We examined the transcriptional profiles of macrophages that reside in the islets of Langerhans of 3-wk-old non-obese diabetic (NOD), NOD.Rag1−/−, and B6.g7 mice. Islet macrophages expressed an activation signature with high expression of Tnf, Il1b, and MHC-II at both the transcript and protein levels. These features are common with barrier macrophages of the lung and gastrointestinal tract. Moreover, injection of lipopolysaccharide induced rapid inflammatory gene expression, indicating that blood stimulants are accessible to the macrophages and that these macrophages can sense them. In NOD mice, the autoimmune process imparted an increased inflammatory signature, including elevated expression of chemokines and chemokine receptors and an oxidative response. The elevated inflammatory signature indicates that the autoimmune program was active at the time of weaning. Thus, the macrophages of the islets of Langerhans are poised to mount an immune response even at steady state, while the presence of the adaptive immune system elevates their activation state.

Introduction

An emerging theme in macrophage biology is that each tissue macrophage is adapted to its local environment and provides homeostatic support (Amit et al., 2016). Although all the tissue macrophages share core gene transcripts, including the transcription factor Spi1 (PU.1) and cell surface receptors such as Mertk (Mer) and Fcgr1 (CD64), a comparison of different organs reveals substantial differences that may relate to a specific function (Gautier et al., 2012). Macrophages share a common set of enhancers, but these are differentially expressed in a tissue-specific manner, leading to their specialization (Kaikkonen et al., 2013; Lavin et al., 2014). For example, red pulp macrophages are specialized to dispose of effete red blood cells by sensing heme and inducing Spic (Haldar et al., 2014). Understanding how the phenotypic qualities of many tissue macrophages originate and are influenced by their surrounding tissues is a work in progress.

The pancreas contains distinct resident macrophages in the exocrine and endocrine regions (Calderon et al., 2015). In each anatomical site, the macrophages differ in their embryonal origin and in their activation status. In an analysis performed in C57BL/6 mice, the interacinar macrophages were found to express genes and cell surface markers that categorize them as M2-like (anti-inflammatory) and tissue supportive. In contrast, the macrophages in the islets of Langerhans expressed M1-like transcripts typically associated with inflammatory macrophages. The islet macrophage has a homeostatic role, as shown in the op/op mouse, which lacks functional CSF-1 protein. In these mice, the islets lack macrophages and develop poorly (Banaei-Bouchareb et al., 2004; Calderon et al., 2008, 2015). Also, the islet macrophages are intimately associated with intra-islet vessels and in close contact with β cells (Calderon et al., 2011). Islet macrophages take up dense core granules and can present the contents of the islet granule to islet reactive CD4+ T cells (Vomund et al., 2015).

There has been confusion in the field about the identity of tissue-resident macrophages and DCs. The main reason has been the long-standing assumption that expression of CD11c and high MHC-II were specific markers of DCs and that high F4/80 was a signature of the macrophage. In fact, CD11c and MHC-II expression are not definitive markers, many tissue-resident macrophages express CD11c and MHC-II, and F4/80 levels vary across the macrophage lineage. Recent data have identified more faithful markers for both cells. In the case of macrophages, the cell surface receptors MerTK and FcγRI are among the most reliable markers (Gautier et al., 2012). In the case of DCs, the conventional DCs are best defined by a group of genes that includes Zbtb46 and Flt3, while the plasmacytoid DC (pDC) lineage is best defined by expression of B220, PDCA, and SiglecH (Miller et al., 2012). The islet macrophages are positive for expression of canonical macrophage proteins or genes such as MerTK, FcγRI, and Sfpi1 but do not express the DC markers Zbtb46, Flt3, B220, and Siglec H (Ferris et al., 2014; Calderon et al., 2015).

Comparing the islet macrophages from nondiabetic mice with early prediabetic mice is important. The macrophages constitute the majority of the leukocytes found in islets at steady state, and they can act as a potential modulator of T cell activation in vivo. Our studies therefore focus on the 3-wk-old mouse, a time before there is overt lymphocytic infiltration of islets. Overall, the role of the macrophage in the autoimmune diabetic process is not well understood. In the non-obese diabetic (NOD) mouse, a strain that develops spontaneous diabetes, the first inflammatory transcriptional signatures that can be detected can be mapped back to myeloid cells generally and macrophages specifically (Carrero et al., 2013). At this early stage in NOD diabetogenesis, a second myeloid cell, the Batf3-dependent XCR1+ DC, enters the islets, but the relationship between the two cells is not known. The importance of myeloid cells in diabetogenesis is reinforced by the finding that the NOD.Batf3−/− mice lacking XCR1+ DCs never develop diabetes (Ferris et al., 2014).

Islet macrophages from two strains of nondiabetic mice were in an activated state comparable with other barrier macrophages, such as those found within the lung and the intestine. They expressed high MHC-II, TNF, and IL-1β, all at the transcript and protein level, as well as a host of other inflammatory mediators and sensors. Importantly, the islet macrophages are sampling blood contents, as there was increased expression of inflammatory transcripts after treatment with intravenous LPS. In the NOD mouse, the islet macrophage was sensitive to the presence of an adaptive immune system, having an increased expression of inflammatory transcripts. Interferon-inducible gene signatures were identified in macrophages from 3-wk-old NOD mice, clearly indicating that the diabetic process was already under way very early in the life of the mouse. Finally, in a deeper analysis of the islet macrophages, a quarter of the NOD macrophages had increased inflammatory transcripts, higher than those found in the basal state.

Results

Core features of the NOD, NOD.Rag1−/−, and B6.g7 islet-resident macrophages

We isolated 3-wk-old islet macrophages (CD45+F4/80+MHC-II+) using the gating strategy shown in Fig. S1 A. Note that 85–95% of the CD45+ cells in the islets of the 3-wk-old NOD mice are macrophages; the remaining cells are mostly CD3ε+ T cells (see also Melli et al., 2009). (Previous work showed that islets at 3 wk or earlier contained B cells, neutrophils, and pDCs [Diana et al., 2013; Diana and Lehuen, 2014]. However, our experimental analysis did not reproduce these findings [Figs. S2 and S3].)

