The MDS diagnosis is difficult in the absence of morphological criteria or non-informative cytogenetics. MFC could help refine the MDS diagnostic process. To become useful for clinical practice, the MFC analysis must be based on parameters with sufficient specificity and sensitivity, and data should be reproducible between different operators.
A working group initiated within the French Cytometry Association (AFC) was developed in order to harmonize the application of multiparameter flow cytometry (MFC) for myeloid disease diagnosis in France. The protocol presented here was agreed-upon and applied between September 2013 and November 2015 in six French diagnostic laboratories (University Hospitals of Saint-Etienne, Grenoble, Clermont-Ferrand, Nice, and Lille and Institut Paoli-Calmettes in Marseille) and allowed the standardization of bone marrow sample preparation and data acquisition. Three maturation databases were developed for neutrophil, monocytic, and erythroid lineages with bone marrow from “healthy” donor individuals (individuals without any evidence of a hematopoietic disease). A robust method of analysis for each myeloid lineage should be applicable for routine diagnostic use. New cases can be analyzed in the same manner and compared against the usual databases. Thus, quantitative and qualitative phenotypic abnormalities can be identified and those above 2SD compared with data of normal bone marrow samples should be considered indicative of pathology. The major limitation is the higher variability between the data achieved using the monoclonal antibodies obtained with the methods based on hybridoma technologies and currently used in clinical diagnosis. Setting criteria for technical validation of the data acquired may help improve the utility of MFC for MDS diagnostics. The establishment of these criteria requires analysis against a database. The reduction of investigator subjectivity in data analysis is an important advantage of this method.
In the absence of phenotypic markers specific to the dysplastic changes occurring in myeloid cells during MDS initiation and progression, a new approach has been proposed in recent years based on the evaluation of the maturation pathways (altered expression of myeloid antigens during the production of mature myeloid cells) or of the abnormal distribution of different cell types within bone marrow (BM) cell compartments1,2.
This article presents a new method for standardized application of MFC in order to detect dysplastic changes in BM myeloid cell compartments related to myelodysplastic syndromes (MDS) or other myeloid hematological diseases. This study also shows the utility of using maturation databases for MFC data analysis.
Standardization of the sample preparation procedure, data acquisition, and analysis using the databases would allow the identification of the most relevant phenotypic abnormalities related to dysplastic changes in BM myeloid cells. Therefore, statistically selected subsets based on well-labeled and well-recognized formats (Automatic Population Separator (APS) diagrams, histograms, and dot plots) are required for developing an analysis strategy that can be used in subsequent analysis rounds. The discovery of robust phenotypic abnormalities in MDS would ease the diagnosis in cases with or without minimal morphological dysplasia and without cytogenetic aberrancies. Identification of discriminatory parameters allowing for the reduction of immunophenotypic panels may simplify the current scores2, permitting their applicability in clinical pathology laboratories.
This method limits the subjective interpretations of cytometry data, as have been signaled in MDS diagnosis3. This step is a prerequisite for the development of automated tools for processing and analyzing flow data4.
MDS comprises a heterogeneous group of clonal hematopoietic stem cell (HSC) disorders in which the spliceosome mutations cooperate with specific epigenetic modifiers to yield the MDS phenotype. It is now known that, along with HSC mutations, other mechanisms are involved in MDS pathophysiology, such as aberrant immune-mediated inflammation and interactions between malignant HSCs and the stromal microenvironment of the BM. However, these mechanisms remain poorly understood. The wide clinical and biological heterogeneity of MDS makes the diagnosis and selection of the optimal therapy a challenge. In the last decade, multiple studies have shown that MFC is often more sensitive in detecting dysplasia2 than morphology, but technical and economic constraints make this technique difficult to standardize, with results often depending on the experience of the interpreter3. In addition, it is unclear how MFC can tip the balance toward MDS in cases with or without minimal morphological dysplasia and in the absence of cytogenetic anomalies, or in borderline cases such as hypocellular MDS, with a low blast count, from other non-clonal BM disorders such as bone marrow failure (i.e., aplastic anemia). It also remains difficult to differentiate borderline cases of MDS with an excess of blasts from acute myeloid leukemia (AML). For all these reasons, the clinical guidelines do not integrate MFC testing into the MDS final diagnosis. In 2011, the US National Comprehensive Cancer Network (NCCN) recommended MFC for the estimation of the percentage of CD34+ cells, detection of paroxysmal nocturnal hemoglobinuria clones, and presence of cytotoxic T-cell clones in hypocellular MDS5. These two latter situations also involve a therapeutic goal because clinical data have shown a good response of these patients to immunosuppressive therapy6. The 2017 NCCN guidelines, citing the International Working Group (IWG) recommendations, listed aberrant immunophenotyping detection by MFC among the co-criteria for MDS diagnosis, but without making any specifications6. In addition, the recently published WHO classification stipulates that MFC findings alone are not sufficient to establish a primary diagnosis of MDS in the absence of conclusive morphological and/or cytogenetic data7. However, MFC can be used as an additional test showing the dysregulation of myeloid cell maturation patterns and quantifying the "distance from normal" for a patient at a specific time in the disease course.
