Subjects and IRB approval

Induced sputum was obtained from both healthy and mild allergic asthmatic or allergic non-asthmatic volunteers under our screening protocol which was reviewed and approved by the University of North Carolina Committee on the Rights of Human Subjects (Institutional Review Board). Asthmatic volunteers had mild symptoms of asthma as defined under section 3 of the 2007 NHLBI guidelines for the diagnosis and management of asthma and all had a positive methacholine challenge test (PC₂₀ ≤ 10 mg/ml), defined as a 20% decrease from the baseline forced expiratory volume in the first second (FEV₁) following an inhaled provocative challenge dose of ≤ 10mg methacholine/ml. Atopy was demonstrated by a positive immediate skin test response to one of the following allergen mixes: 2 species of house dust mite (Dermatophagoides farinaea and Dermatophagoides pteronyssinus), cockroach, tree mix, grassmix, weedmix, moldmix1, moldmix2, rat, mouse, guinea pig, rabbit, cat or dog.

Sample Collection and Processing

Sputum induction and processing techniques are based on the those of Hargreave et al (Hargreave et al. 1998) and are detailed elsewhere. (Alexis et al. 2000) Briefly, only the cell-enriched mucus “plugs” manually selected from the surrounding clear fluid were processed to minimize squamous cell contamination and dilution from saliva. (Pizzichini et al. 1996; Spanevello et al. 1998; Alexis et al. 2006) Sputum samples should immediately be placed on ice and processed within 2 hours.(Efthimiadis et al. 2002a) Excessive or prolonged exposure to dithiothrietol (DTT) during processing adversely affects both cell viability and surface marker expression (Efthimiadis et al. 1997; Loppow et al. 2000) and should be avoided. (See the appendix for additional discussion).

Antibody Panels and Staining Procedures

“Standard” antibody panels (Table 2) were used for identifying leukocyte populations and assessing expression of select cell surface proteins associated with innate and adaptive immunity, antigen presentation and inflammation. Anti-CD45 (leukocyte common antigen) was included in all sample tubes to help differentiate leukocytes from debris and assist in identifying leukocyte populations. Major lineage markers such as CD16, CD14, HLA-DR, CD3, CD19 and CD56 were used to aid in isolating specific populations. Certain antibodies were included in multiple sample tubes to facilitate gating of specific populations. Incorporation of additional fluorochromes would be advantageous, allowing consolidation of tubes and conservation of cells. Though we have done this in some panels for specific protocols, it is often difficult to procure specific antibodies labeled with the desired fluorochrome.

Specific staining procedures have been described elsewhere (Alexis et al. 2000) and in the Appendix. Following staining, cells were fixed in 1% paraformaldehyde in Dulbecco’s phosphate-buffered saline (DPPS) and FCM performed within 24 hours. Stained samples should be refrigerated at 4°C until acquired. Sources for specific antibodies are listed in the appendix (Table 4). Specific antibodies were titrated to determine an appropriate amount for staining. We found it necessary to titrate and dilute isotype controls up to 1:5 (or more), especially for IgG2 isotypes since non-specific binding resulted in fluorescent intensity of the isotype control exceeding that of certain specific antibodies. Though fluorescence minus one (FMO) (Tung et al. 2004) controls would be preferable to isotype controls for determining background fluorescence, it was not feasible since it would require an excessive amount of cells to be performed properly. (See the appendix for additional discussion of isotype controls.)

