Abstract
Flow cytometry (FC) is a highly informative technology that can provide valuable information about immune phenotype monitoring and immune cell states. However, there is a paucity of comprehensive panels developed and validated for use on frozen samples. Here, we developed a 17-plex flow cytometry panel to detect subtypes, frequencies, and functions of different immune cells that can be leveraged to study the different cellular characteristics in different disease models, physiological, and pathological conditions. This panel identifies surface markers to characterize T cells (CD8+, CD4+), natural killer (NK) cells and their subtypes (immature, cytotoxic, exhausted, activated), natural killer T (NKT) cells, neutrophils, macrophages (M1 (pro-inflammatory) and M2 (anti-inflammatory)), monocytes and their subtypes (classical and non-classical), dendritic cells (DC) and their subtypes (DC1, DC2), and eosinophils. The panel was designed to include only surface markers to avoid the necessity for fixation and permeabilization steps. This panel was optimized using cryopreserved cells.
Immunophenotyping of spleen and bone marrow using the proposed panel was efficient in correctly differentiating the immune cell subtypes in inflammatory model of ligature-induced periodontitis, in which we found increased percentage of NKT cells, activated and mature/cytotoxic NK cells in the bone marrow of affected mice. This panel enables in-depth immunophenotyping of murine immune cells in bone marrow, spleen, tumors, and other non-immune tissues of mice. It could be a tool for systematic analysis of immune cell profiling in inflammatory conditions, systemic diseases, and tumor microenvironments.
Keywords: Multi-color flow cytometry, Tumor immune cells, Immune profiling, Phenotypic analysis
1. Introduction
Flow Cytometry (FC) and Flow Cytometry Assisted Sorting (FACS) has been widely used in monitoring immune phenotypes at the single-cell level (Proserpio and Mahata, 2016). Given the high sensitivity of FC, researchers have developed and standardized antibody panels for analyzing cell subtypes and for studying diseases. Over the past decade, FC and FACS have gained importance in studying various diseases, including inflammatory conditions, cancer, chronic conditions, autoimmune diseases, and immunodeficiency (Abraham and Aubert, 2016; Saha et al., 2016; Almubarak et al., 2020; Wu et al., 2021). Standardization and validation for panels have been emphasized to mitigate the variations among different equipment, reagents, protocols, and operators (Kalina et al., 2012; Streitz et al., 2013).
Previous works have stratified human and murine bone marrow, peripheral blood, and tumor mass by FC in health and diseases (Yu et al., 2016; Cardoso and Santos-Silva, 2019; Chuah and Chew, 2020; Bruss et al., 2022). Different panels for mouse immune profiling are suggested; however, many of those panels use intracellular staining. Additionally, several other panels emphasize a special subset rather than comprehensive profiling, or they are not optimized for cryopreserved tissues (Unsworth et al., 2016; Dusoswa et al., 2019). Studying the immune profiling of diseases can further help understand the underlying mechanisms and treatment targets of a disease(Cerbelli et al., 2020; Bobcakova et al., 2021; Botticelli et al., 2022). Furthermore, studying the cellular changes of myeloid and lymphoid cells in the bone marrow (BM) and spleen can potentially provide novel insights about biologically plausible mechanisms in different disease pathogenesis. The aim of the panel is to comprehensively profile the surface and functional characteristics of immune cells in the bone marrow, spleen, and tissue in physiological and different pathological conditions.
Immune cells, including T cells, B cells, Natrual Killer (NK) cells, Natrual Killer T (NKT) cells, macrophages, neutrophils, monocytes, and dendritic cells (DCs), play an important role in both adaptive and immune response and pathogenesis of different diseases, including autoimmune diseases, inflammatory diseases, metabolic diseases, and cancer. Mouse models and recapitulating those disease mechanisms by developing mouse models is an important approach for uncovering the disease mechanism in humans. Thus, the characterization of immune cell subpopulations and functional states in mice under physiological conditions will enhance our understanding of human diseases.
