁻ (red gates) and cDC2 as CD103⁻CD11b⁺ /CD24⁺ cells (blue gates) (D-F).

Figure 131.

Flow cytometric analysis murine dendritic cells in LNs. Examples for basic pre-gating strategy from FSC-A/SSC-A, over doublet exclusion and gating on Live, CD45⁺ LIN⁻ cells (defined as CD3/CD19/CD49b/Ly6G⁻) across murine tissues is shown in Figure 128. In the LNs, murine cDCs can be divided into CD11c^hiMHCII^lo resident DCs and CD11c^loMHCII^hi migratory DCs. Migratory DCs are further split into CD103⁺CD11b⁻ cDC1 (red gates) and CD103⁻CD11b⁺ cDC2, while resident DCs can be split into CD8⁺CD11b⁻ cDC1 (red gates) and CD8⁻CD11b⁺ cDC2, across LNs from different regions (A-D).

Figure 132.

Flow cytometric analysis of human monocytes assays. In vitro stimulation with LPS. Human monocytes were identified as in Figure 126. CD16, CD88 an CD89 expression are shown on monocyte subsets ex vivo (A) and following stimulation with LPS (B). (C) TNFα and IL-6 profile on unstimulated and LPS-stimulated monocytes.

Figure 133.

Flow cytometric analysis for allogenic mixed lymphocyte reaction assay using human PBMCs. (A) Show is a gating strategy to identify blood T cells and measure IFNγ production and proliferation. (B). As an example, the differences in proliferating IFNγ⁺ T-cells when co-cultured with DC2127 2 (Flow cytometric analysis of human dendritic cells).

Figure 134.

Discrimination of human and murine granulocyte subpopulations. A: Human cells after RBC lysis were displayed in a SSC versus (vs) FSC dot plot to show the location of eosinophils (green, high SSC), neutrophils (blue, high SSC), and basophils (red, low SSC). The following Abs CD45, CD11b, CD15, CD16, CCR3, Siglec-F and FcεRIα were used. CD45⁺/CD11b+ cells were gated on CD15 vs CD16 to distinguish granulocyte subpopulations. CD15+/CD16+ cells were determined as neutrophils, CD15⁺CD16− were further designated as eosinophils by their expression of Siglec-8 and CCR3, and the CD15−/CD16− population was depicted in a FcεRIα vs. CCR3 plot to identify the double positive basophil fraction. B: Human neutrophils with low buoyant density are observed in COVID-19. The following Abs CD45, CD11b, CD15, CD16, and CD49d were used after density centrifugation in both cellular fractions to identify neutrophils (blue). C: CD45⁺ murine cells were gated on CD11b/Ly6C to display the CD11b+/Ly6int population which was further analyzed using Ly6G to identify neutrophils (blue). CD11b+/Ly6Cneg-low cells were gated on Siglec-F vs. CD200R3 and were subsequently analyzed for expression of additional cell subset markers. CD200R3-cells expressing Siglec-F and CCR3 were designated as eosinophils (green) and Siglec-F-cells were marked as basophils (red) supported by their expression of CD200R3 and CD49b.

Figure 135.

Apoptosis detection and uptake of nanoparticles in purified human granulocytes. A: Granulocytes were cultivated at 37 °C/ 5% CO₂ for indicated time points and stained according to the cell death protocol. Subsequently, they were subjected to flow cytometry analysis. During apoptosis, granulocytes shrink and increase in granularity, as indicated by a decrease in FSC and an increase in SSC. Viable cells (V) first start to expose ANX-V-FITC and become apoptotic (A), before they lose their plasma membrane integrity and become necrotic as indicated by PI-positivity (N). Note that in the N-gate the population high in PI reflects cells without the loss of nuclear content. In contrast, the population low in PI reflects cells with a subG1 DNA content, which is considered a hallmark of apoptosis. B: 20 μg/ml micro monosodium urate crystals and 250 μg/ml Lucifer Yellow were added to the granulocytes and the suspension was incubated at 37 °C/ 5% CO2 for the time points indicated. Subsequently, flow cytometry analysis was performed. The increase in Lucifer Yellow (see arrow; in red) is restricted to the population of cells which increase in granularity. Therefore, the simultaneous increase in Lucifer Yellow and SSC can be used to monitor the uptake of nanoparticles by granulocytes.

Figure 136.