This method is applicable at clinical laboratories interested in the evaluation of dysplasia in BM myeloid cells using MFC immunophenotyping, in order to refine the diagnosis in MDS or other myeloid disorders with dysplastic abnormalities.
The protocol listed below has been approved by the "Comité de Protection des Personnes" (Independant Ethics Committee) Sud-Est 1 from University Hospital of Saint-Etienne, France.
1. Cytometer Settings
NOTE: The cytometer settings were performed according to France Flow recommendations, in accordance with EuroFlow Procedure "EuroFlow Standard Operating Protocol (SOP) for Instrument Setup and Compensation (https://www.euroflow.org/usr/pub/protocols.php).
2. BM Sample Preparation
NOTE: Perform the cell washing protocol just before the staining procedure.
3. Data Acquisition
4. Data Analysis
NOTE: To construct the normal BM databases were used files from healthy donors and from individuals without any evidence for a hematopoietic disease as follows 11 from 18 files for the Neutrophils_NM database, 10 from 18 files for Monocytes_NM database and 14 from 18 files for NRC_NM database. The files discarded showed various technical problems, as presented in the Representative Results section. The files were individually analyzed using the Infinicyt software (Table of Materials), conforming to the various strategies depicted in Figure 1A(1-3) for neutrophil lineage (Profile Neutrophils_Maturation.inp), Figure 2A for monocyte lineage (Profile Monocytes_Maturation.inp), and Figure 3A(1-2) for erythroid cell lineage (Profile NRC_Maturation.inp).
The 54 BM samples harvested in K-EDTA anticoagulant were included in the study. The MFC data were analyzed in the absence of any information about the patients. Retrospective study showed that the BM samples were from 7 healthy donors (5 males and 2 females with a median age of 47.4 [35-48], 11 individuals with no evidence of a hematopoietic disease (8 males and 3 females with a median age of 57.9 [35-72]) and 36 cases with various pathological conditions: 1 case with anemia and low creatinine level, 3 cases with anemia and high creatinine level, 8 cases with anemia of inflammation, 1 case with anemia caused by vitamin B12 deficiency, 4 cases with anemia ± other cytopenias caused by liver damage, 3 cases with autoimmune hemolytic anemia, 5 cases with idiopathic thrombocytopenic purpura, 1 case with macrophage activation syndrome, 3 cases with complete remission after lymphoma treatment (minimal residual disease < 0.01%) and 7 cases carrying hematological disorders (4 MDS, 1 AML, 1 chronic myelomonocytic leukemia and 1 myeloproliferative syndrome JAK2 positive). This last category included 18 males and 18 females with a mean of age 60.3 [17-100]. All samples were from Caucasian individuals. The building databases allowed identifying and fixing several issues related to sample preparation and acquisition. In our case, the final databases included 11/18 files for neutrophil maturation, 10/18 files for monocyte maturation and 14/18 files for NRC maturation. The files that were not included in databases posed various technical problems. The most common staining issues were observed for CD11b and CD13 in neutrophils, CD300e and HLADR in monocytes, and CD71 and HLADR in NRCs. Exporting data can be a source of errors; the most frequent in our dataset was the absence of FSC-H in exported files and the inversions of FSC-W with FSC-H that sometimes occur in exporting DIVA files8. The evaluation of new cases of cytopenias suspected of being MDS against the Myeloid Normal Maturation Databases allow for the identification of abnormal expression of maturation antigens of neutrophils lineage (Figure 4), monocytes (Figure 5), and NRC (Figure 6) even in cases without cytological or cytogenetic abnormalities (Figure 7). Otherwise, using routine acquisition software, such as Diva, these would be difficult or impossible to realize.
