Circulating lymphocyte subsets in normal adults are variable and can be clustered into subgroups

Cytometry Part B (Clinical Cytometry) 80B:291–299 (2011)

CirculatingLymphocyteSubsets inNormalAdultsare Variable and CanBe Clustered into Subgroups

Debora R. Sekiguchi,1†Sara B. Smith,1

†Jennifer A. Sutter,2 Noah G. Goodman,1

Kathleen Propert,3 Yoram Louzoun,4 Wade Rogers,1 and Eline T. Luning Prak1*1Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine,

Philadelphia, Pennsylvania 191042Section of Pediatric Endocrinology, Department of Pediatrics, Brody School of Medicine at

East Carolina University, Greenville, North Carolina 278343Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine,

Philadelphia, Pennsylvania 191044Department of Mathematics, Bar Ilan University, Ramat Gan, Israel 52900

Background: Flow cytometry is used to monitor lymphocyte subsets in both the clinical and researchsettings. An understanding of the degree of inter- and intrasubject variability of these populations is crit-ical for data interpretation.

Methods: Peripheral blood lymphocytes of 18 healthy adults were analyzed on two separate occasionsusing a multicolor flow cytometric panel with B, T, and NK cell markers. Variability was calculated usingthe coefficient of variation and compared between and within individuals using agglomerative clustering.

Results: Each subject appears to have B and T cell subset profiles that are stable over the two timepoints, but differ from the profiles of other subjects. Thus, the range of measurements for a particular B orT cell subset is larger between subjects and narrower for an individual. In addition, the level of variabilitycorrelates inversely with the size of the lymphocyte subset. When lymphocyte profiles are analyzed byagglomerative clustering, replicate samples from the same individual tend to cluster. When single sam-ples from different individuals are analyzed, individuals appear to cluster into different subgroups.

Conclusions: Variability of lymphocyte subsets is usually greater between individuals than within a singleindividual and each person appears to have a characteristic profile of lymphocyte subsets. These resultsunderscore the importance of obtaining a baseline value for each subject when investigating the impact of atreatment on lymphocyte subsets over time. These results also highlight the potential utility of cluster analysisas a tool for immune subset profiling and biomarker discovery. VC 2011 International Clinical Cytometry Society

Key terms: variability; flow cytometry; clinical trial; longitudinal analysis; cytometric fingerprinting

How to cite this article: Sekiguchi DR, Smith SB, Sutter JA, Goodman NG, Propert K, Louzoun Y, Rogers W, Lun-ing Prak ET. Circulating Lymphocyte Subsets In Normal Adults Are Variable And Can Be Clustered Into Sub-groups. Cytometry Part B 2011; 80B: 291–299.

Flow cytometry is often used to monitor lymphocytesubsets in subjects receiving immunomodulatory thera-pies. In this regard, the average proportion or absolutefrequency of a lymphocyte subset is usually compared ingroups of individuals before, during, and after treatment.Such comparisons assume that the differences observedbetween groups are greater than the differencesbetween individual subjects within a single group. Yet,as we will demonstrate here, the level of interindividualvariability for lymphocyte subsets is considerable andincreases even further in smaller lymphocyte popula-

Additional Supporting Information may be found in the onlineversion of this article.

{Co-first authors have contributed equally to this work.Grant sponsors: Juvenile Diabetes Research Foundation; National

Institutes of Health (grant numbers T32-AR-07442-23 and R56-AI090842) and the Alliance for Lupus Research.*Correspondence to: Eline T. Luning Prak, 422 Curie Blvd, Room

405, Stellar Chance Laboratories, Philadelphia, PA 19104, USA.E-mail: [email protected] 28 August 2010; Revision 3 March 2011; Accepted 4

March 2011Published online 22 March 2011 in Wiley Online Library

(wileyonlinelibrary.com).DOI: 10.1002/cyto.b.20594

Original Article

VC 2011 International Clinical Cytometry Society

tions and with the use of absolute cell counts instead ofpercentages. In contrast, the level of intraindividual vari-ability is lower. These findings indicate that intergroupcomparisons potentially have limited sensitivity for thedetection of biologically meaningful effects. These datasupport alternative study designs, such as longitudinalintraindividual analysis (i.e., fold change relative to abaseline measurement), and cluster analysis for the anal-ysis of immunophenotyping data.

