Immunologic Research

, Volume 53, Issue 1–3, pp 41–57 | Cite as

The three human monocyte subsets: implications for health and disease

  • Kok Loon Wong
  • Wei Hseun Yeap
  • June Jing Yi Tai
  • Siew Min Ong
  • Truong Minh Dang
  • Siew Cheng Wong
Singapore Immunology Network

Abstract

Human blood monocytes are heterogeneous and conventionally subdivided into two subsets based on CD16 expression. Recently, the official nomenclature subdivides monocytes into three subsets, the additional subset arising from the segregation of the CD16+ monocytes into two based on relative expression of CD14. Recent whole genome analysis reveal that specialized functions and phenotypes can be attributed to these newly defined monocyte subsets. In this review, we discuss these recent results, and also the description and utility of this new segregation in several disease conditions. We also discuss alternative markers for segregating the monocyte subsets, for example using Tie-2 and slan, which do not necessarily follow the official method of segregating monocyte subsets based on relative CD14 and CD16 expressions.

Keywords

Monocyte subsets CD14 CD16 Inflammation Classical subset Intermediate subset Non-classical subset 

For over the past two decades, human blood monocytes had been segregated into two subsets based on CD16 expression, the CD14+CD16− and CD14+CD16+ monocyte subsets (hereby designated as CD16− and CD16+, respectively). First identified by Ziegler-Heitbrock [1], extensive research efforts made by several research groups had demonstrated that these two subsets possess distinct phenotypes and functions. These differences between them have also been thoroughly scrutinized using whole genome wide analysis [2, 3, 4, 5, 6] and have been a topic of several excellent reviews [7, 8, 9, 10, 11, 12, 13]. While the differences between the two subsets are clearly established, it has become increasing clear that further heterogeneity exists, particularly within the minor CD16+ monocyte population.

A new nomenclature defines human monocyte subsets into three, where the minor CD16+ population is further segregated into two smaller subpopulations [14]. The intermediate subset expresses relatively higher levels of CD14 coupled with lower CD16 expression (CD14++CD16+), while the non-classical subset expresses lower levels of CD14 with higher expression of CD16 (CD14+CD16++). The major CD16-subset with high CD14 and no CD16 expression is termed the classical subset (CD14++CD16−) (Fig. 1 and Table 1). As the segregation of the intermediate and non-classical subsets can be subjective, gating based on proper isotype controls has been recommended [14]. By definition, the intermediate and classical monocyte subsets possess the same levels of CD14. Hence, we find it convenient to use the end point of CD14 expression by the classical monocytes as a set point to segregate between the intermediate and non-classical subsets, as depicted in Fig. 1.
Fig. 1

Gating strategy of the three monocyte subsets based on relative CD14 and CD16 expression. Flow cytometry dot plot showing the gating of the classical, intermediate and nonclassical monocyte subsets. Classical monocytes express high levels of CD14 but no CD16, intermediate monocytes express high levels of CD14 and low CD16, while non-classical monocytes express low CD14 but high CD16

Table 1

Phenotypic and functional differences between the three monocyte subsets

 

Classical

Intermediate

Non-classical

Approximate proportions to total monocytes

85%

5%

10%

Surface markers expressed

CD62L, CCR2, CLEC4D, CLEC5A, IL13Rα1, CXCR1, CXCR2

CD74, HLA-DR, Tie-2 (CD202B), ENG (CD105)

Siglec10, CD43, SLAN (subpopulation)

Surface markers not expressed

CX3CR1, CD123, P2RX1, Siglec10

CD62L, CXCR1, CXCR2, CLEC4D, IL-13Rα1

CCR5, CD62L, CXCR1, CXCR2, CD163, CLEC4D, IL13Rα1

Preferential responses to LPS

IL-10, G-CSF, CCL2, RANTES, IL-6, IL-8

IL-6, IL-8

TNF-α, IL-1β, IL-6, IL-8

Described functions

Superior phagocytosis

T cell proliferation and stimulation,

Superior ROS production,

Angiogenesis (Tie-2+ subpopulation)

T cell proliferation and stimulation (SLAN+ subpopulation),

“Patroling” behavior in vivo

Defined gene signatures

Wound healing and coagulation,

S-100 proteins

Scavenger receptors

C-type lectin receptors

Anti-apoptosis

Response to stimuli

MHC class II presentation and processing

Cytoskeletal rearrangement

Complement components

Pro-apoptotsis

Negative regulation of transcription

This new segregation raises the possibility that specialized functions and phenotype can be attributed to these newly defined monocyte subsets. Some outstanding questions include: What attributes previously ascribed to CD16+ monocytes are subdivided between the intermediate and non-classical subsets? Can other novel functions be revealed according to this subdivision? Is the intermediate subset a phenotypical and/or developmental intermediate between the classical and non-classical subsets? What are the relationships between the three subsets?

It was only very recently that genome wide analyses of the three monocyte subsets were reported [5, 15, 16]. The results of these studies significantly advance current knowledge on monocyte subsets separated according to the new nomenclature. Apart from providing comprehensive relative gene expression data on the three subsets, these studies also defined their relationships, identified new phenotype and described new functions, setting the ground for future research. In this section, we discuss the results from these recent reports, focusing on areas where the results of these studies were collaborative, on areas where controversy exists, and lastly on aspects where exciting areas for future research are warranted. We have summarized some of the main points from these recent studies in Table 1.

Relationships between the three monocyte subsets

Hierarchical clustering of the gene expression profiles of the monocyte subsets was first reported by Cros et al. [5], who showed that the non-classical monocytes clustered separately from the classical and intermediate monocytes. Their results indicate that the classical and intermediate subsets were most closely related among the subsets, while the non-classical subset was the most distant subset. However, two other independent studies revealed that intermediate and non-classical subsets are more closely related [15, 16]. Correspondingly, the numbers of genes that were significantly different between the intermediate and non-classical subsets were the lowest among the three subsets [15]. This conclusion was also supported by a separate microarray study performed with rhesus monkeys that posses homologous monocyte subpopulations [17]. It is unclear why the results from Cros et al. and the other research groups were different, but there seems to be stronger agreement for the proximity of relationship between the intermediate and non-classical monocyte subset. This close relationship suggests a direct development relationship between these two subsets, although this has yet to be formally proven.

