IT is well established that many immune cell types play critical roles during human immunodeficiency virus (HIV) or simian immunodeficiency virus (SIV) infection in humans and monkeys, respectively, either as targets, viral reservoirs, or host defense (1, 2). Helper CD4+ T lymphocytes, especially activated memory CD4+ T cells, are the main target of HIV and SIV (3). CD8+ T cells have been shown to play a critical role in controlling viremia (4). However, CD8+ T cells ultimately fail to stop infection (5). CD20+ B lymphocytes initially produce neutralizing antibodies against HIV and SIV, but likely because of rapid mutation, overtime the antibodies produced by B lymphocytes become less efficient at viral neutralization (6). Although the natural killer (NK) cells' precise role in HIV infection is unclear, it appears that following infection, they have a decreased ability to kill virus-infected target cells and interact with other cellular components of the adaptative immune system, including dendritic cells (DCs) (7). NK T cells (NKT) are a rare population that is selectively depleted with HIV infection (8). Monocytes/macrophages are considered to be one of the main viral reservoirs (9). In addition, HIV- and SIV-infected bone marrow-derived monocytes are “Trojan horse” cells that traffic to the central nervous system (CNS) and bring virus into the brain (10). Circulating myeloid DC (mDC) and plasmacytoid DC (pDC) are depleted during HIV and SIV infection (11). Finally, a decrease of the pool of CD34+ hematopoietic stem cells from the bone marrow may limit T-cell generation (12). Thus, it becomes critical to precisely identify these cell subsets in clinical viral studies for a broader view of their role in HIV immunopathogenesis.
Today, one of the most powerful tools for immunophenotypic study of the immune system is polychromatic flow cytometry (13, 14). Over the last decade, our knowledge of the immune system has greatly increased, partly due to the development of flow cytometry (15). Cell populations that were considered to be homogenous in the past, appear more complex now. The identification of specialized lymphocyte subsets such as naïve, memory, or cytotoxic T lymphocytes or monocyte subsets has considerably helped our understanding of immunopathogenesis during HIV and SIV infection (16). Moreover, the polychromatic flow cytometry technique has become increasingly useful in identifying rare subsets of cells such as DC, where a minimum of eight fluorescent parameters, in addition to the physical parameters [forward scatter (FSC) and side scatter (SSC)], are ideal to distinguish five nonoverlapping DC subsets simultaneously (17, 18).
Despite access to commercially available flow cytometers that can measure up to 12 colors without significant modifications, a limited number of laboratories are routinely using such instruments. Although developing a reliable multicolor panel is time consuming and requires a number of validation trials, compared to 2 to 4-color assays, the amount of information provided by such a panel will aid in our understanding of the immune system, potentially defining cell subsets that might otherwise be missed (19, 20). In addition, using a multicolor flow cytometry panel can decrease the amount of blood needed for immunophenotyping, which is often limited especially during longitudinal studies. Until recently, most research laboratories were measuring populations of lymphocytes, monocytes, and DC using separate antibody panels in individual tubes, mainly because of technical limitations. With advances of the flow cytometry technology, scientists are now able to measure up to 17 colors in one single panel (21). Using this panel, we characterized T lymphocytes, B lymphocytes, NK cells, NKT cells, monocyte subsets, and DC subsets.
MATERIAL AND METHODS
Peripheral venous blood was obtained from nine healthy adult volunteers and collected in tubes containing anticoagulant EDTA. A properly executed, written, and IRB-approved informed consent was obtained from each volunteer before blood collection.
A Becton Dickinson FACSAria™ cytometer with three lasers (BD Biosciences, San Jose, CA) was used for the study. Our instrument has been optimized to measure up to 12 fluorescent parameters (Fig. 1). The blue laser can independently excite six fluorochromes [FITC, PE, Texas Red-PE (ECD), Cy5-PE, Cy5.5-PerCP, and Cy7-PE], the red laser can excite three fluorochromes (APC, Alexa Fluor 700, and Cy7-APC), and the violet laser can excite three fluorochromes (Pacific Blue, AmCyan, and QDot 655; Fig. 1).