Table 4

List of antibodies and cellular expression of target molecules.^*

Antibody	Clone	Fluorochrome	Source†	Cellular Expression†	Function
CD1a	BL6	APC	Coulter	Mono, Mac, DC, act. T & B cells	Presentation Of Non-Peptide Ag, Cell Mediated Immunity
CD3	13B8.2	PerCP	BD	Pan T cell Marker	Component of T Cell Rcpt. Complex, T Cell Activation
CD4		FICT	Coulter	T-helper cells	Co-Receptor For MHC-II Restricted T Cell Activation
CD8	B9.11	PE	Coulter	Cytotoxic T-cells	Co-Receptor For MHC-I Restricted T Cell Activation
CD11b/CR3	ICRF44	PE-CY5	BD	Gran, Mac, Mono	Phagocytosis, Cell Adhesion
CD11c/CR4	B-ly6	APC	BD	Mac, Mono, Gran	Phagocytosis, Cell Adhesion
CD14	RMO52	APC	Coulter	Mono, Mac, Gran	Lipopolysaccharide (LPS) Receptor
CD16/FcγRIII	3G8	PE	Coulter	PMN, Mac, NK	Phagocytosis, Endocytosis
CD19	J3-119	FITC	Coulter	B cells	B Cell Marker, Component of B Cell Receptor Complex
CD23/FcεRII	9P25	FITC	Coulter	Mono, Mac, B cells	Low Affinity IgE Receptor, Negative Feedback on IgE Production
CD25	B1.49.9	APC	Coulter	CD4(+) T-reg, Activated T&B cells	Component of IL-2 Receptor
CD40	MAB89	PE	Coulter	B cells, Mono, Mac, DC,	B Cell Co-Stimulatory Molecule, Antigen Presentation, B Cell Maturation & Survival
CD45	2D1 N901	APC-Cy7	BD	Pan Leukocyte Marker	Leukocyte Common Antigen, B & T Cell Activation & Survival
CD56	(NHK-1)	FITC	Coulter	NK cells, NKT cells	NK Cell Marker, Cell Adhesion
CD64/FcγRI	22	FITC	Coulter	Mac, Mono,	Phagocytosis, Antigen Capture, Antibody Dependent Cellular Cytotoxicity
CD69	FN50	APC	BD	Activated T&B cells, PMNs & EOS	T Cell Activation, Activation Marker
CD80/B7.1	MAB104	FITC	Coulter	Mac, Mono, DC,	Co-Stimulatory Molecule For Antigen Presentation, T Cell Activation
CD83	HB15a	PE	Coulter	Mac, Mono, DC	Co-Stimulatory Molecule For Antigen Presentation?
CD86/B7.2	HA5.2B7	PE	Coulter	Mac, Mono, DC, B cells	Co-Stimulatory Molecule For Antigen Presentation, T Cell Activation
CD206/Mannose Rcpt	19.2	APC	BD	Macrophages	C-Type Lectin Receptor, Phagocytosis, Antigen Capture
CD282/TLR2	TL2.1	FITC	e-Biosc	Mono, Mac, Gran	Pathogen Associated Molecular Pattern (PAMP) Receptor
CD284/TLR4	HTA125	PE	e-Biosc	Mono, Mac, Gran	PAMP (LPS) Receptor
FcεRIα	AER-37 (CRA1)	PE	e-Biosc	Mono, Mac, basophils, mast cells,	High-Affinity IgE Receptor, Type I Hypersensitivity
HLA-DR	L243 (G46-6)	PerCP	BD	Mono, Mac, DC, B cells	MHC-II, Antigen Presentation
Lineage Cocktail	Multiple‡	FITC	BD	Non-DC leukocytes	Leukocyte Lineage Marker Cocktail

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^*There may be significant differential expression of these markers and some leukocytes not listed may express markers only at very low levels or following cell activation.

^†Coulter = Beckman Coulter, Inc., Brea, CA; BD = BD Biosciences, San Jose, CA ; e-Biosc = eBiosciences, San Diego, CA; Mac = macrophage; Mono = monocyte; Gran = granulocyte; DC = dendritic cells.

^‡Clones used in Lineage cocktail: NCAM16.2, MP9, L27, SJ25C1, 3G8, SK7.