Cytokine specialization by the three monocyte subsets

Thus far, there is poor agreement on the preferential production of cytokines by the three monocyte subsets. TNF-α is produced mainly by the CD16+ monocytes [18, 19], implying that either intermediate, non-classical subsets or both could preferentially produce this cytokine. Unfortunately, recent results on TNF-α production by the three subsets are not reconcilable. In the study by Cros et al. [5], isolated non-classical monocytes were poor producers of several cytokines in response to LPS, including TNF-α, IL-1β, CCL2, IL-10, IL-8, IL-6 and CCL3 but responded most strongly to TLR7/8 ligands. Instead, the intermediate subset treated with LPS produced the most TNF-α, IL-1β and IL-6. Rossol et al. [20] also showed that the intermediate monocytes in vitro produced the most TNF-α and IL-1β upon treatment with LPS and the highest producer of TNF-α when co-culture with pre-activated T cells. However, the study by Wong et al. [15] showed that isolated non-classical monocytes produced in response to LPS the highest levels of TNF-α and IL-1β while equivalent amounts of IL-6 and IL-8 were produced by all three subsets. Using a whole blood intracellular staining method, where samples undergo minimal in vitro manipulation, Belge et al. [19] also showed that the CD14 low population produced greatest amount of TNF-α. Hence, it seems that the non-classical monocytes appear to be capable of producing inflammatory cytokines in response to a wide variety of TLR ligands, and not merely in response to viruses and immune complexes.

There is also inconsistent data on which monocyte subset is the major producer of IL-10. Skrzecynska-Moncznik et al. [21] demonstrated that the intermediate monocytes produced the most IL-10 in response to LPS and zymosan. However, some recent results showed instead that classical monocytes produced the most IL-10 [5, 15, 22], which is consistent with results reported based on segregating monocyte subsets into CD16+ and CD16− monocyte subsets [18].

The differences in cytokine production reported by different groups could be due to the different isolation methods used to purify the monocyte subsets. As such, certain anti-CD14 clones, for example M5E2, can block responses to LPS [23]. CD14 is a co-receptor for LPS and expressed at low levels by the non-classical subset. Hence, the use of blocking CD14 antibodies may selectively inhibit responses of the non-classical monocytes to LPS. Hence, the method where whole blood intracellular staining was utilized to assess cytokine production may be the most ideal, as it will minimize nonspecific effects that may occur during isolation and in vitro culture [19]. Furthermore, dose and time kinetic effects on cytokine production should also be examined.

Antigen processing and presentation by the intermediate monocyte subset

The intermediate monocyte subset expresses high levels of MHC class II processing and presentation genes [15, 16]. This included several paralogues of HLA-DR and MHC class II processing genes, such as CD74 and HLA-DO. The higher protein surface expression of HLA-DR and CD74 was also confirmed by flow cytometry. This subset also expressed higher levels of surface molecules involved in antigen presenting cell–T cell interactions, particularly CD40 and CD54 [15]. Accordingly, intermediate monocytes were shown to be the best inducers of SEB-mediated T cell proliferation [16]. Similarly, CD64+CD16+ monocytes, a profile that matches the intermediate monocyte, were shown to be the best T cell stimulators with influenza type A antigen and mixed leukocyte reactions [24].

As most of these T cell proliferation assays were mostly performed using experimental systems that bypass the need for antigen capture and processing, the relative ability of the intermediate subset to acquire and process antigens for presentation to T cells still need to be properly addressed and should include proper comparisons to blood dendritic cells. The in vivo functional context for this is unclear. Does the intermediate subset serve as a unique type of blood dendritic cell, capable of capturing and processing blood-borne antigens? Does this subset serve to maintain tolerance at steady state, given their low expression of co-stimulatory molecules? Are they capable of migrating into the lymphatic system after activation? Another interesting point was that the expression of MHC class II could be up-regulated on the classical subset, rendering them the ability to induce T cell proliferation after activation with SEB [16]. Hence, it seems that the classical monocytes can acquire T cell proliferative activities if properly activated. Again, how are these phenomena relevant in vivo? One possibility would be the migration of activated monocytes into inflamed tissues to support ongoing T cell responses locally [25, 26].

In contrast to MHC class II, the expression of MHC class I was found to be most highly expressed by the intermediate in the study by Wong et al. [15], while Zawada et al. [16] reported the highest expression of MHC class I associated genes by the non-classical subset. The reason for this difference is not known, but clearly suggests that the CD16+ monocytes are capable of activating CD8 T cells, an area of research that is largely unexplored.

Pro-angiogenic behavior of the intermediate monocyte subset

One prominently feature reported by Zawada et al. [16] was the pro-angiogenic behavior of the intermediate subset. This subset expressed several surface markers associated with pro-angiogenesis including endoglin (ENG), TEK tyrosine kinase (Tie2, CD202b) and KDR (VEGFR2). But these genes were apparent only after the statistical criteria in their analysis were lowered. This may be because pro-angiogenesis is not feature of all intermediate monocytes, and/or that pro-angiogenic monocytes can also be found within non-classical and classical subsets. Interestingly, only the intermediate subset was able to form cell clusters upon vascular endothelial growth factor (VEGF) stimulation.

Patrolling behavior of the non-classical monocyte subset

Cros et al. [5] demonstrated that human non-classical monocytes exhibited crawling behavior on the endothelium after being adoptively transferred into mice. Consistently, in vitro experiments showed the CD16+ monocytes to be far more motile than their CD16− counterparts [27]. In our own hands, we have observed that the movements of CD16+ monocytes in vitro were smooth and mediated through large frontal areas of extended lamellipodium in a seemingly crawling manner (personal observations), which is consistent with that described by Cros et al. in vivo. This behavior suggests that the non-classical monocytes are constantly surveying the endothelium for signs of inflammation or damage and poised to transmigrate rapidly. Interestingly, genes associated with cytoskeleton mobility, such as Rho GTPases, RHOC and RHOF, and several upstream Rho activators and downstream effectors were most highly expressed by the non-classical subset [15, 16]. The high expression of these cytoskeletal genes involved in cell mobility might be responsible for the highly motile behavior of the non-classical subset.

A different way to segregate monocytes using Tie-2 and slan?

The current segregation of monocyte subsets into three based on the relative expression profiles of CD14 and CD16 is rather arbitrary. Nevertheless, recent global gene expression analysis could reveal distinct differences between the populations. The new nomenclature may mask heterogeneity that may be spread across some or all the subsets. Hence, the question arises as to whether there are more meaningful ways to segregate monocytes such as based on tangible functional differences and surface markers?

