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. Author manuscript; available in PMC: 2020 Oct 1.
Published in final edited form as: Transfusion. 2019 Jul 29;59(10):3219–3227. doi: 10.1111/trf.15463

Red blood cell alloimmunization is associated with lower expression of FcγR1 on monocyte subsets in patients with sickle cell disease

Raisa Balbuena-Merle 1,*, Susanna A Curtis 2,*, Lesley Devine 1, David R Gibb 1, Matthew S Karafin 3,4, Chance John Luckey 5, Christopher A Tormey 1,6, Alexa J Siddon 1,6,7, John D Roberts 2, Jeanne E Hendrickson 1,8
PMCID: PMC7075520  NIHMSID: NIHMS1571623  PMID: 31355970

Abstract

BACKGROUND

Despite the clinical significance of red blood cell (RBC) alloantibodies, there are currently no laboratory tests available to predict which patients may be at risk of antibody formation after transfusion exposure. Given their phagocytic and inflammatory functions, we hypothesized that differences in circulating monocytes may play a role in alloimmunization.

STUDY DESIGN AND METHODS

Forty-two adults with sickle cell disease (SCD) were recruited, with data extracted from the electronic medical record and peripheral blood analyzed by flow cytometry for total monocytes, monocyte subsets (CD14 high/CD16 low+ classical monocytes, CD14 high/CD16 high+ intermediate monocytes, and CD14 intermediate/CD16 high+ non-classical/inflammatory monocytes), and FcγR1 (CD64) expression. Thirteen “non-responder” patients (non-alloimmunized patients with documented RBC transfusion at the study institution) were compared to 20 alloimmunized “responder” patients, who had a total of 44 RBC alloantibodies identified.

RESULTS

There were no significant differences in the percentages of total monocytes, monocyte subsets, or measured cytokines between non-responders and responders. However, non-responders had higher CD64 expression on classical monocytes (MFI mean 3424 ± standard deviation 1141) compared to responders (MFI mean 2285 ± 1501), p = 0.029, and on intermediate monocytes (MFI mean 3720 ± 1191) compared to responders (MFI mean 2497 ± 1640), p = 0.033.

CONCLUSIONS

Monocytes and the inflammatory milieu increasingly are being appreciated to play a role in some complications of SCD. The differences in FcγR1 expression on monocyte subsets noted between responders and non-responders, which cannot be directly explained by the serum cytokines evaluated, warrant further investigation.

Red blood cell (RBC) alloimmunization occurs after exposure to non-self blood group antigens, primarily through transfusion or pregnancy. Approximately 3%−10% of transfused individuals will form a RBC antibody, and these antibodies have the potential to be clinically significant during subsequent transfusions or pregnancy.1 Sequelae of RBC alloantibodies include hemolytic transfusion reactions, difficulty locating compatible RBC units for transfusion, and hemolytic disease of the fetus and newborn.

Some transfusion recipients are more likely to become alloimmunized than others. Myelodysplastic syndrome, systemic lupus erythematosus, and rheumatoid arthritis are all associated with increased rates of alloimmunization.2,3 Patients with SCD also have very high rates of alloimmunization.4,5 Up to 40%−50% of such patients will become alloimmunized, with some forming multiple alloantibodies (“hyper-responders”).6 Transfusion burden is a risk factor for RBC alloimmunization, but transfusion burden alone is unlikely to explain the high rates of alloimmunization in patients with SCD. Likewise, exposure to non-phenotypically matched RBCs is a risk factor for RBC alloimmunization,7,8 but antigenic or racial differences between donor and recipient9 are also unlikely to be the sole explanation for the high rates of alloimmunization.6

Recipient inflammation, either exogenous or endogenous, has now been identified in studies in animal models and in humans as being one risk factor for RBC alloimmunization.10,11 SCD is a state of chronic inflammation, with ongoing hemolysis, leukocytosis, thrombocytosis, and elevated pro-inflammatory cytokines.12 RBC exposure during one of the most inflammatory complications of SCD, acute chest syndrome, has been shown to be associated with a relatively high likelihood of becoming alloimmunized.13 Viral infections and viral-like stimulation also have been shown to increase the likelihood of becoming alloimmunized.11