The aim of the present study was to design and test a comprehensive 17-plex flow cytometry panel, intended to phenotype the immune cell subsets by FC in the spleen, BM, and tissue using surface markers and frozen samples.
2. Materials and methods
2.1. Biological sample preparation
The laboratory animal protocol and procedures reported in this study were reviewed and approved by the Institutional Animal Care and Use Committee (IACUC) at Columbia University. A 4–0 silk suture ligature (Henry Schein, USA) was placed between first and second maxillary molars of mice for 7 days to create ligature-induced periodontitis model as described (n = 6) (Abe and Hajishengallis, 2013). An age and sex matched group of mice were used as control (n = 6). This ligature induced periodontitis model is well charachterized and has been shown to induce local and systemic inflammation and alverlar bone loss.(Abe and Hajishengallis, 2013; Li et al., 2020a) bone marrow was collected from the femur and tibia bone of the C57BL/6 mice. A cut above the pelvic-hip joint above the hind leg was made with sharp, sterilized scissors. With a 23-gauge needle and a 10 cc syringe filled with Hank's balanced salt solution (HBSS) (ThermoFisher, USA), the bone marrow was flushed out onto a 70 μm nylon cell strainer (ThermoFisher, USA). The bone marrow was further smashed through the cell strainer with a 5 ml plunger and washed with an additional 5 ml HBSS. The cells were centrifuged at 1000g for 10 mins at 4 °C. the supernatant was discarded and the cell pellets were resuspended and cryopreserved
Spleen cells were collected and processed according to the following protocol: Mouse spleens were examined for necrotic region, enlargement, and discoloration. Excess fat and connective tissue were trimmed and the spleen was placed into a 35 mm petri dish with 5 ml Dulbecco's phosphate-buffered saline (DPBS) (ThermoFisher, USA) and carefully minced into small pieces with a scalpel blade. 1 mM of EDTA (Corning, USA) was added to the petri dish and the excised pieces were incubated for 20–30 min. After the incubation, the cells were strained with a 70 μm nylon cell strainer over a 50 ml conical tube with a circular mashing motion with a plunger end of a syringe into the strainer. 5–10 ml of DPBS was added to help wash th cells through the strainer. The cells were centrifuged at 400–600 g or 5 min at 4 °C. The supernatant was discarded and the cell pellets were resuspended and cryopreserved.
2.2. Cryopreservation procotol
For both bone marrow an spleen, the cell pellets were preserved in 1.8 ml feezing medium (90% FBS + 10% DMSO) in controlled-rate freezing vessels (ThermoFisher, USA) at a cooling rate of −1 °C/min. The tubes in the freezing container were stored at −80 °C for 24 h before transferring to liquid nitrogen storage. Single cell suspension of BM or spleen cells was retrieved and thawed from liquid nitrogen to 37 °C at the time of staining as stated in the staining protocol below.
2.3. Panel development strategy
Standard and best practices were employed to design this panel (Brummelman et al., 2019). These principles take into account: antigen co-expression, antigen expression level, antigen classification, fluorochrome brightness, spillover, and spillover spreading.
First, a gating strategy was established to develop a framework of co-expression. This allowed us to clearly define the practical impact of spillover spreading on the resolution of populations, especially those expressing low levels of target antigens. While spillover spreading was considered on a global level, we focused on the local impact that any spreading would have on specific populations, given the gating strategy. We defined each target population based on the scheme in Table 1. After determining co-expression, we classified the antigens into three categories based on an accepted definition as described in Table 1 (Maciorowski et al., 2017). We classified the 16 antigens probed in this panel as defined in Table 2.
Table 1.
Antigen classifications.
| Classification | Requirements |
|---|---|
| Primary | High-density, often bimodal or “on-off” expression |
| Secondary | High to mid-density, often with gradient expression |
| Tertiary | Low or unknown density; critical to answer an experimental question |
Table 2.