Determination of Background Fluorescence Using Isotype Controls

Mean fluorescence intensity (MFI) of isotype controls (Tubes 1 and 2, Table 2) for specific populations was subtracted from the MFI measured for specific markers to control for background auto-fluorescence and non-specific fluorescence. For individual populations, background fluorescence was determined simultaneously for all fluorescence channels by creating a single histogram and gating 99% of the population (95% at the minimum) in only one fluorochrome channel, usually FITC. As illustrated for the monocyte population (Table 3), background MFIs for the various fluorochromes were relatively constant regardless of which channel was used for gating. This is also true for the other leukocyte populations and avoids the creation of a very large number of histogram gates.

Specific Gating Strategies

We have found that analysis was facilitated if lymphocyte and granulocyte populations were identified and gated first. Lymphocytes could be identified based on SSC and CD45 expression alone (Fig 2A). Granulocytes were identified within the non-lymphocyte gate (P2) using specific markers (e.g., CD64, CD16, CD14 or HLA-DR) which allowed for their discrimination either through positive or negative selection. Remaining non-granulocytes comprised monocyte, macrophage and DC populations, which were further differentiated based on differential SSC properties, CD45 expression and specific surface markers. Depending upon the antibody panel, it was not always possible to differentiate small populations such as DCs or EOS; however, inclusion of additional population-specific antibodies could facilitate their identification (see below).

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Figure 2

Specific Gating Strategies: (Tube 3, 7, 8) Assessment of Innate Immune Markers

(A) Sputum lymphocytes are gated separately from non-lymphoctes (P2). (B) Differential expression of CD64 and CD16 facilitates separation of granulocytes (P3) from non-granulocytes. (C) Eosinophils are differentiated from PMNs by differential expression of CD16 (tube3). Remaining non-granulocytes from P3 are recombined with “Not P3” to comprise the Mono/Mac/DC population. (D) Macrophages and Mono/DCs gated base on CD45 vs. SSC properties. (E) CD14/CD16 dim dendritic cells (tube 3) fall primarily within the Monocyte gate (D). (F and G) Granulocytes are HLA dim/neg cells in tube 7, while DCs are CD14 dim/HLA high cells within the monocyte gate. (H) Granulocytes are CD14 dim/FcεR1 dim cells in tube 8.

SSC = Side Scatter, Mono = monocyte, Mac = macrophage. DC = dendritic cell, EOS = eosinophil, PMN = neutrophil,

Though differential expression of so called “lineage-specific” markers greatly facilitates discrimination of leukocyte populations, some overlap in expression of these molecules on various populations does occur (e.g. CD23 and CD14 on activated PMNs) and may result in less than optimal separation. As a result, gating necessarily becomes relatively more subjective and may vary depending upon the bias of individual analysts. Though this is largely unavoidable, subjectivity and misidentification of populations can be minimized by gating strategies based on two differentially-expressed markers or sequential “Boolean” gating strategies using two or more markers. The use of relatively conservative gating (i.e. gating tightly around populations) in such situations will also help to minimize misidentification of populations.

Evaluation of Innate Immune Response Proteins (Table 2, Tubes 3, 7 & 8)

Lymphocytes could be gated confidently based on SSC and CD45 expression (Fig 2A): however, antibodies against specific cell surface proteins were utilized to differentiate overlapping populations of PMNs, EOS and mononuclear phagocytes. Differential expression of CD64 and CD16 (tube 3) permitted isolation of PMNs, EOS and DCs from remaining monocytes and macrophages (Fig 2B). Neutrophils and EOS were differentiated based on CD16 expression (Fig 2 C). Keep in mind that degranulated EOS will have lower SSC properties and may blend with the PMNs based on SSC. The remaining non-granulocytes (Fig 2C) were monocytes, macrophages and DCs, which were recombined with the “not-P3”population (Fig 2B) and further partitioned on the basis of light scatter and differential expression of CD45, CD16 and CD14 (Fig 2D & E). Dendritic cells (Fig 2E) were identified as CD14_dim/CD16_dim and fell primarily within the monocyte population. Granulocytes in tube 7 (Fig 2F) were identified as HLA-DR_dimCD14_dim/neg cells and DCs could be identified as CD14_dim//HLA-DR_high non-lymphocytic cells (Fig 2G). The proportions of DCs identified in tubes 3 and 7correlated well with the more definitive identification of DCs and their identity can be verified using the lineage cocktail (not shown) as in tubes 4 and 5 (described below).