METHODS

Human Subjects

Peripheral blood lymphocytes were obtained from 18apparently healthy controls. This group of subjects wasused as a control group in a separate unrelated studyevaluating B cell subsets and antibody repertoire inpatients with systemic lupus erythematosus (SLE) (1).Subject demographics are summarized in Table S1 of theelectronic supplement. The same immunophenotypingpanel was performed on the healthy study subjects ana-lyzed in this report at two separate time points rangingfrom 2 to 7 months apart. The first time point was usedfor comparison to subjects with lupus (described in Ref.no. 1) and the second time point was used for compari-son to the first time point in this study. Subject inclusioncriteria include good health and demographic character-istics (age, sex, and race) that matched fairly closelywith our cohort of SLE patients. Exclusion criteriaincluded malignancy, HIV disease or other acute orchronic viral infection, therapy with immunosuppressiveagents including steroids and a history of autoimmunedisease. Peripheral blood was obtained by routine veni-puncture into sodium EDTA tubes. In parallel, bloodsamples were drawn and processed for absolute lympho-cyte counts by Coulter counting (complete blood countwith electronic differential) at the Hospital of the Uni-versity of Pennsylvania. Informed consent was obtainedfrom all patients. These studies were performed in ac-cordance with a protocol that was approved by the Uni-versity of Pennsylvania Institutional Review Board.

Flow Cytometry Methods

One hundred fifty microliter aliquots of the wholeblood (<24 h old, stored at room temperature) werestained and processed for flow cytometry as describedpreviously (2). Cells were stained with the followingantibodies: anti-CD3-APC-AF750 (S4.1), anti-CD4-PE-Texas Red (S3.5), anti-CD5-PE-Cy5.5 (CD5-5D7), anti-CD19-Pacific Blue (SJ25C1), anti-CD24-PE-AF610 (SN3),anti-CD38-APC-Cy5.5 (HIT-2) [Invitrogen]; anti-CD3-Pa-cific Blue (UCHT1), anti-CD8-PE (HIT-8), anti-CD8-PacificBlue (RPAT8), anti-CD10-PE (HI10A), anti-CD14-PacificBlue (N5E), anti-CD16-PE-Cy7 (3G8), anti-CD19-PerCP-Cy5.5 (SJ25C1), anti-CD19-PE-Cy7 (SJ25C1), anti-CD20-PerCP-Cy5.5 (L27), anti-CD27-APC (MT271), anti-CD38-PE (HIT-2), anti-CD45-PerCP (2D1), anti-CD56-APC-AF700 (B159), anti-CD62L-PE-Cy5 (Dreg56), anti-IgD-FITC (IA62), anti-Igj-PE (TB28-1), anti-IgM-FITC

(G20-127), anti-IgM-APC (G20-127) [BD Bioscience]; anti-NKG2D-FITC (1D11), anti-CD23-APC (EBVCS2), anti-CD27-APC-AF750 (0323) [eBioscience]; and anti-NKp44-APC (Miltenyi Biotec; 2.29); 9G4-PerCP-Cy5.5 (kindlyprovided by M. Sanders, Dynavax Technologies Corpora-tion, Berkeley, CA). The 9G4 antibody recognizes B cellsthat express VH4-34 antibody heavy chains and wasincluded as part of our earlier study on SLE patients(Ref. no. 1) because of its association with autoreactiveB cells in SLE. Dead cell exclusion was performed usingthe Live/Dead Fixable Violet Dead Cell Stain Kit (Invitro-gen). All 11-color flow cytometry (both samples fromeach of the 18 subjects) was performed on the sameinstrument (Becton Dickinson LSRII Benchtop Flow Cy-tometer) to control for machine variability. Three-colorflow cytometry was performed in pilot experiments tocompare 11-color to three-color data and evaluate gateplacements (Fig. S1 in the electronic supplement). Thethree-color experiment shown in Figure S1 was per-formed on the Becton Dickinson FACS Calibur. The sam-ple preparation and instrument operation wereperformed by the same single operator at all time pointsto minimize interoperator variability. Cytometer set-upand tracking beads (BD CST) were used daily in combi-nation with BD FACSDiva software (v6.0 or later) byflow cytometry core facility staff to monitor the meanfluorescent intensities and CVs in each of the detectors.PMT voltages were adjusted accordingly and baselineswere re-established with new bead lots. Compensationwas performed digitally using cells stained with singlecolor reagents. Typically, �10,000 B cells were acquiredper tube. Minimum, median, and mean event count datafor the lymphocyte subsets analyzed in this study arepresented in Table S2 of the electronic supplement.

