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How to cite this article: Brooks CR, van Dalen CJ, Hermans IF, Douwes J. Identifying leukocyte populations in fresh and cryopreserved sputum using flow cytometry. Cytometry Part B 2013; 84B: 104–113..
Airway inflammation is commonly assessed by sputum induction followed by a differential cell count (DCC) using light microscopy. This method is prone to intercounter variability and poor reproducibility. We aimed to develop a more objective method using flow cytometry (FCM).
Fifty-six sputum inductions were conducted in 41 adults (23 asthmatics). Sputum was processed, a cytospin prepared for DCC, and the remainder immunolabeled for FCM using CD45, CD14, and CD16-specific antibodies to distinguish major leukocyte populations. Aliquots of 15 samples were frozen at −80°C to assess the effects of cryostorage. DCC and FCM were compared, and viability of individual cell populations was determined by FCM.
FCM and DCC, and fresh and frozen samples, were significantly correlated, R = 0.54–0.87; all P < 0.0001, and R = 0.57 to 1; P < 0.005, respectively. There was a significant neutrophil loss after cryostorage (from median 30.5–17.4% of total leukocytes; P < 0.0001). Cell viability was higher for lymphocytes compared to granulocytes or macrophages (P < 0.001). With the exception of the expected higher levels of eosinophils (P < 0.005), no significant difference in cell differentials or viability was observed between asthmatics and nonasthmatics using either DCC or FCM.
Induced sputum (IS) is a commonly applied technique for assessing airway inflammation in respiratory disease (1, 2), and primarily involves conducting differential cell counts (DCCs) of leukocytes using light microscopy. However, there are significant problems associated with this including interobserver variability, poor reproducibility, and the low cell numbers routinely counted. Assessment is particularly problematic when dealing with poor quality samples containing high levels of squamous-epithelial cells or debris.
These issues can potentially be overcome using flow cytometry (FCM). FCM allows thousands of events to be assessed per sample and provides the ability to gate out squamous-epithelial cells and debris, thus providing a means to remove cytospin reader bias and minimize small cell population bias. However, there is little detailed information available regarding FCM gating for leukocyte populations in IS, although strategies for bronchoalveolar lavage (BAL) have been published (3, 4). To date, most reports using FCM to analyze IS have focussed on one particular cell subset, such as macrophages (5, 6), neutrophils (7), or lymphocytes (8–11). This may be due in part to the specific difficulties associated with the FCM analysis of IS, including contamination with nonviable cells, or the use of DTT (dithiothreitol) for mucus dispersal, which affects FCM detection of certain cell markers (12).
To our knowledge, only one recent study (13) has attempted to correlate cytospin DCC and FCM data for all major cell populations in IS. In addition, although freezing has been reported as a suitable storage strategy prior to assessment of sputum lymphocyte subsets (8) and DCC (14, 15), the effects of freezing on FCM assessment of other leukocyte populations in IS has not been reported. Furthermore, little is known about the viability of individual cell populations in IS, as light microscopic assessment only allows overall cell viability to be determined.
The aims of this study, therefore, were to: (i) develop a robust strategy to detect leukocyte populations in IS using FCM and compare this to DCC data, (ii) assess the effects of cryostorage on FCM analysis, and (iii) determine the viability of individual cell populations. Finally, to test the utility of this approach, we wanted to (iv) compare results obtained using DCC and FCM in samples obtained from asthmatics and nonasthmatics.
MATERIALS AND METHODS
Adult participants (n = 54; 29 asthmatics and 25 nonasthmatics) were recruited from staff and the general population. All completed a respiratory health questionnaire, lung function test, and at least one hypertonic saline sputum induction. Asthma was defined as a physician's diagnosis of asthma, and/or asthma medication use within 12 months of assessment. Exclusion criteria included a respiratory infection within the last 4 weeks, forced expiratory volume in 1 s (FEV1) < 75% predicted or an irreversible drop in FEV1 > 15% during sputum induction. The study protocol was approved by the Upper South A Regional Ethics Committee, New Zealand (URA/08/08/056). Fully informed consent was obtained from all participants.
Sputum induction was conducted as described previously (16) with some modifications. Briefly, participants were pretreated with 400 μg salbutamol. Spirometry was performed according to American Thoracic Society guidelines (17), before and 15 min after salbutamol. Sodium chloride (Baxter Healthcare, Auckland, New Zealand; 4.5% w/v) vapor was produced using an ultrasonic nebulizer (DeVilbiss Ultraneb 2000), and administered orally for increasing intervals from 30 s to 4 min, to a total of 16 min. FEV1 was obtained by spirometry at each interval. At the end of the session, participants produced a sputum sample into a sterile plastic container. The procedure was discontinued if the participant declined, or if a decline >15% FEV1 could not be reversed by salbutamol administration during the procedure.