We compared macrophages isolated from NOD, NOD.Rag1−/−, and B6.g7 mice pancreatic islets for their gene expression profiles by RNA sequencing (RNASeq) and cell surface marker expression. Examination of the NOD.Rag1−/− and B6.g7 mice established their basal features independent of autoimmune inflammation. All mice used in this study were housed in an advanced pathogen-free barrier facility, developed normally, and did not have any indication of infections.

We also included lung macrophages and pancreatic LN (PLN) XCR1+ DCs (CD45+CD11c+MHC-II+CD172anegCD11bnegXCR1+) for additional comparisons (Fig. S1, B and C). A preliminary analysis of the islet macrophage data using the Immunological Genome Project (ImmGen) My GeneSet tool showed that the lung and intestinal macrophages most closely resembled those of the islet (Heng et al., 2008). We chose the lung macrophage to determine both common and unique gene expression signatures of the islet macrophage when compared with other tissue macrophages. (There are two annotated lung macrophages in ImmGen: the “lung CD11b+ macrophage” that is CD11bhiCD11c+MHC-II+CD103negCD24neg and the “lung macrophage” that is CD11c+MHC-IInegCD11bnegCD103negSiglecF+. Our preliminary analysis showed high similarity between the islet macrophage and the “lung CD11b+ macrophage,” leading us to examine that population.) The XCR1+ DCs represent a different phagocyte lineage that is highly relevant for the initiation of diabetes (Ferris et al., 2014). The absence/paucity of these DCs in islets at the 3-wk window made it difficult to analyze them, so instead we isolated them from the PLN on the basis of their expression of XCR1. This combination of tissues and cell types allowed us to interrogate several questions about the transcriptional profile and biology of the islet macrophage.

The top expressed transcripts in 3-wk-old islet macrophages showed similar values (Fig. 1, A–C). Housekeeping genes, such as transcriptional regulators and cell structural genes (i.e., Actb, Fosb, and Hspa5), were comparably expressed (Fig. 1 A and Table S1). Most canonical myeloid genes were comparably transcribed in both islet macrophages and in PLN DCs (Fig. 1 B; Gautier et al., 2012). Included in this group were the MHC-II antigen presentation genes H2-Aa, H2-Ab1, H2-Eb1, and Cd74. The islet macrophages do not initially express uniformly high MHC-II on their surface (Calderon et al., 2015). At 2 wk of age, they have varying levels of MHC-II on their surface. However, by 4 wk of age, MHC-II expression becomes uniformly high (Fig. S1 D). Unlike DCs, high expression of MHC-II genes is not a basal condition in all tissue-resident macrophages. Microglia, the large peritoneal macrophages, and alveolar macrophages express low levels of MHC-II genes and the master regulator Ciita (Fig. S1 E shows data from ImmGen).

Three additional classes of genes integral to the initiation of an adaptive immune response were expressed highly in islet macrophages: (1) costimulatory molecules, (2) pattern recognition molecules (TLRs), and (3) chemokines (Fig. 1, D–G). (1) The most notable costimulatory genes were the activating ligands Cd86 and Pvr (CD155) and the inhibitory ligands Lgals9 (Galectin-9) and Cd274 (PD-L1; Fig. 1 D). (2) Most TLR genes and adaptor proteins were expressed at high levels, including Tlr1, Tlr2, Tlr3, Tlr4, Tlr6, Tlr7, Tlr8, Tlr9, Tlr12, and Tlr13, allowing the islet macrophages to sense different microbial patterns (Fig. 1 E; Kawai and Akira, 2011; Oldenburg et al., 2012; Koblansky et al., 2013). In contrast, the XCR1+ DCs had reduced or absent expression of most of the TLRs, except Tlr3, Tlr9, and Tlr11. (3) Chemokine expression consisted of genes capable of attracting myeloid cells (Ccl3, Cxcl14), macrophages (Ccl9, Cxcl14), neutrophils (Cxcl1, Cxcl2), NK cells (Ccl3), NKT cells (Cxcl16), T cells (Ccl4, Cxcl9, Cxcl10, Cxcl16) and regulatory T cells (Cxcl9; Fig. 1 F; Matloubian et al., 2000; Kurth et al., 2001; Dufour et al., 2002; Zhao et al., 2003; Castellino et al., 2006; Yamada et al., 2012; Griffith et al., 2014). Finally, islet macrophages expressed chemokine receptors found in inflammatory leukocytes (Ccr2, Ccr5), developing leukocytes (Cxcr4), barrier macrophages (Ccr2, Ccr5, Ccrl2, Cx3cr1, Cxcr4), and microglia (Cx3cr1) but whose combined pattern of expression most resembled barrier macrophages (Fig. 1 G; Gautier et al., 2012; Griffith et al., 2014).

Several myeloid genes were differentially expressed in the islet macrophages compared with the PLN DCs. Among these were Atf3, Rasgef1b, and Rgs1 (Fig. 1 B). Atf3 is downstream of TLR4 signaling and acts as a negative regulator of cytokine transcript expression after LPS treatment (Gilchrist et al., 2006). Rasgef1b encodes a guanine-nucleotide exchange factor induced by TLR signaling and believed to activate Ras-like proteins (Andrade et al., 2010). Rgs1 encodes a regulator of G-protein-coupled signaling that antagonizes chemokine receptor signaling (Reif and Cyster, 2000; Moratz et al., 2004). Genome-wide association studies have linked Rgs1 to celiac disease and type 1 diabetes, but its exact function in autoimmunity is poorly defined (Smyth et al., 2008). All three of these genes (Atf3, Rasgef1b, and Rgs1) are also highly expressed in lung and intestinal macrophage (Heng et al., 2008).

