Clinical and Experimental Immunology Original Article doi: 10.1111/cei.13491 Comprehensive flow cytometric reference intervals of leukocyte subsets from six study centers across Europe A. Oras,*,1 B. Quirant-Sanchez,†‡§,1 Summary D. Popadic,¶ S. Thunberg,§** A group of European FOCIS Centers of Excellence adapted panels of the O. Winqvist,§** S. Heck,§†† Human Immunophenotyping Consortium (HIPC) for whole blood analysis. M. Cwikowski,§‡‡ D. Riemann,§‡‡ Using four core panels [T/regulatory T cell/B/natural killer (T/Treg/B/NK) B. Seliger,§‡‡ E. Martinez Caceres,†‡§ §§§ and myeloid cells] the main leukocyte populations were analyzed in a R. Uibo * and T. Giese * Department of Immunology, Institute of clinical–diagnostic setting in a harmonized manner across different plat- Biomedicine and Translational forms. As a first step, the consortium presents here the absolute and rela- Medicine, University of Tartu, Tartu, Estonia, tive frequencies of the leukocyte subpopulations in the peripheral blood † Immunology Division, LCMN, Germans Trias of more than 300 healthy volunteers across six different European i Pujol University Hospital and Research centers. Institute, ‡ Department of Cell Biology, Physiology and Immunology, Universitat Keywords: blood leukocyte counts, flow cytometry, immunophenotyping, Autonoma Barcelona, Barcelona, § Federation reference intervals of Clinical Immunology Societies (FOCIS) Center of Excellence, ¶ Institute of Microbiology and Immunology, School of Medicine, University of Belgrade, Belgrade, Serbia, ** Unit of Immunology and Allergy, Karolinska University Hospital, Stockholm, Sweden, †† NIHR GSTT/KCL Comprehensive Biomedical Research Centre, Guy’s & St Thomas’ NHS Foundation Trust, London, UK, ‡‡ Institute of Medical Immunology, Martin-Luther-University Halle-Wittenberg, Halle/Saale, and §§ Institute of Immunology, Heidelberg University Hospital, German Center for Infection Research (DZIF), Partner site, Heidelberg Accepted for publication 3 July 2020 Correspondence: T. Giese, Institute of Immunology, Heidelberg University Hospital, Im Neuenheimer Feld 305, 69120 Heidelberg, Germany. E-mail: giese@uni-hd.de 1These authors contributed equally to this study. Introduction introduction of novel fluorochromes, polyspectral approaches For decades, flow cytometry has been the standard technique are standard techniques in basic science laboratories, their for phenotyping immune cells in suspension. While, in the utilization in clinical immunology is not yet common prac- last 15 years, due to advances in instrumentation and tice. Furthermore, the analysis of three major lymphocyte © 2020 The Authors. Clinical & Experimental Immunology published by John Wiley & Sons Ltd on behalf of British Society 363 for Immunology, Clinical and Experimental Immunology, 202: 363–378 This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. A. Oras et al. subpopulations T, B and natural killer (NK) is, in most study were recruited at six different study centers partici- cases, not sufficient for clinical decision-making. Therefore, pating in the COST ENTIRE initiative, including Barcelona the characterization of differentiation and activation stages (BCN), Tartu (TAY), Heidelberg (HDB), London (LHR), of the different lymphocyte subpopulations, as well as the Stockholm (ARL) and Halle (HAL). Age distribution of analysis of other subpopulations of the innate immune study participants across study centers is shown in system (monocytes, dendritic cells and granulocytes) is not Fig. 1. All study subjects gave their informed consent only important to understand individual physiology of the according to the approved protocols of research ethics immune system but also for differential diagnosis of immu- committees of the participating study centers (Supporting nodeficiency, activity in immune-mediated diseases and information, Table S1). response towards immune-modulating therapies. After venipuncture, fresh anti-coagulated lithium hep- So far, different approaches have been utilized. As outlined arinized whole blood of the study subjects was aliquoted by Maecker et al. [1], there are several opinions regarding in 100 μl amounts into polystyrene/polypropylene tubes suitable markers and different staining strategies have their and incubated with the appropriate fluorochrome- pros and cons. In order to compare results across different conjugated monoclonal antibodies (Supporting informa- sites, it is important to reach consensus regarding markers, tion, Table S2) to cell surface markers for 30 min at room reagents, instrument settings and gating/enumeration strate- temperature (RT) in the dark. After incubation, stained gies, whereas minor site-to-site variations are of limited samples were subjected to red blood cell lysis using 2 ml practical importance. Unlike in other areas of laboratory 1× BD lysing solution (Cat. no 349202). After erythrocyte diagnostics, analysis of subpopulations of immune cells does lysis for 10 min, the samples were washed and acquired not rely upon fixed reference intervals, but upon the overall on the flow cytometer. Prepared samples were stored at pattern of differentiation/activation cell status and upon RT in the dark until analyzing (maximum 6 h) and for changes of these parameters during therapy. In addition, each sample, a maximum of 300 000 leukocyte events these laboratory values have to be stringently discussed in were recorded as a rule. Using appropriate thresholds, the the clinical context of the individual patient. collection of an excess of debris was avoided. In 2011, under the lead of the Federation of Clinical Flow cytometric analysis was performed on a flow Immunology Societies (FOCIS), a minimal set of multi- cytometer available at the specific study center [either LSR spectral panels to evaluate the major leukocyte subsets in Fortessa, FACS Canto (BD Biosciences, San Jose, CA, USA) the peripheral blood was proposed [1]. So far, these panels or Navios (Beckman Coulter, Brea, CA, USA)] using a have been evaluated in a standardized form by several cent- common set-up for the proposed markers (Supporting ers [2]. However, as starting material this consortium favored information, Fig. S1). The configuration of cytometers and frozen/lyophilized peripheral blood mononuclear cells the laser specifications are included in the Supporting (PBMC). In a clinical–diagnostic setting this approach seems information, Table S3. Briefly, BD cytometer set-up and to be problematic. First, original proportions of subpopula- tracking (CST) beads (Cat. no. 656505) were used to set tions – not biased due to isolation and manipulation tech- the mean fluorescence form each fluorescence channel. niques – and secondly, absolute cell counts are required. Data were analyzed with the FlowJo software (BD Therefore, a group of European FOCIS Centers of Excellence Biosciences) according to the gating strategy developed by with support of the COST Action European Network for the EU COST-ENTIRE Consortium working group Translational Immunology, Research and Education (ENTIRE) (Supporting information, file: HIPC version 3.3 protocol). adapted the proposed Human Immunophenotyping Gating was performed by each center separately using pre- Consortium (HIPC) panels for whole blood analysis. The designed FlowJo gating templates. Gating strategy was pub- basic panels were compatible throughout different platforms. lished previously [3]. Relative numbers of T cells, regulatory The emphasis was put upon harmonization, as due to the T cells (Tregs), B cells, dendritic cells (DC), NK cells, mono- lack of suitable calibrators, the existence of a growing variety cytes, granulocytes