Flow Cytometry Data Analysis

Flow cytometry data were analyzed using FlowJo (v.8.2, Treestar Inc., Ashland, OR). Data analysis was per-formed by one person and checked by two others. ForT cell subsets and NK cell enumeration, gate statisticsand the same combination of antibodies from a singletube were used for all of the samples. After excludingdead cells and gating on lymphocytes utilizing forwardscatter and side scatter, T cells were identified based onCD3 expression and T cell subsets were defined basedon CD4 and CD8 expression into CD4þ, CD8þ, doublepositive (CD4þ, CD8þ), and double negative (CD4�,CD8�). NK cells were identified as CD3 negative, CD56,and/or CD16 positive lymphocytes. B cells were identi-fied based on CD19 expression and B cell subsets weredefined as transitional, naı̈ve mature, mature activated,resting memory, and plasmablast populations, usingCD27 and CD38 expression as described in the legendto Figure 2. Fluorescence minus one controls andmarker expression on other cell types (such as CD27staining on T cells) were used to define positive versusnegative staining in pilot experiments. Three-color and11-color flow cytometry was performed on healthy sub-jects and analyzed to establish the reliability of our B

292 SEKIGUCHI ET AL.

Cytometry Part B: Clinical Cytometry

cell gating scheme, as shown in Figure S1 of the elec-tronic supplement. The percentages of naı̈ve mature,mature activated, and plasmablast subsets were compa-rable for the three-color and 11-color data, whereas thepercentages of transitional cells tended to be slightlyhigher and percentages of resting memory cells slightlylower in the 11-color analysis. To minimize variation dueto potential differences between the three-color and 11-color panels, all of the subsequent lymphocyte immuno-phenotyping studies were performed using the 11-colorpanel. For the analysis of the 18 subjects, B cell subsetswere analyzed in two different tubes. The first B celltube contained antibodies against IgD-FITC, IgM-APC,CD38-APCCy5.5, CD27-AF750, Igj-PE, CD5-PECy5.5,9G4-PerCP, CD19-PECy7, a dump channel, and a viabilitymarker. The second B cell tube contained antibodiesagainst IgM-FITC, CD20-PerCP, CD23-APC, CD38-APCCy5.5, CD27-AF750, Igj-PE, CD24-PE-AF610, CD5-PECy5.5, CD19-PECy7, a dump channel, and a viabilitymarker. Only tube 1 was run in the first time-point(except for subjects 17 and 18), and both tubes wererun in parallel for all subjects at the second time point.Both tubes use the same antibody-fluorophore combina-tions for CD19, CD27, CD38, CD5, anti-kappa, viability(Invitrogen aqua), and the dump gate (CD3, CD8, andCD14 (all Pacific Blue)). Tube 1 was used to define theB cell subsets in all of the subjects for the first timepoint except subjects 17 and 18. For the second timepoint, tube 2 was used in subjects 2, 3, 4, 5, 6, 8, 9, 10,14, 15, 16, 17, and 18 and tube 1 was used for theremaining subjects. When both tubes were available, thetube that provided the clearest discrimination betweenthe B cell subsets was chosen for analysis. Gating wasperformed in an unbiased fashion for each of the sam-ples. Gates were not copied within the same subject orbetween subjects. The placement of the gates was con-firmed by analysis of other markers including CD24,CD5, and IgM. For example, in the case of IgM, naivemature cells express intermediate levels, whereasmature activated cells contain a mixture of IgMþ and iso-type-switched (IgM�) cells (Fig. S1C). In a similar fash-ion, fluorescence intensity levels of CD24 and CD5 wereused to distinguish transitional from naı̈ve mature Bcells. Transitional cells have higher levels of both ofthese markers than naive mature B cells. Absolute T, B,NK cell and B and T cell subset counts were obtainedby multiplying the absolute lymphocyte count (obtainedfrom a complete blood count) by the percentage of lym-phocytes having a particular phenotype (obtained byflow cytometry).