For validation purposes, we identified several cell surface receptors using the Panther classification system (Mi et al., 2013, 2016). In this way, CD44, CD53, CD83, and TLR-7 were identified and validated by flow cytometry, as shown in Fig. S1 F.

NOD islet macrophages expressed higher levels of inflammatory transcripts than NOD.Rag1−/− at 3 wk of age

We compared the gene expression profiles of the NOD, NOD.Rag1−/−, and B6.g7 islet macrophages, the CD11b+ lung macrophage, and the XCR1+ DC. Principal component analysis (PCA), based on the top 5% of gene expression variance, clustered the islet macrophages tightly along the first and second components and related them more closely to the CD11b+ lung macrophage than to the PLN XCR1+ DCs (Fig. 2 A). Pearson correlation and hierarchical clustering reinforced this point, demonstrating the similarity between NOD, NOD.Rag1−/−, and B6.g7 macrophages (Fig. 2, B and C).

The NOD macrophage at 3 wk contained an activation signature when compared with the NOD.Rag1−/− macrophage. Using DESeq2, we found 542 genes differentially expressed at twofold counts (adjusted P ≤ 0.05; Fig. 2, D and E). Of these, 314 were up-regulated in NOD and 228 were up-regulated in NOD.Rag1−/− (Fig. 2 D).

By hypergeometric analysis, the major Gene Ontology (GO) pathways enriched in NOD-up-regulated genes mapped to signal transduction, cellular biosynthetic process, inflammatory response, response to external stimulus, and defense response (Fig. 2 F). The top C2 pathways (curated gene sets) enriched in NOD macrophages mapped to KEGG oxidative phosphorylation, KEGG Huntington’s disease, Naba matrisome associated, and KEGG cytokine/cytokine receptor interaction (Fig. 2 F; Kanehisa and Goto, 2000; Mootha et al., 2003).

We performed gene set enrichment analysis (GSEA) using Broad Institute software (Subramanian et al., 2005), and this revealed a correlation of the NOD-up-regulated genes with the GO MSigDB biological process gene signatures that associate with oxidative phosphorylation, inflammation, cellular response to reactive oxygen species (ROS), and cell proliferation (Fig. 2 G; P < 0.002 and false discovery rate [FDR] q < 0.05: response to oxidative stress [GO term: 0006979], behavior [GO term: 0007610], and inflammatory response [GO term: 0006954]).

These findings show that the NOD macrophages are transcriptionally and metabolically more active than their NOD.Rag1−/− counterparts. First, the behavior and inflammatory response grouping contained NOD-up-regulated genes involved in chemotaxis, including the chemokine receptors Ccr1 and Ccr7 as well as the chemokines Ccl5, Cxcl2, and Cxcl9 (Fig. 2, H and I). Second, the islet macrophages displayed an up-regulation of interferon-inducible genes, including Cxcl9 and Ccl5, along with early innate immune mediators such as Cd40 (Fig. 2, H and I). A majority of these genes are associated with pathogen-associated molecular pattern signaling and are discussed in greater detail in a later section. Third, the response to oxidative stress signature contained up-regulated genes encoding proteins with an antioxidant role in cells (Fig. 2 J). For example, Sod1 encodes superoxide dismutase 1, one of two enzymes responsible for converting superoxide radicals to molecular oxygen and hydrogen peroxide (Fridovich, 1995). Prdx2 and Prdx5 encode peroxiredoxin 2 and peroxiredoxin 5 antioxidant enzymes, which reduce hydrogen peroxide and alkyl hydroperoxides (Wood et al., 2003). Finally, the oxidative phosphorylation gene signature contained genes whose products are components of the four major complexes of the mitochondrial electron transport chain as well as the ATP synthase complex (Fig. 2 K). Combined, these findings point to an increase in the oxidative activity of the NOD macrophages compared with those of the NOD.Rag1−/− macrophages.

NOD macrophages at 3 wk of age already display transcriptional signatures of diabetes progression

We examined whether transcripts found during diabetes progression might be up-regulated in the 3-wk-old NOD macrophage (Fig. 3 A). We did this by comparing the islet macrophage transcriptional signature to our previous chronological analysis of whole islets in NOD diabetes (Carrero et al., 2013). Our previous dataset contains the whole-islet transcriptional analysis of type 1 diabetes progression in the NOD mouse from 2 wk of age until the onset of diabetes (∼20–30 wk). This dataset also included control samples from mice that never developed diabetes: B6.g7, C57BL/6, and NOD.Rag1−/−. We calculated Pearson’s correlation of whole-islet transcriptomes on the basis of the transcripts differentially up-regulated in NOD macrophages (the 314 genes described in Fig. 2, D and E; adjusted P ≤ 0.05 and maximum likelihood estimate–adjusted fold change ≥2). Those same 314 genes were used to calculate Pearson’s correlation between whole-islet transcriptomes that ranged between 2 wk of age and diabetic onset. We found a gradually increasing and positive correlation from 2 wk to diabetic onset (Fig. 3 A and Fig. S4 A).

The up-regulated transcripts parsed into two clusters that showed significant enrichment in the MSigDB Canonical (C2) and GO biological pathways (C5BP) databases (Fig. 3 B). Cluster I included genes that are progressively up-regulated during NOD diabetes (e.g., Cxcl9, S100a4, and Ptgs2). These were enriched by hypergeometric test for inflammatory pathways, namely, defense response, immune system process, and cytokine/cytokine receptor interaction (Fig. 3 C and Fig. S4 B). Cluster II, in contrast, consisted of genes enriched in developmental pathways, such as cellular biosynthetic process, translation, and oxidative phosphorylation. These genes were up-regulated in the islets of 2-wk-old mice and recently diabetic NOD mice (Fig. 3, B and D; and Fig. S4 C). These transcripts indicate increased cellular activity, likely accountable by some level of replication.