One family of inflammatory cytokines associated with viral infections and immunity is the type 1 interferon (IFNα/β) family. Multiple studies have shown that IFNα/β promotes adjuvant-induced antibody responses to soluble antigens by increasing activation of dendritic cells and lymphocytes.14–16 Recently, studies in an animal model related IFNα/β and RBC alloimmunization, with exogenous IFNα/β increasing the likelihood of a transfusion recipient becoming alloimmunized. Further, recipients lacking type 1 IFN receptors failed to become alloimmunized.17,18 Other cytokine genes also have been shown to be more prevalent in alloimmunized patients with SCD compared to non-alloimmunized patients.19

The high affinity Fcγ type 1 receptor (FcγR1),20 CD64, is primarily expressed on mature monocytes and immature neutrophils. It has been described to be expressed to a higher degree in neutrophils isolated from patients of African descent,21 and has also been described to increase in response to IFNγ exposure22 and to be influenced by IFNα/β. Given the central role that monocytes play in immune responses and the importance of the impact of inflammation on monocyte phenotype, we hypothesized that the subsets of monocytes or the degree of CD64 expression on circulating monocytes would differ between non-alloimmunized and alloimmunized patients with SCD.

MATERIALS AND METHODS

Inclusion criteria and data collected

Adults with a known diagnosis of sickle cell hemoglobinopathy (HbSS, HbSC, HbSβ+, or HbSβ0, as previously determined) that were being seen in clinic for routine care were approached for this study, and were financially compensated for their participation. Exclusion criteria included pregnancy, acute crisis or illness, or lack of competency to consent. Demographic data, blood bank data, and hemoglobin electrophoresis/high performance liquid chromatography data were extracted from the electronic medical record. Baseline complete blood counts (CBCs) with differentials and blood samples for flow cytometric analysis and cytokine evaluation were collected for the study. Samples from all patients were collected during a routine clinic visit; samples from patients on chronic transfusion therapy were collected just prior to the scheduled transfusion.

Study approval

The study was approved by the hospital’s Institutional Review Board.

Blood bank data

The Softbank Laboratory Information System was queried for historic and currently detected RBC alloantibodies as well as autoantibodies; the platforms used for antibody detection include solid phase and gel card technology. Clinically significant alloantibodies were classified as recently described.5 Lifetime RBC transfusion history at the study hospital or at hospitals within the healthcare network of the study hospital was obtained from the electronic medical record; transfusions outside of this network were not captured.

Flow cytometric analysis

Patient samples were collected in an EDTA tube between 8 am and 2 pm, and were analyzed within 6 hours of collection. RBCs were removed from samples using Pharmlyse (BD Biosciences), and white blood cells were stained for flow cytometric analysis. Stains included a permeability exclusion dye (Thermofisher) as well as CD11b PE-Cy7 (clone ICRF44, Biolegend), CD64 PerCP-Cy5.5 (clone 10.1, BD Biosciences), CD16 Alexa 700 (Clone B73.1, Biolegend), CD14 V450 (Clone MoP9, BD Bioscience), and CD45 APC-Cy7 (Clone 2D1, Biolegend); gating is described in the results section and shown in Fig. S2, available as supporting information in the online version of this paper. Samples were acquired on a BD LSR-II using FACSDiva Software, and the data were analyzed using FlowJo Version X software.

Serum cytokine measurement

A Milliplex Map kit (EMD Millipore) was used to measure seven cytokines (on previously frozen samples) in one assay (IL-1α, IL-1β, IL-4, IL-6, IFNγ, IL-8, and TNF-α). Plates were read in a Luminex 200 Analyzer (Luminex) controlled by xPONENT software. Values for each analyte were determined using Analyst software (EMD Millipore) from a standard curve of log dose versus median fluorescent intensity using a 5-parameter logistic fit.