and their subsets were determined. For of cytometer platforms in different laboratories and the tech- the determination of absolute cell counts, different centers nical variations inherited to clinical material, standardization used different approaches: (1) single platform: (a) the BD seems unfeasible. As a first step, the consortium presents Multitest™ six-color TBNK reagent and TruCount tubes (Cat. here the percentages and absolute frequencies of major sub- no. 337166) (HDB, TAY, HAL, LHR) or (b) Perfect Count populations in the peripheral blood of more than 300 healthy Microspheres™ (Cat. no. CYT-PCM-100) (BCN); and (2) volunteers among six different European centers. double platform, where the absolute numbers were calculated by the results of hematological analyzer results (ARL). Using Trucount tubes, the absolute cell count was deter- Materials and methods mined by comparing cellular events to bead events and A total of 330 healthy individuals (134 males, 196 females; the absolute counts were calculated according to the age range = 20–81 years) who met the criteria for the manufacturer’s instructions, as follows: 364 © 2020 The Authors. Clinical & Experimental Immunology published by John Wiley & Sons Ltd on behalf of British Society for Immunology, Clinical and Experimental Immunology, 202: 363–378 Flow cytometric reference intervals for blood leukocytes Fig. 1. Violin plots showing age distribution of immunophenotyped subjects across the centers (Kruskal–Wallis test). ARL = laboratory in Stockholm, Sweden; BCN = laboratory in Barcelona, Spain; HDB = laboratory in Heidelberg, Germany; HAL = laboratory in Halle, Germany; LHR = laboratory in London, UK; TAY = laboratory in Tartu, Estonia. events in cell population beads per tube identify T helper type 1 (Th1) (CXCR3+CCR6−), Th2 × = absolute cell count events in beads region sample volume (50𝜇l) (CXCR3−CCR6−) and Th17 (CXCR3−CCR6+) subpopula- tions from CM and EM CD4+ T cells. Activation of For quantification of absolute cell counts using Perfect CD4+ and CD8+ T cells was examined by CD38 and Count Microspheres, 25 μl of peripheral blood samples HLA-DR expression. was incubated with CD45-peridinin chlorophyll (PerCP) In the Treg panel, the following surface markers were (BD Biosciences) for 15 min at room temperature in dark- utilized: CD45, CD3, CD4, CD127, CD25, CD45RO, CCR4 ness. Erythrocytes were removed using 450 μl lysis buffer (CD194) and HLA-DR markers, following the consensus (BD FACS™ lysing Solution; BD Biosciences). Twenty-five of the Human Immunology Consortium using whole blood. microliters of Perfect Count Microspheres was added to The Treg subset was identified as CD4+CD25+CD127− T each sample and acquired on a flow cytometer. The abso- cells, and according to the differential expression of lute counts were calculated as follows: (%sub- CD45RO and CCR4 were characterized as memory Treg, set/100) × counts of the main subpopulation. and the expression of HLA-DR to define activated Treg. In the T cell panel, following markers were used: In the B cell panel, the following subsets were studied: CD45, CD3, CD4, CD8, CD45RA, chemokine receptor CD19+ B cells, naive B cells, transitional B cells, preswitch type 7 (CCR7) (CD197), C-X-C chemokine receptor memory B cells, switched memory B cells, plasmablasts type 3 (CXCR3) (CD183), CCR6 (CD196), human leu- and exhausted memory B cells were identified, which were kocyte antigen D-related (HLA-DR) and CD38. CD4+ distinguished according to differential expression of CD45, and CD8+ T cells were further characterized: using the CD3, CD19, CD20, CD27, CD24, CD38 and immuno- differential expression of CD45RA and CCR7 naive, globulin (Ig)D. central memory (CM), effector memory (EM) and effec- In the NK cell/ myeloid cell panel the following mark- tor memory RA (TEMRA) compartments were analyzed. ers were used: CD45, CD3, CD19, CD20, CD11c, CD123, Anti-CXCR3 and anti-CCR6 antibodies were used to CD14, CD16, CD56, HLA-DR and sulfo-LacNac (SLAN). © 2020 The Authors. Clinical & Experimental Immunology published by John Wiley & Sons Ltd on behalf of British Society 365 for Immunology, Clinical and Experimental Immunology, 202: 363–378 Table 1. Descriptive statistics of T cell frequencies and absolute counts 366 Number of Lower 90% Upper 90% samples Minimum Maximum Median Mean 10% Percentile 90% Percentile 25% Percentile 75% Percentile s.d. s.e. CI of mean CI of mean A. Oras et al. CD3+ (%) 270 35·00 90·00 67·00 66·31 55·00 76·00 61·00 72·00 8·69 0·53 65·44 67·18 CD3+ (cell/μl) 330 487·00 3204·00 1292·00 1361·00 791·00 2014·00 1022·00 1638·00 483·30 26·60 1308·00 1413·00 CD4+ T cell (%) 330 29·00 90·00 60·00 60·73 48·00 73·90 54·00 68·00 10·27 0·57 59·80 61·66 CD4+ T (cell/μl) 330 266·00 2318·00 782·00 823·10 454·50 1249·00 617·30 993·80 319·60 15·59 788·50 857·70 CD4+ T-naive (%) 330 8·00 93·00 44·00 44·22 26·00 62·00 34·00 54·00 14·70 0·81 42·88 45·55 CD4+ T-naive (cell/μl) 330 42·00 1590·00 330·00 378·70 146·00 673·90 218·00 465·00 232·20 1·30 353·60 403·90 CD4+ T-EMRA (%) 330 0·00 18·00 1·00 2·35 0·00 6·00 0·00 3·00 3·02 0·17 2·07 2·62 CD4+ T-EMRA (cell/μL) 330 0·00 146·00 8·00 18·55 2·00 49·80 3·00 22·00 23·55 4·15 16·00 21·10 CD4+ T-EM (%) 330 2·00 72·00 15·00 17·53 7·00 31·00 10·00 22·00 10·51 0·58 16·58 18·48 CD4+ T-EM (cell/μl) 330 7·00 445·00 116·00 133·40 58·00 234·00 78·50 167·00 75·44 2·07 125·30 141·60 CD4+ T-EM Th1 from CD4+(%) 303 0·00 29·00 6·00 6·87 2·00 13·00 3·00 9·00 4·87 0·28 6·41 7·33 CD4+ T-EM Th1 (cell/μl) 303 1·00 231·00 42·00 51·94 17·40 102·60 25·00 71·00 35·98 0·94 47·88 56·01 CD4+ T-EM Th2 from CD4+(%) 303 0·00 25·00 1·00 1·87 0·00 4·00 1·00 2·00 2·36 0·14 1·65 2·09 CD4+ T-EM Th2 (cell/μl) 303 0·00 133·00 9·00 14·10 4·00 28·60 6·00 16·00 16·44 0·73 12·24 15·95 CD4+ T-EM Th17 from CD4+ (%) 303 0·00 13·00 2·00 2·60 1·00 5·00 1·00 3·00 1·98 0·11 2·41 2·79 CD4+ T-EM Th17 (cell/μl) 303 1·00 85·00 17·00 19·44 7·00 36·00 10·00 26·00 12·66 7·87 18·01 20·87 CD4+ T-CM (%) 330 3·00 73·00 35·00 35·87 21·10 52·00 27·00 44·00 12·11 0·67 34·78 36·97 CD4+ T-CM (cell/μl) 330 12·00 852·00 269·00 292·60 142·10 488·90 189·80 372·50 142·90 2·04 277·10 308·00 CD4+ T-CM Th1 from CD4+ (%) 303 1·00 18·00 8·00 8·44 4·00 13·00 6·00 10·00 3·22 0·19 8·13 8·74 CD4+ T-CM Th1 (cell/μl) 303 3·00 216·00 65·00 68·74 28·00 116·60 46·00 87·00 35·58 2·03 64·71 72·76 CD4+ T-CM Th2 from CD4+ (%) 303 1·00 23·00 8·00 8·25 4·00 13·00 6·00 10·00 3·71 0·21 7·90 8·60 CD4+ T-CM Th2 (cell/μl) 303 2·00 181·00 57·00 64·99 27·40 117·20 41·00 81·00 35·35 2·14 61·00 68·99 CD4+ T-CM Th17 from CD4+ (%) 303 0·00 23·00 8·00 8·89 5·00 14·00 6·00 11·00 3·75 0·22 8·54 9·25 CD4+ T-CM Th17 (cell/μl) 303 1·00 247·00 64·00 70·69 31·00 117·20 45·00 92·00 37·23 0·43 66·48 74·90 CD4+ T-activated (%) 330 0·00 7·00 1·00 1·50 1·00 3·00 1·00 2·00 0·95 0·05 1·42 1·59 CD4+ T-activated (cell/μl) 330 0·00 47·00 10·00 12·02 4·00 22·00 7·00 15·00 7·79 0·43 11·17 12·86 Non-activated (CD38− HLA-DR−) 330 16·00 101·00 59·00 58·33 36·00 82·00 47·00 70·00 16·59 0·91 56·83 59·83 CD4+ T cells (%) Non-activated (CD38− HLA-DR−) 330 107·00 1298·00 432·00 464·90 246·10 750·80 321·50 577·30 196·50 10·82 443·70 486·20 CD4+ T cells (cell/μl) CD4+ CD38− HLA-DR+ (%) 330 1·00 22·00 3·00 3·99 2·00 7·00 2·00 5·00 2·57 0·14 3·75 4·22 CD4+ CD38− HLA-DR+ (cell/μl) 330 3·00 116·00 25·00 30·76 13·00 54·90 18·00 38·25 19·26 1·06 28·68 32·85 CD4+ CD38+ HLA-DR− (%) 330 1·00 80·00 37·00 36·22 12·00 58·00 24·75 48·00 16·99 0·94 34·68 37·75 CD4+ CD38+ HLA-DR− (cell/μl) 330 2·00 1643·00 262·50 315·90 81·00 651·20 148·80 401·50 237·70 13·08 290·10 341·60 CD8+ T (%) 330 7·00 67·00 32·00 31·66 20·00 42·00 26·00 37·00 8·73 0·48 30·87 32·45 