Inflammatory transcripts in the islet macrophage are the hallmark features of a barrier macrophage

GSEA of the genes differentially expressed between NOD and NOD.Rag1−/− (Fig. 2 G) revealed a correlation with gene signatures identified in cultured bone marrow–derived macrophages treated with LPS (dataset: GSE14769 samples, unstimulated vs. 40-min LPS bone marrow–derived macrophages dominant-negative (Litvak et al., 2009). Similar findings were made interrogating datasets from macrophages treated with different inflammatory stimuli. In total, we found a significant enrichment score for genes expressed after LPS, TNF, and IL-1 treatment of macrophages (Fig. 4 A).

When further compared with ImmGen datasets, the macrophages that displayed genes up-regulated in an LPS signature were all barrier macrophages and exposed to either intestinal or airborne stimuli. This association was revealed both by hierarchical clustering (Fig. 4 B) and by correlation analysis. Both techniques grouped the intestinal and CD11b+ lung macrophages together and showed that barrier macrophages as a group had a higher enrichment score for LPS signatures than the nonbarrier macrophages (Fig. S4 D).

Gene signatures induced by LPS, TNF, and IL-1 were found highest in the CD11b+ lung macrophage, NOD, NOD.Rag1−/−, and B6.g7 islet macrophages but not in the bone marrow macrophage (Fig. 4, C and D). This indicated that at steady state, the islet macrophages were in an activated state comparable with those at a barrier surface.

The similarities between the islet-resident macrophage and barrier macrophages led us to compare our own gene expression profiles between the islet and the CD11b+ lung macrophage to identify islet macrophage–enriched signatures. The top statistically significant canonical signature by GSEA that distinguished the islet macrophage from the CD11b+ lung macrophage was lysosome (FDR < 1.0E-5, P < 1.0E-5; Fig. 4, E and F). The lysosome signature included genes encoding eight cathepsins (A/B/C/D/H/O/S/Z), Laptm4b, Laptm4a (lysosomal protein transmembrane 4 alpha and beta), and Lgmn (legumain cysteine protease). This gene signature suggested a higher lysosomal activity in the islet macrophages than in the CD11b+ lung macrophages (Fig. 4 F; Ewald et al., 2011). The lysosomal gene signature was found in all islet datasets, indicating that heightened lysosomal function was a steady-state feature of islet macrophages.

Inflammatory mediator production by the islet macrophages

Intracellular flow cytometry showed production of IL-1β and TNF, confirming the RNASeq analysis (Fig. 5 A). About half of the NOD.Rag1−/− islet macrophages were producing detectable levels of TNF and IL-1β without any stimulation. Activation with PMA and ionomycin led to limited increases in TNF and IL-1β proteins, revealing that they were at or near maximal production (Fig. 5, A and B). For comparison, the CD11b+ lung macrophage produced almost no detectable TNF but equivalent IL-1β (Fig. 5, A and B). Even when pulsed with PMA/ionomycin, CD11b+ lung macrophage TNF cytokine production never reached that of steady-state islet macrophage levels (Fig. 5 B). Their production of IL-1β protein was equivalent to the islet macrophages and did not increase when pulsed with PMA/ionomycin (Fig. 5, A and B).

Activation of IL-1β from its pro form to its active form requires processing by an active inflammasome-family caspase (Dinarello, 2009). Islet macrophages from NOD and NOD.Rag1−/− expressed a detectable level of active caspase-1 when examined with the fluorescent form of the caspase substrate YVAD. The CD45-negative fraction (i.e., the endocrine cells) was negative (Fig. 6 A).

The data indicate that the islet macrophages are making some level of both active IL-1β and TNF. Because both receptors converge on the NF-κB pathway, we analyzed our transcriptional data, searching for NF-κB family members expressed in the whole islet. The only members of the transcription factors and coactivators contained in the NF-κB and Rel family detectable above background were Nfkb1 (p105/p50) and Rela (p65; Carrero et al., 2013). Therefore, we examined the nuclear localization of the NF-κB component RelA/p65 (Rütti et al., 2016). Staining for RelA/p65 revealed a predominantly cytosolic pattern of staining in NOD islets at 6 wk of age (Fig. 6 B, top). As a positive control, islet cells treated with IL-1β induced nuclear localization (Fig. 6 B, bottom). Therefore, despite the expression of IL-1β and TNF by islet macrophages, there was limited detectable nuclear NF-κB in the endocrine pancreas. These data indicate that despite the levels of cytokine being made by the islet macrophages, the β cells are not strongly signaling.

Islet macrophages respond to blood-borne stimuli

A blood-borne stimulus might be able to induce part of activation profile in the macrophage, as suggested by the expression of Tnf, Il1a, and Il1b transcripts. This idea is supported by our previous finding that the islet macrophage extended filopodia into the lumen of the blood vessel and could interact with blood-borne particles (Calderon et al., 2011).

To determine if a blood-borne TLR-agonist could stimulate the islet macrophage, we injected LPS at low concentrations that would not induce systemic effects. LPS was injected intravenously into 3-wk-old NOD and NOD.Rag1−/− mice. Islets were isolated 3 h later, and the islet macrophage was sorted using the strategy outlined in Fig. S1. Transcripts associated with signaling of TLR4 were assayed using quantitative RT-PCR (qRT-PCR). The transcripts for Cxcl9, Ccl5, Il12b, Nos2, and Il6 were significantly up-regulated after LPS injection of NOD.Rag1−/− mice when compared with control PBS-injected mice (Fig. 6 C). Cxcl9, Ccl5, and Il12b were up-regulated in the NOD mice. Notably, the NOD mice displayed higher baseline expression than the NOD.Rag1−/− mice for all markers examined. This was not surprising, as some of these markers were the most differentially expressed between NOD and NOD.Rag1−/− mice by RNASeq (Fig. 2 D). In sum, the islet macrophage is accessible and reacting to circulating LPS, a strong representative pathogen-associated molecular pattern.