Gating of human umbilical cord blood neutrophils. Flow cytometric analysis of human umbilical cord blood neutrophil subsets. Doublets and dead cells are first excluded, followed by the exclusion of CD45⁻ cells. Lineage cells and monocytes are excluded before total granulocytes are gated with CD15 and CD66b. From there, eosinophils are excluded before gating on the classical nomenclature of neutrophil subsets using CD11b and CD16. P_M = Promyelocytes and Myelocytes, MM = Meta-myelocytes, BN = Band cells, SN = Segmented neutrophils. Gating is adapted from [1469, 1480].

Figure 137.

Alternative gating strategy to classical nomenclature for identifying human neutrophils. An alternative to using CD11b vs CD16 (Figure 136, panel 10), total human cord blood neutrophils can be divided into CD11b⁻ neutrophil progenitors and CD11b⁺ neutrophils [1479, 1481]. The proliferative CD11b⁺ preNeus can be separated using CD101 and CD49d as shown previously [1478]. The remaining non-proliferative pool of neutrophils can be separated with CD10 as described previously [1482].

Figure 138.

Flow cytometric analysis of murine bone marrow neutrophil subsets. Murine bone marrow samples are first gated to exclude doublets and dead cells. Debris are also excluded based on forward and side scatter information. Within the cKit^hi expressing cells, Ly-6C and CD81 are used to mark all neutrophil progenitors. CD106 distinguishes between pro-neutrophil 1 and pro-neutrophil 2. Other subsets are gated by first removing lineage positive cells (T, B and NK cells) followed by the exclusion of eosinophils and monocytes. Ly6C is then used to further remove any Ly-6C^hi and Ly-6C^lo monocyte contamination. Gr-1 and CD11b gates for total bone marrow neutrophils. cKit and CXCR4 is used to gate proliferative pre-neutrophils and CD101 distinguishes mature neutrophils from immature neutrophils.

Figure 139.

Gating strategy for human bone marrow MSCs. Biocoll gradient-purified and in vitro-expanded cells from human bone marrow are gated to exclude doublets and cells expressing CD45. As a next step, live single MSCs are identified as cells negative for CD34 and CD31 and then analyzed for their positive expression of CD73, CD105 and CD90.

Figure 140.

Gating strategy for murine BM stroma cells. Live single non-platelet cells were identified as CD45⁻ and VCAM-1⁺ and further analyzed regarding their CD31 expression. After sorting for CD45⁻/VCAM-1⁺/CD31⁻ a purity of 96% was achieved.

Figure 141.

Phenotypic characterization of HSCs from CD34-enriched human cord blood. HSPCs were identified as CD34⁺ CD38⁻ cells within the human CD45⁺ Lin⁻ compartment. The lineage cocktail contained the following Abs: CD3, CD10, CD14, CD15, CD16, CD19, and CD235. HSCs were identified as CD34⁺ CD38⁻ CD90⁺ CD45RA⁻ cells and MPPs as CD34⁺ CD38⁻ CD90⁻ CD45RA⁻ cells [1551]. LT-HSCs with increased expansion potential can be identified within cells expressing the top 20% of Kit [1553], and LT-HSCs containing the highest repopulating activity express high levels of CD49f [1553]. Gatings for CD38 and CD90 were set according to isotype controls. Gating strategies for Kit and CD49f are chosen according to the original publications [1552, 1553]

Figure 142.

Gating strategy of mouse hematopoietic stem cells. Phenotypic characterization of mouse bone marrow derived HSCs. LSK cells were identified as kit⁺ Sca1⁺ cells within the Living/Lin⁻ compartment. LSK cells were further characterized by CD34 and Flk2 (CD135) expression. HSCs are CD150⁺ CD48⁻ within the CD34⁻CD135⁻ gate. MPPs are further characterized as CD150⁺ CD48⁻ (MPP1) CD150⁺ CD48⁺ (MPP2) and CD150⁻ CD48⁺ (MPP3) cells within the CD34⁺CD135⁻ gate. MPP4 population is determined as CD34⁺CD135⁺CD150⁻CD48⁺cells. Gating for all colors were set according to the isotype control staining (not shown). Forward and side scatter voltages can be increased to dissect bone marrow cell populations into more differentiated subpopulations, differing in size and density.

Figure 143.