The expression of CD14 on activated PMNs (tube 8) often prevented good separation of PMNs from mononuclear phagocytes based solely upon CD14 expression. Differential expression of CD14 in combination with either CD23 or FcεRIα (dim expression on granulocytes) was used to facilitate isolation of granulocytes in this tube (Fig 2H). Though some activated PMNs and EOS may express low to moderate levels of these surface proteins, they are expressed at relatively low levels compared to mononuclear phagocytes. Inclusion of anti-HLA-DR would allow better discrimination of mononuclear phagocytes and identification of DCs.

Evaluation of Adaptive Immune Response Proteins (Table 2, Tubes 4, 5 and 6)

Dendritic cells were specifically identified (tubes 4 and 5) within the entire CD45(+) leukocyte population as Lineage_dim/negative/HLA-DR_high cells (Fig 3A). The lineage cocktail (Table 2) allowed differentiation of DCs from monocytes, macrophages, granulocytes, T-cells, B-cells and NK cells. Similar to blood, sputum DCs comprised a very small proportion (0.5 – 1.5%) of total sputum leukocytes and were dispersed mainly within the monocyte population (Fig 3B). The majority (≈ 90 to 95%) of sputum DCs were CD11c (+) myeloid DCs (Fig 3C), expressing high levels of CD86. Around 50% or more constitutively expressed CD83 while only a small proportion expressed CD1a (Fig 3D), suggesting that airway luminal DCs are relatively mature and at least partially activated. Subsets of DCs (Fig 3E) expressed both TLR2 and TLR4 (tube 7).

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Figure 3

Specific Gating Strategies: (Tube 4, 5 & 6) Assessment of Adaptive Immune Markers

(A) Dendritic cells are lineage-dim/HLA-DR high cells from CD45(+) population and are located primarily within the monocyte gate (B). DCs are primarily CD11c(+) myeloid DCs (C) which express high levels of CD86. (D) The majority of DCs expressed CD83 (tube 5), while relatively few expressed CD1a. (E) Subpopulations of DCs (tube 7) expressed TLR2 and TLR4. Granulocytes gated based on differential expression of CD86 and lineage cocktail (F) or HLA-DR vs. SSC (G). A small proportion of granulocytes may express low levels of CD80 (tube 6) (H).

SSC = Side Scatter, DC = dendritic cell, EOS = eosinophil, PMN = neutrophil

Granulocytes were differentiated within the remaining (non-DC) cells based on a combination of SSC properties, differential expression of lineage markers and the negative or dim expression of either HLA-DR or co-stimulatory molecule CD86 (Fig 3F) in tube 4 or CD83 in tube 5. EOS were differentiated from PMNs as lineage-dim granulocytes (Fig 3F), similar to Fig 2C. Though granulocytes can usually be differentiated from mononuclear phagocytes on the basis of HLA-DR alone (Fig 3G), the use of a combination of two differentially expressed markers is preferable when possible. In most cases, the separation of granulocytes was well-defined; however, some PMNs and EOS may express low levels of HLA-DR and/or co-stimulatory molecules (CD80 and CD86), sometimes making the distinction between the populations less discrete. Mononuclear phagocytes were further dissected via SSC and differential expression of CD45 (see Fig 2D).

Differential expression of CD206 and HLA-DR (tube 6) allowed discrimination of granulocytes from macrophages (see Fig 1H). We found that CD80 was occasionally expressed at moderate levels on a small proportion of sputum granulocytes (Fig 3H). Though HLA-DR (+) lymphocytes are, for the most part, CD40 (+) B-cells (see Fig 4D) and also express some level of CD86, some activated T-cells may also express these proteins. Inclusion of additional antibodies to specifically identify B cells (tubes 4 and 5) and either lineage cocktail or CD14 to allow identification of DCs (tube 6) would be advantageous.