Single-cell analysis of islet macrophages reveals heterogeneity in the NOD but not the NOD.Rag1−/− mice

We examined if the inflammatory gene expression differences between NOD and NOD.Rag1−/− islets was due to homogeneous or heterogeneous autoimmune activation of the NOD macrophages. To interrogate transcriptional heterogeneity at the cellular level, we examined individual macrophages from NOD and NOD.Rag1−/− islets. Macrophages were sorted directly into RNA lysis buffer, cDNA was synthesized and preamplified, and qRT-PCR was performed for 95 different markers using the Fluidigm Biomark HD system. The starting cell inputs were 184 NOD and 95 NOD.Rag1−/− macrophages. After outlier detection using Singular analysis software, 143 NOD and 79 NOD.Rag1−/− macrophages were kept for further analysis.

By PCA, the majority of macrophages from both mice clustered together, except for a subset in the NOD samples represented by 42 of 184 (22.8%; outlined in Fig. 7 A). The combination of ANOVA and fold change analysis was performed on the normalized expression values (log2ex) of the single macrophage PCR data (Fig. 7 B). We identified several genes previously shown to be differentially expressed between NOD and NOD.Rag1−/− samples by RNASeq, including Ccl5, Cxcl9, Ly6a, Cd38, and Dnase1l3. However, Cxcl9 was the most significant differentially expressed gene. All of the cells contained in the NOD outlier population expressed high levels of Cxcl9. The population level macrophage RNASeq data showed that Cxcl9 was the most differentially expressed gene between the NOD and NOD.Rag1−/− samples, and the expression of this chemokine was detected only in the NOD outlier population (Fig. 2 Dand7 A and Table S1). In total, 26 of 95 genes interrogated by single cell qRT-PCR were differentially expressed between NOD and NOD.Rag1−/− samples at twofold change and P < 0.01. Selection of these genes revealed a cluster enriched in only NOD but not NOD.Rag1−/− samples (Fig. S5 A, left side of the heat map; for the heat map of the entire dataset, see Fig. S5 B).

In brief, the islet-resident macrophages in the NOD.Rag1−/− mouse were homogeneous. In the NOD mouse at 3 wk, there was heterogeneity in their activation state. There was a major set identical to the NOD.Rag1−/− macrophage but also a second that expressed the same up-regulated transcripts revealed by the RNASeq analysis.

Discussion

We made three key findings that place the resident macrophage in a unique position to regulate islet function. First, the islet macrophages are in an activated state comparable with macrophages exposed at barrier surfaces. This result leads to the obvious question: why are these macrophages in such a state? The islet is a mini-organ that is constantly responding to several stimuli that vary and oscillate, such as blood glucose, the most prevalent bioactive molecule. We have reported that the macrophages capture dense core granules and therefore must be exposed to the products of the β cell (Vomund et al., 2015). We also showed here how blood products, in our example a low dose of LPS, were accessible to the islet macrophage. In a previous paper, we indicated that the islet macrophages extended filopodia into the blood vessel lumen, which could be the interacting structure with the circulation (Calderon et al., 2011). In essence, the islet macrophages may be activated by intra-islet components as well as by blood products.

A second finding, as mentioned above, is the capacity of the islet macrophages to sense blood-borne stimuli. This finding opens the question of the biological significance of such sensing as well as on the repertoire of stimulants, an issue that needs critical evaluation. One needs to keep in mind that the Tlr2−/−, Tlr9−/−, and Myd88−/− mice have reduced incidences of diabetes (Kim et al., 2007; Wen et al., 2008; Tai et al., 2016). It is tempting to postulate that during weaning, profound changes occur in the islet macrophage, as was evident by MHC-II expression. This occurs at a time when the islet is subjected to more demands for growth as well as to a new intestinal flora. It is possible that the islet macrophage senses systemic inflammation, as suggested by the modulation of type 1 diabetes by gut flora and systemic infections (Wen et al., 2008; Christen and von Herrath, 2011; Markle et al., 2013; Yurkovetskiy et al., 2015). We speculate that through this sensory function, the macrophages play a protective role for the islets against blood-borne pathogens. Finally, one component that may contribute to NOD macrophage activation compared with NOD.Rag1−/− mice is the presence of antibodies, particularly those derived from the mother. Evidence in favor of this is that the wild-type heterozygote pups born from B cell–deficient mice (µMT) display decreased incidence and penetrance of diabetes (Greeley et al., 2002).

The third finding is that there are genes characteristic of diabetes progression being expressed in 3-wk-old islet macrophages. This implies that even without overt lymphocyte infiltration of the islet or destruction of β cells, the adaptive immune system is enforcing a degree of activation on the islet macrophage. Thus, superimposed on the basal activation state, the diabetes susceptible traits impose a further activation program that is likely to be critical for the development and progression of the disease. The single-cell analysis of the islet-resident macrophages showed that only a subset of the NOD macrophages were more inflamed than those of the NOD.Rag1−/−. We cannot conclusively state which signal/cell type is responsible for the activation of the macrophage in a young NOD mouse, but the presence of an interferon signature points to an activated diabetogenic T cell acting on the islets at these early times. We have found a few T cells in islets at this early time period (Carrero et al., 2013). Also, up-regulation of the oxidative phosphorylation in NOD macrophages, as well as self-defense antioxidant enzyme expression (response to oxidative stress, GO term 0006979), suggests antimicrobial generation of ROS. Higher ROS synthesis in NOD macrophages compared with NOD.Rag1−/− might be a result of synergistic action of adaptive and innate immune response (T cell and macrophage activation) as opposed to only an innate response in NOD.Rag1−/−. During diabetogenesis, it is the lymphocytic infiltrate that is most likely responsible for the cell division and cellular activity signature in whole islets. Activated macrophages are known to express genes responsible not only for cytokine and chemokine production but also for cell cycle progression and proliferation (Buxadé et al., 2012).