Antibodies Used for Flow Cytometry
Our ultimate goal was the development of a 12-color flow cytometry panel. To determine the accuracy of this panel, we compared this panel to smaller panels of select lineages, including lymphocytes (six colors), monocytes (five colors), and DCs (eight colors). For the 12-color flow cytometry panel and the lineage-specific panels, the following monoclonal antibodies were used: CD16-FITC (clone 3G8), CD141-PE (clone 1A4), CD11c-Cy5-PE (clone B-Ly6), CD123-Cy5.5-PerCP (clone 7G3), CD20-Cy7-PE or CD20-Cy7-APC (clone B27), CD56-Cy7-PE (clone B159), CD3-Alexa Fluor 700 or CD3-Cy7-PE (clone SP34-2), HLA-DR-Cy7-APC (clone L243), CD14-Pacific Blue or CD14-Cy7-PE (clone M5E2), CD4-AmCyan (clone SK3) (all from BD Pharmingen, San Jose, CA); CD34-ECD (clone 581, Beckman Coulter, Miami, FL); CD1c-APC (clone AD5-8E7, Miltenyi Biotech, Auburn, CA); and CD8-QD655 (clone 3B5, Invitrogen, Carlsbad, CA). All antibodies were titrated to determine optimal concentrations. Antibody-capture beads (CompBeads, BD Biosciences) were used for single-color compensation controls for each reagent used in the study, with the exception of CD3-Alexa Fluor 700 that does not bind anti-mIgGκ CompBeads. In this case, cells were stained with CD3-Alexa Fluor 700. Gating controls were determined using fluorescence minus one (FMO) controls for CD1c, CD14, CD16 where all antibodies are used except one (22, 23) (Supporting Information Figs. 1 and 2). A description of the different panels and the compensation matrix are shown in the Table. 1.
Table 1. Panel description (A) and compensation matrix (B)
Compensation matrix obtained by staining Antibody-capture beads Compbeads with each of the mAbs used in the panel, with the exception of Alexa 700-anti-CD3 used on cells. Spectral overlap or spillover between fluorochromes was calculated using flowJo software 8.7.1.
CD3+ CD14+CD20+ CD56
(B) Compensation Matrix
Blood Samples and Staining Protocol
Erythrocytes within 100 μl of whole blood were lysed using a cell lyse preparation workstation (TQ-Prep instrument, Beckman Coulter). We routinely use two tubes of 100 μl of whole blood, to ensure we have enough DC for each data point. For studies strictly focusing on DC, larger volumes of blood are required. For lymphocyte and monocyte staining, we used only 100 μl of whole blood and followed the same protocol. Erythrocyte lysis was performed before the staining to avoid a reduction in fluorescence of CD16 on cells because of endogenous Fcγ RIII-IgG in the plasma that can potentially bind to the CD16 antibody (24). After lysis, samples from both tubes were pooled, washed with 3 ml of phosphate buffered saline (PBS) containing 2% fetal bovine serum (FBS), and then incubated with a premixed antibody cocktail for 15 min at room temperature. Stained cells were washed with 3 ml of PBS containing 2% FBS and fixed with freshly made 1% paraformaldehyde (PFA).
Data Acquisition and Sample Analysis
The BD FACSAria™ cytometer was set up with a pressure of 20 psi and a 100-μm nozzle was used. Instrument calibration was checked daily by use of rainbow fluorescent particles (BD Biosciences). After acquiring unstained and single color control samples to calculate the compensation matrix, we acquired 1 × 106 events within the combined lymphocyte-monocyte gate, based on the FSC and SSC parameters. Finally, the Flow Cytometry Standard (FCS) files were analyzed using FlowJo software 8.7.1 (Treestar, Ashland, OR) on a MAC® workstation.
Percentages and Absolute Numbers of Lymphocytes, Monocytes, and DC Subsets
Absolute numbers of peripheral blood lymphocytes, monocytes, and DC populations were calculated by multiplying the total white blood cell count determined with an automated hematology analyzer, with the total percentage of each cell population determined by flow cytometry from the whole blood sample (see Table 2).
Table 2. Percentages and absolute cell numbers of lymphocyte, monocyte and DC subsets
Absolute Count (Cells/μl)
Data were obtained from nine healthy adult donors (age range 21–48 years) by an automated hematology blood analyzer and FACS Aria flow cytometer. Median and range are reported.