Sputum samples were processed by plug selection (18). In several cases (n = 7), this was not possible due to a dispersed mucoid appearance, and the whole sample was processed (19). Total cell count and viability (trypan-blue exclusion) were determined using light microscopy. The sample was centrifuged (350g, 8 min), and the cell pellet resuspended in RPMI 1640 medium (Invitrogen, Auckland, New Zealand) containing 10% fetal calf serum (FCS, Invitrogen) and 1% penicillin-streptomycin (Invitrogen) at 1 × 106 cells/ml.
Identification of Cell Populations by Differential Cell Count
Cell suspension (75 μl) was centrifuged in a Labofuge 400 cytospin column (Heraeus, Hanau, Germany) at 44g for 5 min. The slide produced was air dried, fixed and stained with the Diff-Quik® fixative and stain set (Dade Behring, Deerfield, IL). 400 Nonsquamous cells were counted using a DME light microscope (Leica Microsystems, Wetzlar, Germany). Twenty-nine slides (51.8%) were counted separately by a second researcher and compared with the original counts to determine interobserver variation.
Identification of Cell Populations by Flow Cytometry
Cell suspension (100 μl) was used for each FCM stain, conducted in 96 well plates. Cells were washed in FCM buffer (phosphate-buffered saline (PBS) containing 1% FCS, 0.01% sodium azide, and 2 mM ethylenediaminetetraacetic acid (EDTA; both Sigma-Aldrich, Auckland, New Zealand; also for all subsequent wash steps), incubated with polyclonal IgG (Intragam, Commonwealth Serum Laboratories, Sydney, Australia) at 2 mg/ml (10 min, 4°C) to block nonspecific binding as described previously (20), washed again, then labeled in a total volume of 25 μl (25 min, 4°C). Antibodies used were CD45-APC (clone HI30), CD14-FITC (clone M5E2), and CD16-PE (clone 3G8) (all BD Biosciences, San Jose, CA). All antibodies were optimally titrated and matching isotype controls were used as appropriate. Cells were washed again and resuspended in FCM buffer containing 4,6-diamidino-2-phenylindole (DAPI; Molecular Probes, Eugene, OR) at a final concentration of 0.5 μg/ml. FCM data acquisition was performed on a LSR II flow cytometer (BD Biosciences), equipped with five lasers. FITC was detected using a 515/20 bandpass filter with the 488 nm laser, PE using a 575/26 filter with the 532 nm laser, APC using a 670/14 filter with the 640 nm laser, and DAPI using a 450/50 filter with the 355 nm laser. Data were collected using FACSDIVA™ software (BD Biosciences). Instrument voltage settings were kept the same throughout the study. Instrument compensation was set-up using fluorescently-labeled antibodies and Compbeads (BD Biosciences) according to manufacturer's instructions. Flowjo software (Treestar Incorporated, Ashland, OR) was used for FCM analysis.
Freezing and Thawing of Induced Sputum Samples
For 15 randomly selected sputum samples, a further aliquot of between 5 × 105 and 2 × 106 cells was cryopreserved. Samples were centrifuged at 350g for 5 min, resuspended in 1 ml freeze medium (FCS containing 10% dimethyl sulfoxide (DMSO: Sigma-Aldrich), placed in a cryostorage container (cooling rate of 1°C/min) and transferred to a −80°C freezer. Samples were stored between 1 and 3 months. Prior to FCM analysis, frozen samples were thawed rapidly by adding 10 ml prewarmed RPMI 1640, counted, centrifuged (350g, 8 min), then resuspended and processed for FCM analysis. A further antibody panel was used in thawed samples to assess the effect of cryostorage on the major CD4/8 T cell subsets (CD45, CD4/8 BD Biosciences multitest).
Spearman's rank correlation and Bland–Altman plots were used to compare FCM/DCC data, and fresh/frozen samples. Additional comparisons were made using the paired t-test or Wilcoxon matched-pairs test. Differences between groups were assessed using the unpaired t-test or Mann–Whitney u-test. Data normality was determined using the D'Agostino and Pearson omnibus test. Multi-group comparison was conducted using Kruskal–Wallis with Dunn's post-test. All statistical analyses were conducted using Prism 5 software (Graphpad Software, La Jolla, CA).