CD8+ T cell (cell/μl) 330 61·00 1527·00 388·50 435·30 218·10 690·70 295·00 547·00 217·00 11·95 411·80 458·90 CD8+ T-naive (%) 330 3·00 88·00 35·50 37·67 14·00 62·00 23·00 52·00 18·34 1·01 36·01 39·33 CD8+ T-naive (cell/μl) 330 9·00 631·00 136·50 159·10 42·10 307·90 82·00 210·00 105·80 5·82 147·60 170·50 CD8+ T-EMRA (%) 330 1·00 85·00 33·00 33·80 12·00 59·00 19·00 47·00 17·60 0·97 32·20 35·39 Immunology, Clinical and Experimental Immunology, 202: 363–378 © 2020 The Authors. Clinical & Experimental Immunology published by John Wiley & Sons Ltd on behalf of British Society for Table 1. (Continued) Number of Lower 90% Upper 90% samples Minimum Maximum Median Mean 10% Percentile 90% Percentile 25% Percentile 75% Percentile s.d. s.e. CI of mean CI of mean CD8+ TEMRA (cell/μl) 330 1·00 820·00 120·00 157·10 34·10 288·60 61·75 210·00 138·10 7·60 142·10 172·00 CD8+ T-EM (%) 330 0·00 58·00 19·00 19·95 9·00 33·90 13·00 25·00 9·63 0·53 19·08 20·83 CD8+ T-EM (cell/μl) 330 0·00 602·00 68·50 84·92 30·00 161·00 47·00 105·00 62·36 3·43 78·16 91·67 CD8+ T-CM (%) 330 0·00 34·00 7·00 8·51 2·00 17·00 4·00 12·00 5·89 0·32 7·98 9·04 CD8+ T-CM (cell/μl) 330 0·00 251·00 29·00 34·04 8·10 59·90 17·00 44·00 27·62 1·52 31·05 37·03 CD8+ T-activated (%) 330 0·00 40·00 2·00 3·16 1·00 6·00 1·00 4·00 3·57 0·20 2·84 3·49 CD8+ T-activated (cell/μl) 330 0·00 349·00 9·00 13·40 2·00 27·00 5·00 16·00 22·12 1·22 11·00 15·79 Non-activated (CD38− HLA-DR−) 330 24·00 112·00 82·00 78·05 59·00 92·00 70·00 88·00 13·47 0·74 76·83 79·26 CD8+ T cells (%) Non-activated (CD38− HLA-DR−) 330 43·00 1329·00 301·50 340·60 154·40 568·80 217·50 413·00 186·90 10·29 320·40 360·90 CD8+ T cells (cell/μl) CD8+ CD38− HLA-DR+ (%) 330 0·00 46·00 7·00 8·98 3·00 19·90 4·00 12·00 7·04 0·39 8·34 9·62 CD8+ CD38− HLA-DR+ (cell/μl) 330 3·00 317·00 25·00 38·69 8·00 84·00 15·00 50·00 37·73 2·08 34·60 42·78 CD8+ CD38+ HLA-DR− (%) 330 0·00 65·00 6·00 9·90 1·00 25·00 3·00 13·00 10·32 0·57 8·97 10·84 CD8+ CD38+ HLA-DR− (cell/μl) 330 1·00 407·00 23·50 42·99 5·00 106·80 9·00 57·00 52·70 2·90 37·28 48·69 T DP (%) 330 0·00 50·00 1·00 1·37 0·00 2·00 1·00 1·00 2·98 0·16 1·10 1·64 T DP (cell/μl) 330 2·00 848·00 11·00 18·96 5·00 31·90 7·00 18·00 50·26 2·77 13·52 24·41 T DN (%) 324 0·00 37·00 5·00 6·30 2·00 8·00 3·00 8·00 4·88 0·27 5·85 6·74 for Immunology, Clinical and Experimental Immunology, 202: 363–378 T DN (cell/μl) 324 5·00 639·00 65·00 84·33 24·00 157·50 41·25 106·00 73·57 4·09 76·29 92·37 Centers: ARL, Stockholm, Sweden; BCN, Barcelona, Spain; HDB, Heidelberg, Germany; HAL, Halle, Germany; LHR, London, UK; TAY, Tartu, Estonia. EM = effector memory; CM = central memory; DP = double-positive; DN = double-negative; s.d. = standard deviation; s.e. = standard error; CI = confidence interval; CM = central memory; EM = ef- fector memory; Th = T helper; HLA-DR = human leukocyte antigen D-related. © 2020 The Authors. Clinical & Experimental Immunology published by John Wiley & Sons Ltd on behalf of British Society 367 Flow cytometric reference intervals for blood leukocytes Table 2. Descriptive statistics Treg cell frequencies and absolute counts 368 Number of Lower 90% Upper 90% samples Minimum Maximum Mean Median 10% Percentile 90% Percentile 25% Percentile 75% Percentile s.d. s.e. CI of mean CI of mean A. Oras et al. Treg (%) 263 3 13 7·74 8 5 10 6 9 1·87 0·12 7·55 7·93 Treg cells (cell/μl) 263 18·00 173·00 64·00 67·18 36·00 104·60 48·00 80·00 26·80 1·65 63·92 70·43 Memory Treg from CD4+ (%) 263 2 11 4·45 4 3 6 4 5 1·42 0·09 4·31 4·60 Memory Treg (cell/μl) 263 10·00 88·00 36·00 37·78 22·00 59·00 27·00 46·00 14·47 0·89 36·02 39·54 HLA-DR+ from CD4+ (%) 263 0 6 1·70 2 1 3 1 2 0·81 0·05 1·62 1·78 HLA-DR+ Treg (cell/μl) 263 2·00 46·00 13·00 14·37 6·40 25·00 9·00 18·00 7·51 0·46 13·45 15·28 Centers: BCN, Barcelona, Spain; HDB, Heidelberg, Germany; HAL, Halle, Germany; LHR, London, UK; TAY, Tartu, Estonia. s.d. = standard deviation; s.e. = standard error; CI = confidence interval; Treg = regulatory T cell; HLA-DR = human leukocyte antigen D-related. Table 3. Descriptive statistics B cell frequencies and absolute counts Number 10% 90% Lower 90% Upper 90% of samples Minimum Maximum Median Mean Percentile Percentile 25% Percentile 75% Percentile s.d. s.e. CI of mean CI of mean B cells (cell/μl) 260 53·00 628·00 211·00 229·00 106·00 364·60 151·50 271·80 106·60 3·65 216·00 242·00 Switch memory B cells (%) 260 2·06 82·07 19·90 17·27 8·38 33·03 12·00 24·62 11·39 0·71 18·74 21·06 Switched memory B cells (cell/μl) 260 3·00 349·00 37·00 44·23 15·00 76·80 25·00 54·00 34·06 11·39 40·07 48·39 Plasmablasts from B cell (%) 260 0·00 10·67 1·04 0·76 0·06 2·09 0·31 1·27 1·19 0·07 0·92 1·16 Plasmablasts (cell/μl) 260 0·00 13·00 1·50 2·07 0·00 4·00 1·00 3·00 2·09 1·19 1·81 2·32 Preswitch memory B cells (%) 260 0·82 79·93 16·75 14·98 4·86 29·59 8·00 22·59 12·05 0·75 15·52 17·98 Preswitch memory B cells (cell/μl) 260 1·00 471·00 29·00 37·31 9·00 68·60 16·25 44·00 39·97 12·05 32·43 42·19 Exhausted memory B cells (%) 260 0·79 39·37 4·61 3·00 1·19 9·81 1·98 5·17 4·93 0·31 4·11 5·11 Exhausted memory B cells (cell/μl) 260 1·00 63·00 7·00 10·07 2·00 26·90 4·00 12·00 10·05 4·93 8·85 11·30 Naive B cells (%) 260 1·23 87·68 58·81 61·00 36·27 78·00 50·00 69·67 16·56 1·03 57·12 60·50 Naive B cells (cell/μl) 260 1·00 475·00 126·50 137·50 45·10 232·50 81·50 177·00 81·04 16·56 127·60 147·40 B-transitional from B cell (%) 260 0·00 19·09 6·36 5·80 2·04 11·63 3·78 9·00 3·87 0·24 5·97 6·76 B-transitional (cell/μl) 260 0·00 75·00 12·00 14·66 3·00 30·90 6·00 20·00 12·06 3·87 13·19 16·13 Centers: BCN, Barcelona, Spain; HDB, Heidelberg, Germany; HAL, Halle, Germany; LHR, London, UK; TAY, Tartu, Estonia. s.d. = standard deviation; s.e. = standard error; CI = confidence interval. Immunology, Clinical and Experimental Immunology, 202: 363–378 © 2020 The Authors. Clinical & Experimental Immunology published by John Wiley & Sons Ltd on behalf of British Society for Flow cytometric reference intervals for blood leukocytes Statistical analysis was carried out in two centers significantly higher proportions compared to female sub- (Tartu and Barcelona) using the r language and envi- jects (Supporting information, Table S8). ronment and Graphpad Prism version 7. For analysis Age-associated differences were analyzed in the different of differences between females and males the Mann– Treg subpopulations divided into three groups (Supporting Whitney U-test was used. Association between age and information, Tables S9 and S10). Absolute and relative cell counts was examined using Spearman’s rank cor- cell frequencies of the total Treg subpopulation did not relation test and age groups were compared using the show age-specific anomalies (Supporting information, Kruskal–Wallis test. Participants were divided into three Tables S9 and S10). However, the absolute number of groups aged 20–39, 40–59 and 60–81 years. The results memory Tregs and the absolute and relative count of acti- of statistical tests were considered statistically significant vated Treg increased with age (Supporting information, when P ≤ 0·05. For the calculation of reference intervals, Tables S9 and S10). a non-parametric method was applied because of the non-normal distribution. B cell panel: centers BCN, HDB, HAL, LHR and TAY Table 3 shows the absolute and relative counts of the Results total B cell population and their subpopulations. The absolute number and the relative amount of the T cell panel: centers ARL, BCN, HDB, HAL, LHR and total population of peripheral blood CD19+ B cells remained TAY stable throughout adulthood and did not show sex-related (Supporting information, Tables S11 and S12) or age-related Table 1 shows the distribution