Abstract
Macrophages are a heterogeneous population of cells involved in tissue homeostasis, inflammation and cancer. Although macrophages are densely distributed throughout the human intestine, our understanding of how gut macrophages maintain tissue homeostasis is limited. Here we show that colonic lamina propria (LpM) and muscularis macrophages (MM) consist of monocyte-like cells that differentiate into multiple transcriptionally distinct subsets. LpM comprise subsets with proinflammatory properties and subsets high antigen presenting and phagocytic capacity. The latter are strategically positioned close to the surface epithelium. Most MM differentiate along two trajectories; one that upregulates genes associated with immune activation and angiogenesis, whereas the other upregulates genes associated with neuronal homeostasis. Importantly, MM are located adjacent to neurons and vessels. Cell-cell interaction and gene network analysis indicated that survival, migration, transcriptional reprogramming, and niche-specific localization of LpM and MM are controlled by an extensive interaction with tissue-resident cells and a few key transcription factors.
Introduction
Over the last years it has been shown that tissue macrophages, residing in virtually all organs, are extremely heterogeneous. Originally, macrophages were described as professional phagocytes being fundamental to our immune defense. However, it is now clear that macrophages have a broad range of both immune and non-immune functions. Moreover, many macrophage functions are tissue-specific such as electrical conduction in the heart (Hulsmans et al., 2017), synaptic pruning in the brain (Hong et al., 2016), alveolar surfactant clearance in the lung (Guilliams et al., 2013) and limb regeneration in salamanders (Godwin et al., 2013). These functions are thought to be controlled by tissue-specific gene modules regulated by specific transcription factors imprinted by the microenvironment. Interestingly, tissue resident macrophages (TRM) can be reprogrammed when transferred to a new microenvironment (Lavin et al., 2014), indicating that mature TRM retain their plasticity. In addition to the local microenvironment, macrophage functions also depend on their ontogeny (embryonic precursors or adult monocytes), intrinsic factors, and time spent in the tissue (Bleriot et al., 2020). The gut is an organ composed of several compartments with unique functions. The mucosa is covered by a one-cell thick epithelial layer that separates our body from the outside world. This compartment stretches the local immune system to its limits, which has to rapidly and efficiently eliminate pathogens and toxins and at the same time tolerate harmless molecules (e.g. food proteins) and the gut microbiota. Below the mucosa is a thin layer of tissue termed submucosa, which is overlying a thick layer of smooth muscle, termed muscularis propria, consisting of a circular and longitudinal muscular layer. The muscular layers are responsible for segmental contractions and peristaltic movement of the intestinal tract. The gut has an extensive enteric nervous system with a plexus of ganglia cells in both the muscular layers (Auerbach’s plexus) and the submucosa (Meissner’s plexus).
Macrophages populate all layers of the gut wall and studies in mice suggest that they have niche-specific functions that are essential to maintain tissue homeostasis (De Schepper et al., 2018). Under steady state conditions lamina propria macrophages (LpM) constantly phagocytose apoptotic epithelial cells and are critical for gut microbiota composition (Arandjelovic and Ravichandran, 2015; Earley et al., 2018). Recently, LpM in distal colon were shown to maintain epithelial integrity by limiting fungal product adsorption (Chikina et al., 2020). Muscularis propria macrophages (MM) interact with both neurons and vessels and loss of MM leads to loss of enteric neurons, vascular leakage, impaired secretion and reduced intestinal motility (De Schepper et al., 2018; Muller et al., 2014). Moreover, Matheis et al showed that MM protected against post-infectious neuron damage (Matheis et al., 2020). At birth, the mouse gut is populated with fetal-derived macrophages. However, the LpM are rapidly (within weeks) replaced by bone marrow-derived circulating monocytes (Bain et al., 2014). Monocytes also emigrate to muscularis propria, but in this compartment fetal-derived macrophages appear to be more persistent (De Schepper et al., 2018). However, all studies referred to above have been performed in mice, and translation of these results to understand human biology should be made with caution.