Variability Analysis

Interindividual variability of a particular subset overtime was defined using the coefficient of variation (CV),calculated as (standard deviation/mean of a particularsubset) � 100. An unequal variance Student’s t test wasused to compare the significance of the difference ofwithin versus between individual pair differences. Vari-ability was also evaluated using agglomerative clustering.

Cluster analysis was performed in the R statistical pro-gramming environment (3) using the algorithm agnes(4). Cluster analysis on paired samples was performedon three different representations of the same dataset:standardized percentages, percentages (definedabove), and absolute cell counts (defined above). Stand-ardization for each subset percentage consists of sub-tracting the arithmetic mean of the subset and dividingby the mean absolute deviation of the subset. The meanabsolute deviation is the mean value of the absolute dif-ferences of the values from their mean. Cluster analysison single samples was performed on lymphocyte subsetpercentages and not subjected to standardization.

Assessment of the Likelihood of Replicate Pairing

A Euclidean distance-based phylogenetic tree was builtusing the UPGMA algorithm with mean distances.Within the tree, we counted how many of the firstneighbor couples corresponded to replicate samplesfrom the same subject (hereafter referred to as a ‘‘pair’’).The significance of this number was computed using alabel mixing technique. The labels of all samples werescrambled one million times and the number of firstneighbors that corresponded to a pair was computed ineach tree. The maximal number of pairs was 5 in onemillion scrambles (for all trees built in the current analy-sis). Thus, the probability of observing more than 5pairs is lower than about 1 � 10�6.

RESULTS

The Average Intrasubject Variability Is SignificantlyLess Than Intersubject Variability

To evaluate how lymphocyte subsets vary within andbetween individuals, we performed immunophenotypingon peripheral blood of 18 healthy adults at two timepoints that were at least 2 months apart. The absoluteand relative counts of the major lymphocyte subsets areshown in Figures 1A and 1B, respectively. Most subjectshave fairly similar profiles at each of the two timepoints. The similarity in lymphocyte profiles within indi-viduals is more pronounced when the relative frequen-cies of B and T cell subsets are evaluated (Figs. 2A and3A, respectively). The range of subset frequency meas-urements tends to be narrow in replicate samples fromthe same individual, whereas it is more variable in differ-ent individuals. This is illustrated graphically in Figure2B for B cell subsets and in Figure 3B for T cell subsets.Replicate measurements, indicated by black dots con-nected by lines, are fairly close together for most sub-jects, whereas the ranges of values for all of theindividuals are further apart. However, because the num-ber of observations is larger for the interindividual analy-sis (n ¼ 36) than the intraindividual analysis (n ¼ 2 persubject), these data will tend to show a narrower rangeof measurements within individuals than between indi-viduals. We therefore wished to compare intra- andinterindividual variability using a complementarymethod (agglomerative clustering) that was not

VARIABILITY AND CLUSTERING OF LYMPHOCYTE SUBSETS 293


confounded by this issue. Clustering determines the rela-tive similarities of samples. By evaluating the frequencywith which samples from the same individual cluster orpair together versus the frequency with which a sampleclusters with a sample from a different individual, it ispossible to evaluate the likelihood that paired samplestend to be more similar to each other than they are tothe general population.