Based on current evidence, there seems to be at least two distinct functional populations within the CD16+ monocyte population, defined according to Tie-2 and slan expression. In the following section, we discuss the use of these two surface molecules to segregate monocytes and how they define monocytes with seemingly different functions. Importantly, reports so far suggest that the expression of these surface markers does not follow the current definition monocyte subsets based on CD14 and CD16 expression.

Tie-2+ monocytes comprise of approximately 20% of total monocytes [28]. They are enriched but not necessarily restricted to the intermediate subset. For example, a small percentage (2–7%) is actually found within the non-classical monocyte subset [28, 29]. Tie-2 is also expressed on rare circulating endothelial cell progenitors, but their co-expression of CD133 and CD146 distinguished them from Tie-2+ monocytes [29]. Furthermore, Tie-2+ monocytes are also CD11b+, CCR5+, CD33+ and CD115+.

Tie-2+ monocytes represent a distinct functional subset endowed with pro-angiogenic properties. Tie-2+ monocytes are clearly involved in promoting tumor angiogenesis [30, 31]. For example, Tie-2+ monocytes were found within tumor but not the adjacent healthy tissues. Tie-2+ monocytes could promote angiogenesis of xenotransplanted human tumors [29]. They expressed higher levels of genes associated with angiogenesis, including metalloproteinase-9 (MMP-9), VEGFA, COX-2, WNT5A and TNF-α than Tie-2− monocytes [32]. Tie-2+ monocytes are responsive to angiopoietin-2 (ANG-2), which serves as a chemoattractant for them but not the Tie-2− counterpart [28, 29]. Furthermore, ANG-2 treatment of Tie-2+ monocytes suppressed their production of inflammatory cytokines, including TNF-α, CCL2 and IL-12 [28] and induced regulatory T cell expansion [33].

Overall, evidence suggests that Tie-2+ monocytes might play important roles in physiological wound healing processes and angiogenesis. As Tie-2 expression does not necessarily follow the current three monocyte subsets nomenclature based on CD14 and CD16 expression, the current nomenclature for monocytes could have unwittingly masked this Tie2+ monocyte population.

A monoclonal antibody, M-DC8, specifically labeled a subpopulation, consisting of 0.5–1% of all leukocytes in the blood [34]. These cells were found to be highly phagocytic, expressed CD16, several myeloid-associated markers and have the capacity to stimulate CD8 T cells. These slan+ cells were considered as a new distinct subset of blood dendritic cells because their expressions of surface markers markedly differ from CD1c+ and plasmacytoid DCs [35]. The epitope recognized by M-DC8 was subsequently identified as 6-sulfo LacNAc, a novel O-linked carbohydrate modification of P-selectin glycoprotein ligand 1 (PSGL-1), also known as slan [36]. These cells were capable of stimulating CD4 T cells under various conditions, including allogeneic naive CD4+ cord blood T cells, autologous recall antigen tetanus toxoid and autologous neoantigen KLH responses [36, 37].

Based on flow cytometry, slanDCs are CD16+CD14low and nestled within the non-classical monocyte gate [37]. They constitute about 30–50% of the non-classical population in healthy individuals [5, 15]. Analysis of the surface marker expression by the slan+CD16+, slan−CD16+ and CD16− monocyte populations revealed several differences between them [35]. SlanDCs (slan+CD16+) expressed the highest levels of CD45RA and C5aR. They also expressed lowest levels of CD11b, CD14, CD32, CD33, CD45RO, CD62L, CD64 and C3aR, surface markers highest on the CD16− monocytes [35]. Slan−CD16+ monocytes most highly expressed HLA-DR and CD86 [35]. Strikingly, the patterns of expression of these surface molecules are similar to that described among the classical, intermediate and non-classical subsets. Furthermore, the slanDC’s ability to produce high levels of TNF-α in response to stimulation is also consistent as that described for the non-classical subset. SlanDCs are potent producers of inflammatory cytokines like TNF-α and IL-12 in response to LPS or CD40L [35, 37, 38]. Comparatively, slan−CD16+ monocytes were poorer producers of IL-12 [35]. Overall, the evidence suggests that slanDCs are potent pro-inflammatory Th1 [35] and perhaps even Th17-inducing cells [39]. And the current three-subset definition may mask this unique pro-inflammatory slan+ monocytes that are actually a subset within the non-classical monocytes.

It is unclear whether specific differences exist between non-classical monocytes that are slan+ and slan−. According to Cros et al. [5] based on global gene expression analysis, slan did not allow discrimination of the slan+ and slan− non-classical subsets. However, this does not exclude the possibility that there could be subtle differences between the slan+CD16+ monocytes and slan−CD16+ monocytes, and such studies are needed.

Our preliminary experiments showed that Tie-2 and slan expression are mutually exclusive and is not consistent with the current definition for the intermediate and non-classical subsets (data not shown). It could be possible, with further experimental evidence, that the CD16+ monocytes could be reclassified based on their relative expression of Tie-2 and slan, namely Tie2+slan−, Tie2−slan+ and Tie2−slan− monocyte subpopulations. This method of segregation could allow separation of the CD16+ monocytes based on bona fide functional differences.

Diseases associated with CD16+ monocyte subsets

As discussed in the earlier sections, segregating monocytes into 3 subsets based on CD14 and CD16 or with the use of additional surface markers, that is, Tie-2 or slan have led to the discovery of new phenotypic and functional features in the different subsets. But have these new features aid us in our understanding of these subsets’ contribution in diseases?