Figure 4: Representative flow cytometry analysis of neutrophils in a del(7) MDS case. (A) In the Normalized Maturation Differences diagram, the gray area corresponds to the normal expression of antigens resulting from the analysis of the 11 normal files included in the neutrophil database (median± 2SD). The abnormal expression of several antigens was observed when compared the del(7) MDS case against Neutrophils_NM database (n=1 versus n=11 normal controls samples). (B) Parameter Band Maturation Diagrams allow the comparisons between the median intensity of expression of each marker at different stages of maturation (continuous full lines) and 2SD curves calculated for the 11 normal BM cases included in the database (continuous dashed lines). Phenotypic abnormalities of neutrophils observed in a del(7) MDS case as compared with the Neutrophil Normal Maturation Database are as follows: overall increased expression of CD34, slight increase of CD11b expression on immature neutrophils (stages 2), diminished expression of CD13 on immature neutrophils (stages 1-3) and slight increase of CD16 expression on the first two stages of neutrophil maturation followed by moderate increases of CD16 expression on the mature neutrophils (stages 4-5). (C) shows the corresponding dot plots as visualized in DIVA software. Image evaluation using the DIVA software allows for identification of a small number of phenotypic abnormalities in this MDS case: an increased percentage of CD34+ CD13+ precursors and the downregulation of CD13 expression. The abnormal expression of CD16 is difficult to observe in this type of representation. Please click here to view a larger version of this figure.
Figure 5: Representative flow cytometry analysis of monocytes in a del(7) MDS case. (A) The Normalized Maturation Differences diagram offers an overall view of antigen expression at various stages of monocytic cell lineage maturation. The gray area corresponds to the normal expression of antigens resulting from the analysis of 10 normal files included in the monocytic database (median ± 2SD). The abnormal expression of several antigens exceeds 2SD, but for some markers, such as CD35 and HLADR, it exceeds 4SD. (B) In the Parameter Band Maturation Diagrams, the phenotypic abnormalities of monocytic cells observed in a SMD del(7) case when compared with Monocyte Normal Maturation Database (n=1 versus n=10 normal controls samples) are as follows: diminished expression of CD45 during stages 1-4 of monocytic cells, of CD117 at stages 1-2 of monocytic precursors, and of HLA-DR overall. The increased expression of CD35 in the first 3 stages of maturation of the monocytic cells was the most significant abnormality for this lineage. (C) The DIVA dot plots that allow evaluation of monocytic cells in a single normal BM. (D) The DIVA dot plots that allow evaluation of monocytic cells in del(7) MDS case. Image evaluation using the DIVA software allows for identification of two phenotypic abnormalities in this MDS case: the diminished expression of HLA-DR and the increased expression of CD35 in a part of monocytic precursors (red population). Please click here to view a larger version of this figure.
Figure 6: Representative flow cytometry analysis of nucleated red cells in a del(7) MDS case. (A) The Normalized Maturation Differences diagram offers an overall view of antigen expression at different stages of NRC lineage maturation. The gray area corresponds to the normal expression of antigens resulting from the analysis of the 14 normal files included in the NRC Normal Maturation Database (median ± 2SD). The abnormal expression of several antigens exceeds 2SD, but for some markers, such as CD34, CD117, CD71, and CD105, it exceeds 4SD. (B) In the Parameter Band Maturation Diagrams, the phenotypic abnormalities observed in a SMD del(7) case when compared with the NRC Normal Maturation Database (n=1 versus n=14 normal controls samples) are as follows: diminished expression of CD34 in the first two stages of maturation, of CD117, CD105, CD71 and HLA-DR on stage 1 of erythroid lineage precursors. Image evaluation using the DIVA software allows for identification of CD71 abnormal expression on erythroid cells in this MDS case, but other modifications are not detectable. Please click here to view a larger version of this figure.