of cell percentages and differences (Supporting information, Tables S13 and S14) total counts of T cell subpopulations. The data divided The sex-related differences appeared only in the absolute for gender groups are presented in Supporting informa- count and frequency of preswitch memory B cells, show- tion, Tables S3 and S4 and for age groups in Supporting ing significantly higher values in males compared to female information, Tables S5 and S6. participants. Females had a higher CD4+ T cell count than males We used anti-IgD and anti-CD27 antibodies to separate (878 ± 325 versus 744 ± 296/µl). This difference was sta- B cells into four subsets: naive B cells (IgD+CD27−), pres- tistically significant in both the naive (416 ± 241 versus witch memory B cells (IgD+CD27+), switched memory B 325 ± 208/µl) and central memory (CM) (310 ± 147 versus cells (IgD−CD27+) and exhausted double-negative B cells 267 ± 134/µl), but not in the effector memory (EM) com- (IgD−CD27−). Interestingly, the proportion of naive B cells partments. In-depth analysis of CM T cells revealed a does not decrease with age and remains the predominant higher count of CM Th1 cells in females versus males B cell subset throughout adulthood. None of the three (75 ± 38; 59 ± 30/µl). memory B cell subsets (preswitch memory, switched memory, As expected, there was a clear age-relation in the dis- exhausted memory) showed statistically significant age- tribution of naive and memory subsets of T cells. The related changes. However, the absolute count and frequency cell frequencies and the absolute count of naive CD4+ of the developmentally earliest B cell subset in the periphery, and CD8+ T cells decreased with age (Supporting infor- termed transitional B cells (IgD+CD27−CD38+CD24+), as mation, Tables S5 and S6), while the CM and EM CD4+ well as the frequency of plasmablasts (CD20−IgD−CD27+ T cell compartments, as well as the CM CD8+ T cells, CD38+), decreased with age. increased. In our cohort, we also observed an increase of EM Th1 and CM Th2 cells (Supporting information, Tables S5 and S6). In addition, there was a marked increase of NK cell and myeloid cell panel: centers BCN, HDB, activated HLA-DR+ CD4+ and CD8+ T cells in both the HAL, LHR and TAY CD38+ and CD38− compartments. We also observed an Table 4 shows the relative distribution and the absolute age-dependent decline of the DN population (Supporting counts of NK cell, monocyte and DC cell subpopulations. information, Tables S5 and S6). NK cells and myeloid cells were identified by the absence of T and B cell-specific lineage markers (CD3, CD20 and Treg cell panel: centers BCN, HDB, HAL, LHR and TAY CD19). NK cells were then divided into four subsets based Table 2 shows the distribution of the absolute and relative on the differential expression of CD56 and CD16. counts of Treg subpopulations. CD56+CD16+ (CD56dimCD16+) subset represent the pre- Although, in comparison between male and female dominant NK cell subset in all age groups followed by subjects, we identified no differences in any of the Treg CD56++CD16− (CD56brightCD16−), CD56+CD16− subsets when absolute counts were analyzed (Supporting dim − ++ (CD56 CD16 ) and CD56 CD16 (CD5 + brightCD16+) NK information, Table S7), sex-related differences appeared in cell subsets. The proportion of the total NK cell population the relative distribution of all Treg subsets: males have increases with age (Supporting information, Table S17), © 2020 The Authors. Clinical & Experimental Immunology published by John Wiley & Sons Ltd on behalf of British Society 369 for Immunology, Clinical and Experimental Immunology, 202: 363–378 Table 4. Descriptive statistics NK cells, monocytes, dendritic cells and granulocyte frequencies and absolute counts 370 Lower Number of 90% CI of Upper 90% A. Oras et al. samples Minimum Maximum Median Mean 10% Percentile 90% Percentile 25% Percentile 75% Percentile s.d. s.e. mean CI of mean NK cells (%) 270 2·00 55·00 12·00 12·64 6·00 20·00 8·00 16·00 6·74 0·41 11·97 13·32 NK cells (cell/μl) 270 33·00 1345·00 242·00 264·10 98·10 441·20 154·50 327·30 166·60 10·14 244·10 284·10 CD56bright CD16− (%) 270 0·00 20·00 4·00 5·07 2·00 9·00 3·00 7·00 3·22 0·20 4·75 5·40 CD56bright CD16− (cell/μl) 270 1·00 34·00 10·00 10·83 4·00 18·00 7·00 14·00 5·89 0·36 10·13 11·54 CD56bright CD16+ (%) 270 0·00 15·00 2·00 2·19 1·00 4·90 1·00 3·00 1·93 0·12 1·99 2·38 CD56bright CD16+ (cell/μl) 270 0·00 28·00 4·00 4·90 1·00 10·00 2·00 6·25 4·21 0·26 4·40 5·40 CD56dim CD16− (%) 270 0·00 95·00 4·00 5·89 1·00 10·00 2·00 6·00 9·53 0·58 4·93 6·84 CD56dim CD16− (cell/μl) 270 1·00 318·00 8·00 12·71 3·10 20·90 5·00 13·00 22·47 1·37 10·02 15·41 CD56dim CD16+ (%) 270 0·00 99·00 89·00 86·87 79·00 95·00 84·00 93·00 11·07 0·67 85·76 87·98 CD56dim CD16+ (cell/μl) 270 0·00 1288·00 208·00 235·70 74·40 413·80 132·50 297·30 163·00 9·92 216·20 255·20 NT-cells (%) 270 0·00 48·00 5·00 6·51 1·00 13·00 3·00 8·00 6·06 0·37 5·90 7·11 NT-cells (cell/μl) 270 7·00 1052·00 95·00 140·10 28·10 300·10 48·00 168·80 150·70 9·17 122·00 158·20 Dendritic cells (DC) (%) 269 0·30 5·11 1·16 1·27 0·70 1·88 0·89 1·56 0·56 0·03 1·21 1·32 Dendritic cells (DC) 269 16·00 404·00 72·00 79·80 35·00 137·00 49·50 98·00 45·31 2·76 74·36 85·23 (cell/μl) CD11c−CD123− DC (%) 229 0·00 75·00 10·00 14·90 2·00 38·00 4·00 20·00 14·13 0·93 13·37 16·44 CD11c−CD123− DC 229 0·00 274·00 7·00 12·13 1·00 28·00 3·00 16·00 20·73 1·37 9·43 14·83 (cell/μl) Plasmacytoid DC (%) 229 1·00 44·00 11·00 13·43 5·00 24·00 7·00 18·00 7·79 0·51 12·58 14·27 Plasmacytoid DC (cell/μl) 229 2·00 32·00 8·00 9·20 4·00 17·00 5·00 12·00 5·54 0·37 8·48 9·92 Myeloid DC (mDC) (%) 229 20·00 95·00 73·00 71·11 49·00 89·00 62·50 83·00 15·29 1·01 69·45 72·77 Myeloid DC (mDC) 229 7·00 222·00 49·00 55·49 23·00 99·00 32·00 74·00 31·44 2·08 51·40 59·58 (cell/μl) Slan+ CD16+ mDC (%) 204 0·00 87·00 45·50 45·42 23·00 68·50 34·00 57·00 17·30 1·21 43·43 47·41 Slan+ CD16+ mDC (cell/μl) 204 0·00 129·00 21·00 25·27 6·00 49·00 12·00 34·00 19·04 1·33 22·64 27·90 Slan− CD16− mDC (%) 204 3·00 79·00 26·00 29·35 14·00 51·00 19·00 37·00 14·15 0·99 27·72 30·98 Slan− CD16− mDC (cell/μl) 204 1·00 43·00 12·00 13·77 6·00 24·00 8·25 17·00 7·52 0·53 12·73 14·81 Slan− CD16+ mDC (%) 204 0·00 73·00 22·50 23·96 7·00 41·50 14·25 31·00 13·74 0·96 22·37 25·54 Slan− CD16+ mDC (cell/μl) 204 0·00 83·00 9·00 14·46 2·00 34·00 5·00 17·75 14·98 1·05 12·39 16·53 Monocytes (%) 270 0·28 19·04 5·81 5·99 3·75 8·25 4·66 7·14 1·97 0·12 5·79 6·19 Monocytes (cell/μl) 270 48·00 896·00 354·50 370·60 185·40 568·40 261·80 477·00 148·20 9·02 352·80 388·30 CD14+ CD16−monocytes 270 62·00 100·00 96·00 95·22 92·00 98·00 94·00 97·00 3·58 0·22 94·86 95·58 (%) CD14+ CD16− 270 30·00 892·00 337·00 353·50 176·20 538·90 247·80 451·30 143·30 8·72 336·30 370·60 monocytes (cell/μl) CD14+ CD16+ 270 0·00 37·00 4·00 4·71 2·00 8·00 3·00 6·00 3·54 0·22 4·36 5·07 monocytes (%) Immunology, Clinical and Experimental Immunology, 202: 363–378 © 2020 The Authors. Clinical & Experimental Immunology published by John Wiley & Sons Ltd on behalf of British Society for Flow cytometric reference intervals for blood leukocytes while the absolute count and proportion of CD56brightCD16− CI of mean Upper 90% and CD56dimCD16− NK cells show an age-related reduction 18·48 60·47 4092·00 (Supporting information, Tables S17 and S18). In contrast, the absolute and relative count of NK T cells are inversely correlated with age. When we studied the effect of sex, 90% CI of Lower mean we observed that there is a difference between cell pro- 15·34 58·40 1808·00 110·00 3659·00 portion and/or absolute count in male and female in the total NK cell population and the in CD56brightCD16− NK 0·80 