Statistical analysis

Statistical analysis was performed using Graph Pad Prism software; where applicable, means are shown plus or minus one standard deviation (mean ± sd). Statistical significance between two groups was determined using an unpaired t-test or Mann Whitney U test for parametric and non-parametric analysis, respectively. Significance between three groups of non-parametric data was determined using a Kruskal-Wallis test with a Dunn’s posttest.

RESULTS

Patient description

Forty-four patients were enrolled in this study, and 42 of 44 had interpretable flow cytometry data available for analysis. Of these, 26 of 42 (61.9%) were female and the mean age was 34.5 years (range 19–81). Of the 42 total patients studied, 29 received RBC transfusions at the study hospital (mean RBCs transfused 73.7 units, 95% confidence interval [CI] 37.4–110).

RBC alloantibody status

Twenty of 42 (47.6%) of the patients had clinically significant RBC alloantibodies detected either at the study hospital or elsewhere and will be referred to as “responders” throughout this study (Fig. 1). Of the responders, 13 of 20 (65%) were female and the mean age was 36 (range 19–81). Twenty-two patients (52.4%) had only negative antibody screens at the study hospital and had no known RBC alloantibodies detected elsewhere. Thirteen (59.1%) of these non-alloimmunized patients were female, and the mean age was 33 (19–59).

Fig. 1.

Fig. 1.

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Schematic of patient groups. Flow chart of study patients, with respect to RBC alloantibody status and past transfusion history at the study hospital.

Of the responders, 14 of 20 (70%) had a Hgb SS genotype, 1 of 20 (5%) had a Hgb Sβ0 genotype, 1 of 20 (5%) had a Hgb Sβ + genotype, and 4 of 20 (20%) had a Hgb SC genotype. Of the non-alloimmunized patients, 10 of 22 (45.5%) had a Hgb SS genotype, 1 of 22 (4.5%) had a Hgb HbSβ0 genotype, 5 of 22 (22.7%) had Hgb Sβ + genotype, and 6 of 22 (27.3%) had a Hgb SC genotype. By genotype, 14 of 24 (58.3%) patients with Hgb SS were alloimmunized, 1 of 2 (50%) patients with HbSβ0 genotype were alloimmunized, 1 of 4 (25%) patients with Hgb Sβ + were alloimmunized, and 4 of 10 (40%) with Hgb SC were alloimmunized (Fig. S1, available as supporting information in the online version of this paper).

The RBC transfusion burden (within the study healthcare system) of the alloimmunized responders was a mean of 82.7 units (95% CI 33.1–132.3 units), and the transfusion burden of the non-alloimmunized patients was a mean of 65.5 units (95% CI 9.2–121.8 units); p = 0.14 between alloimmunized and non-alloimmunized. Thirteen of the 22 non-alloimmunized patients were transfused with RBCs at the study hospital (mean of 110.8 units, 95% CI 19.7–202 units) and had subsequent antibody screens 15 or more days post-transfusion with no clinically significant RBC alloantibodies; these will be referred to as “non-responders” throughout this study (Fig. 1).

The 20 alloimmunized patients had a total of 44 antibodies identified at some point in their lives, with 13 having more than one antibody and being classified as “hyper-responders” (Fig. 2A). The alloantibodies detected included anti: D (1), C (3), c (1), E (12), Cw (3), V (3), K (10), Jkb (2), Fya (2), Fyb (2), S (3), and other (1; Fy3); the distribution of these antibodies is shown in Fig. 2B.

Fig. 2.

Fig. 2.

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RBC alloantibody number and specificity. (A) RBC alloantibody distribution in the 20 alloimmunized patients, and (B) RBC alloanlibody specificity in these patients.