Figure 7: Representative flow cytometry analysis of neutrophils, monocytes and NRCs in an early stage MDS case without morphological dysplasia and without cytogenetic aberrancies. (A) The distribution of immature and mature cells within different maturation stages for the three myeloid lineages: neutrophils, monocytes and NRCs. An increased number of mature monocytes (stage 5) and the slightly diminution of monocytic immature cells (stage 2 of maturation) was observed. (B) The Normalized Maturation Differences diagram shows that the phenotypic abnormalities exceed 2SD, but for CD34, CD117, CD11b, CD16 and HLADR, they are higher at 4SD. Phenotypic abnormalities observed for neutrophils are as follows: slightly increased SSC values in neutrophil immature precursors (stage 1), diminution of expression of CD34 (stage 1), of CD117 (stage 1), of CD16 (stage 1-2) and of HLADR (stages 1-3 of maturation). An increased expression of CD11b was observed on neutrophil immature precursors (stage 1). (C) The Normalized Maturation Differences diagram shows that the most important phenotypic abnormalities observed for the monocytic lineage (exceeding 10SD) are increased expression of CD35 and early acquisition of CD300e on the immature monocytic precursors. Other phenotypic abnormalities observed on the monocytic lineage are as follows: diminution of FSC and SSC on more mature monocytes (stages 3-5) and slight diminution of expression of CD14 on stages 2-3 of monocytic cells. (D) The Normalized Maturation Differences diagram and the Parameter Band Maturation Diagrams shows that the most important phenotypic abnormalities observed for the NRC are: increased expression of CD117 (exceeding 10SD) on stages 2-3 of NRC maturation, increased expression of CD34 (exceeding 5SD) during the second stage of maturation of NRC and decreased expression of CD36 on the early erythroid precursors (stages 1-3). In addition, the SSC on immature NRC is lower than on the normal counterparts. Please click here to view a larger version of this figure.
Moreover, the interpretation of the significance of differences between antigen expression on pathological settings and normal counterparts allows for "quantification" of these abnormalities, with the identification of those that are discriminant for a group of cases. This may also make it possible to rank them based on importance for the purposes of a follow-up. In this study, all 7 cases of hematological disorders with myeloid dysplasia features (4 MDS, 1 AML, 1 chronic myelomonocytic leukemia and 1 myeloproliferative syndrome JAK2 positive) were classified as abnormal when compared with the NBM databases. In addition, the evaluation against the Neutrophils_NM database eliminated the suspicion of myeloid dysplasia in 7 cases with toxic, inflammatory, autoimmune hemolytic anemia and anemia from chronic kidney disease. The evaluation against the Monocytes_NM database eliminated the suspicion of myeloid dysplasia in 4 cases with idiopathic thrombocytopenic purpura (ITP), while the evaluation against NRC_NM allowed for differentiation in 3 cases, 2 of inflammatory anemia and 1 of ITP. The BM aspirates from the patients in complete remission after lymphoma or solid tumor treatments were within 2 SD from the median of the normal databases for all three lineages, and thus these samples may be alternatives to healthy donor BM samples.
Figure 1: Analysis strategies for neutrophil cell lineage. (A) Selection of CD34+ CD117+ HLADR+low CD10- CD13+ CD11b- neutrophil progenitors was realized by intersection of seven gates as depicted in A1. The next step of maturation, CD117+ CD34- CD13+ CD11b- HLADR+low neutrophil precursors was chosen using an intersection of six gates as depicted in A2. The more mature neutrophils are finally identified using an intersection of four gates that allow discrimination of CD45dim SSCint-hi CD117- HLADR- cells (A3). The most discriminant markers for neutrophil lineage were CD34, CD117, CD11b, and CD16. Along with CD13, these parameters allow identification of five subpopulations: CD34+ progenitors (CD34+ CD117+ CD13+low CD11b- CD16-) (dark blue); CD34- CD117+ CD13+hi CD11b- CD16- neutrophil precursors (blue); CD34- CD117- CD13+low CD11b- CD16- neutrophils (3rd step of maturation; light blue); CD34- CD117- CD13+low CD11b+ CD16+low neutrophils (4th step of maturation; pink); mature neutrophils (CD34- CD117- CD13+hi CD11b+hi CD16+hi; violet). Each colored circle represents the median of a subpopulation for the marker of interest from one sample (B). (C) displays the homogeneous distribution of the tested parameters during the neutrophil cell maturation when compared with 2 SD of the normal maturation database (n=11). Please click here to view a larger version of this figure.