The expansion of the CD16+ monocytes has been well described in many different types of diseases, mostly in infection or inflammatory conditions [40, 41, 42, 43, 44, 45, 46]. This subset is generally termed “pro-inflammatory” monocytes because of their ability to produce high amounts of TNF-α and IL-1β [18, 19]. Hence, it is not unusual that they might play important roles in promoting inflammation in diseases. But what is still not known is what causes them to expand in numbers? Are they playing a protective or pathogenic role in these different diseases? With the CD16+ monocytes now further divided into the intermediate and non-classical subsets as discussed in the previous section, it will be important to determine if only one of the subsets within the CD16+ population is expanded, and if so, can we deduce their roles in disease based on what is known about phenotypic and functional properties from the gene expression profiling studies. In this section, we will be discussing clinical studies of diseases where an expansion in the intermediate and/or non-classical subsets was reported. Table 2 summarizes a list of diseases the papers reported on the three monocyte subsets together with the frequencies of each subset in patients versus healthy controls. In addition, Table 3 lists the surface markers reported to be altered on monocyte subsets in the diseases found in Table 2. The diseases are broadly divided into bacterial infection, viral infection and inflammatory diseases including autoimmune disorders and asthma.
Table 2

Frequencies of the three monocyte subsets in different types of diseases

Disease states

Patient groups/timeline

Percentage of total monocytes (%)

Expanded population

References

Classical

Intermediate

Non-classical

Bacterial infections

Sepsis

Healthy controls

~87.5

6.0 ± 1.6

6.4 ± 2.5

Intermediate

[48]

Severe sepsis or septic shock

~78.0

17.9 ± 6.2

3.0 ± 5

Immunoparalysis

~78.0

~18.0

~3.0

Sepsis

Healthy controls

NR

~11.0

~5.0

Intermediate and non-classical

[49]

Children without symptoms

NR

~12.5

~6.7

Children with clinical symptoms

NR

~15.0

~14.0

Children sepsis symptoms and positive blood culture

NR

~22.5

~11.0

Tuberculosis

Patients

~60.0

~17.0

~23.0

Intermediate and non-classical

[41]

Vaccinated/immunised

~70.0

~8.0

~10.0

Non-vaccinated

~70.0

~8.0

~10.0

Viral infections

Dengue fever

Healthy controls

77

5

4

Intermediate

[62]

Mild dengue fever

59

12

5

  

Severe dengue fever

65

8

4

  

Hepatitis B

Healthy controls

~87.5

~5.0

~5.0

Intermediate and non-classical

[59]

Immune tolerant carriers

~87.5

~5.0

~6.0

Immune activated subjects

~81.3

~8.8

~7.5

Chronic Hepatitis C

Healthy controls

~86.0

~1.0

~1.0

Intermediate and non-classical

[60]

Chronic hepatitis patients

~84.0

~5.0

~2.0

HIV

Healthy controls

~90.0

~7.0

~7.0

No expansion

[65]

HIV-1 infected Individuals (HAART treated)

~87.5#

~10.0#

~10.0#

HIV

Healthy controls

~95.0

~5.0

~2.0

Intermediate and non-classical

[61]

HIV-1 infected individuals

~85.0

~7.5.0

~6.0

Autoimmune diseases

Active Crohn’s Disease

Healthy controls

~75.0

2.8 (IQR 2.0–4.3)

~10.0

Intermediate

[89]

Active Crohn’s disease

~80.0#

10.3 (IQR 7.3–13.7)

~6.0#

Eales’ Disease

Healthy controls

69.06 ± 4.47

10.92 ± 2.98

2.38 ± 0.83

Intermediate

[100]

Eales’ disease

27.09 ± 3.26

56.83 ± 6.7

3.99 ± 0.84

Rheumatoid arthritis

Healthy controls

NR

~5.0

NR

Intermediate

[20]

Rheumatoid arthritis

NR

~10.0

NR

Inflammatory conditions

Aseptic loosening

Group I: aseptic loosening

68.7 ± 11.3

8.8 ± 5.9

13.7 ± 7.5

Non-classical

[122]

Group II: mechanical loosening

75.9 ± 8.5

7.0 ± 4.7

6.9 ± 3.4

Group III: stable implants

75.4 ± 5.4

6.7 ± 2.4

9.2 ± 5.6

Group IV: osteoarthritis

74.9 ± 8.5

8.2 ± 4.8

8.5 ± 3.1

Asthma@

Healthy controls

84.1

5.4

2.9

Intermediate

[120]

Mild asthma

83.4

8.8

3.1

Moderate asthma

60.3

27.3

4.7

Severe asthma

17.1

68.2

3.2

Coronary artery disease

Healthy controls

87 (IQR 78.5–95.9)

3.3 (IQR 1.1–7.5)

5.8 (IQR 1.7–10.2)

Non-classical

[117]

Coronary artery disease (statin-treated)

82 (IQR 73.6–96.2)

3.6 (IQR 3.0–14.3)

9.2 (IQR 5.2–13.8)

End stage renal disease

Stage 5 kidney failure patients

NR

NR

NR

Intermediate

[112]

Hemodialysis and cardiovascular outcome

Prevalent CKD stage 5 patients on hemodialysis treatment

NR

NR

NR

Intermediate (decline)

[116]

Periodontitis§

Healthy controls

59 ± 5.0

23 ± 5.3

8.5 ± 1.0

Non-classical

[123]

Chronic periodontitis

49 ± 4.0

20 ± 3.2

13 ± 1.3

Aggressive periodontitis

57 ± 5.1

20 ± 4.0

11 ± 1.2

Stroke

Controls

~88.0

~3.5

~6.0

Intermediate

[108]

At admission

~87.0

~4.5

~6.0

Day 2 after admission

~87.5

~6.5

~3.5

Day 7 after admission

~87.0

~5.5

~5.0

Day 90 after admission

~87.0

~4.0

~6.0

Values are median ± SD except §where SE was reported

IQR Inter-quartile range, NR not reported

~Values estimated from graphs

#Not significant

@1 representative data/patient group

Table 3

Changes in surface markers expression on the three monocyte subsets in different types of diseases

Disease states

Phenotypic changes during disease conditions versus healthy controls

References

Classical

Intermediate

Non-classical

Bacterial infections

Sepsis

↑ HLA-DR

↓ HLA-DR

↓ HLA-DR

[48]

Tuberculosis

 

↑ CD11b

↓ HLA-DR

[41]

Viral infections

Hepatitis C (HIV co-infection)

↑ CD81

↑ CD81

[79]

HIV

↑ CXCR4

[63]

HIV

↑ APP#

[71]

HIV

↑ CD163

↑ CD163

[65]

Autoimmune diseases

Active Crohn’s disease

↓ CX3CR1

↓ CCR2

↓ CX3CR1

↓ CCR2

↓ CX3CR1

↑ CCR2

[89]

Eales’ disease

↓ TLR2

↑ TLR2

↑ TLR2

[100]

Rheumatoid arthritis

↑ CD64

↑ CD64

↑ CD64

[20]

Inflammatory diseases

Allergic rhinitis

↑ FcεRI

[89]

Asthma

↑ CD163

↑ CD163

↑ CD163

[100]

Coronary artery disease

↑ CX3CR1

↓ CCR2

[20]

End stage renal disease

↓ CD143

↑ CD143

↑ CD143

[114]