10·34 0·63 cell subset (Supporting information, Tables S15 and S16). s.e. The parameters of the whole population ofs DC (LIN− 13·13 CD14−CD56−LA−DR+) are relatively stable throughout adult- s.d. hood (Supporting information, Tables S17 and S18). In DC subsets, age has a differential effect. Absolute counts of the 10% Percentile 90% Percentile 25% Percentile 75% Percentile relative proportion of myeloid DCs (mDC) (CD123−CD11c+), which is the major subset of DC in the peripheral blood, 21·00 67·08 4653·00 s.d. = standard deviation; s.e. = standard error; CI = confidence interval; mDC = myeloid dendritic cells; NK = natural killer; Slan = sulfo-LacNac. are expanding in parallel with increasing age, accompanied by a decrease in the absolute count and frequency of plas- macytoid DCs (pDC) (CD123+CD11c−) (Supporting infor- mation, Tables S17 and S18). We also studied mDC subsets 9·00 52·75 2664·00 divided based on the differential expression of CD16 and Slan. The majority of mDCs express CD16. Interestingly, Centers: BCN, Barcelona, Spain; HDB, Heidelberg, Germany; HAL, Halle, Germany; LHR, London, UK; TAY, Tartu, Estonia. the proportion of CD16+ single-positive mDC increases with age, while CD16+Slan+ cells show a reverse trend. The sex- 31·00 71·30 6176·00 specific difference appeared only in the proportion of DC, as males have a slightly higher frequency of DCs than females (Supporting information, Table S15). It seems that the absolute number of cells in the mono- cyte population remain unchanged over age, whereas the 5·00 45·12 1967·00 relative proportion of monocytes slightly decreases with age (Supporting information, Tables S17 and S18). As in the total population of DCs, males also show a slightly 16·91 59·43 3875·00 Mean higher proportion of monocytes when compared with females (Supporting information, Table S15). When we Median divided monocytes were into two subsets based on the 14·50 60·38 3602·00 expression of CD16 (also known as FcγRIII): (1) CD14+CD16− classical monocytes, which represent the Maximum majority of monocytes, and (2) a minor subset of CD14+/ 113·00 83·31 13414·00 lowCD16+ non-classical monocytes, we did not notice any difference between them. Absolute counts and relative size of both monocytes subsets are comparable between males Minimum and females (Supporting information, Tables S15 and S16) 0·00 16·26 287·00 and over age (Supporting information, Tables S17 and S18). The absolute count of granulocytes remains strongly Number of stable through adulthood and is not influenced by the samples 270 270 270 sex of the study participants; the relative size of the total granulocyte population is positively correlated with age (Supporting information, Table S17). CD14+ CD16+ monocytes (Continued) Granulocytes (cell/μl) Discussion Granulocytes (%) The diversity in the healthy human immune system has been only partially described, and the focus has been (cell/μl) Table 4. more upon characterizing disorders related to the immune system. The reason is that the data concerning © 2020 The Authors. Clinical & Experimental Immunology published by John Wiley & Sons Ltd on behalf of British Society 371 for Immunology, Clinical and Experimental Immunology, 202: 363–378 A. Oras et al. interindividual variation are limited and the metrics of proportion of CD8+ TEMRA is highly dependent upon the immune health are not clearly defined [4]. CMV-status of healthy individuals [7]; however, these data Since its invention in the 1960s, flow cytometry has were not available in our cohort and is a limitation of undergone rapid development and has become the first- our study. choice method in the evaluation, prognosis and diagnosis Our core panel for analysis of peripheral T cells pro- of a variety of clinical conditions, including autoimmune vides various opportunities to add markers that answer diseases, immunodeficiency, infection, malignancy and specific questions. In one of our study sites, 17 different transplantation [1]. The availability of accurate reference markers were studied in this panel for diagnostic purposes. intervals obtained from a healthy population, against which For example, an important question is the expression of the patients’ results can be evaluated, is essential for all CD28 on the different T cell subpopulations, as CD28- diagnostic methods [5]. negative cells are, for example, potentially resistant to Further, the accurate measurement of variations in the cytotoxic T lymphocyte antigen 4 (CTLA-4) blockade. immune system depends upon a standardized assessment Naive and CM CD8+ T cells are considered CD28-positive, procedure, which is critical to demarcate between true EM-variable, and TEMRA CD28 mainly negative [8,9]. In biologically important changes from artifacts [4]. the CD4 compartment, the number of CD28− cells is In this study, we used a standardized protocol for flow much less frequent [10]. cytometric analysis to describe sex- and age-related vari- In our cohort, we observed infrequently a high num- ations of cellular components of the adaptive and innate ber of CD4−CD8− double-negative T cells (DN). Most immune system in more than 300 healthy volunteers from of these cells turned out to be T cell receptor (TCR)-γδ six different study centers. cells. Therefore, we considered it important to also include an anti-TCR-γδ antibody into the panel. We T cells observed an age-dependent decline of the DN popula- Sex-specific differences in relation to the total CD4+ T tion, which might reflect the lower counts of γδ T cell numbers, as well as both the naive and central memory cells observed with age [11]. The presence of TCR-αβ CD4+ populations, have been previously reported [6]. DN T cells above 2·5% of the CD3+ T cells is a cri- However, considering the large spread and overlap in and terion for the diagnosis of an autoimmune lymphopro- between the groups this observation seems to be not rel- liferative syndrome (ALPS) [12], and the addition of evant in a diagnostic setting. Therefore, both cohorts were this marker might be meaningful in the clinical immu- combined for further analysis. nology laboratory. As previously described [7], there is a clear relation to For the estimation of activated T cells a combination age in the distribution of naive and memory T cell subsets. of HLA-DR and CD38 was used, as this has been sug- Using age as a continuous parameter, the strongest cor- gested for the monitoring of HIV-positive patients [13]. relation was found between the decline of the number For further characterization of Th cells a combination of of naive CD8+ T cells and age (Fig. 2). However, there anti-CCR6 and anti-CXCR3 antibodies were employed was no significant accumulation of memory type CD8+ [14]. Here, Th1 cells were defined as CD4+/CXCR3+/CCR6−, T lymphocytes. Therefore, the reported relative accumula- Th2 as CD4+/CXCR3−/CCR6− and Th17 as CD4+/CXCR3−/ tion of the latter with age is only due to the decline of CCR6+. naive CD8+ T lymphocytes. This confirms the findings of In the originally proposed HIPC panels three tubes Wertheimer et al., although a different staining strategy were utilized to characterize peripheral T lymphocytes: was employed in this study [7]. T basic, T regulatory and T helper. In the present study, The enumeration of naive and the different memory the basic and the helper panels were combined resulting populations based on the expression of CCR7 and CD45RA in a 10-color panel. At study sites that lack the equip- is straightforward for CD4+ cells; however, in the CD8+ ment above eight-color detection, the Thelper panel can population the border between effector-memory and TEMRA still be measured separately in