Panel markers
| Marker | Classification |
|---|---|
| CD45 | Primary |
| TCRβ | Primary |
| CD8a | Primary |
| CD4 | Primary |
| MHC II (I-A I-E) | Secondary |
| Ly-6G | Secondary |
| Ly-6C | Secondary |
| XCR-1 | Secondary |
| CD11c | Secondary |
| CD11b | Primary |
| NK-1.1 | Secondary |
| CD80 | Secondary |
| CD206 | Secondary |
| Siglec F | Secondary |
| TIGIT | Secondary |
| CD69 | Secondary |
All of the antigens were classified in either the primary or secondary categories. None of the markers were designated as tertiary markers because none are particularly of low or unknown density. Moreover, there is no group of antigens in this panel that are uniquely critical to the experimental question (e.g. activation or other differential expression based on an experimental condition).
2.4. Titration of each antibody
The concentration used for each antibody was determined after titration performed. We performed 8-plex titration of the antibodies on mice spleen cells and final concentrations were selected according to the saturation concentration of each antibody.
2.5. Fluorescence minus one and single staining quality controls
Three sets of compensation control were performed: antibody-capture beads (CompBeads, BD Biosciences, USA) and single staining of mice spleen cells for single color compensation, and mice spleen cells for Fluorescence Minus One (FMO) compensation (Fig. 1). Spleens and BM cells isolated from healthy mice were examined.
Fig. 1.
Fluorescence minus one (FMO) compensation. FMOs were performed for each antibody and used for negative control when setting the gate for target populations to facilitate accurate sorting. The top row are the FMOs staining and the bottom row is the full staining. We can observe the absence of staining in FMOs.
2.6. Staining protocol, data acquisition, and data analysis
Prior to reconstitution, the vials of reagent/antibody were spun down in a microcentrifuge to ensure the reagent was at the bottom of the vial.
The kit was pre-warmed to room temperature for reconstitution of Zombie Aqua™ (BioLegend, USA) dye. 100 μl of DMSO (Corning, USA) was added to one vial of Zombie Aqua™ dye and mixed until fully dissolved.
Single cell suspension of BM or spleen cells was retrieved and thawed from liquid nitrogen to 37 °C. Afterward, 15 ml DBPS without calcium and magnesium (ThermoFisher, USA) was added to a 15 ml tube and mixed with the cells.
The samples were centrifuged at 0.4 g at 4 °C for 7 min.
The suspension was removed, and the cell pellets were transferred and resuspended in 250 μl DPBS.
Zombie Aqua™ dye was diluted in PBS, at a concentration of 1:250.
1 × 106 cells were resuspended in diluted 100 μl Zombie Aqua™ solution.
The cells were incubated at room temperature in the dark for 20 min.
The cells were wash with 2 ml Cell Staining Buffer (BioLegend, USA) at 4 °C.
After washing, the cells were centrifuged at 0.4 g at 4 °C for 7 min and resuspended in 100 μl of Cell Staining Buffer.
Prior to staining, the antibodies were vortexed and each of the16 desired antibodies was added to the sample according to the desired concentration. At completion, the stained samples were incubated for 40 min in dark at 4 °C
The cell suspensions were washed with 1.2 ml of Cell Staining Buffer and then centrifuged at 0.4 g for 5 min.
The suspension was removed, and cell pellets were resuspended in 400 μl Cell Staining Buffer at 4 °C.
Instrument quality control was run before each assay using SPHERO™ Beads (Spherotech, USA).
Flow cytometry performed with cytometer (BD FACSAria II, BD Life Sciences, USA) based on the instrument configuration (Table 3).
Table 3.