Up and down arrows indicate up or down-regulated expression of the markers in disease versus healthy controls, respectively, # Amyloid precursor protein, –no change

Perturbation of monocyte subsets in bacterial infection

Sepsis is a systemic inflammatory response syndrome that occurs during infection [47]. The expansion of CD16+ monocytes in sepsis has been documented since 1993 [40] with most subsequently studies done on based on two monocyte subsets. To date, there are no studies on the prognostic value of the expansion of the CD16+ monocytes in sepsis. Recently, our group (unpublished data) and Poehlmann et al. [48] noted that the expanded CD16+ monocytes mainly consisted of monocytes of the intermediate phenotype. Another study looking at monocyte subsets perturbation in the course of sepsis in small children and neonates showed that both intermediate and non-classical subsets numbers were elevated when these children are diagnosed with sepsis. Interestingly, the increase in the non-classical subsets was more significant in the patient group with clinical symptoms of sepsis but negative blood culture while the intermediate subset showed the greatest increase in the patient group with positive blood culture [48, 49]. These data would suggest that the non-classical subset plays role in controlling the infection while the intermediate subset is involved in promoting the infection. The positive association of both pro-inflammatory cytokines like TNF-α and IL-6 as well as anti-inflammatory cytokine, IL-10 with CD16+ monocytes had previously been reported [18, 19, 21, 40]. Unfortunately, the serum levels of these cytokines were not investigated in this study. And due to the current inconsistent in vitro data relating to the production of pro- and anti-inflammatory cytokines by the three monocyte subsets in response to LPS as discussed in the earlier section, it is difficult to deduce the exact source of these cytokines and the precise contribution of the intermediate and non-classical subsets toward the disease. Furthermore, this report by Skrzeczynska et al. is thus far the only one and their study focused on patients of a specific age group. Hence, further studies on larger group of sepsis patients comprising of wider age groups, different disease severity and positivity for blood cultures will be warranted.

During homeostasis, the intermediate subset has the highest expression of surface markers for antigen processing and presentation [15, 16]. The expression of HLA-DR expression was significantly reduced in sepsis patients as compared to healthy controls [48, 49, 50]. Numerous reports indicated the drop in HLA-DR expression is independently associated with development of sepsis and the degree of recovery in surface expression associated with mortality [51, 52, 53]. However, these were observed in all monocyte subsets and not just pertaining to the intermediate population. The intermediate subset has also been reported to be pro-angiogenic and the main producers of ROS during homeostasis [16]. These properties, however, have not been studied in monocytes of sepsis patients, but we speculate that high ROS production during sepsis may worsen the systemic inflammation, while it is not apparent how the pro-angiogenic property can be beneficial or harmful for host defense during sepsis.

Another bacterial infection where increased monocyte numbers had been reported is tuberculosis (TB) [54]. Castano et al. found that this increase is contributed by the CD16+ monocytes, with the intermediate subset showing a more significant expansion than the non-classical subset [41]. The expansion of both the intermediate and non-classical subsets has been speculated to be a consequence of high IL-10 production by the monocytes in TB patients, as IL-10 has been shown to induce CD16 expression on CD16− monocytes in vitro [55]. To date, there are no studies on the prognostic value of the expansion of the intermediate and non-classical subsets in TB. The roles these two subsets play in TB can only be speculated from phenotypical characterization ex vivo, or from functional characterization in vitro. Many markers related to maturation, differentiation and function in the three monocyte subsets were found to be similar between TB patients and healthy controls, except for higher CD11b and lower HLA-DR in the non-classical subset of TB patients [41]. CD11b has been described to enhance intracellular survival of the M tuberculosis [56], while low HLA-DR expression suggests reduced antigen processing and presentation capability of the expanded subset [57], both inferring that the expanded non-classical subset is unfavorable to host defense against TB. On the other hand, upon infection with M tuberculosis in vitro, the CD16+ monocytes from TB patients produced more TNF-α [41], which has been associated with the control of TB [58]. However, it is not known which subset within the CD16+ monocytes is producing the TNF-α. Hence, the exact role of the expanded intermediate subset in TB is still uncertain. More detailed studies on the functions of the expanded subsets and their prognostic values would help to determine whether the expanded subsets are beneficial or detrimental to host defense against TB and whether they represent a target for treatment.

Perturbation of monocyte subsets in viral infection

Besides bacterial infections, the CD16+ subset was also expanded in numerous viral infections like hepatitis B (HBV) [59], hepatitis C (HCV) [60], HIV [61] and Dengue [62]. Similar to TB infection, the proportions of both intermediate and non-classical subsets were increased in chronic HBV, HCV and HIV infections [59, 60, 61].

In HIV patients, the CD16+ monocytes are more permissive to infection by HIV than the classical subset due to their higher expression of CCR5 and CD4 [61, 63, 64]. Besides being less permissive to HIV entry, the classical subset also exhibits higher antiretroviral activity due to their expression of the low molecular mass (MM) form of apolipoprotein B mRNA-editing enzyme, catalytic polypeptide-like 3G (APOBEC3G). On the contrary, the CD16+ monocytes express in addition to the low MM form, also the high and mid MM forms of APOBEC3G, which would explain for their higher susceptibility to HIV infection [63]. While both intermediate and non-classical subsets are increased in HIV patients not subjected to highly active antiretroviral therapy (HAART), the proportion of intermediate and not the non-classical subset was found to correlate with increased plasma viral loads and a decrease in CD4+ T cell counts [61]. More interestingly, only the intermediate subset proportion returned to baseline 12 weeks post-HAART [61]. However, it is not known if the intermediate monocytes are expressing higher levels of the high-MM form of APOBEC3G than the non-classical monocytes. Hence, rendering the intermediate monocytes less inhibitory to viral replication [63]. In another HIV study where 80% of the HIV patients recruited were receiving HAART, no significant increase in the proportion of both intermediate and non-classical subsets was observed [65]. However, the proportion of intermediate monocytes expressing CD163 was increased [65]. Fischer-Smith et al. [66] observed an increase in CD16+ monocytes that co-expressed CD163 in active HIV patients. This population is highly probable to be the same as the CD163+ intermediate subset described in the study by Tippett et al. [65]. A positive correlation between increased CD163+CD16+ monocyte frequency and viral load was observed. Hence, these CD163+CD16+ monocytes may be a useful biomarker for HIV-1 infection and also a possible target for therapeutic intervention.