Because NOD macrophage autoimmune activation is dependent on the presence of the adaptive immune system, it is likely that there is heterogeneity in immune components within a single islet or among them, with some affected and others not. Resolving this will require isolating single macrophages from individual hand-picked islets, a difficult task given current technologies. However, this will be an important issue to tackle, as it will instruct us as to the nature of the activation of the earliest sensors of autoimmune diabetes, the macrophage, as well as the distribution of islet involvement in the autoimmune process. Thus, intervention and identification of those who will progress to diabetes must be done much earlier than diagnosis to intervene and prevent diabetes.

The islet macrophages express IL-1β and TNF at the steady state. Expression of IL-1β and TNF (and other cytokines) is a complex process (Aggarwal, 2003; Dinarello, 2009; Pasparakis, 2009). It involves several transcriptional regulators that modulate the level and time of expression of the gene (Foster et al., 2007; Amit et al., 2009; Yosef and Regev, 2011, 2016; Kotas and Medzhitov, 2015). This regulation is of particular relevance. As noted earlier, Atf3 is expressed by the macrophages, indicating a degree of regulation of TLR signaling. Regarding IL-1β, we find an intermediate expression of active caspase-1 but failed to detect RelA by nuclear staining in the β cells. However, we may well be missing a low level of activation and are guarded in concluding a lack of IL-1β signaling in the β cell. Indeed, it is still likely that there is a level of homeostatic role for IL-1R signaling in endocrine function. For example, the p85α component of the PI3 kinase is activated by IL-1R signaling (Reddy et al., 1997). Deficiency of p85α leads to reduced insulin secretion after glucose challenge (Terauchi et al., 1999). Also, IL-1β treatment of islets instigates down-regulation of the glucose transporter GLUT2 and glucokinase (Park et al., 1999). Finally, IL-1R1-deficient mice develop late-onset obesity (García et al., 2006). In sum, IL-1 production by the islet macrophage may be one component in its general role as a modulator of glucose homeostasis influencing the pancreas, fat depots, and central nervous system (del Rey et al., 2006; Dror et al., 2017). Concerning its role in the autoimmune process, much has been discussed on IL-1’s role in diabetes initiation, largely on the basis of the harmful effects of IL-1 on long-term cultures of β cells (Mandrup-Poulsen et al., 2010). However, a role in diabetes progression in the NOD mouse model is not evident. Genetic ablation of caspase-1 does not alter diabetes development (Schott et al., 2004). IL-1R deficiency delays progression to diabetes, albeit modestly (Thomas et al., 2004). As for TNF, its effects are pleiotropic but are also under strict control. TNF can affect not only β cells but also the vascular endothelium and T cells. In contrast to IL-1β, TNF has complex effects on diabetes development, most likely on the effector stage of the disease by modifying not only β cells but also T cells (Yang et al., 1994; Kägi et al., 1999; Pakala et al., 1999; Wu et al., 2002; Chee et al., 2011).

The data indicate that the islet macrophage is subjected to stimuli derived from β cells as well as by blood products (Calderon et al., 2011; Vomund et al., 2015). Therefore, this leads to a transcriptional program that is complex, representing the diverse stimuli, and these responses need to be integrated. In the absence of macrophages as in the op/op mouse the development of islets is impaired (Banaei-Bouchareb et al., 2004). Studies are now under way in our laboratory to determine the activation state of islet macrophages derived from different strains and ages of mice and under different biological conditions. The islet macrophages of all strains examined contain macrophages with core features similar to NOD.Rag1−/−. In contrast, the signature of diabetic inflammation that includes Cxcl9 and other interferon-inducible genes has been found only in NOD, not in any of the control strains.

Materials and methods

Mice

NOD/ShiLtJ (NOD), NOD.129S7(B6)-Rag1tm1Mom/J (NOD.Rag1−/−), and B6.NOD-(D17Mit21-D17Mit10)/LtJ (B6.g7) mice originally obtained from The Jackson Laboratory were bred and maintained under specific pathogen-free conditions in our animal facility. All experiments were approved by the Division of Comparative Medicine of Washington University School of Medicine in St. Louis (Association for Assessment and Accreditation of Laboratory Animal Care, accreditation number A3381-01).

Antibodies

The following antibodies were purchased from BioLegend: FITC/PE/PerCP-Cy5.5 anti-B220 (RA3-6B2), PE-Cy7 anti-CD11b (M1/70), PE/APC-Cy7 anti-CD11c (N418), PE/APC anti-CD274 (10F.9G2), PE-Cy7 anti-CD4 (RM4-5), APC-Cy7 anti-CD44 (IM7), BV510/APC anti-CD45 (30-F11), FITC anti-CD53 (OX-79), FITC/PerCP-Cy5.5 anti-CD8a (53-6.7), PE-Cy7 anti-CD83 (Michel-19), FITC/APC anti-F4/80 (BM8), and APC anti-TNF (MP6-XT22). The following antibodies were purchased from eBioscience: fixable viability dye eflour780 and PerCP efluor710 proIL-1β (NJTEN3). The following antibodies were purchased from BD: FITC/APC anti-CD3e (145-2C11), PE anti-IFNγ (XMG1.2), and PE anti-TLR7 (A94B10). Pacific Blue I-Ag7 (AG2.42.7) was made in our laboratory (Suri et al., 2002).