Instrument configuration: laser wavelength, power, fluorochrome, mirror, and bandpass of the panel.
| Laser Power | Detector | Detector name | Dichroic | Fluorochrome used | Bandpass |
|---|---|---|---|---|---|
| 355 nm | A | UVA | 690LP | BUV737 | 740/35 |
| UV | B | UVB | 450LP | BUV496 | 515/30 |
| 60 mW | C | UVC | X | BUV395 | 379/28 |
| 405 nm | A | V780 | 750LP | BV785 | 780/60 |
| Violet | B | V710 | 695LP | BV711 | 710/50 |
| 100 mW | C | V670 | 635LP | BV650 | 670/30 |
| D | V610 | 595LP | BV605 | 610/20 | |
| E | V525 | 495LP | Zombie Aqua | 525/50 | |
| F | V450 | X | BV421 | 450/50 | |
| 488 nm | A | B695 | 685LP | PerCP-eFluor 710 | 695/40 |
| Blue 100 mW | B | B530 | 505LP | Alexa Fluor 488 | 530/30 |
| 561 nm | A | Y780 | 750LP | PE-Cy7 | 780/60 |
| Yellow/Green | C | Y610 | 595LP | PE-eFluor 610 | 610/20 |
| 100 mW | D | Y586 | X | PE | 586/15 |
| 637 nm | A | R780 | 750LP | APC-Fire 750 | 780/60 |
| Red | B | R730 | 685LP | Alexa 700 | 730/45 |
| 140 mW | C | R670 | X | Alexa Fluor 647 | 670/30 |
3. Results
3.1. Reagent panel
We developed and primed a novel comprehensive, optimized multiplex flow cytometry panel to be able to study immune cell changes in the BM and spleen of mice using the following markers in Table 4. Each cell population was defined with corresponding markers as described in Table 5.
Table 4.
Reagents used for panel development.
| Specificity | Fluorochrome | Titration | Purpose | Clone | Source |
|---|---|---|---|---|---|
| Viability | Zombie Aqua | 1:250 | Viability | N/A | BioLegend |
| CD45 | Alexa Fluor 488 | 1:200 | Differentiation | 30-F11 | BioLegend |
| TCRβ | BUV737 | 1:50 | Lineage | H57–597 | BD |
| CD8a | Alexa 700 | 1:200 | Lineage | 53–6.7 | BioLegend |
| CD4 | BV605 | 1:200 | Lineage | RM4–5 | BioLegend |
| MHC II (I-A I-E) | BV711 | 1:200 | Lineage | M5/114.15.2 | BioLegend |
| Ly-6G | BUV395 | 1:100 | Lineage | 1A8 | BD |
| Ly-6C | BV786 | 1:100 | Lineage | HK1.4 | BioLegend |
| XCR-1 | PE | 1:100 | Lineage | ZET | BioLegend |
| CD11c | APC-Fire 750 | 1:50 | Lineage | N418 | BioLegend |
| CD11b | BV421 | 1:100 | Differentiation | M1/70 | BioLegend |
| NK-1.1 | BV650 | 1:20 | Lineage | PK136 | BioLegend |
| CD80 | Alexa Fluor 647 | 1:100 | Differentiation | 16-10A1 | BioLegend |
| CD206 | PE-Cy7 | 1:100 | Differentiation | C068C2 | BioLegend |
| Siglec F | PE-eFluor 610 | 1:50 | Lineage | 1RNM44N | Thermo Fisher |
| TIGIT | PerCP-eFluor 710 | 1:20 | Exhaustion | GIGD7 | Thermo Fisher |
| CD69 | BUV496 | 1:20 | Activation | H1.2F3 | BioLegend |
Table 5.
Cell population and staining markers.