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Figure 4

Sputum Lymphocyte Subpopulations

(A) CD3(+) T cells are comprised predominantly of CD4(+) T-helper cells. (B) NK cells and B cells comprise small and variable proportions of the lymphocyte population. (C) CD3(+) NK-T cells comprise the majority of sputum NK cells. (D) Sputum B cells express high levels of CD40. (E) Identification of NK cells (tube 3)

SSC = Side Scatter, FSC = Forward Scatter, NK = natural killer, T-ctl = cytotoxic T cell

Sputum Lymphocytes

Lymphocytes (tubes 9 and 10, Table 2) comprise a small and quite variable proportion of sputum leukocytes which is influenced by various factors including the health status of the individual and inflammatory processes in the bronchial airways. In this cohort of healthy,mild asthmatic and atopic individuals, the majority (≈ 80 to 90%) of sputum lymphocytes were CD3 (+) T cells, predominantly (≈ 60 to 65%) CD4 (+) T-helper cells (Fig 4A). B cells and natural killer (NK) cells comprised small and highly variable proportions (Fig 4B) of the lymphocytes. The majority of CD16/56 (+) NK cells were CD3 (+) NKT cells (Fig 4C). B cells (tube 6) were identified as HLA-DR_high/CD40 _high lymphocytes (Fig 4D). In the absence of CD56, NK cells could be tentatively identified as CD14_dim/CD11b_high/CD16_high cells (tube 3) within the lymphocyte population (Fig 4E).

Gating for Isotype Controls

Measurement of background auto-fluorescence and non-specific staining was accomplished using isotype controls (Tubes 1 & 2 – Table 2). Lymphocytes could be gated easily since they do not overlap significantly with other populations. Simple gating of major populations based on SSC and CD45 expression may be adequate (see Fig 1G) for isotype controls; however, gating based on auto-fluorescence (FITC channel) and SSC (Fig 5A) usually provided better discrimination of overlapping granulocyte and macrophage populations. EOS overlap with both PMN and macrophage populations, while DCs are dispersed within the monocyte population. If present in sufficient numbers, the EOS gate could be based on the segregated granulocyte population (Fig 5B). After isolating granulocytes, the remaining monocytes and macrophages could be gated based on differential CD45 and SSC (Fig 5C) Regardless of which approach was used (FITC autofluorescence or CD45 vs. SSC), small populations such as EOS, DCs and basophils (extremely rare) were difficult (or impossible) to isolate without specific antibodies. Figure 5D shows the locations of the EOS and DC populations as determined by specific gating methods using tube 4 (see Fig 3 A and F). A simplistic alternative solution was to “non-specifically” gate the locations where these small populations are expected to exist or to substitute background auto-fluorescence values of their parent populations (i.e. monocytes for DCs, lymphocytes for basophils and PMNs for EOS). This is the approach we have taken for determining background fluorescence for DCs.

Specific Examples for Evaluating Airways Disease and Response to Inhaled Pollutants (Ozone)

The gating strategies and antibody panels described above are useful for monitoring expression of cell surface proteins or quantification and characterization of rare-event populations such as dendritic cells. The usefulness of these strategies for evaluating airways inflammation associated with asthma or exposure to environmental air pollution is demonstrated in Figure 6. As an example, using FCM, a slight yet significant (p<0.01) increase in the proportion of sputum dendritic cells was demonstrated (Fig 6A) in allergic individuals sensitive to dust mites (N=29; 19 mild allergic asthmatics and 10 allergic non-asthmatics) compared to healthy individuals (N=44). The value of flow cytometry in evaluating the effects of air pollutant exposure is exemplified by changes in surface expression of CD14 and HLA-DR on sputum monocytes (Fig 6B) from healthy volunteers 4 hours following a 2-hour inhalation exposure to 0.4 ppm ozone. (Hernandez et al. 2010).