RBC autoantibody status

A strong association between RBC alloantibodies and warm autoantibodies has previously been reported in multiple patient populations,5 including those with SCD.23,24 Ten patients (23.8%) in this study had warm autoantibodies detected at some point in their lives, with all (100%) of the warm autoantibodies being present in patients who also have RBC alloantibodies. Of the alloimmunized patients, 10 of 20 (50%) had warm autoantibodies detected at some point.

Complete blood count data and RBC alloantibody status

Consistent with the findings from past studies,23 we found no relationship between total white blood cell count, white blood cell subsets, or platelet count by CBC and alloantibody status. The mean WBC of responders was 10.74 × 109 cells/liter (± 3.68), and of non-responders was 10.44 × 109 cells/liter (± 3.03), p = 0.82. The absolute neutrophil count (ANC) in the CBC was similar between responders and non-responders (mean 6.15 ± 3.20 and mean 6.64 ± 4.24, respectively, p = 0.72). The monocyte percentage by CBC was similar as well between responders and non-responders (mean 9.2 ± 3.37 and mean 9.67 ± 4.53, p = 0.75). The platelet count by CBC was also similar between responders and non-responders (mean 331.9 ± 124 and mean 379.6 ± 151.5, p = 0.34).

Monocyte subset data and RBC alloantibody status

Despite responders and non-responders having comparable monocyte percentages by CBC, we hypothesized that responders might have a different distribution in the types of monocytes. We evaluated monocyte subsets by flow cytometry, gating first on leukocytes by FSC-A and SSC-A, then gating on live CD45 high leukocytes (to exclude dead cells and debris), then gating on CD14 positive, CD64 positive cells as total monocytes,25 and then sub-classifying these monocytes26–28 based on CD14 and CD16 expression (CD14 high, CD16 low monocytes are “classical” monocytes; CD14 high, CD16 high monocytes are “intermediate” monocytes, and CD14 intermediate CD16 high monocytes are “non-classical” monocytes) (Fig. S2, available as supporting information in the online version of this paper, shows example flow cytometric gating). There were no significant differences in monocyte subset percentages between responders and non-responders (Fig. 3A–C). Absolute monocyte subset numbers were also evaluated, with a higher median number of classical monocytes in non-responders compared to responders (Table S1, available as supporting information in the online version of this paper).

Fig. 3.

Fig. 3.

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Monocyte subset percentages in non-responder and responder patients. Percentage of (A) classical monocytes, (B) intermediate monocytes, and (C) non-classical monocytes in non-alloimmunized and alloimmunized patients with SCD; all non-alloimmunized patients were transfused at the study hospital and are referred to as “non-responders.”

FcγR1 (CD64) mean fluorescence intensity on CD14 + and CD16+ monocytes and RBC alloantibody or autoantibody status

Given the reflection of the inflammatory milieu on macrophages, we hypothesized that the intensity of CD64 expression on total monocytes would differ based on alloimmunization status. After gating on all monocytes as described above, we evaluated the mean fluorescence intensity (MFI) of CD64 expression. We found a non-statistically significant trend for a decreased CD64 MFI on monocytes from responders (mean 2422 ± 1580) compared to the non-responders (mean 3372 ± 1078, p = 0.08, Fig. 4A). Next, we compared the CD64 MFI on monocytes between the non-responders and the 13 hyper-responders (i.e., those with more than 1 alloantibody) and found no difference (mean 2759 ± 1491 for the hyper-responders, p = 0.84, Fig. 4B). The CD64 MFI on monocytes was also similar between patients in the absence or presence of history of warm autoantibodies (mean 2192 ± 1683 vs. mean 2762 ± 1405, respectively, p = 0.49, Fig. 4C).

Fig. 4.

Fig. 4.

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FcγR1 (CD64) mean fluorescence intensity on total monocytes and RBC alloantibody or autoantibody status. CD64 mean fluorescence intensity (MFI) on total monocyte population separated by (A) non-responders and responders, (B) non-responders and hyper-responders (with more than one alloantibody detected), and (C) presence of warm autoantibodies.