The increase in CD16+ monocytes had been associated with HIV-associated dementia (HAD) [67]. HIV patients who developed HIV encephalopathy had an accumulation of CD14+CD16+ in the microglia nodules and perivascular regions of the brain [66, 68]. As CD16+ monocytes are more permission to HIV infection, it has been strongly suggested that this subset may be acting as Trojan horses and are playing important role in viral dissemination [69, 70]. For instant, these infected monocytes might play critical role in the development of encephalitis and dementia if their trafficking to the brain was increased. Interestingly, the intermediate monocytes exhibit the highest expression of amyloid precursor protein (APP) while non-classical monocytes do not express APP [71]. APP accumulation had been observed in brain tissues from HIV patients [72] and also shown to participate in cell migration [73, 74]. It will be of interest to determine whether the high APP expression on intermediate subset contributes to a preferential recruitment of these cells to the brain resulting in disease pathogenesis.

Besides their contribution during active HIV infection, monocytes from HIV patients who had received HAART and responded with a drop in plasma viral load had detectable HIV DNA [67, 75, 76]. In HAD patients, the HIV DNA were predominantly found in the intermediate subset and HIV DNA copy number in this subset appeared to be linked to HAD [67, 75].

One potential explanation for the increase in CD16+ monocyte numbers could be due to an enhanced survival upon infection by HIV. Indeed, monocytes infected with HIV were found to exhibit an anti-apoptotic gene signature and were less apoptotic [77]. Since CD16+ monocytes are more susceptible to undergo spontaneous apoptosis than the CD16− monocytes, the tendency that the CD16+ monocytes will increase in numbers will be higher [78].

For chronic HBV, there is only one study reporting on the perturbation of monocyte subsets frequencies [59]. In that study, two-thirds of the HBV patients were having active disease (IA) while the remaining were carrier status (IT) during recruitment. The proportion of CD16+ monocytes in the IA patient group was significantly increased when compared to both healthy controls and the IT group while no significant difference was observed between IT and healthy controls. Further segregation of the CD16+ monocytes into the intermediate and non-classical subsets revealed the expansion to be the intermediate subset. While the numbers of non-classical subset in the IA patient group was also significantly higher when compared to healthy controls, the difference was not significant when compared to the IT group. The intermediate subset positively correlated with serum ALT levels, which is a measure of liver damage. No such correlations were observed with non-classical monocytes. This indicated that the intermediate subset might be involved in liver injury and be used as an indicator for active disease in chronic HBV patients. Accordingly, the numbers of CD16+ monocytes within the liver correlated with the severity of hepatic inflammation in IA patients, but no distinction of the intrahepatic CD16+ monocytes into intermediate or non-classical subset was made. Similar to HBV, an increase in CD16+ monocytes, meaning both intermediate and non-classical, was also reported for chronic HCV patients [60]. In this study, the patient cohort consisted of both treatment naïve patients as well as those receiving anti-viral treatments, but the increase in CD16+ monocytes were observed regardless of whether the patients were treated or not. In this study, the serum enzymes measured to determine liver damage were different from that the HBV study by Zhang et al. [59] and they also did not report whether there is any correlation between CD16+ monocyte numbers and the levels of serum liver function enzymes. They also did not use CD16 to detect for intrahepatic monocytes in HCV patients. Hence, no conclusion could be made as regards intermediate subset contribution to liver damage in this case. However, as in the case with HIV, HCV infect both intermediate and non-classical but not classical monocytes [79]. The preferential ability to infect CD16+ monocytes may be attributed to the higher expression of CD81 on these two subsets as compared to the classical monocytes [79], and CD81 has been reported previously as the primary receptor for HCV [80, 81]. More importantly, monocytes infected with HCV were found to facilitate the co-infection of the HIV virus and vice versus [79, 82], and the intermediate monocytes were the ones that exhibit co-infection by both HCV and HIV, indicating that they are capable of supporting replication of both viruses [79]. This would also imply the relevance of the intermediate subset as a reservoir for viral persistence and dissemination. Hence, how this monocyte subset respond to therapy will be critical to disease resolution or progression.

Another virus where perturbation in the CD16+ monocytes numbers was reported in infected individuals is Dengue virus [62]. The numbers of CD16+ monocytes were markedly increased in dengue patients when compared to healthy controls. However, within the CD16+ monocytes, the intermediate subset was the population that was specifically modulated. And this modulation was most significant in patients suffering from mild dengue and only slightly elevated in severe dengue patients. The proportion of the non-classical subset was not affected in either patient group. Interestingly, a significant negative correlation was observed between the intermediate subset and the plasma levels of several pro-inflammatory cytokines like TNF-α, IFN-γ and IL-18 in dengue patients. These cytokines have been associated with the severity of dengue disease [83, 84]. Unfortunately, the authors did not compare the levels of these cytokines in mild versus severe dengue patients. In this case, the finding appears consistent with the intermediate subset producing little TNF-α but a major producer of IL-10 as reported by Skrzeczynska-Moncznik et al. [21]. Although the levels of IL-10 were also measured in dengue patients, it was not mentioned whether this cytokine had a positive correlation with the intermediate subset. Nevertheless, the author suggested that the intermediate subset is most likely involved in controlling exacerbated inflammatory responses in dengue patients [62].

Perturbation of monocyte subsets in autoimmune disease

In autoimmune disorders, studies reporting monocyte subsets perturbation were mostly rheumatoid arthritis (RA) [20, 44, 85, 86] and Crohn’s disease (CD) [87, 88, 89]. While the expansion in the CD16+ monocytes had been clearly demonstrated in RA in several studies [44, 85], it is not clear which subsets within the CD16+ monocytes were specifically increased. A study by Baeten et al. mentioned that monocytes positively stained for the RA autoantigen HC gp-39 were expanded in numbers both in the blood as well as synovial tissues of RA patients. And these HC gp-39 positive cells were further characterized to have low CD14 expression, matching the non-classical monocytes [86]. In that study, the expression of HC gp-39 in the synovial lining of RA patients correlated with joint destruction. This would suggest that the non-classical subset might be promoting disease pathology. Conversely, another more recent study indicated that it was the intermediate subset that was expanded in RA [20]. It is currently unclear whether the difference reported in the two studies is attributed to the demographics of the patient groups with respect to disease duration, disease severity and forms of treatments.