| Cell Population | Viability | CD45 | TCRβ | CD4 | CD8 | CD11b | NK1.1 | MHC-II | Ly6C | Ly6G | CD11c | XCR-1 | CD80 | CD206 | TIGIT | CD69 | SiglecF |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| CD4 T | − | + | + | + | − | − | − | − | − | − | − | − | − | − | − | + | − |
| CD8 T | − | + | + | − | + | − | − | − | − | − | − | − | − | − | − | + | − |
| NKT | − | + | + | − | − | + | + | + | − | − | − | − | − | − | − | + | − |
| Activated NK | − | + | − | − | − | + | + | + | − | − | − | − | − | − | − | + | − |
| Exhausted NK | − | + | − | − | − | + | + | + | − | − | − | − | − | − | + | − | − |
| Mature NK | − | + | − | − | − | high | + | + | − | high | − | − | − | − | − | + | − |
| Immature NK | − | + | − | − | − | low | + | + | − | low | − | − | − | − | − | − | − |
| Neutrophils | − | + | − | − | − | + | − | + | − | + | − | − | − | − | − | − | − |
| M1 | − | + | − | − | − | + | − | + | − | − | − | − | + | − | − | − | − |
| M2 | − | + | − | − | − | + | − | + | − | − | − | − | − | + | − | − | − |
| DC1 | − | + | − | − | − | − | − | + | − | − | + | + | − | + | − | − | − |
| DC2 | − | + | − | − | − | + | − | + | − | − | + | − | − | + | − | − | − |
| Inflammatory monocytes | − | + | − | − | − | + | − | + | + | + | − | − | − | + | − | − | − |
| Non-Inflammatory monocyte | − | + | − | − | − | + | − | + | + | − | − | − | − | + | − | − | − |
| Eosinophils | − | + | − | − | − | − | − | − | − | − | − | − | − | − | − | − | + |
3.2. Gating strategy and immune cell subpopulations
We developed the gating strategy as shown in Fig. 2. Cell populations were defined as shown in Table 6. First, we identified the single cells and live lymphocytes with CD45 and Zombie Aqua™ markers. From the CD45+Zombie Aqua™- population, we gated over TCRβ (pan T cell marker) and from the TCRβ+ population and identified CD4+ T cells (TCRβ+CD4+), double negative (DN) T cells (TCRβ+CD4−CD8−), double positive DP T cells (TCRβ+CD4−CD8−) and CD8+ T cells (CD4−CD8+) based on the presence of CD4 and CD8 surface markers. Secondly, also from CD45 + Zombie Aqua™- population, we gated over TCRβ and NK1.1 markers and identified NK (TCRβ−NK1.1+) and NKT (TCRβ+NK1.1+) cells. Further stratification of the NK cell population by level of CD11b is performed to differentiate cytotoxic or mature NK cells (CD11bhigh NK) and immature NK cells (CD11blow NK). The activation and exhaustion of NK cells were marked by the presence of CD69 and TIGIT, respectively. Thirdly, from CD45+Zombie Aqua™- a population we gated over various markers to stratify hematopoietic cells. Neutrophils are defined as Ly6G+CD11b+ and from the Ly6G−CD11b+ subset, we further stratified macrophages and monocytes. M1 macrophage is defined as CD80+CD206− while M2 macrophage is defined as CD80−CD206+. Monocytes were stratified according to surface Ly6C expression into inflammatory monocytes (Ly6C+) and noninflammatory monocytes (Ly6C−). The eosinophils were defined as SigliecF+CD11b+.
Fig. 2.
Gating strategy for BM cells isolated from ligature-induced periodontitis mouse. Firstly, we identified the single cells and live lymphocytes with CD45 and Zombie Aqua™ markers in the BM of healthy mice. A) We gated over TCRβ and from the TCRβ+ population, we identified CD4+ T cell, DN T cell, DP T cell, and CD8+ T cell based on the CD4 and CD8 surface markers. B) From CD45+Zombie Aqua™- population, we gated over TCRβ and NK1.1 and identified NK (TCRβ−NK1.1+) and NKT (TCRβ+NK1.1+) cells. Further stratification of the NK cell population by level of CD11b is performed to differentiate cytotoxic NK cells (CD11bhigh NK) and immature NK cells (CD11blow NK). The activation and exhaustion of NK cells were marked by the presence of CD69 and TIGIT. C) From CD45+Zombie Aqua™- a population we gated over various markers to stratify hematopoietic cells. Neutrophils are defined as Ly6G+CD11b+ and from the Ly6G−CD11b+ subset, we further stratified macrophages and monocytes. M1 macrophage is defined as CD80+CD206− while M2 macrophage is defined as CD80−CD206+. Monocytes were stratified according to surface Ly6C expression into inflammatory monocytes (Ly6C+) and noninflammatory monocytes (Ly6C−). The eosinophils were defined as SigliecF+CD11b+.