Single cell preparations from human tumor vs. non-tumor tissues and characterisation of human tumor vs. non-tumor epithelial cells. (A) Human tumor (upper row) and adjacent non-tumor tissue (lower row) was obtained as surplus tissue in the course of a pulmonary tumor resection with informed consent (MHH Nr. 1747). After tissue digestion, single cells were stained with a live/dead dye (QDot585) and anti-human CD45 (Alexa-Fluor700) mAb. The hierarchical gating strategy starts with exclusion of doubles and aggregates in the FSC-A/FSC-H plot, followed by exclusion of dead cells in the QDot585/SSC-A plot and leukocytes, i.e., CD45-positive cells in the CD45/SSC-A plot. The remaining living CD45-negative single cells are shown in the FSC-A/SSC-A plot and in the blue gate, epithelial cells including tumor cells in the tumor tissue, can be identified according to their relative size and granularity. (B) A renal tubular cancer cell (RTCC) and the corresponding non-tumor tubular cell line (RNTC) derived from tumor and adjacent non-tumor tissue of the same patient are compared with respect to surface expression of the following markers: HLA class I (mAb W6/32) and the adhesion molecule ICAM-1 (CD54, mAb gp89). All primary mAb are mouse IgG2a and were stained with a goat-anti-mouse PE-labeled secondary Ab.

Figure 144.

FANS analysis of nuclei prepared from human surgical brain tissue. Nuclei were prepared from frozen adult brain tissue (>100mg), stained with nuclear marker NeuN (monoclonal mouse anti-NeuN, clone A60, 1:1000 and phycoerythrin (PE)-conjugated goat anti-mouse IgG 1:1000) and submitted to sorting. Gating for identification of NeuN-PE positive neuronal and non-neuronal cell populations or respective nuclei was based on the first 20,000 events. FITC fluorescence was included to identify and exclude autofluorescent nuclei. SSC-A/FSC-A gate covered 60% of counts. Next 80% were gated positively following doublet exclusion based on SSC-A/SSC-H. Two distinct populations of nuclei were identified and gated, of which 30% were identified as neuronal (NeuN+) and 70% as non-neuronal (NeuN−) based on the intensity of the phycoerythrin signal (PE-H). FSC and SSC axes are linear, fluorochrome axes are log.

Figure 145.

Flow cytometric analysis of murine neonatal astrocytes. (A) Neonatal astrocytes were harvested and stained with the cell surface marker ACSA-2 (recombinant human anti-mouse, APC-conjugated, 1:10 dilution, Miltenyi Biotec). (B) Neonatal astrocytes were harvested, fixed in 2% PFA and permeabilized in 0.5% saponin. Cells were stained with the intracellular marker GFAP (mouse monoclonal, Alexa Fluor-488-conjugated, 1:20 dilution, BD Biosciences). The gating was based on unstained controls for each Ab as shown on the far right. 10,000 cells of the SSC-A/FSC-A gate were set as a stopping point during flow cytometry. FSC and SSC axes are linear, fluorochrome axes are log.

Figure 146.

Gating strategy for the identification of the murine resident microglia and infiltrating macrophages and lymphocytes. (A) Analysis of brain cell suspension from a non-immunised wildtype mouse via CD45 and CD11b marker expression. (B) Analysis of monocyte-derived macrophages, infiltrating lymphocytes and microglia of a mouse immunized with MOG35-55 at chronic phase. Cell populations were distinguished by CD45 and CD11b expression levels with macrophages showing high expression of both CD11b and CD45 (CD45^hiCD11b⁺), microglia showing intermediate expression of CD45 and high expression of CD11b (CD45^intCD11b⁺), and infiltrating lymphocytes showing high expression of CD45 and no expression of CD11b (CD45^hiCD11b⁻) and non-leukocytes being negative for both CD11b and CD45 (CD45⁻CD11b⁻). Antibodies used: rat anti-mouse CD45, PerCP-conjugated, 1:200 clone 30-F11, Biolegend; rat anti-mouse CD11b, APC-conjugated, 1:400 clone M1/70, Biolegend. 100,000 cells of the SSC-A/FSC-A gate were set as a stopping point during flow cytometry. FSC and SSC axes are linear, fluorochrome axes are log.

Figure 147.