Agglomerative clustering starts by considering eachsample as its own cluster. In a pair-wise fashion, themost similar instances are joined together into a largercluster recursively until a single cluster containing allsamples is achieved. The result is displayed in the formof a dendrogram. Figure 4 shows the resulting dendro-gram when standardized lymphocyte subset data fromboth time points are clustered for all of the subjects.Standardization controls for the sizes and variances ofpopulations (see Materials and Methods). Under the den-drogram is a map showing the lymphocyte subsetscolor-coded based upon their values expressed in stand-ard deviations relative to the mean. This figure showsthat the replicate samples from 10 of the 18 individualspair together (occupy neighboring branches in the den-drogram). The chance of obtaining 5 pairs out of 18 is

less than one in a million if the observations are unre-lated (see Methods). Therefore, these data suggest thatobservations from individual subjects are correlated. Themap below the dendrogram shows that there are twoways in which samples from the same individual cancluster together. One is for the individual to have sub-sets that differ from the rest of the group. For example,subject 17 has multiple subsets that are more than twostandard deviations from the mean of the group. Other

FIG. 1. B, T, and NK cell numbers and percentages in replicate sam-ples from normal subjects. A: Absolute cell counts. Stacked bar graphsof T cells (CD3þ lymphocytes), B cells (CD19þ lymphocytes), and NKcells (CD3�, CD56þ, and/or CD16þ lymphocytes). Cell counts (inthousands per microliter) are plotted on the y-axis versus the subjectnumber on the x-axis. Each vertical bar represents a single sample.Samples from two different time points are shown for each subject. B:Relative cell counts (Percentages). Stacked bar graphs of T cells, Bcells, and NK cells expressed as a percentage of lymphocytes on they-axis versus the subject number on the x-axis. [Color figure can beviewed in the online issue which is available at wileyonlinelibrary.com]

FIG. 2. B cell subsets in replicate samples from normal subjects. A:B cell subsets are plotted on the y-axis as percentages of CD19þ lym-phocytes. B cell subsets are defined on the basis of CD27 and CD38staining as follows: Transitional CD27�CD38þþ, Naı̈ve MatureCD27�CD38þ, Mature Activated CD27þCD38þ, Resting MemoryCD27þCD38�, and Plasmablasts CD27þþCD38þþ. Subject num-bers are given on the x-axis. Replicate samples are shown for eachsubject. B: B cell subsets (as defined in Fig. 2A) are plotted on the y-axis as a percentage of CD19þ lymphocytes. Dots indicate individualmeasurements. Vertical lines link the replicate measurements fromeach subject. Horizontal lines intersecting the vertical lines indicatethe mean for each subject. The range of B cell subset percentages forall of the subjects is represented by the shaded box extending fromthe smallest individual mean to the greatest individual mean measure-ment. [Color figure can be viewed in the online issue which is avail-able at wileyonlinelibrary.com]



individuals who have more dissimilar lymphocyte sub-sets include 16, 10, and 2. These subjects are all foundon one end of the dendrogram, where there are longbranches, illustrating their dissimilarity to the other sub-jects. The other, nonmutually exclusive way in whichsamples from the same individual can cluster together isif the lymphocyte subsets are very similar in both sam-ples. A low level of intraindividual variability is the mostlikely explanation for clustering of the samples from sub-jects 9, 8, 12, 4, and 6.

Next, we performed agglomerative clustering on thebasis of relative versus absolute lymphocyte subsetfrequencies. Figure 5 shows a map of the subsets, color-coded based upon their relative frequencies, and theclustering dendrogram, which reveals that replicate sam-

ples from 12 out of the 18 individuals cluster together. Forabsolute cell counts, 7 out of 18 replicate samples fromindividual subjects cluster together (Fig. S2). Qualitatively,the dendrograms for standardized, absolute, and relativelymphocyte subset data appear similar to one another(some of the differences in branch order can be resolvedby pivoting branches of the dendrogram). Nine out of the10 individuals whose samples clustered together basedupon the standardized data also clustered together withthe unstandardized percentage data. Six of the seven sub-jects whose samples clustered together based upon theabsolute count data also clustered together when relativelymphocyte subset percentages were used.