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Figure 6

Examples of Data Derived via Flow Cytometry of Sputum

(A) Proportions of sputum dendritic cells was significantly higher in dust-mite sensitive allergic individuals (N=29) compared to healthy non-allergic volunteers (N=44). (B) CD14 and HLA-DR expression was significantly higher on sputum monocytes from healthy volunteers 4hours after a 2-hr inhalation exposure to 0.4 ppm ozone (adapted from (Hernandez et al. 2010). (C) There was a significant (p=0.0001) correlation between %PMNs derived by FCM vs. stained slides (N=45). Filled circles = high viability (>70%) and low squamous cells (<35%). Open circles = low viability or high squamous cells, (D) Lymphocyte proportions tended to be higher based on FCM vs. stained slides.

The authors wish to acknowledge and thank our highly skilled clinical and technical staff. These include Lynne Newlin-Clapp, Martha Almond, Carole Robinette, Margaret Herbst-Saunders, Aline Kala and Sally Ivins who perform sputum inductions, as well as Heather Wells, Fernando Dimeo, Danuta Sujkowski, Nolan Sweeney, and Katherine Mills who process the sputum samples and acquire data on the flow cytometer.

Funding: This research was funded in part by grants from The National Institutes of Health U19AI077437, R01-ES012706, RC1ES018417 and P01AT002620, as well as cooperative agreement CR 83346301 from the US Environmental Protection Agency. Although the research described in this article has been funded wholly or in part by the United States Environmental Protection Agency through cooperative agreement CR 83346301 with the Center for Environmental Medicine and Lung Biology at the University of North Carolina at Chapel Hill, it has not been subjected to the Agency’s required peer and policy review and therefore does not necessarily reflect the views of the Agency, and no official endorsement should be inferred.

Construction of antibody panels

When constructing antibody panels for sputum analysis, it is essential to include anti-CD45 (leukocyte common antigen, pan leukocyte marker) in all sample tubes to help differentiate leukocytes from debris and facilitate identification of leukocyte populations. We take advantage of major lineage markers such as CD16, CD14, HLA-DR, CD3, CD19, CD56, as well as differential expression of other population specific markers as an aid in isolating specific populations and subpopulations. Certain specific antibodies are included in multiple sample tubes to aid in identifying specific populations for gating purposes. Using these principles, we have developed “standard” panels of fluorescent antibodies (See manuscript Table 2) for identifying leukocyte populations and assessing expression of select cell surface proteins associated with innate and adaptive immunity, antigen presentation and inflammation. Many of the innate immune proteins also interact with the adaptive immune system.

Cytometers equipped with multiple lasers are capable of simultaneously using many more than the five fluorochromes incorporated in our standard 5-color panel. It would be desirable to incorporate more fluorochromes in order to reduce duplication of certain markers, consolidate tubes and conserve cells. We have done this for certain of our more specific protocols, however it is often difficult (short of labeling antibodies in-house) to procure the desired antibody labeled with the necessary fluorochrome to accomplish this. Individual investigators can tailor antibody panels to incorporate additional fluorochromes and to fit their specific research objectives.

Staining Procedures

Cells should be labeled (stained) in a timely manner (as soon as possible) following sample processing and kept at 4 °C in the dark until acquired on the flow cytometer. Data acquisition should be done within 24 hours for best results.(Stewart et al. 2007) The specific antibody staining procedures have been described elsewhere.(Alexis et al. 2000) We routinely label 100 μl of cell suspension (1 × 10⁶ cells/ml) based on total non-squamous nucleated cells (i.e. leukocytes and bronchial epithelial cells). Following staining, cells were fixed in 1% paraformaldehyde in Dulbecco’s phosphate-buffered saline (DPPS). Sources for specific antibodies used in these panels are listed in Table 4..