Figure 2: Analysis strategy for monocytes. Identification of monocytic lineage cells (CD117+/- CD64+hi HLADR+hi) is performed using an intersection of four gates (A). The most discriminant markers for monocytic lineage were CD14, CD300e (IREM2), CD35, and HLA-DR. Along with CD117, these parameters allow identification of three subpopulations: CD34- CD117+low/- HLADR+hi CD35- CD14- CD300e- (red); CD34- CD117- HLADR+med CD35+med CD14+med CD300e- (orange); mature monocytes CD34- CD117- HLADR+med CD35+hi CD14+hi CD300e+ (green) (B). (C) displays the homogeneous distribution of the tested parameters during the monocytic cell maturation when compared with 2 SD of the normal maturation database (n=10). Please click here to view a larger version of this figure.
Figure 3: Analysis strategies for NRCs. (A) The identification of CD34+ erythroid committed blasts was realized using an intersection of seven gates that allow selection of CD34+ CD117+ erythroid progenitors (A1). The more mature NRCs are identified using an intersection of five gates (A2). Exclusion of the platelets (CD36+hi SSClow cells) from NRC population is required (A2). The most discriminant markers for erythroid cell lineage were CD34, CD117, CD71, and CD105. Along with CD33, these parameters allow identification of three subpopulations: CD45+low CD34+ CD117+ HLADR+med CD71+med CD36+med CD105+hi (red); CD34- CD117+ HLADR+low CD71+hi CD36+hi CD105+hi NRCs (2nd step of maturation; pink); CD34- CD117- HLADR-/+low CD71+hi CD36+med CD105+low/- NRCs (more mature NRCs; pink salmon) (B). (C) displays the homogeneous distribution of the tested parameters during the NRC maturation when compared with 2 SD of the normal maturation database (n=14). Please click here to view a larger version of this figure.
Table 1: List of fluorochrome-conjugated antibody reagents used to setup the fluorescence compensation matrices with their reference populations. Please click here to view a larger version of this table.
Table 2: Combination and quantities of antibodies used for evaluation of maturation of neutrophil, monocytes and erythroid cells in BM. Please click here to view a larger version of this table.
Supplementary File 1: Monocytes_Maturation.inp Please click here to download this file.
Supplementary File 2: Monocytes_NM_GMFF.cyt Please click here to download this file.
Supplementary File 3: Neutrophils_Maturation.inp Please click here to download this file.
Supplementary File 4: Neutrophils_NM_GMFF.cyt Please click here to download this file.
Supplementary File 5: NRC_Maturation.inp Please click here to download this file.
Supplementary File 6: NRC_NM_GMFF.cyt Please click here to download this file.
The quality of BM aspirate could impact on the final results. The hemodilution of the BM aspirate could distort the distribution of cells in different stages of maturation due to the absence of progenitors or precursors cells. Probably employing a bulk lysing method may help in normalization of BM aspirates for hemodilution in flow cytometric analyses. In addition, the critical steps for the evaluation of BM myeloid dysplasia by flow-cytometry are the sample processing and staining, data acquisition, and interpretation2,3. The sample processing and staining should be performed up to 72 h after BM harvesting. The data should be acquired, preferably, immediately after staining or store the samples at 4 °C, protected from light, for no more than 1 h until measuring in the flow cytometer. The database-guided interpretation avoids the subjective evaluation of BM myeloid cell maturation.