The highest level of intraindividual clustering (12pairs) was achieved with the unstandardized lymphocytepercentage data. To gain further insight into this result,we repeated the cluster analysis with subsets of thedata: the major lymphocyte subset fractions, the B cellsubsets and the T cell subsets (Fig. 5B). Subjects 3, 5,and 14 clustered on the basis of the general subsets.Subjects 14, 17, and 16 clustered on the basis of their Bcell subsets and subjects 3, 12, 10, 11, and 17 clusteredon the basis of their T cell subsets. When all of thesedata were used collectively (Fig. 5A), subjects 14, 15, 5,2, 4, 6, 8, 11, 10, 12, 16, and 17 clustered together.Thus, while some subjects can be clustered on the basisof distinctive lymphocyte subsets, for others such as 15,2, 4, and 6, clustering was only revealed in the full dataset. On the other hand, subject 3 clustered in the gen-eral and T cell subsets, but not when all of the subsetswere incorporated into the clustering algorithm. In thissubject, the B cell subsets are highly variable and mayhave had the effect of ‘‘canceling out’’ the correlationseen amongst the general and T cell subsets. To deter-mine the level of variability within each lymphocyte sub-set, we analyzed the coefficient of variation for thepercentage and absolute lymphocyte counts.

Variability Is Greater in Small Lymphocyte Subsets and forAbsolute Cell Counts

For the basic lymphocyte subsets expressed as per-centages, the coefficient of variation for T cells is 9%, Bcells is 49%, and NK cells is 44% (please see Table S3 inthe electronic supplement). Amongst the lymphocytes,the major subsets, CD4þ and CD8þ T cells, are lessvariable than the minor subsets, such as CD4þCD8þ Tcells, CD4�CD8� T cells, B cells, and NK cells. Thelevel of subset variability is inversely correlated with thesize of the population (Fig. S3A). When absolute fre-quencies are analyzed, the coefficients of variation arehigher than the corresponding relative subset coeffi-cients of variation (Table S3). As with relative frequencyvariation, absolute subset frequency variation is inverselycorrelated with the size of the lymphocyte subpopula-tion (Fig. S3B). The finding that absolute count datayielded fewer intraindividual pairs was consistent with agreater level of variability seen in absolute lymphocytecounts. Furthermore, clustering is skewed toward thelarger cell populations, which exhibit less variability. For

FIG. 3. T cell subsets in replicate samples from normal subjects.Repeat measurements of basic T cell subsets expressed as a percent-age of CD3þ lymphocytes are presented for all subjects. A: Eachgrouping represents one subject and each bar represents a single sam-ple. Replicate samples are shown for each subject. B: CD4þ, CD8þ,Double Positive (CD4þ, CD8þ), and Double Negative (CD4�, CD8�)T cell subsets are plotted on the y-axis as a percentage of CD3þ lym-phocytes. Dots indicate individual measurements. Vertical lines linkthe replicate measurements from each subject. Horizontal lines inter-secting the vertical lines indicate the mean for each subject. Therange of T cell subset percentages for all of the subjects is representedby the shaded box extending from the smallest individual mean to thegreatest individual mean measurement. [Color figure can be viewed inthe online issue which is available at wileyonlinelibrary.com]



example, CD4þ T cells are given more weight in theunstandardized clustering than plasmablasts. Thus inthis analysis, the use of unstandardized subset percent-age data appears to result in the most robust clustering,in which replicate samples from the same individualstend to pair the most often.

Individuals Fall into Different Clusters on the Basis ofTheir Lymphocyte Subset Profiles

We next used nonstandardized subset percentage datato cluster single samples from individuals. By clusteringsingle samples from individuals, we hoped to determineif individuals fell into one or more clusters on the basisof internal similarities in their lymphocyte subsets(Fig. 6). This analysis revealed three to four clustersamongst different individuals.