We then evaluated CD64 expression on each monocyte subset. Statistically significant differences were noted between the CD64 MFI on classical monocytes between responders and non-responders (mean 2285 ± 1501 vs. mean 3424 ± 1141, p = 0.029, Fig. 5A) and between the CD64 MFI on intermediate monocytes in these same patients (mean 2497 ± 1640 vs. mean 3720 ± 1191, p = 0.033, Fig. 5B). No statistically significant differences were observed in CD64 MFI on non-classical monocytes (mean 1440 ± 882 vs. mean 1841 ± 600, p = 0.194, Fig. 5C). The genotype of each patient was evaluated in relation to CD64 expression on monocyte subsets, with no significant differences observed between genotypes (Fig. S3, available as supporting information in the online version of this paper).

Fig. 5.

Fig. 5.

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CD64 expression on monocytes, by subset. CD64 mean fluorescence intensity (MFI) on (A) classical monocytes, (B) intermediate monocytes, and (c) non-classical monocytes in non-responders and responders *p < 0.05 by Mann Whitney U test.

CD11b expression on monocytes

It has previously been described that CD11b expression on the monocytes of patients with SCD is significantly higher than that of race matched healthy controls.29 We thus compared CD11b expression on monocytes in responders and non-responders, and found no differences between the groups (mean 37,639 ± 23,458 vs. mean 43,069 ± 17,525, p = 0.39). We also found no statistically significantly differences in CD11b MFI by monocyte subset in these groups (Fig. S4, available as supporting information in the online version of this paper).

Serum cytokines

Th2 cytokines such as IL-4 and IL-6 are associated with humoral responses, and a recent study suggests polymorphisms of certain cytokine genes are more prevalent in alloimmunized patients with SCD compared to non-alloimmunized patients.19 In the present study, serum cytokines were evaluated by a Luminex multiplex assay from the same blood draw as that used for flow cytometric analysis. No statistically significant differences in any cytokines measured were noted between responders and non-responders (Fig. 6). Further, no obvious differences were noted between hyper-responders compared to non-responders (data not shown).

Fig. 6.

Fig. 6.

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Serum cytokine measurements. Serum cytokines in non-responders and responders. No statistically significant difference in any cytokine measured by Luminex was noted between groups.

DISCUSSION

RBC alloimmunization is a major clinical problem in a significant subset of patients with SCD. However, the factors that dictate whether a patient will form RBC alloantibodies remain largely unknown.4,30 Strategies to decrease the likelihood of alloimmunization via extended RBC matching in a uniform manner are resource-intensive, and a method to identify those at highest risk of developing antibodies would have significant clinical as well as financial value.31 In this study, we focused on peripheral blood monocytes as an immune cell that may impact alloimmunization. We show, for the first time, differences in the degree of FcγR1 (CD64) expression on classical and intermediate monocytes in responder and non-responder adult patients with SCD. Our data demonstrate that CD64 expression is lower on classical and intermediate circulating monocytes in alloimmunized responder patients relative to non-alloimmunized non-responders.

CD64 expression is typically observed in response to IFNγ stimulation32 of monocytes,33,34 and recent in vitro data has shown that IFNα/β suppresses IFNγ responses in macrophages, leading to CD64 downregulation.33 Although we did not find differences in measured IFNγ levels between responders and non-responders, our findings in sickle cell patients may be consistent with recent work from our laboratory showing that IFNα/β impacts the ability of a murine transfusion recipient to form RBC alloantibodies to the KEL human blood group antigen expressed on murine RBCs.17,18 Taken together, these data suggest that alloimmunized patients with SCD may have higher IFNα/β levels that prevent IFNγ from leading to the same increase in CD64 expression as would be seen in the absence of IFNα/β. IFNα/β or surrogate measurements were not included in the current study, and ongoing studies are investigating such measurements.