The antibody combinations and the sample preparation were broadly conformed to the EuroFlow procedures9,10. The difference compared with the EuroFlow panel was that all antibodies except CD300e were provided by BD Bioscience. The flow cytometry standardization made it possible to obtain data with high levels of reproducibility11, but this entails joining a group working on the standardization of the panels in question. In our group, the standardization of SSC remains an area to be improved. The major issue regarding MFC databases building is the staining procedure. The initial distribution of the non-backbone antibodies in FACS tubes before adding the equal amounts of the sample-backbone mix avoids repeating the backbone staining in case of eventual errors in the distribution of other non-backbone antibodies. In addition, to overcome the staining problems there are two possibilities: either increase the incubation time of antibodies from 15 to 30 minutes, or use the recombinant antibodies, which provide greater reproducibility, according to the manufacturer's specifications12. The analysis was performed in conformity to recently published data13,14,15. Concerning monocytes analysis, we frequently observed the absence of CD34+ CD117- monocytic precursors, as has been previously reported by Shen et al.16. For this reason, we eliminated from the analysis strategy a supplementary gate for isolation of this particular population. The intersection of gates allowing isolation of CD64+F HLADR+F/int and CD117+/- includes the immature precursors (CD117+ CD34+low/-) and the more mature monocytes.
The evaluation of new FCS files compared to a database contributes to a more objective, standardized analysis and data interpretation and avoids misinterpretations of the so-called patterns recognition, which is difficult to standardize3. The quantification of the amplitude of phenotypic abnormalities by comparison with normal databases makes it possible to rank these abnormalities, which may help improve MFC scores for MDS diagnosis. Moreover, this method allows precise identification of the phenotypic abnormalities on immature CD34+ and/or CD117+ precursors and in more mature cells, which are not evident when using the current analysis strategies in acquisition software. This method is relevant in the diagnosis of MDS cases that do or do not have evident morphologic abnormalities, or that do or do not carry cytogenetic recurrent aberrancies.
Improvement of antibody staining is needed, and the addition of new normal BM data in databases is required in order to increase the robustness, reliability, and sensitivity of the analysis. This method could be applied, with further investigation, to cytopenias from other causes, or to clonal hematopoiesis of indeterminate potential (CHIP), in order to identify the phenotypic changes reliably related to dysplastic processes.
The authors have nothing to disclose.
The antibodies used in this study were provided by BD Biosciences. The authors would like to thank their colleague, Dr. Pascale Flandrin-Gresta, from the Department of Molecular Biology, Hematology laboratory, University Hospital of Saint-Etienne, France, who provided expertise for interpretation of NGS data for the second MDS case. The authors are thankful for the clinician hematologists for their interest and involvement in this study and for the patients and healthy donors for their agreement to participate in this study. The authors would also like to thank the “Les Amis de Rémi” Foundation for financial support for publication.