DISCUSSION

There are many well-understood reasons for lympho-cyte subset measurements to vary. These reasonsinclude preanalytical variables such as the source, condi-tion, and manner of preservation of the sample, thetime of day that the blood was drawn, subject age, dis-ease state, and medications (5–8). Analytical variablesinclude instrumentation, instrument settings, the typesand quantities of antibody-fluorophore conjugates, andthe manner in which lymphocyte subsets are definedand electronically gated (5,9,10). We attempted to limitsome of these sources of variation by using fresh wholeblood, drawing all samples in the morning, using anidentical panel of core antibodies and having a singleoperator perform all of the staining and run all of thesamples on the same instrument. Furthermore, data anal-ysis was performed using very similar gate settings.

However, we did not run bead calibrators with eachsample to standardize instrument settings between dif-ferent days, so it is likely that some of the measurementscould have been less variable. Another caveat to thisstudy is that a two-platform technique was employed forabsolute quantification: the absolute lymphocyte countwas obtained from a CBC that was performed on thesame blood sample, in parallel with the flow cytometry.Dual platform methods have greater variation than singleplatform methods for determining absolute cell countsand the use of the absolute lymphocyte count in particu-lar is less reliable than the absolute white blood cellcount (11). Thus, our finding that absolute cell countshave greater variability than lymphocyte subset percen-tages may be attributable entirely or in part to technicalvariations introduced by using a dual platform method.Nevertheless, the remarkable finding is not that lympho-cyte subsets are variable but that the level of variabilitywithin subjects is much lower than between subjects.

This finding negates the standard concept of a refer-ence interval for lymphocyte subsets; the referenceinterval for a population is quite broad compared withthe baseline measurements for an individual. Therefore,in some instances, the use of reference ranges or aver-age values will lead to erroneous conclusions, unlessbaseline measurements are obtained. For example, with-out a baseline measurement, one might incorrectly con-clude that a high or low proportion of a particularlymphocyte subset is due to therapy, rather than beingdue to where that subject’s lymphocyte set-point tendsto reside. Conversely, a small change in the proportionof a particular lymphocyte subset compared with base-line might be quite meaningful in an individual, even ifthe changed value still falls within the ‘‘normal’’ range.

FIG. 4. Cluster analysis with standardized percentage data. Agglomerative cluster analysis was performed on the percentages of T, B, and NK cellsand the T and B cell subset percentage data (as defined in the legends to Figs. 2 and 3) but the data were first subjected to standardization. Stand-ardization for each subset percentage consists of subtracting the arithmetic mean of the subset and dividing by the mean absolute deviation of thesubset as defined in Methods. The data are displayed graphically below the dendrogram using colored squares that correspond to the lymphocytesubsets, color-coded based upon their values expressed in standard deviations relative to the mean. Samples from each subject are represented by avertical bar with different colored squares. [Color figure can be viewed in the online issue which is available at wileyonlinelibrary.com]



Thus, the use of a baseline measurement is preferable toan average or reference value when evaluating theeffects of therapy on lymphocyte subsets in an individ-ual over time. We acknowledge that patients who havehad treatments or longstanding disease before enroll-ment into a study may have ‘‘baseline’’ samples that donot accurately reflect their true baseline in health. Avery important extension of this work will be to deter-mine how stable lymphocyte subset composition is inindividuals with active disease, and ultimately in individ-uals at different stages of disease.

Another finding of this study is that not all lympho-cyte subset measurements have a similar level of variabil-ity. Absolute cell counts and smaller lymphocyte subsetstend to have more inter- and intraindividual variability.This is illustrated by less robust clustering of pairedmeasurements from individuals when absolute lympho-

cyte counts or standardized lymphocyte fractions areused. In the case of small populations, which are givensimilar weight in standardized lymphocyte fractions,small variations in staining intensity can lead to large