A total of 56 sputum samples adequate for analysis by both FCM and DCC were produced by 41 individuals, representing 75.9% of participants (23 asthmatics and 18 nonasthmatics). Repeat visits were conducted once with five participants (4 asthmatic, 1 nonasthmatic) twice with one participant (1 asthmatic), and four times with two participants (2 nonasthmatics) as part of a study assessing stability of airway inflammation (data not shown). Thirteen participants (6 asthmatics and 7 nonasthmatics) were unable to provide an adequate sample for FCM and/or DCC and were excluded. Demographic data is provided in Table 1.
Table 1. Participant Characteristics
Expressed as median (IQR) or number (percentage).
FEV1 (% predicted)
ΔFEV1 (pre/post bronchodilator)
Current ICS use
Identification of Leukocyte Populations by FCM
FCM was conducted by gating on forward scatter (FSC)-H and FSC-A to exclude doublets, followed by gating on CD45 expression to discriminate leukocytes from contaminating squamous-epithelial cells. Subcellular debris was then excluded on the basis of FSC and side scatter (SSC). Specific populations were identified on the basis of marker expression and/or autofluorescence (21) as well as size and granularity. Using this approach, lymphocytes were identified as CD45high, CD14− (SSClow); macrophages as CD45high, autofluorescencehigh, CD14+, CD16+ (SSChigh); monocytes as CD45high, autofluorescencelow, CD14+, CD16+ or − (SSC/FSCmid); neutrophils as CD45mid, CD14dim, CD16+; and eosinophils as CD45mid, CD14dim, CD16dim. Representative gating is shown in Figure 1. An alternative gating strategy was attempted which also excluded nonviable DAPI+ events prior to leukocyte gating (as is commonly conducted with FCM), but this resulted in overestimation of lymphocytes and underestimation of macrophages when compared to either DCC or the FCM strategy above (Supp. Info. Figs. 1 and 2).
Comparison of FCM and DCC Data in Fresh Sputum Samples
We observed significant correlations between FC and DCC for all cell populations (P < 0.0001), with correlation coefficients of 0.87 for neutrophils, 0.83 for macrophages/monocytes (combined in FCM to allow for comparison to DCC), 0.84 for eosinophils, and 0.54 for lymphocytes (Fig. 2). In contrast to previous findings (22), the strength of these correlations was not improved by the exclusion of samples with relatively high squamous-epithelial cell contamination, or processing whole rather than selected sputum (data not shown). Neutrophils and macrophages represented a slightly greater percentage of leukocytes by DCC (medians of 30.2 and 60.8, respectively, compared to 26.0 and 57.9 by FCM; Table 2). This increase was small but significant in both cases (paired t-test; P = 0.005 and 0.001 respectively). Conversely, lymphocytes were detected as a greater percentage of leukocytes using FCM rather than DCC (5.7% vs. 1.7%; P < 0.0001). Eosinophil percentage was similar using either method (0.5% and 0.6%).
Table 2. Sample Characteristics and Percentage of Leukocyte Populations Determined Using DCC and FCM (Expressed as Median (IQR))
DCC and light microscopy
P value determined by T test or Mann–Whitney test. Between asthmatics and nonasthmatics.
Total nonsquamous cells/ml (x106)
Sputum volume selected (ml)
Total nonsquamous cells (x 106)
Viability nonsquamous cells %)
% Squamous cells (of all)
% Neutrophils (of nonsquamous)
% Lymphocytes (of nonsquamous)
% Macrophages (of nonsquamous)
% Eosinophils (of nonsquamous)
% Col epithelial cells (of nonsquamous)
Monocytes (SSC mid CD14+CD16+)
Monocytes (SSC mid CD14+CD16–)
Despite the significant correlations observed between FCM and DCC, there was some interobserver variation when conducting DCC on less frequent leukocyte populations, with a coefficient of variance (CV) of 60.9% for eosinophils and 32.9% for lymphocytes, compared to a CV of 7.1% for the more abundant macrophages/monocytes (65.2% of leukocytes) (Table 3).
Table 3. Mean Coefficient of Variance (CV) Values for Inter-Observer Variation During Differential Cell Count (DCC) (n = 29)
SD, standard deviation.
Viability of Leukocyte Populations by FCM
Individual leukocyte populations were further gated based on their uptake of DAPI into viable/nonviable populations (Fig. 1B). Significant differences in viability were observed, with lymphocytes significantly more viable than all other cell populations identified (median 98.59% viability; P < 0.001), while macrophages had the lowest overall viability (71.93%; P < 0.001; Table 4).