case this information is cells requires a consensus opinion. The re-expression of required. CD45RA is a continuous process, and depending on the Treg cells instrument settings a distinguished intermediate population appears. The use of additional markers such as CD27, Tregs play a crucial role in immune homeostasis and are CD28, CD57 and CD127 suggested that the intermediate associated with prevention of autoimmune diseases, aller- population relates more to the TEMRA population (data gies and allogenic responses in organ transplantation not shown). Naive cells were defined as CD45RA++/CCR7+; [15–17].Characterization of Treg cells was initially difficult CM cells as CD45RA−/CCR7+; EM cells as CD45RA−/ because they do not depict specific markers, and no con- CCR7−; and TEMRA cells as CD45RA+/++/CCR7−. The sensus standardized phenotypical panel exists for them. 372 © 2020 The Authors. Clinical & Experimental Immunology published by John Wiley & Sons Ltd on behalf of British Society for Immunology, Clinical and Experimental Immunology, 202: 363–378 for Immunology, Clinical and Experimental Immunology, 202: 363–378 © 2020 The Authors. Clinical & Experimental Immunology published by John Wiley & Sons Ltd on behalf of British Society Fig. 2. Correlation between age and relative or absolute cell counts in T cell panel (only data with correlation coefficient ≥ 0·3 or ≤ −0·3 are shown). Spearman’s rank correlation was performed and the cell subset dependency with age is illustrated by regression lines and confidence intervals. 373 Flow cytometric reference intervals for blood leukocytes A. Oras et al. The heterogeneity in utilized monoclonal antibodies made plasma cells. Our study revealed an age-related reduction it difficult to compare studies, either in healthy individuals in the proportion of plasmablasts, which is consistent with or in patients with immune-mediated diseases [18]. previous studies [28,33,34] showing an age-associated Treg can also be subdivided into naive and memory Treg decline in plasma cell absolute counts and/or proportions. through the expression of CD45 isoforms, together with These findings confirm the findings of Pritz et al., who chemokine receptors [20,21]. Expression of CCR4 is demonstrated a reduction of plasma cells in the bone restricted to circulating memory Treg [22]. Most of the Treg marrow in relation to aging [34]. In contrast to our results, in the peripheral blood of adults show a CCR4+CD45RO+ Caraux et al. demonstrated that memory cells are also in effector/memory phenotype. Moreover, most research to reverse relationship with age [28]. The controversy between date on Treg focuses upon this subset [22–24]. these results might result from different marker choices, Interestingly, memory Treg cells can be further subdivided as they used CD27 in combination with CD10 and CD38, on the basis of HLA-DR expression. Mature and highly instead of IgD, which was used in the present study. activated Treg express HLA-DR [25,26]. Double-negative (IgD−CD27−) B cells have been char- Therefore, in our study, we characterized Treg subsets acterized as senescent/exhausted memory B cells, but so (memory and activated) by the surface expression of CD4, far their biological function remains elusive [35]. Expansion CD25, CD127, CD45R0, CCR4 and HLA-DR following of these exhausted B cells has been associated with chronic the consensus of the Human Immunology Consortium antigenic stimulation and with diseases such as systemic [1], but using whole blood. Our results show that there lupus erythematosus [36] and Alzheimer’s disease [37], is an increase in the percentage of memory Treg and acti- but also with aging [38]. Our data did not support the vated Treg with age, in accordance with previous results latter finding, as in our study population we surprisingly in which these populations of Treg have been analyzed, observed the decline of exhausted B cells in elderly patients; although using different markers [26]. Nevertheless, no however, this did not reach statistical significance. age- or sex-specific differences in the total count of Treg Conflicting results might be derived from the choice of lymphocytes were found in our cohort. study material, as Colonna-Romano et al. used separated PBMCs and our data are based on the analysis of fresh B cells whole blood, which again illustrates the necessity of har- B cells play an essential role in adaptive humoral immunity, monization of flow cytometric methods in order to compare as they are the only cells capable of differentiang into anti- results from different studies in a meaningful way. Many body producing plasma cells. In addition, they can act as authors have emphasized the critical part of methodical antigen-presenting cells and initiate an immune response. differences as the main source of variability in flow cyto- B cells also have regulatory and effector functions mediated metric immunophenotyping [1,39,40]. by cytokines and chemokines [27]. Different marker com- NK cells, dendritic cells, monocytes and granulocytes binations have been proposed for the identification of memory and naive B cells [28,29]. Here, we used anti-IgD and anti- In concordance with several other studies, we found that CD27 antibodies for dividing B cells into four subsets. CD27 the number of NK cells increases with age [41,42]. We is considered to be a memory B cell marker and its expres- used the expression of CD56 and CD16 to study four sion is in correlation with cell size, proliferative capacity, NK cell subsets. Immature NK cells, forming approximately antigen presentation efficacy and ability to differentiate into 10% of the peripheral blood NK cells, have high levels antibody-secreting plasma cells [30]. Similarly, as has been of CD56 on their surface and lack expression of CD16 shown by other studies [31], we also found that the majority (CD56brightCD16−). As NK cells mature they start to express of B cell subsets are not affected by sex. CD16, while the expression of CD56 is down-regulated As reviewed by Frasca and colleagues [32], there are (CD56dimCD16+) [43]. In relation to age we observed an conflicting results in the field of studies focusing upon increase in the proportion of whole NK cells accompanied the effect of aging on B cells. In our study, the absolute by the decrease in both CD16− NK cell subsets number and relative size of the total population of CD19+ (CD56brightCD16− immature NK cells and CD56dimCD16−). B cells, as well as naive and memory B cell subpopula- Numbers of mature NK cells (CD56dimCD16+) had a ten- tions, showed a slight but not statistically significant age- dency for age-related increase, but this was not statistically associated trend of reduction, which is also shown by significant. In contrast to our observation, a previous study other authors [11,33]. In contrast to our study, other studies reported a significant difference in the absolute number have not shown an age-dependent reduction of immature of mature NK cells; however, they were comparing par- transitional B cells (here identified as CD19+IgD+CD27− ticipants ranging from 18 to 60 years and participants CD38+CD24+) in adults [28,31,33]. over 80 years [43]. Unfortunately, we were not able to Plasmablasts (here identified as CD19+CD20−IgD−CD2 include a higher number of older participants in our study 7 CD38+) are short-lived precursor cells for long-lived + populations. 