pDC CD123+ CD11c−
mDC CD123− CD11c+
Panel Strategy and Development
The goal of this study was to design and develop a 12-color flow cytometry panel that would allow simultaneous analysis of the lymphocyte, monocyte, and DC populations. Bandpass and dichroic filters used in this study are described in Figure 1. The first step was to choose bandpass filters based on the maximum emission spectra of each fluorochrome that we would use for our study. This would ensure that the fluorescence intensity of each directly conjugated antibody would be the highest possible. The second step was to ensure that there was no overlap between bandpass filters and that the dichroic filters separate the two contiguous bandpass filters. Additionally, we used directly conjugated antibodies to avoid an additional step of indirect labeling, which can result in a background signal. After several unsuccessful attempts, we also elected to avoid using two quantum dot (QD)-conjugated antibodies together (CD4-QD605 and CD8-QD655), because of that increased double positive CD4+CD8+, which we found to be two- to three-fold higher in percentage when compared with a six-color lymphocyte panel using CD8-QD655 and CD4-AmCyan (data not shown) (25). Very low percentages of double positive CD4+CD8+ are normally found in humans (26). We note that the CD4-AmCyan results in a reasonable separation of CD4+ T lymphocytes in normal blood, but may present a problem if CD4 is significantly down-regulated in specimens from samples of patients with Acquired Immune Deficiency Syndrome (AIDS). For such studies, other CD4 conjugates may be preferable. Finally, for efficiency we used together in one channel anti-CD20 and anti-CD56 antibodies, both Cy7-PE, as their target cells (B cells and NK cells, respectively) can be identified using HLA-DR, which is expressed on B cells and not NK cells. Thus, our final panel is described in Table 1.
Phenotypic Analysis of Lymphocyte Populations
To identify lymphocyte populations, we first gated on cells based on FSC and SSC properties (Fig. 2A). We excluded monocytes based on CD14 expression (Fig. 2B). From this gate, CD3+CD16− T lymphocytes (median: 60.6%, range: 48.7–77.1%, n = 9) were selected (Fig. 2C) and further divided into CD4+CD8− (median: 35.46%, range: 26.15–48.26%, n = 9), CD4−CD8+ (median: 21.13%, range: 12.38%–77.1%, n = 9), CD4+CD8+ (median: 0.48%, range: 0.18%–0.75%, n = 9), and CD4−CD8− (likely gamma delta T cells) (median: 3.51%, range: 1.02–7.12%, n = 9) lymphocytes (Fig. 2D). In addition, using an anti-CD16 antibody we can identify CD3+CD16+ NKT cells (median: 2.35%, range: 0.26–4.42%, n = 9) (Fig. 2C) as well as CD3−CD16+ cells. Within the CD3−CD16+ cells, we can identify a major NK population (median: 14.42%, range: 5.08–33.37%, n = 9) that does not express HLA-DR (Fig. 2E) as well as a small population expressing HLA-DR (CD3−CD16+HLA-DR+) but not CD56 (data not shown), which might be a myeloid DC subset (17). CD3−CD16− cells (Fig. 2C) were further divided into HLA-DR+CD20+ B cells (median: 13.15%, range: 6.36–16.51%, n = 9) (Fig. 2F) and CD56+ NK cells (median: 0.39%, range: 0.23–0.85%, n = 9) (Fig. 2F). HLA-DR+CD20+ B cells can be further divided into CD1c+ resting (median: 3.3%, range: 1.23–5.48%, n = 9) and CD1c− activated (median: 9.83%, range: 5.13–13.03%, n = 9) B cells (Fig. 2G). Finally, the CD20−CD56− cells (Fig. 2F) represent the overall contamination of nonlymphocyte cells within the lymphocyte gate and are usually <5% of the total lymphocyte population. Of these, ∼1% of the overall contamination are HLA-DR+ cells corresponding to DC. Four percent are HLA-DR− cells (Fig. 2), half of which are CD123+ basophilic granulocytes. The remaining 2% are not defined.
Phenotypic Analysis of Monocyte Populations
The monocyte populations were analyzed by first gating using FSC and SSC (Fig. 3A) and then excluding T lymphocytes, B lymphocytes, and NK cells, expressing CD3, CD20, and CD56, respectively (Fig. 3B). From the HLA-DR+ and CD14+ population (median: 9.28%, range: 6.04–11.56%, n =9) (Fig. 3C), three monocyte subsets were distinguished: CD14+CD16− classical monocytes (median: 8.11%, range: 7.83%–8.54%, n = 9) and two subsets of activated monocytes: CD14+CD16+ (median: 0.62%, range: 0.18–0.75%, n = 9) and CD14dimCD16+ (median: 0.35%, range: 0.16–0.81%, n = 9) (Fig. 3D). Granulocytes that are HLA-DR− and CD14 dim are excluded from monocyte analysis, based on FSC vs. SSC and HLA-DR and CD14 expression (Fig. 3C).