CD64 expression is also known to be altered by factors besides IFNα/β and IFNγ. Interleukin-10 increases CD64 expression, and less mature monocytes express higher levels of CD64.35 Interleukin-4 has been shown to downregulate CD64 expression,36 though no association between the one-time measurements of interleukin-4 and CD64 were noted in the current study. Further, a body of literature exists evaluating the degree of CD64 expression on other cell subsets, predominantly neutrophils, as a marker of inflammation.37,38

Monocytes have been extensively studied in patients with SCD, with a focus on their association with inflammation12,39 and their impact on the vascular endothelium.29,40 Patients with SCD have higher monocyte counts than race matched controls,41 and it has been proposed that a hemolytic phenotype further increases monocyte count and activation.39,42 Free heme has been shown to activate toll-like receptor 4 signaling in monocytes,43 to augment lipopolysaccharide-induced toll-like receptor 4 signaling in monocytes,44 and to act like a soluble damage associated molecular pattern.45 A monocyte-tumor necrosis factorendothelial activation axis has recently been described in animal models of SCD,46 with monocytes generating large amounts of pro-inflammatory cytokines. A recent study also has shed light on the potential importance of non-classical “patrolling” monocytes and vaso-occlusion.47

The cytokine milieu of patients with SCD has also been studied,12 with increases in tumor necrosis factor-α and interleukin-6 noted in crisis, and increases in IFNγ, interleukin-1, and interleukin-6 noted in steady state.48 A 2017 manuscript reported polymorphisms of tumor necrosis factor-α and interleukin-1β being over-represented in alloimmunized compared to non-alloimmunized Brazilian patients with SCD.19 Further, interleukin-6 receptor signaling has been shown to impact RBC alloimmunization in an animal model.49 However, overall serum levels of multiple cytokines were not noted to be associated with RBC alloimmunization in a past study of patients with SCD.50 The fact that the present study found no statistically significant differences in pro-inflammatory cytokines in a one-time measurement of alloimmunized and non-alloimmunized patients indicates that the differences in FcγR1 expression on monocyte subsets noted between responders and non-responders is unlikely to be directly explained by the serum cytokines measured.

It is important to remember that both our macrophage phenotyping and our cytokine measurements presumably reflect the steady state, with all the patients being alloimmunized in the past and with the analyzed samples being drawn at a routine clinic visit. Thus, one cannot comment on the immunophenotypic status of the patient at the time that the RBC alloantibody was initially formed. It would be most informative, though logistically difficult, to capture the immunophenotypic status of a patient at the time of antibody formation. It would also be informative to capture the immunophenotypic status of patients in states associated with a relatively high risk of alloimmunization following RBC exposure (e.g., acute chest syndrome13). All blood for this study was drawn with patients in their baseline states of health, but disease fluctuations inevitably constantly occur. Monocyte subsets were gated based on recent literature, but the distinction of these three subsets is typically not made in clinical flow cytometric laboratories. While our data demonstrate that CD64 expression correlates with non-responder status, it is also possible that CD64 or other markers studied may differ with complications or sequela of SCD other than RBC alloimmunization, or may be impacted by transfusion, hydroxyurea, or other therapies; ongoing studies are investigating this possibility. Finally, race matched healthy controls were not included in this study, and thus differences between patients with SCD and Hgb AA cannot be commented on.

In sum, this study found that expression of FcγR1, also known as CD64, was lower on classical and intermediate monocytes in responder alloimmunized patients with SCD than in non-responder non-alloimmunized patients. It is plausible that the inflammatory milieu found in patients with SCD plays a role in these findings, though the altered CD64 levels cannot directly be explained by the serum cytokines evaluated. While not studied, it is possible that the altered FcγRl expression is impacted by IFNα/β, which has recently been shown to influence RBC alloimmunization in animal models. Continued investigation of immunophenotypic differences between responder and non-responder patients with SCD is warranted, given the advantages of being able to distinguish patients who are at high risk of forming RBC alloantibodies from those who may be transfused hundreds of times without ever forming an antibody.

Supplementary Material

Supplemental Material

TABLE S1 Absolute monocyte subset counts of responders and non-responders.