Gating strategy showing the simultaneous analysis of viable, human hepatocytes and leukocytes sampled by FNA. Cells are discriminated by their SSC-A. Parenchymal viable hepatocytes are defined as high SSC-A, CD45 negative and albumin positive compared to intrahepatic leukocytes (encompassing myeloid and lymphoid cells) as low SSC-A, CD45 positive and albumin negative. We recommend the use of albumin-APC (R&D Systems Cat. No. MAB1455, clone 188835).

Figure 148.

Gating strategy showing the simultaneous analysis of innate and adaptive IHL sampled by perfusate, FNA and from biopsy tissue samples. Representative example showing a sequential gating strategy used for the identification of major subsets of IHL isolated from a) perfusates, b) fine needle aspirates (FNA) and c) liver biopsy tissue. Lymphocytes are discriminated by plotting FSC-A against SSC-A (Gate 1). Single cells are further identified using FSC-A against FSC-H (Gate 2). Live cells are defined as LIVE/DEAD^™ Fixable Dead Cell Stain negative (Gate 3) and CD45 expression is used to identify pan-lymphocytes (Gate 4). CD3+CD56- T cells, CD3+CD56+ NKT cells and CD3-CD56+ NK cells are defined using Gate 5, with the CD3-CD56- cells being further gated to identify CD19⁺ B cells (Gate 6). From the CD3+CD56- T cell gate sequential gating allows for the identification of CD4+ and CD8+ T cells respectively (Gate 7) and Gate 8 shows the discrimination of liver infiltrating, non-resident (CD69-CD103-) from liver resident tissue memory CD8 T cells (Trm; CD69⁺CD103⁺).

Figure 149.

Gating strategy for Kupffer cell identification in LNPC samples isolated from C57BL/6 mice (9 weeks-old). Single cells were discriminated by plotting FSC-A against FSC-H (gate 1). Live cells were defined as DAPI negative (gate 2), while hepatic leukocytes are CD45⁺(gate 3). Note that in this gate liver sinusoidal endothelial cells (LSEC) can be identified as CD45⁻ CD31⁺. Gate 4 is then used to get rid of lineage⁺cells (CD3⁺, CD19⁺, CD49⁺, Ly6G⁺). Note that the concomitant use of CD11b allows the identification of neutrophils as Lineage⁺CD11b^high, while T and B cells are Lineage⁺CD11b⁻. F4-80⁺macrophages are then defined as CD11b^int F4-80⁺(gate 5). Finally, plotting I-A/I-E against TIM4 (gate 6) allows the discrimination between Kupffer cells (TIM4⁺, I-A/I⁻ Eint) and capsular macrophages (TIM4⁻, I-A/I-E⁺).

Figure 150.

Gating strategy for LSEC identification in liver samples isolated from C57BL/6 mice (9 weeks-old). Single cells were discriminated by plotting FSC-A vs FSC-H and SSC-A vs SSC-H (gate 1 and 2, respectively). Then, the population of interest were gated based on its morphology (gate 3). Living cells were defined as Live/Dead NIR negative (gate 4). Kupffer cells and macrophage-related population were excluded using F4/80 (gate 5). Other leukocytes (e.g., lymphocytes, NKT cells, ILCs, and neutrophils) were excluded using CD45.2 (gate 6). LSEC are finally identified as CD31⁺CD146⁺(gate 7).

Figure 151.

Gating strategy for NKT cells and γδ T cells in murine liver. Hepatic leukocytes from Mdr2−/− mice, which develop chronic liver inflammation, were used for analysis. Single cells were discriminated from doublets by plotting FSC-A against FSC-H. To exclude dead cells, a fixable dead cell staining was performed. Hepatic leukocytes were stained with anti-TCRβ-PE/Cy7 (H57-597; BioLegend), anti-TCRδ-PerCP/Cy5.5 (GL3; BioLegend) and anti-CD1d tetramer-AF647 (NIH Tetramer Core Facility) Abs to distinguish between TCRαβ- TCRγδ⁺cells and TCRαβ⁺CD1d tetramer⁺NKT cells.

Figure 152.

Comparison of LSEC frequency from the same WT mouse using CD146 MicroBeads positive selection. Gating strategy for single and living cells is the same as shown in Figure 150. The LSEC fraction is about 30-40% of total living cells before purification. After CD146 MicroBeads positive selection, the LSEC population reaches about 90- 95% of total living cells. Note in the dot plots that the presence of non-LSEC is minimal after purification.