Table 6.
Cell population definition.
| Cell Population | Marker Definition |
|---|---|
| CD8 T cells | Zombie−CD45+TCRβ+CD4−CD8+ |
| DNT | Zombie−CD45+TCRβ+CD4−CD8− |
| DPT | Zombie−CD45+TCRβ+CD4+CD8+ |
| Natural Killer T cells | Zombie−CD45+TCRβ+NK1.1+ |
| Natural Killer (NK) cells | Zombie−CD45+TCRβ−NK1.1+ |
| Mature (cytotoxic) NK cells | Zombie−CD45+TCRβ−NK1.1+CD11bhigh |
| Immature NK cells | Zombie−CD45+TCRβ−NK1.1+CD11blow |
| Activated NK | Zombie−CD45+TCRβ−NK1.1+CD69+ |
| Exhausted NK | Zombie−CD45+TCRβ−NK1.1+TIGIT+ |
| Neutrophil | Zombie−CD45+TCRβ−Ly6G+CD11b+ |
| Inflammatory Monocyte | Zombie−CD45+TCRβ−Ly6G−CD11b+Ly6C+ |
| Noninflammatory Monocyte | Zombie−CD45+TCRβ−Ly6G−CD11b+Ly6C− |
| M1 Macrophage | Zombie−CD45+TCRβ−Ly6G−CD11b+CD80+CD206− |
| M2 Macrophage | Zombie−CD45+TCRβ−Ly6G−CD11b+CD80−CD206+ |
| Eosinophil | Zombie−CD45+TCRβ−SiglecF+CD11b− |
| Dendritic Cell DC1 | Zombie−CD45+TCRβ−CD11c+MHCII+XCR1+CD11b− |
| Dendritic Cell DC2 | Zombie−CD45+TCRβ−CD11c+MHCII+XCR1−CD11b+ |
3.3. Identifying inflammation
The panel is able to distinguish frequency and distribution of different immune cell populations in the state of inflammation and in health. Fig. 3 presents representative images of NK and NKT frequency in spleen (naïve), BM in health, and BM in ligature-induced periodontitis. The frequecny of NKT cells (p < 0.01), activated NK cells (p < 0.05), and mature or cytotoxic NK cells (p < 0.01) were incleased significantly in the ligature-induced periodontitis model.
Fig. 3.
The panel was able to demonstrate high capacity for detection of difference in the frequency of cell populatins including NKT cells, NK cells, and NK activation and mature states in BM of ligature induced periodontitis versus health. A) Representative images of current validated panel in the spleen (Naïve), bone marrow (health), and bone marrow isolated from ligature-induced periodontitis model. B.C·D) statistically significant increase in NKT, activated NK (CD69+), and mature NK (CD11b+) cells in BM in ligature-induced periodontitis model. Results are representative of 12 biolgical replicates (n = 6 per goup). Parametric Students' t-test was used for statistical analysis. ** indicates P < 0.01 and * indicates P < 0.05. ** indicates P < 0.01 and * indicates P < 0.05.
4. Discussion
In the present panel design, to assign markers to fluorophores, we combined information about expression patterns with fluorochrome brightness and potential spillover/spillover spreading issues, specifically in the context of the instrument being used. We took a “top-down” approach, first tackling the primary antigens - in our case, markers expressed on a variety of cell types (CD45, CD11b, and TCRβ). In order to avoid potential severe spillover, we assigned fluorophores to primary markers by either (1) using a dim fluorochrome with generally minimal spillover or (2) using a fluorochrome with potential spillover spreading in other used channels while also ensuring that affected channels are not expressed on the same cell type. For example, we selected Alexa Fluor 488 to probe for CD45 because of minimal spillover in nearly every other channel being used in the experiment. The instrument used (BD FACSAria II, BD Life Sciences, USA) is equipped with a 561 nm laser, from which all of the PE-based dyes are measured. Therefore, due to Alexa 488's lack of excitation at 561 nm, we can avoid the spillover that can occur on an instrument where all of the PE-based dyes are measured at 488 nm excitation. Similarly, we selected Brilliant Violet 421 to measure CD11b, which is expressed on a variety of cell types examined in this panel, taking advantage of its narrow emission spectra and lack of potential spillover/spillover spread into other channels. In the case of TCRβ, which also defines multiple cell types, we chose Brilliant Ultraviolet 737, which does present spillover potential via cross-laser excitation between the 405 nm and 637 nm lasers. However, the channels we chose for downstream T cell markers - Alexa 700 and BV605 - are spectrally unaffected by BUV737, precluding potential spreading issues.