Specific antibodies were titrated to determine an appropriate amount for staining. The majority of the antibodies were still on the plateau of the titration curve when used at half the manufactures suggested amount (i.e. 10 μl vs. 20 μl). A few (e.g., CD16, CD3) required further dilution. Individual investigators should titrate their antibodies to determine appropriate concentrations for their specific applications. Isotype antibodies also were titrated; however, when we attempted to match protein concentrations and fluorochrome/protein concentrations, we found that the isotype controls had a much higher MFI signal than did certain of the specific antibodies, particularly for the IgG2 isotypes. We therefore found it necessary to dilute isotype control antibodies up to 1:5 (or more). The cause of this disparity is not entirely clear. (Hulspas et al. 2009) Some labs have advocated doing away with isotype controls for this and other reasons (Keeney et al. 1998) while others do not. (O’Gorman and Thomas1999). It may also be useful to employ nonspecific unlabeled blocking antibody to help minimize non-specific staining.

Alternatively, a “cleaner” method for determination of background fluorescence might be the use of “fluorescence minus one” (FMO) analysis (Tung et al. 2004), however, to be done properly, this requires large numbers of cells when multiple populations, multiple specific antibodies, multiple fluorochromes and two different isotypes (IgG1 and IgG2) are used. Since the number of leukocytes obtained from sputum samples is often limited, the FMO approach is usually not feasible for the typical sputum sample. Substitution of blood leukocytes for estimating background fluorescence (Dua et al. 2010) may be adequate for sputum lymphocytes, granulocytes and monocytes; however the absence of macrophages in blood is an obvious problem; although this might be overcome by “spiking” sputum isotype control tubes with blood leukocytes. In our experience, however, sputum monocytes tend to be more granular (higher SSC) with moderately higher autofluorescence (especially in the FITC channel) than peripheral blood monocytes.

Determination of Background Fluorescence Using Isotype Controls

Gating populations in the isotype control tubes is more complex for sputum than for blood since, unlike blood, major non-lymphocyte populations overlap and small or rare populations are imbedded within larger populations. More precise determination of background fluorescence for these small populations, requires inclusion of specific antibodies [e.g. CD9 or CD16 (EOS), CD203c (basophils), or CD14 (DCs)] in addition to CD45 in the isotype control tube.

Mean fluorescence intensity (MFI) of isotype controls (Tubes 1 and 2, Table 2) for specific populations was subtracted from the MFI measured for specific markers to control for background auto-fluorescence and non-specific fluorescence. For individual populations, background fluorescence was determined simultaneously for all fluorescence channels by creating a single histogram and gating 99% of the population (95% at the minimum) in only one fluorochrome channel, usually FITC. As illustrated for the monocyte population (Table 3), background MFIs for the various fluorochromes were relatively constant regardless of which channel was used for gating. This is also true for the other leukocyte populations. This approach avoided the creation of a very large number of histogram gates.

Instrumentation

The majority of our samples are acquired on a BD^™ LSR-II digital flow cytometer equipped with 405nm solid state, 488nm argon-ion, and 633nm Helium-Neon lasers, appropriate filters and capability for cross-laser compensation (BD Biosciences Immunocytometry Systems, San Jose, CA). Compensation for spillover and spectral overlap are set for a particular set of instrument settings using BD^™ CompBeads (BD Biosciences, cat 552843) and an automated compensation algorithm in BD^™ FACSDiva 6.1 software (BD Biosciences). Once established, compensation values are not changed unless the established parameter settings are altered. Our “standard” panel uses 5 colors exciting off the 488 and 633 lasers; however, additional fluorochromes exciting off the 405 laser are used in some studies. This is determined by the availability of specific antibodies in a particular fluorochrome and the amount of sample available. While it is true that less sample is required when more fluorochromes are employed in a particular panel, the trade-off is that the complexity of instrument setup, compensation and data analysis also increases.