HUMAN IMMUNOLOGY doi: 10.1111/sji.12024 ..................................................................................................................................................................
Age-Matched Dendritic Cell Subpopulations Reference Values in Childhood A. Heinze*1, M. C. Elze†1, S. Kloess‡, O. Ciocarlie§, C. K€onigs*, S. Betz*, M. Bremm*, R. Esser*, T. Klingebiel*, M. Serban§, J. L. Hutton† & U. Koehl‡
Abstract *Department of Pediatrics, Johann Wolfgang Goethe-University Hospital, Frankfurt am Main, Germany; †Department of Statistics, University of Warwick, Coventry, UK; ‡Institute of Cellular Therapeutics, GMPDU/CTC, Hannover Medical School, Hannover, Germany; and §Pediatric Department, University of Medicine and Pharmacy ‘Victor Babes’ Timisoara, Timisoara, Romania
Received 19 July 2012; Accepted in revised form 25 December 2012 Correspondence to: A. Heinze, Pediatric Hematology and Oncology, Lab for Stem Cell Transplantation and Immunotherapy, Johann Wolfgang Goethe-University Hospital, TheodorStern-Kai 7, 60590 Frankfurt; Germany. E-mail:
[email protected] 1
AH and ME contributed equally to this work.
Dendritic cells (DCs) are the most potent antigen-presenting cells and are the key link between the innate and adaptive immune response. Only a few reports with study populations of up to 50 individuals have been published with age-based reference values for DC subpopulations in healthy children. Therefore, we aimed to establish reference ranges in a larger study population of 100 healthy children, which allowed age-matched subgroups. Most previous studies were performed using a dual-platform approach. In this study, a single-platform approach in a lyse no-wash procedure was used. DC subpopulations were defined as follows: CD45+CD85k+HLA-DR+CD14 CD16 CD33+ cells as myeloid DCs (mDCs) and CD45+CD85k+HLA-DR+CD14 CD16 CD123+ cells as plasmacytoid DCs (pDCs). Reference ranges were established using a semi-parametric regression of age-matched absolute and relative DC counts. We found a significant decline with increasing age in the medians of mDCs (P = 0.0003) and pDCs per ll peripheral blood (PB) (P = 0.004) and in the 50%, 90% and 95% reference ranges. We also identified significantly lower absolute cell counts of mDCs per ll PB in girls than in boys for all age groups (P = 0.0015). Due to the larger paediatric study population and single-platform approach, this study may give a more precise overview of the normal age-matched development of DC subpopulations and may provide a basis for analyzing abnormal DC counts in different illnesses or therapies such as post stem cell transplantation.
Dendritic cells (DCs) are the most potent antigenpresenting cells and have an important role in linking innate and adaptive immunity [1–3]. Only a small proportion of the blood circulating leucocytes are DCs. Immature DCs capture apoptotic cells and present selfantigens to T cells [4]. They also sample different selfantigens, presenting them to T cells and inducing peripheral self-tolerance by promoting T cells to differentiate into T regulatory/suppressor cells [5, 6]. Additionally, DCs can directly activate na€ıve and memory B cells, support the differentiation of activated-na€ıve B cells to plasma cells and directly stimulate the production of antibodies. The activity of natural killer (NK) cells and NK-like T cells through the release of cytokines is regulated by DCs [1–3]. Two subtypes of DCs are present in the peripheral blood (PB): myeloid DCs (mDCs) and plasmacytoid DCs (pDCs). Different antibodies can be used to define DC subtypes: CD33 or CD11c for mDCs, CD123,
CD303(BDCA-2) or CD304(BDCA-4) for pDCs. Furthermore, mDCs can be divided into two subtypes: CD1c+(BDCA1+) mDC1s and CD141+(BDCA3+) mDC2s [7, 8]. The total number of all blood circulating lymphocyte subpopulations in children declines with age [9–11]. To our knowledge, age-matched reference ranges for DC subpopulations in children are only available from a few studies with a sample size of 19 to 50 children [12–17]. Most of these studies are based on a dual-platform approach, while measurements using a single-platform approach are only available from two paediatric studies by Vuckovic et al. [14] and Smolewska et al. [18]. A singleplatform approach is the preferred technique for analyzing scarce cell populations [19]. The aim of this study was to establish reference values in a large paediatric study population allowing semi-parametric regression and creation of age-matched reference ranges based on an advanced single-platform approach on a five-colour flow cytometer.
Ó 2013 The Authors. Scandinavian Journal of Immunology © 2013 Blackwell Publishing Ltd.
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Introduction
214 Reference Values of Dendritic Cell Subpopulations A. Heinze et al. ..................................................................................................................................................................