To assign fluorochromes to secondary markers, which can be expressed at lower levels and are thus more sensitive to spreading error than primary markers, we used expression pattern and spillover potential to minimize any practical effect of spillover spreading. For example, to assign fluorochromes to cell populations defined by TCRβ expression, probed by BUV737, we chose fluorochromes whose channels would be minimally affected by BUV737 spillover, including cross-laser spillover, namely Alexa Fluor 700 and Brilliant Violet 605. Similarly, we ensured that channels with high spillover spreading potential were assigned to markers without co-expression of markers assigned to problematic fluorochromes. For example, we assigned PE-eFluor 610 (561 nm - 610/20), which can be affected by spreading contributed by PE and Brilliant Violet 605, to Siglec F, expressed on eosinophils, while PE and Brilliant Violet 605 were assigned to XCR-1 (expressed on DCs) and CD4, expressed on T cells, respectively. Similarly, we assigned Alexa 647, which can be affected by spillover from Brilliant Violet 650, to CD80 (expressed on macrophages) and Brilliant Violet 650 to NK-1.1, which is expressed in NK cells. While we did not generate a spillover spread matrix (SSM) specifically for the instrument being used, we based our decisions based on channels that can often be affected by spreading error in the presence of dyes with spillover (e.g., 637–670/30, 637–730/45, 561–610/20).
In order to test the panel and perform any optimization, we acquired data from fully stained, and FMO controls after antibody titration in order to assess resolution and any potential loss thereof due to spreading error. We did not encounter any issues with the panel at this point and deemed that further optimization was not required. Because this panel was used for cell purification of live cells, it was not tested under fixation conditions, so the impact of fixation was not considered during the fluorochrome selection process. Nevertheless, this panel can be expanded to include intracellular markers after fixation and permeabilization conditions have been tested.
Using this panel, we successfully stratified CD4+ T cells, CD8+ T cells, double negative (DN) T cells, double positive (DP) T cells, NK T cells, natural killer (NK) cells, neutrophils, M1 macrophages, M2 macrophages, inflammatory monocytes, noninflammatory monocytes, dendritic cells (DC), eosinophils and their subtypes. Besides, we were able to identify statistical significant changes in BM in the ligature-induced periodontitis versus BM in health. Using ou panel, we were able to identify the the signficat increased frequency NKT cells, activated NK cells (CD69+), and mature/cytotoxic NK cells (CD11b+) in BM of ligature-induced periodontitis versus health.
This work has similarities with other panels (Unsworth et al., 2016; Dusoswa et al., 2019), which characterize murine innate and adaptive immune cell subsets in bone marrow and spleen. These markers were used to detect T cells, NK cells, DC, macrophages, monocytes, and neutrophils: CD4, CD8, CD45, TCRβ, Ly6C, CD49b, Ly6G, CD11c, MHC II (I-A I-E), CD206, CD62L, NKp46, and CD44. The above-mentioned markers overlap with our panel except for CD49b, NKp46, CD44, and CD62L. Our panel also looked into the activation and exhaustion of T cells and NK cells, thus, we included NK1.1, CD69, and TIGIT. To achieve more comprehensive immune profiling of in mice, we added XCR1 for the DC subset, CD11b for neutrophil/macrophage/monocyte subset, CD80 for the M1 macrophage subset, Siglec F for the eosinophil subset, NK1.1 for NK cell subset, TIGIT for exhaustion and CD69 for activation and metabolism.