Gut macrophages may also be detrimental to the host. Aberrantly activated macrophages have been shown to play a key role in IBD pathology (Martin et al., 2019; Smillie et al., 2019) and tumor-associated macrophages are often associated with worse prognosis (Katzenelenbogen et al., 2020; Zhang et al., 2020). Because of their heterogeneity and plasticity, there is currently a great interest in the search for strategies to reprogram macrophages as a therapeutic tool to treat both inflammatory disorders and cancer (Jahchan et al., 2019). However, to target macrophages in human diseases a deeper understanding of their heterogeneity and tissue-specific functions is necessary.
Our current knowledge about macrophages in the human gut is limited. Using bulk RNA sequencing (RNA-seq) we have shown that LpM the small intestine consist of several transcriptional states (Bujko et al., 2018) and by following their turnover kinetics in transplanted duodenum we found that host circulating monocytes rapidly entered the graft and differentiated into TRM that completely replaced donor LpM within months after transplantation. Here we extend these findings by performing a detailed characterization of both LpM and MM from adult human colon applying single cell (sc) RNA-seq and multi-color immunostaining in situ. Spatiotemporal analysis shows that both compartments contain multiple coexisting TRM subsets with sub-tissular specific functions. We furthermore identify a limited number of transcription factors that may control TRM diversity and we reveal an extensive cross-talk between TRM subsets and tissue resident cells, including stromal cells, epithelial cells, neurons and immune cell lineages. These cell-cell interactions are most likely responsible for the imprinting of tissue-specific macrophage identity.
Results
Macrophages in colonic mucosa and muscularis propria comprise transcriptional states associated with monocytes and tissue resident macrophages
Samples from the large intestine were obtained from colorectal cancer resections. Macroscopically and microscopically normal tissue at least 10 cm from the tumor tissue was used. Mucosa and muscularis propria were separated longitudinally and pieces of tissue from each compartment were enzymatically digested to obtain single cell preparations. Cells from mucosa and muscularis were analyzed separately (Figure 1). Flow cytometric analysis revealed that CD45+CD3−CD19−HLA-DRint/+ cells separated into either CD14+ macrophages or CD14− classical dendritic cells (cDC), including CD141+ cDC1 and CD1c+ cDC2 (Figure S1A), as previously shown in the small intestine (Bujko et al., 2018). Thus, tissue-derived macrophages were sorted as CD45+HLA-DR+CD14+ (Figure S1B) and processed on a 10X Genomics Chromium platform. A total of 63.970 cells with high quality mRNA were analyzed. To present high dimensional data in low dimension we constructed UMAP plots of each compartment. We found that sorted CD14+ cells expressed the macrophage markers CD163 and CD68, further demonstrating that cells included in the analysis were all macrophages (Figure S1C). We have previously shown that the macrophage population in the small intestine contains several transcriptionally distinct subpopulations (Bujko et al., 2018); including “transient” monocyte-like macrophages expressing high levels of calprotectin (heterodimer of S100A8 and S100A9) and long-lived calprotectin-negative macrophages expressing a TRM phenotype (Bujko et al., 2018). To examine whether colonic LpM and MM contained similar phenotypes we first assessed the expression of S100A8 and S100A9, and the complement component 1 chains (C1QA, C1QB, C1QC); the latter highly expressed on small intestinal TRM (Bujko et al., 2018). UMAP visualization of both compartments showed that most LpM and MM expressed either S100A8/S100A9 or C1QA/C1QB/C1QC genes (Figure 2A). Further transcriptomic analysis of S100A8/S100A9+ macrophages showed a high number of differentially expressed genes (DEG, e.g. FCN1, VCAN, AQP9, TREM1) that are preferentially expressed in blood monocytes and small intestinal monocyte-like macrophages (Figure 2B). C1QA/C1QB/C1QC+ macrophages, on the other hand, expressed many genes associated with TRM (e.g. MRC1, APOE, SELENOP, CSF1R, MERTK, and LYVE1). Flow cytometry of dispersed tissue macrophages confirmed that LpM and MM consisted mainly of calprotein+C1q− and calprotectin−C1q+ macrophages (Figure 2C).