Proinflammatory cytokines, particularly TNF-α and IL-1β, play important role in promoting joint destruction in RA [90]. The blockage of TNF-α with therapeutic antibody in RA patients could delay disease progression and alleviate disease severity [91]. Unfortunately, there is currently no study correlating the levels of serum pro-inflammatory cytokines in RA patients with active disease and in remission with the proportion of monocyte subsets. In addition, which monocyte subset is the main producer of the pro-inflammatory in RA patients is still an open question. In terms of surface marker, CD64 expression was observed to be up-regulated on all the three monocyte subsets in RA patients compared to healthy controls [20]. In SLE and lupus nephritis patients, the increase in CD64 expression on monocytes parallels systemic inflammation and renal disease in these SLE patients [92]. This could be due to the activation of monocytes by immune complexes and/or proinflammatory mediators. A similar scenario could be happening in RA patients.

The two main forms of human inflammatory bowel diseases (IBD) are Crohn’s disease (CD) and ulcerative colitis (UC), and the expansion of CD16+ monocytes had been reported in both forms of IBD [87, 88]. In CD, it is during active disease that the monocyte subsets perturbation was observed [87, 89]. In the study by Grip et al., it was the intermediate subset that was specifically expanded in active CD patients [89]. Interestingly, these expanded intermediate subset from CD patients had CCR2 expression levels similar to those of the classical subset and therefore significantly higher than the intermediate population in healthy controls. High levels of CCL2 could be detected within the colon of IBD patients and may attract CCR2-expressing monocytes into the inflamed tissues. CCL2 was also found to correlate with disease severity [93]. An increase in CD16-expressing monocytes could be observed within the non-inflamed regions of the mucosal tissues of CD patients, and the numbers were further enhanced in active inflamed tissue samples [87]. These colonic CD16+ cells also expressed CD36 and were more highly stained for TNF-α than the colonic CD16-monocytes. Unfortunately, it was not determined whether these colonic CD16-expressing monocytes were the intermediate or the non-classical subset [87]. Hence, we still do not know which subset within the CD16+ monocytes is the actual producer of the TNF-α.

So far, there is only one report indicating an expansion in CD16+ monocytes in active UC patients not receiving treatment [88]. Based on the way the CD16+ monocytes were gated, they were predominantly the intermediate population but more detailed studies looking at the three monocyte subsets in the future will better verify whether the intermediate subset is indeed the monocyte subset preferentially expanded in UC.

In both types of IBD, high levels of TNF-α have been associated with disease progression [94, 95] and alleviation of disease symptoms could be achieved either by leukocytapheresis or anti-TNF treatment [96, 97]. In line with this observation, numerous studies reported the specific reduction or removal of CD16+ monocytes in IBD patients upon leukocytapheresis [88, 98, 99]. As the process of leukocytapheresis is nonspecific, both intermediate and non-classical subsets will be depleted. Hence, it will be important to determine which subset is the main producer of TNF-α and provides more targeted therapy.

In two other autoimmune diseases: Eales’ disease and psoriatic arthritis (PsA), it was the intermediate monocyte subset that was expanded [100]. While the CD16+ monocytes from Eales’ patients clearly produced more TNF-α, IL-6 and IFN-γ, there are no data to indicate that the intermediate monocytes were the major producers of these pro-inflammatory cytokines [100]. Again, this will be an important point to be addressed since the systemic levels of these inflammatory markers correlated with increase severity of retinal inflammation [101, 102, 103]. In patients with PsA, the CD16+ monocytes exhibit a preferential ability to differentiate into osteoclasts (OC), the cell type that mediates bone erosion. In healthy individuals, it is the classical monocytes that harbor the highest propensity to differentiate into OC [104]. This led to the suggestion that the CD16+ cells in PsA patients may have derived from classical subset since they exhibit similar property, in this case the ability to become OCs [104]. It is not known what induces CD16 expression on the classical but M-CSF and/or RANKL are likely candidates in this disease setting since these factors together in vitro can induce CD16 expression on monocytes [104].

Perturbation of monocyte subsets in inflammatory disease

There are numerous inflammatory diseases where monocyte subsets perturbation had been observed, which we will briefly discuss several of these diseases where analyses were performed on three subsets.

Increase in monocyte numbers has been reported in patients with acute stroke, and the increase occurs early after stroke onset [105, 106, 107]. A study by Urra et al. [108] found an increase in the frequency of intermediate subset that peaked at day 2 after stroke. This was mirrored by a significant decrease in the frequency of the non-classical subset at the same time. While the non-classical subset was found to be inversely correlated to poor outcome and infraction size, the intermediate subset showed a strong inverse correlation with mortality and the expansion of this subset was thus deemed to be beneficial to stroke patients.

Patients with chronic kidney diseases (CKD) are at higher risk of suffering from atherosclerosis and subsequent cardiovascular disease mortality [109, 110, 111]. And these patients also have an increase in their intermediate monocyte subset [112]. It was observed that the intermediate subset numbers correlated with risk factor of cardiovascular events as well as mortality [113]. The correlation was further enhanced if the expanded intermediate subset within the circulation also had a higher expression of angiotensin-converting enzyme (ACE), also known as CD143 [112, 114]. Expression of ACE was observed to be significantly higher on the intermediate monocytes of patients with severe atherosclerosis than those with little or no atherosclerosis [115]. In line with the findings that intermediate subset may be contributing toward disease severity in CKD, a study on end stage renal disease patients on hemodialysis treatment showed that the extent of which the intermediate subset is depleted by dialysis correlated with cardiovascular event-free survival in these patients [116]. At present, it is not completely clear as to how the intermediate subset with the high expression of ACE promotes cardiovascular events in CKD patients. Hence, a better understanding of the function of the intermediate subset in terms of its migration and adhesion property to endothelium, and the types of cytokines they produced may shed light on their involvement and aid in potential therapy. Somehow for both CKD and coronary artery disease (CAD) patients undergoing statins treatment, the non-classical monocyte subset was observed to increase the proportion rather than the intermediate subset [113, 117]. As no correlation with cardiovascular event was done in either of these studies, the significance of the preferential increase in non-classical monocyte numbers is currently unclear and renders further investigation.