FIG. 5. Lymphocyte subset profilesfrom individual subjects cluster to-gether. Lymphocyte profiles were sub-jected to agglomerative cluster analysisas described in Methods. A: A dendro-gram displaying the resulting clusters isshown. Individual subject numbers areshown at the base of the dendrogramand link the dendrogram to the coloredgraphical display below. The percen-tages of the lymphocyte subsets are dis-played graphically using coloredsquares that correspond to different per-centage ranges. Samples from eachsubject are represented by a vertical barwith different colored squares. B: Sub-sets of the lymphocyte data were usedfor agglomerative clustering. Generalsubsets are defined in the legend forFigure 1. B cell subsets are defined inthe legend for Figure 2A. T cell subsetsare defined in the legend for Figure 3A.[Color figure can be viewed in the onlineissue which is available atwileyonlinelibrary.com]

FIG. 6. Cluster analysis with single samples from different individu-als. Agglomerative cluster analysis of unstandardized lymphocyte sub-set percentage data is shown for the first sample of each of the 18subjects.



relative changes. Others have also found that variabilitywas proportionally greater for measurements of numeri-cally smaller subsets of lymphocytes (5,6,10,12). In thecase of absolute cell counts, additional sources of varia-tion including analytical (e.g., the complete blood count(13), and the use of a dual platform method, as dis-cussed above) and biological [e.g., diurnal shifts (14)],contribute to increased inter- and intraindividual variabil-ity. It is also possible that shifts in clonal populationsand responses to immunological stressors are moreapparent in smaller lymphocyte subsets and/or withabsolute cell counts.

While the proportions of different lymphocyte subsetsare stable in most subjects over the two time points, thedegree of intrasubject variability does vary. Some of thesubjects exhibit larger shifts in their subsets. It isunclear if this is due to unique unidentified circumstan-ces in these subjects or if some individuals have inher-ently more variable lymphocyte subset set points. Whenthe change in lymphocyte subset percentages was ana-lyzed as a function of the length of time between timepoints, there was no positive or negative correlation(data not shown). Lymphocyte subsets and their level ofvariability also differ in subjects with diseases such asSLE (6,15–17). Therapy with corticosteroids and otherimmunomodulatory agents can have profound effects onlymphocyte subset distributions as well (2,18–21), fur-ther complicating longitudinal data interpretation in thesetting of disease.

One of the most intriguing aspects of this analysis isthat healthy individuals can be segregated into differentgroups on the basis of their lymphocyte subsets. Due tothe small number of subjects studied, we were not ableto correlate the clusters with demographic characteris-tics of the subjects such as age or race. The subjects an-alyzed in this study are mostly females of childbearingage (as described in Methods, these subjects were usedas controls for an analysis of B cell subsets in patientswith SLE and replicate samples were analyzed specifi-cally for this study). Thus, the group of subjects is moreuniform than a randomly recruited group of healthy con-trol subjects with respect to age and sex but is not wellcontrolled for race. The ability to cluster individuals sug-gests that there may be similarities between individualswho tend to fall into a particular cluster (such as race,HLA type, or other genetic factors, although race didnot correlate in this small sample). Furthermore, theremay be under-appreciated relationships between differ-ent lymphocyte subsets. Additional correlations maybecome apparent when larger numbers of individualsare analyzed.

Overall, these data suggest that each individual has acharacteristic lymphocyte profile that defines his or herimmunologic set point. Lymphocyte subset frequenciesmake up a multidimensional profile that can be studiedlongitudinally to determine the effects of disease or ther-apy on the immune system. The use of colored graphi-cal displays or maps of lymphocyte percentages may beparticularly useful in visualizing longitudinal trends. In a

similar fashion, Petrausch and colleagues have usedevent acquisition histograms (FCOMTM analysis, availableon Winlist software) and applied hierarchical clusteringto multiparameter flow cytometry data to demonstratethat this data analysis method reveals distinctive lympho-cyte surface marker profiles in lymph node cells stimu-lated with different cytokine cocktails (22). Moreprovocatively, it is possible that subjects with specificlymphocyte profiles are predisposed to develop certaindiseases or immune responses.

ACKNOWLEDGMENTS

The authors are indebted to Lytia Fisher for help withsubject recruitment and to the Pathology BioResourceFlow Cytometry and Cell Sorting facility at the Univer-sity of Pennsylvania for expert technical assistance.

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Circulating lymphocyte subsets in normal adults are variable and can be clustered into subgroups

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