Materials and methods Blood donors A reference population for paediatric DC normal values was established from 100 healthy children aged between 11 days and 18 years, 49 females and 51 males. The median age was 7.5 years. Blood samples were collected during routine examinations for minor elective surgery or routine checkups in the outpatient clinic or admissions for minor disorders (e.g. mild concussion). There were no clinical or laboratory signs of inflammation at time of blood collection (differential blood counts, C-reactive protein), and the medical history showed no indications for an immune deficiency, allergic, autoimmune or malignant disease. Additionally, study participants were not taking any medications affecting the immune system or the hematopoiesis. This study was approved by the local ethic committee (Frankfurt, Germany).
bias and variability in the analysis, every measurement was gated by three technicians with extensive experience in flow cytometric analyses. Quality control The optical alignment and fluidics stability of the flow cytometer were tested with Flow-Check Pro Fluorospheres (Beckman Coulter, Villepinte, France) daily, before measurements. Immuno-Trol control cells (Beckman Coulter, Villepinte, France) were used and evaluated for verification twice daily. Flow-Set Fluorospheres (Beckman Coulter, Villepinte, France) served to set up the photomultiplier tube values weekly. In addition, stained Cyto-Comp cells (Beckman Coulter, Villepinte, France) were used to compensate the fluorescence overlap. Isotype controls were used to account for non-specific staining antibodies of a particular isotype conjugated to a particular fluorochrome. Statistical analysis
Blood sample preparation Peripheral blood was collected into EDTA tubes and processed within 24 h to preserve cell surface markers. For mDC detection, 100 ll of PB were stained with monoclonal antibodies against CD14 (FITC/fluorescein isothiocyanate, clone RMO52, isotype IgG2a), CD16 (FITC, clone 3B8, isotype IgG1), CD33 (PC5/ phycoerythrincyanin-5, clone D3HL60.251, isotype IgG1), CD45 (PC7/ phycoerythrin-cyanin-7, clone J.33, isotype IgG1), CD85k (PE/ phycoerythrin, clone ZM3.8, isotype IgG1) and HLADR (ECD/ phycoerythrin-Texas-Red, clone Immu-357, isotype IgG1). For pDC measurements, CD123 (PC5/ phycoerythrin-cyanin-5, clone 107.D2, isotype IgG1) was applied instead of CD33. The tubes were incubated at room temperature for 20 min and processed in a lyse nowash procedure with 1 ml Versa-Lyse Lysing Solution for 15 min. Immediately before immunophenotyping, 100 ll of Flow-Count Fluorospheres were added to the lysed samples. All reagents were procured from Beckman Coulter, Villepinte, France. Immunophenotyping of mDCs and pDCs Flow cytometry data were acquired on a five-colour flow cytometer (FC500, Beckman Coulter, Krefeld, Germany) for up to 300 s or 100,000 events for each subsample to collect adequate numbers of rare events. The mDCs were defined as CD45+CD85k+HLA-DR+CD14 CD16 CD33+ cells. The pDCs were defined as CD45+CD85k+HLADR+CD14 CD16 CD123+ cells. The data were analyzed with the CXP v2.2 software (Beckman Coulter, Krefeld, Germany). The absolute cell numbers of mDCs and pDCs per ll PB were calculated using flow-count fluorospheres in a single-platform approach (Fig. 1). To detect potential
All analyses were performed using the R language for statistical computing, version 2.14.1 [20]. For hypothesis tests, P-values of 0.95 in all cases). For further analyses, the mean of all three results was used. To assess if age or sex influence DC counts, linear models were fitted. Mann–Whitney U tests were employed to confirm sex differences to avoid overfitting in the linear models. To establish reference values for data of this nature, semi-parametric regression methods that allow some flexibility not just in the mean, but also in the coefficient of variation and skewness are appropriate. We employed the LMS method that applies a Box-Cox transformation to give data an approximately normal distribution [22]. The modelling parameters mean, coefficient of variation and the Box-Cox power parameter k summarize the distribution of the data for a given age. Each parameter is calculated using nonparametric regression with a smooth spline with given degrees of freedom. Degrees of freedom were chosen based on likelihood ratio tests for nested models, although they are approximate, as well as visual inspection of the resulting fitted spline curves [23]. For relative mDCs counts, three degrees of freedom were chosen for the mean curve and two degrees of freedom for the coefficient of variation and Box-Cox power parameter curves. For absolute mDC, absolute pDC and relative pDC counts, one degree of freedom was selected for all three curves.
Scandinavian Journal of Immunology, 2013, 77, 213–220
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Figure 1 Gating strategy for identification of mDCs and pDCs. (A) Region CD45+ is set to include all CD45+ events (leucocytes). This plot excludes region Beads. (B) Region Cells is set to identify leucocytes via granularity (SS) and cell size (FS). Smaller cells are excluded. Region Cells is gated on region CD45+ and excludes region Beads. (C) Identification of CD16 granulocytes as region EOS. This region is gated on Cells and region Beads is excluded. (D) Identification of CD85k+ cells as region CD85k+. This region is also gated on region Cells and excludes region Beads. (E) Identification of region Beads as flow-count fluorospheres and region DC+ as CD85k+, CD14 and CD16 cells. This plot is gated on region CD45+. (F) Identification of HLA-DR+ cells as region HLA-DR+. This region is also gated on region Cells and region Beads is excluded. (G) Control plot to check if the DC cloud is not dissected. (H) Plot of the fluorescence signal of events in region Beads. Region CAL is set to define the signal of fluorospheres and to monitor the occurrence of fluorospheres doublets, which should be