Phenotypic Analysis of DC Populations
As DCs have an intermediate size that falls between that of monocytes and lymphocytes, the FSC vs. SSC gate included both populations (Fig. 4A). DCs were gated as HLA-DR+ cells and lineage-negative (Lin−) cells by excluding CD3+ T lymphocytes and NK cells (Fig. 4B), CD14+ monocytes (Fig. 4C), and then CD20+ B lymphocytes and CD56+ NK cells (Fig. 4D). HLA-DR+Lin− cells (median: 1.16%, range: 0.63–1.46%, n = 9) were further divided into CD123+CD11c− plasmacytoid DC cells (pDC, median: 0.27%, range: 0.12–0.35%, n = 9) and CD123−CD11c+ myeloid DC cells (mDC, median: 0.75%, range: 0.54–0.86%, n = 9) (Fig. 4E). Consistent with previous publications (18), we distinguished three nonoverlapping subsets of CD11c+ mDC expressing CD16 (median: 0.55%, range: 0.13–0.74%, n = 9), CD1c (BDCA-1; median: 0.12%, range: 0.09–5.48%, n = 9), or CD141 (BDCA-3; median: 0.01%, range: 0–0.03%, n = 9) (Fig. 4E). Finally, CD34+ hematopoietic stem cells (median: 0.03%, range: 0.01–0.06%, n = 9) were identified within CD123− CD11c− cells. For flow cytometric analysis focusing solely on DC, analysis of >500 cells per subset is ideal. This would require more than 200 μl of whole blood that we used in the current study.
Individual Lineage-Specific Panels Versus 12-Color Panel
To further validate the 12-color flow cytometry panel, we compared data obtained using three flow cytometry panels, each specific for each single population, lymphocytes, monocytes, and DCs, with our multicolor flow panel. The different panels are described in Table 1. Comparing the two panels (Fig. 5A), we noticed that within the leukocyte population the percentage of CD3+ T lymphocytes were lower when using the lymphocyte panel compared to the 12-color panel (percent change: 3.2%, P = 0.02). This difference is probably due to monocyte contamination in the lymphocyte gate on the FSC vs. SSC that we can measure consistently with the 12-color panel (Fig. 2). We also found a statistical difference in the total NK population, with a higher percentage of CD16+CD56dim NK cells using the lymphocyte panel when compared with the 12-color panel (percent change: 28.7%, P = 0.006). This difference is likely due to the CD16+ mDC that express HLA-DR as opposed to the CD16+ NK cells that do not express HLA-DR (Fig. 2). No statistical differences were measured on the monocyte subsets using either panel (Fig. 5B). Although DC subsets are usually very rare populations and difficult to precisely identify, we were able to consistently measure higher percentages of all five previously defined subsets, when using a 12-color panel as compared with a specific DC panel (Fig. 5C). Statistical differences were measured on the pDC (percent change: 42%, P = 0.04), CD141+ mDC (percent change: 64%, P = 0.04), and CD34+ cells (percent change: 31%, P = 0.01) subsets. When comparing a phenotypic analysis of DC subsets using the two panels, we found that it is difficult to gate HLA-DR+Lin− cells when all the exclusion markers are in the same channel (Fig. 6A). Therefore, the percentage of CD11c−CD123− cells, most likely contaminating cells (that also contains CD34+cells), is important (Fig. 6A, red circles, ∼40.0%). However, when using exclusion markers in specific channels as demonstrated in the 12-color panel, the gating strategy is clear and leads to less contamination and more precise data (Fig. 6B, red circles, ∼12.0%).
Absolute Cell Numbers of Lymphocyte, Monocyte, and DC Subsets by a Single 12-Color Flow Cytometry Assay
Using the gating strategy previously described, we find a normal distribution of lymphocyte, monocyte, and DC subsets in humans that are in agreement with data obtained using three flow cytometry panels specific for each population. Percentages and absolute numbers of lymphocytes, monocytes, and DC subsets are shown in Table 2.