Together, these findings demonstrated that both LpM and MM contained monocyte-like macrophages and macrophages with a TRM phenotype.
Mucosal macrophages comprise transcriptionally distinct and niche-specific subsets
To characterize LpM further we performed clustering analysis using Seurat 3.0 (Stuart et al., 2019). Based on DEG, 13 clusters were identified (Figures 3A and S2) that encompassed macrophages from all patients included in the study (Figure S3A). DEG in clusters 0-4 (LpM0-LpM4) were reminiscent of small intestinal monocyte-like macrophages (Bujko et al., 2018) and enriched gene ontology (GO) terms were typical for innate immune responses such as responses to bacterium, fungus and LPS (Figure 4). LpM0 expressed the highest levels of S100A8, S100A9 and S100A12 (Figure 3B), indicating that this cluster constitutes the most recently elicited monocytes. LpM2 expressed high levels of many proinflammatory genes (e.g. IL-1B, IL-1A, IL-6, IL23A, CXCL2, CXCL3, CXCL8) reminiscent of inflammatory macrophages found in IBD lesions (Martin et al., 2019) (Figure 3B, C). The immunoregulatory cytokine IL10 showed highest expression in LpM2-LpM4 (Figure 3B). LpM5 contained DEG encoding many heat shock proteins (Figure S2) and top GO terms were related to unfolded protein responses (Figure 4). Interestingly, LpM6 expressed many IFNγ-inducible genes virtually absent in the other clusters; (e.g. CXCL9, CXCL10, CXCL11, IDO1, GBP1, GBP2, GBP4, GBP5) (Figure 3B, C). The top GO terms were antigen processing and presentation of exogenous peptide antigen via MHC class I, and type I IFN-, IFNg- and TNF-signaling pathway (Figure 4). This phenotype has recently been reported to promote anti-tumor immunity in colorectal cancer of mice (Qu et al., 2020). Cluster 8 expressed low levels of both S100A8/S100A9 and C1Q genes (Figure 1), but many DEG were associated with DC (e.g. FCER1A, CD1C, CLEC10A, CD1E) (Figure 3B). This cluster also expressed high levels of MHC class II genes (Figure 3B, C) and top GO term was antigen processing and presentation of exogenous peptide antigen via MHC class II (Figure 4). Flow cytometric analysis confirmed the presence of CD14+FcER1+CD1c+ cells in dispersed mucosa (Figure S4). This phenotype is reminiscent of a recently identified DC population - termed DC3, which originates independently of both classical DC and monocytes (Bourdely et al., 2020).
LpM9 and LpM10 expressed high levels of HLA class II genes (Figures 3B, C). Accordingly, enriched GO terms were antigen processing and presentation of exogenous peptide antigen via MHC class II (Figure 4). GO terms for LpM10 also included wound healing and receptor-mediated endocytosis (Figure 4). Interestingly, LpM9 (and to a lesser extent LpM10) expressed CD9, TREM2, SPP1, and ACP5 (Figure 3B, C), genes that were recently shown to identify monocyte-derived macrophages enriched in liver fibrosis (Ramachandran et al., 2019) and in adipose tissues of obese patients (Jaitin et al., 2019). LpM12 selectively expressed many typical TRM genes, such as LYVE1, COLEC12, F13A1, and FOLR2 (Figure 3B, C), and several chemokine genes (e.g. CXCL2, CXCL3, CXCL8, CCL3, CCL4, (Figure 3B, C). GO terms included chemokine-mediated signaling pathway, synapse pruning, apoptotic cell clearance and receptor-mediated endocytosis (Figure 4).