374 © 2020 The Authors. Clinical & Experimental Immunology published by John Wiley & Sons Ltd on behalf of British Society for Immunology, Clinical and Experimental Immunology, 202: 363–378 Flow cytometric reference intervals for blood leukocytes Fig. 3. Correlation between age and relative or absolute cell counts in natural killer (NK) cell and myeloid cell panel (only data on cell subsets with correlation coefficient ≥ 0·3 or ≤ −0·3 are shown). Spearman’s rank correlation was performed and the cell subset dependency with age is illustrated by regression lines and confidence intervals. Results of previous reports show conflicting results might be hormone-related, as has been shown by several regarding the effect of age to DC populations [44–47]. studies [51]. We observed a strong association between age and the subsets of DC (Fig. 3). Despite similar marker choice in Conclusions some previous studies, we observed a significant age- dependent increase in mDC and its CD16 expressing In summary, the presented core panel is well applicable subsets [31,48] (Fig. 3). On pDCs, aging has the opposite to analyze the main leukocyte subpopulations in whole effect, as the number of pDCs decreases in elderly patients, blood in a clinical–diagnostic setting. This panel serves both absolutely and relatively (Fig. 3). Although Stervbo as a comprehensive starting-point for characterization of et al. [44] used a combination of BDCA1 (CD1c), BDCA2 main peripheral blood cells populations, which can be (CD303) and BDCA3 (CD141) for distinguishing DC expanded to resolve specific questions. subsets, they also observed the age-dependent decline in pDC and expansion of mDC. Acknowledgements We next investigated the total population of peripheral This study was supported by EU COST Action BM0907, blood monocytes and its two main subsets: classical partially by Estonian Research Council grants IUT20-43 (CD14+CD16−) and non-classical monocytes (CD14+CD16+). and PRG712, by Spanish grant FIS PI14/01175 and CD16-expressing non-classical monocytes have been PI16/01737 and the GRK PROMOAGE (B. S.). The German described as proinflammatory cells, and accumulation of these cells has been associated with aging [47,49,50]. However, cohort was supported by the German Centre for Infectious our results do not support these findings, and suggest that Diseases (DZIF) – Thematical Translation Units: Infections age does not significantly affect the composition of the of the immunocompromised host. Open access funding peripheral blood monocyte compartment. The difference enabled and organized by Projekt DEAL. from other studies may be partly explained by the fact that we divided monocytes into two subsets, and others have Disclosures also distinguished a third subset called ‘intermediate mono- cytes’ [47,49]. Authors have no conflicts of interest to declare. Although the number of most cells representing com- ponents of the innate immune system cells do not differ Author contributions between males and females, we found that absolute and/ or relative proportions of some subsets are significantly D. P., O. W., D. R., B. S., E. M. C., R. U. and T. G. par- higher in men. In accordance with previous studies [41], ticipated in the design of the study and/or supervised we also detected a higher proportion of total NK cells the study sites. A. O., B. Q. S., S. T., S. H., M. C., D. R. in males and absolute numbers and frequency of immature and T. G. recruited the donors and collected the primary (CD56brightCD16−) NK cells in females. The relative size data. A. O., B. Q. S., D. P. and T.G. analyzed the data and of the monocyte population was also significantly smaller performed the statistics. A. O., B. Q. S., E. M. C., R. U. in females, as reported previously [42]. These differences and T. G. wrote the paper. © 2020 The Authors. Clinical & Experimental Immunology published by John Wiley & Sons Ltd on behalf of British Society 375 for Immunology, Clinical and Experimental Immunology, 202: 363–378 A. Oras et al. References 17 Pierini A, Alvarez M, Negrin RS. NK cell and CD4+FoxP3+ regulatory T cell based therapies for hematopoietic stem cell 1 Maecker HT, McCoy JP, Nussenblatt R. Standardizing engraftment. Stem Cells Int 2016; 2016:9025835. immunophenotyping for the Human Immunology Project. Nat 18 Santegoets SJAM, Dijkgraaf EM, Battaglia A et al. Monitoring Rev Immunol 2012; 17:191–200. regulatory T cells in clinical samples: consensus on an essential 2 Finak G, Gottardo R. Promises and pitfalls of high-throughput marker set and gating strategy for regulatory T cell analysis biological assays. Methods Mol Biol 2016; 1415:225–43. by flow cytometry. Cancer Immunol Immunother 2015; 3 Oras A, Peet A, Giese T, Tillmann V, Uibo R. A study of 51 64:1271–86. subtypes of peripheral blood immune cells in newly diagnosed 19 Miyara M, Yoshioka Y, Kitoh A et al. Functional delineation young type 1 diabetes patients. Clin Exp Immunol 2019; and differentiation dynamics of human CD4+ T cells expressing 198:57–70. the FoxP3 transcription factor. Immunity 2009; 30:899–911. 4 Davis MM. A prescription for human immunology. Immunity 20 Sallusto F, Mackay CR, Lanzavecchia A. The role of chemokine 2008; 29:835–8. receptors in primary, effector, and memory immune responses. 5 McCoy JP, Overton WR. Quality control in flow cytometry for Annu Rev Immunol 2000; 18:593–620. diagnostic pathology: II. A conspectus of reference ranges 21 Taams LS, Smith J, Rustin MH, Salmon M, Poulter LW, Akbar for lymphocyte immunophenotyping. Cytometry 1994; AN. Human anergic/suppressive CD4+CD25+ T cells: a highly 18:129–39. differentiated and apoptosis-prone population. Eur J Immunol 6 Valiathan R, Deeb K, Diamante M, Ashman M, Sachdeva N, 2001; 31:1122–31. Asthana D. Reference ranges of lymphocyte subsets in healthy 22 Booth NJ, McQuaid AJ, Sobande T et al. Different proliferative adults and adolescents with special mention of T cell maturation potential and migratory characteristics of human CD4 + subsets in adults of South Florida. Immunobiology 2014; regulatory T cells that express either CD45RA or CD45RO. J 219:487–96. Immunol 2010; 184:4317–26. 7 Wertheimer AM, Bennett MS, Park B et al. Aging and 23 Seddiki N, Santner-Nanan B, Tangye SG et al. Persistence of cytomegalovirus infection differentially and jointly affect distinct naive CD45RA+ regulatory T cells in adult life. Blood 2006; circulating T cell subsets in humans. J Immunol 2014; 107:2830–8. 192:2143–55. 24 Baecher-Allan C, Hafler DA. Response to comment on ‘MHC 8 Gupta S, Bi R, Su K, Yel L, Chiplunkar S, Gollapudi S. Class II Expression Identifies Functionally Distinct Human Characterization of naïve, memory and effector CD8 + T cells: Regulatory T Cells’. J Immunol 2008; 180:3626. effect of age. Exp Gerontol 2004; 39:545–50. 25 Schaier M, Seissler N, Schmitt E et al. DR high+CD45RA –tregs 9 Mahnke YD, Brodie TM, Sallusto F, Roederer M, Lugli E. The potentially affect the suppressive activity of the total treg pool who’s who of T-cell differentiation: human memory T-cell subsets. in renal transplant patients. PLOS ONE 2012; 7:e34208. Euro J Immunol 2013; 43:2797–809. 26 Santner-Nanan B, Seddiki N, Zhu E et al. Accelerated age- 10 Chen G, Lustig A, Weng N-P. T cell aging: a review of the dependent transition of human regulatory T cells to effector transcriptional changes determined from genome-wide analysis. memory phenotype. Int Immunol 2008; 20:375–83. Front Immunol 2013;4(121):1–11. https://doi.org/10.3389/ 27 Fe L. Cytokine-producing B lymphocytes-key regulators of fimmu.2013.00121 immunity. Curr Opin Immunol 2008; 20:332. 