T lymphocytes have an essential role in immunity and are responsible for both inflammatory and regulatory events. The development of T cells takes place in the thymus, passing through a CD4−CD8− (double negative, DN) stage, then a CD4+CD8+ (double positive, DP) stage, and eventually commit to either CD4+ or CD8+ lineage (Anderson and Jenkinson, 2001; Li et al., 2020b). CD4+ T cells are identified by their cytokine and transcription factor features, while CD8+ T cells are characterized with cytotoxic functions. DN T cells have been proposed to originate from the thymus as the precursor of DP cells (D'Acquisto and Crompton, 2011). DN T cells present both proinflammatory and immunoregulatory features. They are also directly linked to the development of different autoimmune diseases including, myasthenia gravis, autoimmune lymphoproliferative syndrome, and Behcet's disease (D'Acquisto and Crompton, 2011; Li and Tsokos, 2021).
Aside from the fact that CD4+ T cell, CD8+ T cell, DN T cell, and Treg, natural killer T cells (NKT) cells are a special subset of T lymphocytes bridging the innate and adaptive immunity (Van Kaer et al., 2011; Kumar et al., 2017). NKT cells demonstrate the ability to initiate and amplify both innate and adaptive immune responses, as well as immunoregulatory functions such as fine-tuning the nature of the magnitude of immune responses (Brennan et al., 2013). When activated, NKT cells can produce proinflammatory cytokines such as INF-γ and IL-4, which could induce an immune response and tissue destruction(Nilsson et al., 2020) (Nowak et al., 2013).
NK cells are a distinct lineage of cytotoxic lymphocytes that play fundamental roles in the innate- and cell-mediated immune response. NK cell development is an intricate yet highly controlled process, depending on both extrinsic and intrinsic factors (Kim et al., 2002; Chiossone et al., 2009; Goh and Huntington, 2017). The acquisition of CD11b divides NK cells into immature and mature subsets (Kim et al., 2002). As NK cells lose CD27 and start to acquire CD11b, they lose proliferative potential, and produce fewer inflammatory cytokines. Meanwhile, NK cells demonstrate more cytotoxic function against target cells as they mature (Kim et al., 2002; Goh and Huntington, 2017). Moreover, it has been reported that NK cells also express higher levels of Ly6C in the late developmental stage (Goh and Huntington, 2017). Ly6Chigh NK cells could serve as a reservoir and allow for effective and strong responses to infections (Omi et al., 2014).
In summary, this work designed and tested a 17-color panel for immune profiling to be used in laboratories that perform FC for BM, spleen, tumors and tissues. Analysis of this panel and identification of immune cell subtypes, together with functional assays, can provide important insights about the immune cell changes in different disease models. Thus, this is a promising tool for immune profiling in preclinical studies to gain insights about immune cell changes for both diagnostics and therapeutics applications.
Funding
This research received funding from the National Institute of Health (NIH)/National Institute of Dental and Craniofacial Research (DE029546) (DE031112), NIH/National Cancer Institute (P30 CA013696), NIH/National Center for Advancing Translational Sciences (TR001874) (UL1 TR001873), American Association of Cancer Research and The Mark Foundation for Cancer Research (20–60-51-MOME).
Footnotes
CRediT authorship contribution statement
Yi-Chu Wu : Conceptualization, Methodology, Software, Data curation, Investigation, Validation, Formal analysis, Visualization, Project administration, Writing – original draft, Writing – review & editing. Michael Kissner : Conceptualization, Methodology, Software, Data curation, Validation, Formal analysis, Supervision, Project administration, Resources, Writing – original draft, Writing – review & editing. Fatemeh Momen-Heravi : Conceptualization, Methodology, Data curation, Formal analysis, Supervision, Funding acquisition, Project administration, Resources, Writing – original draft, Writing – review & editing.
Declaration of Competing Interest
The authors disclosed no conflict of interests.
Data availability
Data will be made available on request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Data will be made available on request.