Flow cytometry and cell sorting

Islets were harvested as described previously (Calderon et al., 2015) and dispersed using Cell Dissociation Solution Non-Enzymatic (Sigma-Aldrich) for 5 min at 37°C. PLNs and lungs were harvested, finely chopped with scissors, placed in DMEM (Thermo Fisher Scientific) containing 125 μg/ml Liberase TL (Roche) and 50 μg/ml DNase I, grade II (Roche), and incubated at 37°C for 30 min and dispersed via gentle agitation. Single-cell suspensions incubated in PBS, pH 7.4 (Thermo Fisher Scientific) having 1% BSA (Sigma-Aldrich), and 50% of 2.4G2 conditioned DMEM at 4°C for 15 min to block Fc receptors. Cells were stained with fluorescently labeled antibodies and sorted using a FACSAria II or analyzed using the FACSCanto II (BD). Macrophages were sorted on the basis of their expression of CD45, F4/80, and I-Ag7. DCs were selected on the basis of their expression of XCR1. In all cases CD3e+ and CD19+ events were eliminated from the sort (dump gated). For cell sorting experiments, ∼500–5,000 macrophages were isolated from 6–10 mice, and this represented 1 biological sample in our analysis. At least three samples were used for each population studied by RNASeq analysis. All flow cytometry data were analyzed using FlowJo software version 10.2 (Tree Star).

Intravenous LPS injection

NOD and NOD.Rag1−/− mice were intravenously injected with 100 ng LPS in 0.1 ml PBS. Mouse islets were harvested 3 h later and dispersed, and macrophages were sorted using the FACSAria II (BD) after staining with antibodies to CD45, CD3, CD19, F4/80, and I-Ag7.

RNA isolation and real-time PCR

Sorted cell RNA was isolated using the Ambion RNAqueous-Micro kit (Thermo Fisher Scientific) following the manufacturer’s instructions. RNA was quantified by OD260 using NanoDrop (Thermo Fisher Scientific). cDNA was synthesized using iScript Reverse Transcription Supermix for qRT-PCR (Bio-Rad Laboratories). qRT-PCR was performed using the 5′ nuclease (TaqMan) chemistry using iTaq Universal Probes Supermix (Bio-Rad Laboratories). The 18S ribosomal RNA probe was obtained from Thermo Fisher Scientific, and all other primers and probes were designed and obtained from IDT DNA. All quantitative PCRs were performed on a StepOnePlus Real-Time PCR system (Thermo Fisher Scientific) running StepOne 2.3 Software (Thermo Fisher Scientific). Quality control was performed by the StepOne 2.3 software. RT-PCR data analysis was done using Excel (Microsoft).

RNASeq analysis

Library preparation was performed with 1–50 ng total RNA; integrity was determined using an Agilent Bioanalyzer (Agilent Technologies). Double-stranded cDNA was prepared using the SeqPlex RNA Amplification kit (Sigma-Aldrich) per the manufacturer’s protocol. cDNA was blunt ended, had an adenosine added to the 3′ ends, and then had Illumina sequencing adapters ligated to the ends. Ligated fragments were then amplified for 12 cycles using primers incorporating unique index tags. Fragments were sequenced on an Illumina HiSeq-2500 using single reads extending 50 bases. All RNASeq work was performed by the Genome Technology Access Center at Washington University School of Medicine.

We assessed the mean quality of sequencing reads for base pair positions in each sample using the fastqc utility from fastq-tools (Droop, 2016). Samples with a quality score less than 25 (Sanger/Illumina 1.9 encoding) across all bases were truncated using the fastx_trimmer utility, removing base pairs from the end of each read in the library. All libraries were aligned to the GRCm38.p4 assembly of the mouse genome using the STAR aligner (Dobin et al., 2013) with GENCODE version M7 (Harrow et al., 2012) transcripts included into the pregenerated reference. We generated raw counts of the number of tags on each gene by using htseq-count utility from the HTseq package (Anders et al., 2015). GENCODE version M7 (with -t exon option) was used as a reference GTF. The DESeq2 package (version 1.6.3; Love et al., 2014) was used to detect differentially expressed genes between the analyzed conditions (output with P < 0.05 and a Benjamini-Hochberg FDR of 5% was used). Variance between samples was stabilized for the purposes of hierarchical clustering and PCA to address the problem of heteroscedasticity (dependence of variance on the mean counts). Pathways analysis was done using the Broad Institute’s GSEA software (Subramanian et al., 2005). All heat maps are in log2 scale.

LPS, TNF, and IL-1 signature analysis

The LPS signature consists of genes up-regulated in macrophages treated with LPS for 40 min when compared with untreated macrophages at P < 0.05, log fold change >1 (GSE14769). The IL-1 signature consists of genes up-regulated in human monocyte-derived macrophages 4 h after stimulation with IL-1 compared with control cells (GSE8515, P < 0.05, log fold change >0.7). The donor effect for the GSE8515 data set was corrected with ComBat (Johnson et al., 2007). The limma package was used for differential expression analysis of microarray data (Ritchie et al., 2015). The TNF signaling gene set was taken from the Broad Institute Molecular Signatures Database (MSigDB) hallmark gene sets (Hallmark TNF signaling via NF-κB). The ImmGen dataset was downloaded from GEO (GSE15907; Heng et al., 2008).

Intracellular flow cytometry

Dispersed islet or PLN cells were placed at 37°C in DMEM, 10% FBS (GE Healthcare), and 10 μg/ml Brefeldin A for 4 h. As a positive control for activation, samples were pulsed with 100 ng/ml PMA (Sigma-Aldrich) and 500 ng/ml ionomycin (Sigma-Aldrich). Cells were washed with PBS, 1% BSA, cell surface stained with fluorescent antibodies for 30 min at 4°C, and fixed with PBS and 2% formaldehyde (Sigma-Aldrich) for 15 min at 4°C. Cells were washed with PBS and 1% BSA, incubated with 2% saponin (Sigma-Aldrich) and PBS for 5 min at 4°C, and then with fluorescent antibodies recognizing intracellular proteins for 30 min at 4°C. Cells were washed with 2% saponin and PBS and then with PBS and 1% BSA. Flow cytometry was performed using a FACSCanto II.