BD FACSCanto II flow-cytometer | BD Biosciences, CA, USA | SN: V33896301336 | 3-laser, 4-2-2 configuration, Filters and mirrors details: https://www.bdbiosciences.com/documents/BD_FACSCanto_II_FilterGuide.pdf |
Awel C48-R Centrifuge | AWEL Industries, FR | SN: 910120016; Model No: 320002001 | low speed centrifuges; capacity 60 FACS tubes |
Pipetts of 10µl and 200µl | |||
Pasteur pipettes | |||
15 mL Falcon tubes | |||
polypropylene tube for FACS | |||
Mouse Anti-Human HLA-DR | BD Biosciences, CA, USA | 655874 | clone L243 Mouse BALB/c IgG2a, κ Fluorochrome Horizon V450 (Ex max 404 nm/ Em max 448 nm) |
Mouse Anti-Human CD45 | BD Biosciences, CA, USA | 560777 | clone HI30 Mouse IgG1, κ Fluorochrome Horizon V500 (Ex max 415 nm/ Em max 500 nm) |
Mouse Anti-Human CD16 | BD Biosciences, CA, USA | 656146 | clone CLB/fcGran1 Mouse BALB/c IgG2a, κ Fluorochrome FITC (Ex max 494 nm/ Em max 520 nm) |
Mouse Anti-Human CD13 | BD Biosciences, CA, USA | 347406 | clone L138 Mouse BALB/c X C57BL/6 IgG1, κ Fluorochrome PE (Ex max 496 nm/ Em max 578 nm) |
Mouse Anti-Human CD34 | BD Biosciences, CA, USA | 347222 | clone 8G12 Mouse BALB/c IgG1, κ Fluorochrome PerCP-Cy5.5 (Ex max 482 nm/ Em max 678 nm) |
Mouse Anti-Human CD117 | BD Biosciences, CA, USA | 339217 | clone 104D2 Mouse BALB/c IgG1 Fluorochrome PE-Cy7 (Ex max 496 nm/ Em max 785 nm) |
Mouse Anti-Human CD11b | BD Biosciences, CA, USA | 333143 | clone D12 Mouse BALB/c IgG2a, κ D12, Fluorochrome APC (Ex max 650 nm/ Em max 660nm |
Mouse Anti-Human CD10 | BD Biosciences, CA, USA | 646783 | clone HI10A Mouse BALB/c IgG1, κ Fluorochrome APC-H7 (Ex max 496 nm/ Em max 785nm) |
Mouse Anti-Human CD35 | BD Biosciences, CA, USA | 555452 | clone E11 Mouse IgG1, κ Fluorochrome FITC (Ex max 494 nm/ Em max 520 nm) |
Mouse Anti-Human CD64 | BD Biosciences, CA, USA | 644385 | clone 10.1 Mouse BALB/c IgG1, κ Fluorochrome PE (Ex max 496 nm/ Em max 578 nm) |
Mouse Anti-Human CD300e | Immunostep | IREM2A-T100 | clone UP-H2 Mouse BALB/c IgG1, k Fluorochrome APC (Ex max 496 nm/ Em max 578 nm) |
Mouse Anti-Human CD14 | BD Biosciences, CA, USA | 641394 | clone MoP9 Mouse BALB/c IgG2b, κ Fluorochrome APC-H7 (Ex max 496 nm/ Em max 785nm) |
Mouse Anti-Human CD36 | BD Biosciences, CA, USA | 656151 | clone CLB-IVC7 Mouse IgG1, κ Fluorochrome FITC (Ex max 494 nm/ Em max 520 nm) |
Mouse Anti-Human CD105 | BD Biosciences, CA, USA | 560839 | clone 266 Mouse BALB/c IgG1, κ Fluorochrome PE (Ex max 496 nm/ Em max 578 nm) |
Mouse Anti-Human CD33 | 345800 | clone P67.6 Mouse BALB/c IgG1, κ Fluorochrome APC (Ex max 496 nm/ Em max 578 nm) |
|
Mouse Anti-Human CD71 | BD Biosciences, CA, USA | 655408 | clone M-A712 Mouse BALB/c IgG2a, κ Fluorochrome APC-H7 (Ex max 496 nm/ Em max 785nm) |
Lysing Solution 10X Concentrate (IVD) | BD Biosciences, CA, USA | 349202 | |
FACSFlow Sheath Fluid | BD Biosciences, CA, USA | 342003 | |
FACSDiva CS&T IVD beads | BD Biosciences, CA, USA | 656046 | |
RAINBOW CALIBRATION PARTICLES, 8 PEAKS | Cytognos, Salamanca, Spain | SPH-RCP-30-5A | lots EAB01, EAC01, EAD05, EAE01, EAF01, EAG01, EAH01, EAI01, EAJ01, EAK01 |
Compensation Particles Multicolor CompBeads(CE/IVD) | BD Biosciences, CA, USA | ref. #51-90-9001229 + #51-90-9001291 | |
Diva software versions 6.1.2 and 6.1.3 | BD Biosciences, CA, USA | ||
Phosphate buffered saline tablets | R&D Systems, Minneapolis, USA | 5564 | |
Bovine serum albumin (BSA) | Sigma-Aldrich, France | A9647 | |
Sodium azide 99% | Sigma-Aldrich, France | 199931 | |
Infinicyt software version 1.8.0.e | Cytognos, Salamanca, Spain |