11 Stervbo U, Bozzetti C, Baron U et al. Effects of aging on human 28 Caraux A, Klein B, Paiva B et al. Circulating human B and leukocytes (part II): immunophenotyping of adaptive immune plasma cells. Age-associated changes in counts and detailed B and T cell subsets. Age 2015:93. characterization of circulating normal CD138- and CD138 12 Oliveira JB, Bleesing JJ, Dianzani U et al. Revised diagnostic plasma cells. Haematologica 2010; 95:1016–20. criteria and classification for the autoimmune lymphoproliferative 29 Yiu L, Jiewan K, Metter ET et al. Changes in blood lymphocyte syndrome (ALPS): report from the 2009 NIH International numbers with age in vivo and their association with the levels Workshop. Blood 2010;116(14):e35–e40. of cytokines/cytokine receptors. Immunity 2016; 13:24. 13 Kestens L, Vanham G, Gigase P et al. Expression of activation 30 Kaminski DA, Wei C, Qian Y, Rosenberg AF, Sanz I. Advances antigens, HLA-DR and CD38, on CD8 lymphocytes during in human B cell phenotypic profiling. Front Immunol 2012; HIV-1 infection. AIDS 1992; 6:793–7. 3:302. 14 Sallusto F, MacKay CR. Chemoattractants and their receptors 31 Kverneland AH, Streitz M, Geissler E et al. Age and gender in homeostasis and inflammation. Curr Opin Immunol 2004; leucocytes variances and references values generated using the 16:724–31. standardized ONE-Study protocol. Cytometry A 2016; 15 Tao JH, Cheng M, Tang JP et al. Foxp3, regulatory T cell, and 89:543–64. autoimmune diseases. Inflammation 2017; 40:328–39. 32 Frasca D, Diaz A, Romero M, Landin AM, Blomberg BB. Age 16 Takeuchi Y, Nishikawa H. Roles of regulatory T cells in cancer effects on B cells and humoral immunity in humans. Ageing immunity. Int Immunol 2016; 28:401–9. Res Rev 2011; 10:330–5. 376 © 2020 The Authors. Clinical & Experimental Immunology published by John Wiley & Sons Ltd on behalf of British Society for Immunology, Clinical and Experimental Immunology, 202: 363–378 Flow cytometric reference intervals for blood leukocytes 33 Blanco E, Pérez-Andrés M, Arriba-Méndez S et al. Age-associated 49 Ong SM, Hadadi E, Dang TM et al. The pro-inflammatory distribution of normal B-cell and plasma cell subsets in peripheral phenotype of the human non-classical monocyte subset is blood. J Allergy Clin Immunol 2018; 141:2208–19.e16. attributed to senescence article. Cell Death Dis 2018; 9:266. 34 Pritz T, Lair J, Ban M et al. Plasma cell numbers decrease in 50 Merino A, Buendia P, Martin-Malo A, Aljama P, Ramirez R, bone marrow of old patients. Eur J Immunol 2015; 45:738–46. Carracedo J. Senescent CD14+ CD16+ monocytes exhibit 35 Bulati M, Caruso C, Colonna-Romano G. From lymphopoiesis proinflammatory and proatherosclerotic activity. J Immunol 2011; to plasma cells differentiation, the age-related modifications of 186:1809–15. B cell compartment are influenced by ‘inflamm-ageing’. Ageing 51 Ghazeeri G, Abdullah L, Abbas O. Immunological differences Res Rev 2017; 36:125–136. in women compared with men: overview and contributing 36 Jenks SA, Cashman KS, Zumaquero E et al. Distinct effector factors. Am J Reprod Immunol 2011; 66:163–9. b cells induced by unregulated Toll-like receptor 7 contribute to pathogenic responses in systemic lupus erythematosus. Supporting Information Immunity 2018; 49:725–39.e6. 37 Bulati M, Martorana A, Gervasi F et al. Double negative Additional supporting information may be found in the on- (IgG+IgD-CD27–) B cells are increased in a cohort of moderate– line version of this article at the publisher’s web site: severe Alzheimer’s disease patients and show a pro-inflammatory Fig. S1. Statistic view to see the means of fluorescence for trafficking receptor phenotype. J Alzheimers Dis 2015; 44:1241–51. each fluorochrome channel used. Instrument settings to 38 Colonna-Romano G, Bulati M, Aquino A et al. A double-negative make sure that we all have the same configurations. (IgD–CD27–) B cell population is increased in the peripheral blood of elderly people. Mech Ageing Dev 2009; 130:681–90. Table S1. Overview of study centers, method used and stud- 39 Thyagarajan B, Barcelo H, Crimmins E et al. Effect of delayed ied immune cell populations. cell processing and cryopreservation on immunophenotyping Table S2. Antibody panels used in the different cytometers. in multicenter population studies. J Immunol Methods 2018; 463:61–70. Table S3. Configuration of cytometers and filters used for 40 Verschoor CP, Kohli V, Balion C. A comprehensive assessment fluorochromes. of immunophenotyping performed in cryopreserved peripheral Table S4. Comparison between male and female in T cell whole blood. Cytom Part B Clin Cytom 2018; 94:662–70. frequency, Mann-Whitney Test. 41 Phan MT, Chun S, Kim SH et al. Natural killer cell subsets Table S5. Comparison of T cell panel absolute counts be- and receptor expression in peripheral blood mononuclear cells tween male and female. of a healthy Korean population: Reference range, influence of age and sex, and correlation between NK cell receptors and Table S6. Comparision between age groups for T cell fre- cytotoxicity. Hum Immunol 2017; 78:103–12. quencies, Kruskal-Wallis Test. 42 Valiathan R, Ashman M, Asthana D. Effects of ageing on the Table S7. Comparison between age groups for absolute immune system: infants to elderly. Scand J Immunol 2016; count of T cell. Kruskal -Wallis test. 83:255–66. Table S8. Comparison of Treg absolute counts between male 43 Béziat V, Duffy D, Quoc SN et al. CD56 bright CD16 + NK and female. Mann-Whitney test. cells: a functional intermediate stage of NK cell differentiation. Table S9. Comparison of Treg frequencies between male J Immunol 2011; 186:6753–61. and female. Mann-Whitney Test. 44 Stervbo U, Meier S, Mälzer JN et al. Effects of aging on human leukocytes (part I): immunophenotyping of innate immune cells. Table S10. Comparison between age groups for Treg fre- Age 2015; 37:92. quencies. Kruskal-Wallis Test. 45 Della Bella S, Bierti L, Presicce P et al. Peripheral blood dendritic Table S11. Comparison between age groups for Treg abso- cells and monocytes are differently regulated in the elderly. lute counts. Kruskal-Wallis test. Clin Immunol 2007; 122:220–8. Table S12. Comparison of B cell frequency between male 46 Orsini G, Legitimo A, Failli A, Massei F, Biver P, Consolini R. and female. Mann-Whitney Test. Enumeration of human peripheral blood dendritic cells throughout the life. Int Immunol 2012; 24:347–56. Table S13. Comparison of B cell absolute count (cells/μl) be- 47 Puissant-Lubrano B, Apoil PA, Guedj K et al. Distinct effect of tween male and female. Mann-Whitney Test. age, sex, and CMV seropositivity on dendritic cells and monocytes Table S14. Comparison between age groups for B cell fre- in human blood. Immunol Cell Biol 2018; 96:114–20. quencies. Kruskal-Wallis Test. 48 Garbe K, Bratke K, Wagner S, Virchow JC, Lommatzsch M. Plasmacytoid dendritic cells and their Toll-like receptor 9 expression Table S15. Comparison between age groups for absolute selectively decrease with age. Hum Immunol 2012; 73:493–7. count of B cell. Kruskal-Wallis Test. © 2020 The Authors. Clinical & Experimental Immunology published by John Wiley & Sons Ltd on behalf of British Society 377 for Immunology, Clinical and Experimental Immunology, 202: 363–378 A. Oras et al. Table S16. Comparison between male and female for NK Table S18. Comparison between age groups for NK cells, cells, Monocytes, Dendritic cells and Granulocyte frequen- Monocytes, Dendritic cells and Granulocyte frequencies. cies, Mann-Whitney Test. Kruskal-Wallis Test. Table S17. Comparison of NK cell, dendritic cell, monocytes Table S19. Comparison between age groups for abso- and granulocyte absolute counts between male and female. lute count of NK cells, Monocytes, Dendritic Cells and Mann-Whitney test. Granulocytes. Kruskal-Wallis test. 378 © 2020 The Authors. Clinical & Experimental Immunology published by John Wiley & Sons Ltd on behalf of British Society for Immunology, Clinical and Experimental Immunology, 202: 363–378