Fig. S1. Red blood cell (RBC) alloimmunization as related to sickle genotype. Alloimmunization rates were 58% (14/24) for patients with Hgb SS, 50% (1/2) for patients with Sβ0 thalassemia, 25% (1/4) for patients with Hgb Sβ + thalassemia, and 40% (4/10) for patients with HgbSC.

Fig. S2. Flow gating for monocyte subsets. (A) General leukocytes were gated based on forward and side-scatter properties, (B) of the general leukocyte gate, a CD45 high live cell gate was set to exclude cell fragments and to yield the most accurate nucleated cell counts possible, and (C) of the CD45 high live cell gate, total monocytes were gated as CD14+ CD64+. (D) Monocytes were then grouped into classical, intermediate (also known as non-classical CD14 hi) and non-classical (also known as non-classical CD14 lo) subpopulations, with (E) CD64 mean fluorescence intensity assessed on each subpopulation.

Fig. S3. Fc gamma R1 (CD64) expression on monocytes, by subset, by type of sickle cell disease (SCD). CD64 mean fluorescence intensity (MFI) on (A) classical monocytes, (B) intermediate monocytes, and (C) non-classical monocytes in non-responders (non-alloimmunized patients with a documented transfusion history) compared to alloimmunized responder patients, separated by type of SCD.

Fig. S4. CD11b expression on monocytes, by subset. CD11b mean fluorescence intensity (MFI) on (A) classical monocytes, (B) intermediate monocytes, and (C) non-classical monocytes in non-responders and responders.

ACKNOWLEDGMENTS

RBM, SAC, LD, JDR, and JEH designed and completed the research. RBM, SAC, LD, and JEH analyzed the data. DRG, MSK, CJL, CAT, and AS interpreted and critically analyzed the data. RBM and JEH wrote the first draft of the manuscript, and all authors edited the manuscript.

This work was supported in part by R01 HL132951 (to JEH), T32 HL007974-14 (to Brian Smith, for RBM and SAC), and 5K08HL141446 (to DRG).

Footnotes

CONFLICT OF INTEREST

The authors have disclosed no conflicts of interest.

SUPPORTING INFORMATION

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

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Associated Data

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Supplementary Materials

Supplemental Material

TABLE S1 Absolute monocyte subset counts of responders and non-responders.

Fig. S1. Red blood cell (RBC) alloimmunization as related to sickle genotype. Alloimmunization rates were 58% (14/24) for patients with Hgb SS, 50% (1/2) for patients with Sβ0 thalassemia, 25% (1/4) for patients with Hgb Sβ + thalassemia, and 40% (4/10) for patients with HgbSC.

Fig. S2. Flow gating for monocyte subsets. (A) General leukocytes were gated based on forward and side-scatter properties, (B) of the general leukocyte gate, a CD45 high live cell gate was set to exclude cell fragments and to yield the most accurate nucleated cell counts possible, and (C) of the CD45 high live cell gate, total monocytes were gated as CD14+ CD64+. (D) Monocytes were then grouped into classical, intermediate (also known as non-classical CD14 hi) and non-classical (also known as non-classical CD14 lo) subpopulations, with (E) CD64 mean fluorescence intensity assessed on each subpopulation.

Fig. S3. Fc gamma R1 (CD64) expression on monocytes, by subset, by type of sickle cell disease (SCD). CD64 mean fluorescence intensity (MFI) on (A) classical monocytes, (B) intermediate monocytes, and (C) non-classical monocytes in non-responders (non-alloimmunized patients with a documented transfusion history) compared to alloimmunized responder patients, separated by type of SCD.

Fig. S4. CD11b expression on monocytes, by subset. CD11b mean fluorescence intensity (MFI) on (A) classical monocytes, (B) intermediate monocytes, and (C) non-classical monocytes in non-responders and responders.

NIHMS1571623-supplement-Supplemental_Material.docx (548.5KB, docx)

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