In this study, we developed a 12-color flow cytometry panel for human specimens, capable of characterizing and measuring precisely major lymphocyte, monocyte, and DC populations. To optimally study monocyte/macrophages and DCs in normal and HIV-infected individuals, a panel for simultaneous detection of all cell types is advantageous. In addition to such work, CD4+ and the CD8+ T lymphocyte cell subsets need to be monitored, as these lymphocyte populations change during AIDS development (21, 27). Furthermore, a multicolor panel allows the simultaneous phenotypic analysis of different cell types that express the same antigen. This leads to more accurate data and a better understanding of the immune responses during viral infection. For example, a “lymphocyte” panel used to analyze T and B lymphocytes usually comprises anti-CD3, anti-CD4, anti-CD8, and anti-CD20 antibodies. However, by using this four-color panel only, one potentially excludes the NKT cells (CD3+CD16+), a unique population that has been recently thought to play a critical role in HIV infection (28).
The power of multiparameter flow cytometry lies in the ability to study rare subsets of cells like DCs. DCs are important professional antigen-presenting cells (29–32). DCs comprise <1% of total leukocytes and are heterogeneous, thus requiring multiparameter flow cytometry to correctly identify the DC subsets. Classically, DCs have been defined as two main subsets: Lin−HLA-DR+ CD11c+ CD123− mDC and the Lin−HLA-DR+ CD11c− CD123+ pDC (30, 32–34). More recently, MacDonald et al. distinguished five cell subsets within Lin−HLA-DR+ cells: Lin−HLA-DR+ CD123+ pDC, CD34+ hematopoietic stem cells, and three subsets of CD11c+ mDCs expressing CD16, CD1c (BDCA-1), or CD141 (BDCA-3) similar to what we describe here (18). However, the authors had to use a cell purification technique to remove all the unwanted cells, prior to the staining. Another technique to “remove” the unwanted cells from the analysis is the use of a lineage cocktail used as exclusion markers for DC (CD3, CD14, CD20, CD56). Often, these exclusion markers are all grouped in one single channel, referred to as a dump channel, and, thus, it is impossible to also identify lymphocyte and monocyte populations (35, 36). With our 12-color panel, we used the exclusion markers in different fluorescent channels, and thus were also able to assess the major lymphocytic populations (T, B, NK, and NKT cells) and monocyte subsets. Using a channel for “removing” all the unwanted cells and/or dead cells from the analysis is commonly employed. However, gating out these unwanted cells is difficult and there is usually no clear separation between the unwanted cells and the cells of interest. Furthermore, when the cells of interest are rare populations like DCs, it is important to clearly remove cells that can lead to contamination. With the availability of measuring more parameters on flow cytometers, it is now possible to use exclusion markers in individual channels and this should be used whenever possible.
Another important advantage of a multiparameter flow panel over four- or six-color flow panels is the decreased level of contamination among populations. An example of that is the NKT population that expresses CD3 and CD16. If the anti-CD16 antibody is not used in a “lymphocyte” flow cytometry panel, the entire NKT population can potentially “contaminate” the T lymphocyte population and results in an overestimation of the percentage of T cell subsets, as 50% of the NKT population express CD4 (8). Another example is a well-characterized, both phenotypically and functionally, CD11c+CD16+ mDC subset (17). The CD16 molecule is expressed on many cells such as NK cells, monocytes, macrophages, granulocytes, and DCs. It is virtually impossible to gate DCs only based on their size and granularity, as DC overlap with the lymphocyte and monocyte gates. Therefore, it is very easy to misidentify these cells as being NK cells, unless one includes HLA-DR or CD11c in the flow cytometry panel as we have done, or even CD4, as all DCs express low levels of CD4, in contrast to NK cells.
Finally, one last advantage of a 12-color flow cytometry panel is the ability to use small volumes of blood to monitor changes in the phenotyping of lymphocyte, monocyte, and DC subsets. During longitudinal studies, the volume of blood from patients can be limited. Using one flow cytometry panel with markers for all major blood cell populations is of interest and can save material for other analysis such as cell sorting or in vitro assays.
In summary, we show in this study that all the main players of the immune system, lymphocytes, monocytes, and DCs, can be precisely measured using a 12-color multiparameter flow cytometry approach. This assay is rapid, does not involve a cell isolation technique, and requires a minimum amount of blood. Moreover, this assay is precise with minimal contamination between populations. Most importantly, the 12-color panel is an important tool to study the interactions between different immune cell populations during HIV and potentially other diseases, and to better understand the vital role that DCs play in disease progression.
The authors thank Cynthia Lubianez, Assistant Director of the Health Services Primary Care Center of Boston College and Anyeris Paulino, Nursing assistant, for help with the blood draw protocol.