Asthma is another form of condition driven by inflammation and where an expansion in the CD16+ monocytes was reported [118, 119]. Moniuszko et al. [120] determined that the expansion was specifically in the intermediate subset. Although an increase in numbers were observed for mild, moderate-to-severe asthmatics compared to healthy controls, the difference was significant only in the severe asthmatic group. For asthmatics given glucocorticoid (GC) treatment, both intermediate and non-classical subset numbers decreased but the decrease was more pronounced in the non-classical subset. In addition, intermediate monocytes from severe asthmatics patients had higher CCR4 and IL-4 receptor expression as opposed to non-classical subset [120]. This subset may thus be important in regulating immune responses since the ligands for CCR4, TARC and MIP-1 as well as IL-4 are found at significantly higher levels in atopic asthmatics [121].

Aseptic loosening (AL) involves local inflammation within the affected joint, and the appearance of the loosening periprosthetic tissue is thought to share similar histological characteristics of RA. Analysis of a group of 43 patients with AL showed that the proportion of non-classical subset was significantly elevated while the frequencies of the classical and intermediate subsets were not significantly changed [122]. In this inflammatory condition, the non-classical subset positively correlated with serum TNF-α and IL-1β, suggesting that this subset may be involved in promoting the inflammation. This is in line with reports of non-classical subset being the main producer of these pro-inflammatory cytokines [15, 19].

Disease associations of slan+ and Tie-2+ monocytes

SlanDCs were strong inducers of Th1 responses and are present in high frequency of diseased tissues associated with strong Th1 responses, including psoriasis, RA [35], tonsillitis and CD [38]. The expansion of slanDCs can also be indirectly inferred during periodontitis [123], where an expansion of CD45RA-expressing monocytes, a marker highly expressed by slanDCs [35], was observed. Notably, all these diseases are Th1 type inflammatory disorders, consistent with the Th1 pro-inflammatory properties of slanDCs in vitro [35, 36]. Interestingly, Tie-2+ monocytes were elevated in chronic HCV [60]. The presence of high percentages of Tie-2+ monocytes was related to poor response to antiviral therapy, whereas the percentages of Tie-2−CD16+ monocytes associated with decreased viral load after anti-viral therapy [60]. This suggests that Tie-2+ and Tie-2−CD16+ differentially contribute to the responses toward chronic HCV. While Tie-2+ monocytes are not particularly suited for pro-inflammatory responses, they are associated with strong angiogenic properties. In contrast, slan+ monocytes are associated with pro-inflammatory activities.

Gaps in our understanding of monocyte subsets in disease

Within the CD16+ monocytes, it appears that the intermediate subset is the main population to be perturbed in almost all of the disease conditions discussed in this review (Table 1). We also noticed that in infection, be it bacterial or viral, most studies observed a concurrent expansion of both the intermediate and non-classical subsets. In the few studies on autoimmune disease, all reported expansion only in the intermediate subset. And for other inflammatory conditions excluding autoimmune disease, the expansion was either the intermediate or the non-classical subset.

First and foremost, more studies and perhaps with larger patient cohorts are definitively needed in order to corroborate the current results for the various diseases since only one, at most two studies were performed for each of the disease conditions thus far. We also find difficulty in comparing the results for different diseases and even the same disease from two independent studies. This is mainly due to several reasons: the first being the description of the subsets was not consistent and sometimes not lucid; the second being the difference in gating strategy to segregate the subsets; and the third being the composition of the patient cohorts with respect to treatment, stage of the disease, survival status, etc. Moving on, we would strongly recommend that the subsets nomenclature and the gating strategy as put forth in the paper by the nomenclature committee be implemented for studies on three monocyte subsets [14].

Other questions where there are currently no definitive answers are the origin of the expanded subset, is their expansion a cause or consequence of the disease, and the role they play in disease condition. The increase in proportion due to enhanced survival has been proposed in the case of HIV [77]. It would be interesting to explore whether other bacteria and viruses that infect monocytes also utilize such a mechanism as this may have important implications in the persistent and dissemination of the pathogen. Another proposal for the origin of the expanded population is the up-regulation of surface CD16 on the classical subset as in the case for CD [87] and PsA [104]. To address this, an in-depth phenotypic and functional characterization of the three monocyte subsets in the various diseases based on what is reported during homeostasis [5, 15, 16] might aid in the better identification of the expanded subset in disease conditions.

To determine whether the expanded population is contributing to protection or pathogenesis and potentially be used as a biomarker, numerous studies investigated the correlation between the proportions of the expanded subset either with disease severity or mortality. In many but not diseases, the intermediate subset numbers appear to be positively associated disease severity based on clinical scores or tissue damage particularly in inflammatory diseases. However, there is currently not enough evidence to establish that the intermediate subset is the main producer of pro-inflammatory mediators as a likely explanation for its contribution toward promoting inflammation and leading to disease pathogenesis. One possibility could be due to difference between in vitro and in vivo data. Another could be the differential response of the subsets to different stimuli. Hence, the ability to identify the appropriate subset involved in pathogenesis as the selective therapeutic target may help avoid adverse events associated with indiscriminate monocyte inhibition, for example, leukocytapheresis.

In diseases where inflammation is localized to a particular tissue location such as the joints in RA, the intestines in IBD, the lungs in asthma, etc., many studies had tried to also characterize the monocyte infiltrates within the affected tissues and make comparison to monocyte perturbation observed in the periphery. Unfortunately, there is never a clear indication if the monocyte subset expanded in the blood that correlated with disease severity is the same monocyte subset that was observed within the tissues. This is mainly hampered by the lack of markers to definitively identify these monocyte subsets both in the periphery as well as within the tissue microenvironments. Again, a more detailed knowledge of the phenotypic and functional properties of the subsets would allow for better identification of the expanded population in the different microenvironments.

Conclusion

The heterogeneity of blood monocytes indicates that each subpopulation may play different roles during homeostasis and in disease conditions. Only with a better phenotypic and functional characterization of each of these subsets can one associate their respective contributions toward either disease elimination or progression.

Notes

Acknowledgments

We would like to thank the Health Science Authority (HSA), Singapore, and NUH Blood Donation Center, Singapore, for the supply of buffy coats; the staffs of the flow cytometry and microarray units in the Biopolis Shared Facilities and the Singapore Immunology Network, Agency for Science, Technology and Research, Singapore (A*STAR) for their assistance in cell sorting and sample processing. This work was supported by the Biomedical Research Council (BMRC), A*STAR, Singapore.

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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Kok Loon Wong
    • 1
  • Wei Hseun Yeap
    • 1
  • June Jing Yi Tai
    • 1
  • Siew Min Ong
    • 1
  • Truong Minh Dang
    • 1
  • Siew Cheng Wong
    • 1
  1. 1.Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR)SingaporeSingapore

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