Immunofluorescence microscopy

For nuclear RelA detection, islets from 6-wk-old NOD females were isolated and dispersed as described previously. Dispersed cells were placed in chamber slides and incubated at 37°C overnight in 20 ng/ml IL-1β (PeproTech) DMEM (Thermo Fisher Scientific) supplemented with 10% FGE (Healthcare Life Sciences) or in DMEM 10% FBS alone (Rütti et al., 2016). Cells were washed with 1% BSA in PBS and fixed and permeabilized using the BD Fix/Perm kit according to the manufacturer’s instructions. Cells were stained with NF-κB p65 (D14E12) XP rabbit mAb (Cell Signaling Technology) overnight at 4°C. Cells were washed twice with PBS and stained with Alexa 488 anti-rabbit IgG F(ab')2 (Cell Signaling Technology) for 2 h at room temperature. After washing with PBS, the chamber was removed, and Prolong Gold Anti-Fade Reagent with DAPI (Cell Signaling Technology) was applied. Slides were imaged using a Leica SPE confocal microscope.

For pancreas immunofluorescence, frozen sections from each tissue were cut on a cryotome at 10 μm. Sections were fixed for 5 min in ice-cold acetone. Then the sections were blocked with Cas-Block (Thermo Fisher Scientific) following the manufacturer’s instructions. The sections were then stained with PE anti-B220 (RA3-6B2), PE anti-Siglec H (551), or PE anti-Ly6G (1A8; eBioscience) and polyclonal guinea pig anti-insulin (Thermo Fisher Scientific) for 1 h at room temperature. Sections were washed three times with PBS and then stained with goat anti–guinea pig Alexa-488 (A11073; Thermo Fisher Scientific) for 1 h at room temperature. Background autofluorescence was quenched with 70% ethanol supplemented with 0.5% Sudan Black B (Sigma-Aldrich) for 2 min at room temperature and washed with PBS. Nuclei were stained with bisBenzimide H 33258 (Sigma-Aldrich) at a concentration of 1 μg/ml in water for 5 min at room temperature and washed three times with PBS. Sections were mounted with PBS containing 50% glycerol and visualized via an Olympus BX51 microscope.

Active caspase-1 detection

Islets were isolated and dispersed as described previously. Cells were incubated for 30 min at 37°C with the fluorescent inhibitor probe FAM-YVAD-FMK (Immunochemistry Technologies) to label active caspase-1 enzyme. Cells were washed and stained for cell surface antigens, acquired using a FACSCanto II flow cytometer, and analyzed using FlowJo software.

Single-cell sorting and quantitative PCR

Islet macrophages were isolated, stained, and single-cell-sorted into 96-well plates containing 5 μl Cellsdirect 2X Reaction Mix (Thermo Fisher Scientific) and 0.1 μl RNase inhibitor (SUPERase-In; Thermo Fisher Scientific) per well. For reverse transcription and specific cDNA preamplification, all the 96 20× Taqman assays (Fig. S5; IDT DNA) were mixed and diluted to 0.2×. Subsequently, 2.5 μl of the 0.2× Taqman assay mix, 0.2 μl of the SuperScript III RT/Platinum Taq (Thermo Fisher Scientific), and 1.2 μl PCR-certified water (Thermo Fisher Scientific) were added together to the sorted single cells. Reverse transcription (15 min at 50°C) was performed, followed by Taq activation (2 min at 95°C) and 18 cycles of cDNA preamplification (each cycle: 15 s at 95°C, 4 min at 60°C). The resulting cDNA product was diluted with PCR-certified water (1:5) and submitted for single-cell quantitative PCR using the Biomark HD system (Fluidigm).

Data availability

RNAseq data are available at the Gene Expression Omnibus under accession no. GSE100067.

Online supplemental material

Fig. S1 shows the gating strategy for the isolated cells, expression of MHC on macrophages from ImmGen, and flow cytometry of cell surface markers on islet macrophages. Fig. S2 shows immunofluorescence of pancreas and lymphoid structures. Fig. S3 shows different islet isolation techniques and outcomes. Fig. S4 shows Pearson correlation, heat maps, and gene set enrichment analysis. Fig. S5 shows single-cell islet macrophage qRT-PCR data. Table S1, included in a separate Excel file, shows the normalized gene counts for the dataset.

Acknowledgments

We wish to thank Katherine E. Frederick for maintaining the mouse colony. We are grateful to Dr. Jesse W. Williams for assistance with confocal microscopy.

This work was funded by grants provided by the National Institutes of Health (NIH; DK058177 and AI14551) and a Janssen Pharmaceuticals–Washington University agreement. We thank the Genome Technology Access Center in the Department of Genetics at Washington University School of Medicine for help with genomic analysis. The Center is partially supported by National Cancer Institute Cancer Center Support Grant P30 CA91842 to the Siteman Cancer Center and by Institute for Clinical and Translational Science/Clinical and Translational Sciences Award grant UL1 TR000448 from the National Center for Research Resources, a component of the NIH, and the NIH Roadmap for Medical Research. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or Janssen Pharmaceuticals.

The authors declare no competing financial interests.

Author contributions: S.T. Ferris, J.A. Carrero, and E.R. Unanue conceived and designed the experiments. S.T. Ferris, J.A. Carrero, and X. Wan performed the experiments. S.T. Ferris, J.A. Carrero, and P.N. Zakharov curated and analyzed the data. S.T. Ferris, P.N. Zakharov, X. Wan, M.N. Artyomov, E.R. Unanue, and J.A. Carrero interpreted results and/or wrote the paper.

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    Abbreviations used:
     
  • FDR

    false discovery rate

  •  
  • NOD

    non-obese diabetic

  •  
  • PCA

    principal component analysis

  •  
  • pDC

    plasmacytoid DC

  •  
  • PLN

    pancreatic lymph node

  •  
  • qRT-PCR

    quantitative RT-PCR

  •  
  • ROS

    reactive oxygen species

Author notes

*

S.T. Ferris and P.N. Zakharov contributed equally to this paper.

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