Table 4. Viability of Leukocyte Populations in Induced Sputum (IS) as Determined by Flow Cytometry (FCM) (Expressed as Median (IQR), P Value Determined by Mann–Whitney Test)
% Viability (DAPI low to negative cells)
Assessment of the Effects of Cryostorage on FCM Analysis of Sputum Samples
Significant correlations were observed between FCM data obtained with fresh and frozen samples (all P < 0.001) for all leukocyte populations. The correlation coefficients ranged from 1 for ratio of CD4+/CD8+ T cell subsets to 0.57 for macrophages/monocytes (Fig. 3). Although we observed only minor reductions in most cell populations after freeze-thawing, a significant loss was seen with neutrophil percentage, from a median of 30.54% of total leukocytes (interquartile range (IQR) 18.78, 34.01) to 17.44% (11.19, 20.41); P < 0.0001), although a strong correlation was observed between fresh and frozen samples (Fig. 3). The majority of cryopreserved samples did not contain a significant number of eosinophils, so no comparative data for eosinophils is available.
Analysis of Sputum Samples from Asthmatics and Nonasthmatics
There were no differences between asthmatics and nonasthmatics for any light microscopic assessment, such as total cell yield or viability. A significantly increased eosinophil proportion was observed in asthmatics using FCM (1.63% vs. 0.32%; P = 0.007) which corresponded to the increased eosinophil differential also observed using DCC (1.30% vs. 0.24%; P = 0.0004; Table 2).
In this study, we described a robust FCM strategy (using a panel of three markers: CD14, CD16, and CD45, alongside the viability dye DAPI) to identify the major leukocyte populations in IS. The differential leukocyte count determined by FCM correlated well with DCC for all the cell populations studied, i.e. lymphocytes, neutrophils, monocytes/macrophages, and eosinophils. A strong correlation was also observed when comparing FCM analysis of fresh and cryopreserved sputum samples, although neutrophil numbers were significantly reduced after thawing. We were able to determine the viability of the individual leukocyte cell populations and found the lowest viability levels (approximately 70%) for macrophages and the highest (approximately 99%) for lymphocytes. Finally, analyses of induced sputum from asthmatics and nonasthmatics by FCM and DCC were comparable, with a higher eosinophil percentage seen in asthmatics by both methods.
Several approaches to FCM analysis of airway leukocyte populations in IS and BAL have been attempted. Some studies suggested that sputum leukocytes could be differentiated by size and granularity alone (23). We found it extremely difficult to differentiate leukocyte populations on this basis, and therefore used the pan-leukocyte marker CD45+ prior to subsequent analysis. A similar approach was also used in two recent publications. Lay et al. (22) describe work conducted over the last decade, including use of an antibody panel similar to that used in our laboratory. However, while comprehensively describing gating strategies for several cell populations, only neutrophil correlation data (R = 0.82) was provided. Vidal et al. (13) describe a study of 38 patients, using CD45, CD66b, CD125, and CD14. When comparing FCM and DCC, this group found correlation coefficients of 0.73 for neutrophils, 0.75 for eosinophils, and 0.53 for macrophages, but no reported values for lymphocytes. These correlations are similar but not as strong as the correlation coefficients we observed (R = 0.87 for neutrophils, 0.84 for eosinophils, 0.83 for macrophages, and 0.54 for lymphocytes). Vidal et al. express caution about the use of CD14 and CD16 to identify macrophages and neutrophils due the co-expression of these markers on both. Interestingly, we did not find an obvious overlap. CD14 was expressed at low levels on granulocytes in comparison with macrophages. When CD14 was assessed in combination with SSC, discrete populations representing granulocytes, lymphocytes, and macrophages could be observed. CD16 expression could then be used to confirm the identity of neutrophils and eosinophils, as has previously been described in blood analysis (24).
Two studies have compared leukocyte populations in BAL using FCM and DCC. Both (3, 4) describe slightly different methods, but show strong correlations between FCM and DCC for lymphocytes, macrophages, and neutrophils. However, BAL does not have the same issues with poor viability as IS, and leukocyte proportions in BAL are different to IS (25); results therefore cannot be directly compared. Nonetheless, both studies observed a tendency for DCC to underestimate lymphocytes and overestimate macrophages, similar to that described earlier.
Our study offers several advances on the studies discussed earlier. First, the strong correlations between FCM and DCC suggest that our approach compares well (in some cases better) for identification of major leukocyte populations, compared to previous studies (13, 22). Second, we measured populations before and after cryostorage. Although it has been suggested that freezing has no effect on subsequent DCC counts (14), deterioration of neutrophils and macrophages after freezing has been reported (3). We observed a significant reduction in neutrophil percentage after freezing (from 30.5% to 17.4% of leukocytes), suggesting that FCM analysis conducted after freezing may bias results. Despite this, neutrophil loss during freezing was consistent, with a strong correlation between fresh and frozen samples observed (R = 0.88). Therefore, comparison between cryopreserved samples in a study may be feasible, although results are not directly comparable with those obtained from fresh samples.
Finally, we routinely used a dye (DAPI) to identify viable cells. A similar approach has been conducted in BAL analysis using 7-aminoactinomycin (3). When conducting functional experiments (such as oxidative burst assays) a viability dye is likely to be essential for avoiding staining artifacts, particularly when assessing the relatively low viability macrophage population. Since conducting this study, we have adapted our protocols to use the LIVE/DEAD® fixable blue reagent (Invitrogen), which adds further convenience by allowing downstream fixation.
Some previous studies have only analyzed 10,000 events per run during FCM analysis (13). However, the large amount of debris/noncellular material in IS may negatively affect results with such a limited number of events. For example, using the gating strategy described in our study, we observed a median of 94.4% nondoublets per sample, of which 30.6% were CD45+, and 61.7% of which were larger than debris. If only 10,000 events were run on such a sample, only 1,781 events would be leukocytes. Although this number is greater than that used routinely in DCC, it may produce some imprecision in detected levels of eosinophils or lymphocytes, which regularly represent 0–5% of total leukocytes. We therefore routinely conducted analysis on >100,000 events per sample during this study.
When comparing data obtained from asthmatics and nonasthmatics we observed no significant differences by either DCC or FCM, with the exception of increased eosinophils in asthmatics using either technique (median 1.3% eosinophils using DCC and 1.64% eosinophils using FCM). Although some studies have reported considerably higher IS eosinophil percentages in asthmatics (e.g. Fahy et al. (19) reported a mean of 8.1% in 18 asthmatics), our findings are consistent with those of Munoz et al. (26) and Grootendorst et al. (27), who reported a median IS eosinophil percentage of approximately 1% in studies assessing 84 adult asthmatics and 18 mild-to-moderate adult asthmatics respectively. The regular use of inhaled corticosteroids in 65% of asthmatics in our study may also have resulted in a lower eosinophil percentage than would be observed in studies assessing steroid-naïve patients.
Interestingly, using FCM we observed that five asthmatic individuals (21.7%) had a CD16+ monocyte population making up more than 4% of their total IS leukocyte population, compared to only one individual (5.6%) in the nonasthmatic group. Although not statistically significant, this may be indicative of active inflammation, and could (like eosinophils) be another indicator of inflammatory airway disease. Indeed, one study (28) has shown an increase in a population of small macrophages in another respiratory disorder, chronic obstructive pulmonary disease (COPD).
There are several advantages of FCM compared with DCC analysis of sputum. Using DCC, counts of cell populations with low frequency (such as lymphocytes and eosinophils) are prone to considerable variation and imprecision, as shown by the lower correlation coefficients and increased CV values observed when assessing these populations. DCC is also prone to underestimating lymphocyte percentages. Both issues been previously described (3), and the variation in particular is unsurprising, as only 400 leukocytes are counted as standard during DCC, while we routinely assessed more than 100,000 cells using FCM. Furthermore, the use of well-defined, specific antibodies in combination with distinct FSC/SSC characteristics allows accurate and objective identification of each leukocyte population using FCM. Slide preparation and staining for DCC can be variable, often making accurate identification of cell populations difficult and subjective. However, it must be acknowledged that there are also some limitations with FCM. It takes longer to prepare IS samples for analysis, needs access to a flow cytometer, the use of more expensive reagents, and requires more technical expertise.
In summary, we have described a robust method of FCM analysis of IS that correlates strongly with the conventional approach using light microscopy, and thus provides a convenient alternative method of enumerating sputum leukocytes. We show that this approach can be used with cryopreserved samples, and therefore may be useful for epidemiological studies in which immediate analysis of sputum samples is difficult. Although this study was limited to four fluorophores, FCM analysis of IS could be extended to identify several more important cell populations, such as DCs and lymphocyte subsets (e.g. 29,20), within a single antibody panel, as has been recently conducted with a 12-color panel in peripheral blood (30). Such an approach could be of considerable benefit for the detailed immunophenotyping of IS in respiratory diseases such as asthma.
We thank Elizabeth Harding for her support during clinical assessments (lung function, skin prick tests, and sample collection), and Kathryn Farrand and Kylie Price for their support and guidance with FCM analysis. We also thank the staff, students, and other participants who kindly donated their time for this study.