In agreement with other reports (Bain et al., 2014; Bujko et al., 2018) our findings strongly suggested that the vast majority of LpM originates from bone marrow-derived monocytes, which constantly emigrate to the mucosa and differentiate into TRM. To further understand the process of monocyte-to-macrophage differentiation we reconstructed their developmental trajectories using the Monocle 3 algorithm (Trapnell et al., 2014). We excluded DC3 from the analysis because this subset is reported to originate independently of the monocyte lineage (Bourdely et al., 2020). Selecting random cells in LpM0 as root, we identified at least two distinct branches. A short branch ended in Cluster 2, compatible with a distinct proinflammatory trajectory, whereas the majority of the cells went through a more extensive reconfiguration program from LpM0 to LpM9-12 as a function of pseudotime (Figure 3D). As expected, genes typical for monocytes were downregulated following this trajectory, whereas typical TRM genes were increased (Figure 3E). Together, the findings clearly showed that LpM consisted of multiple transcriptional cell states indicating that incoming monocytes constantly differentiate into multiple distinct TRM subsets with time (Bujko et al., 2018).
Studies in mice have shown that TRM occupy distinct tissue niches where they display niche-specific functions (Chakarov et al., 2019; Guilliams et al., 2020). To determine the anatomical localization of LpM subsets we performed multi-color immunofluorescence stainings in situ. Calprotectin-expressing LpM were found scattered in the lower part of the mucosa between crypts (Figure 5A), whereas C1q was strongly expressed by LpM positioned in the subepithelial region (Figure 5B). To further determine the localization of distinct TRM clusters we stained for Acp5 (expressed by LpM9 and LpM10), Lyve-1 and Colec12 (expressed by LpM12). Interestingly, whereas Acp5+ LpM were located in the subepithelial region (Figure 5C), Lyve-1+ and Colec12+ macrophages were confined to the submucosal region (Figures 5D and S5A) and not found in the mucosa. The latter finding indicated that LpM12 represented submucosal macrophages (SmM). Several studies have shown that Lyve-1 is associated with perivascular macrophages, whereas macrophages expressing Colec12, a scavenger receptor for uptake of myelin, has been associated with neurons (Bogie et al., 2017). Here we found that the vast majority of SmM expressed both Lyve-1 and Colec12.
Collectively, our analysis indicated that the colonic mucosa contains monocyte-derived LpM that differentiate into multiple LpM subpopulations; some subsets with proinflammatory properties (e.g. LpM2 and LpM6), and other subsets with high antigen presenting and phagocytic capacity that were strategically positioned in the subepithelial region (LpM9 and LpM10). In contrast, LYVE1+ SmM displayed a transcriptomic profile indicating low antigen presenting capacity, but with high chemotactic and tissue-protective properties.
Muscularis propria macrophages comprise transcriptionally distinct subsets displaying different developmental trajectories
Next, we analyzed macrophages isolated from the muscular compartment. Following high-resolution clustering, the cells were separated into 12 transcriptionally distinct clusters (Figures 6A and S6) encompassing cells from all donors (Figure S3B). In three of the patients, we sampled muscularis propria from two different sites. Clustering analysis revealed that MM from these sites were very similar (Figure S7) indicating that MM subpopulations were distributed homogeneously throughout the muscular compartment. Cluster 0 (MM0) expressed high levels of S100A8, S100A9, S100A12, IL1B, IL1A and CXCL chemokines (Figure 6B, C), and enriched GO terms were cellular response to bacterium and LPS as well as type I- and IFNγ-mediated signaling pathway together with MHC class I-mediated antigen presentation (Figure 4). MM1, MM2, and MM4, which clustered adjacent to MM0, expressed genes associated with immune activation (e.g. HLA class II genes) (Figure 6B, C) and enriched GO terms were IFNg-mediated signaling pathway and antigen processing and presentation of exogenous peptide antigen via MHC class II (Figure 4). Cluster 3 was reminiscent of DC3 (Bourdely et al., 2020), demonstrating that this DC subset also resided in muscularis propria (Figure S4). The clusters transcriptionally most distant from MM0, could be broadly divided into clusters with “proinflammatory” and “homeostatic” properties, respectively. MM5 (and MM8) expressed high levels of proinflammatory cytokines (e.g. IL1A and IL1B) and multiple chemokines (e.g. CXCL2, CXCL3, CXCL8, CCL3, CCL4), whereas MM11 expressed low levels of proinflammatory cytokines and chemokines, but high levels of genes such as LILRB5, MARCO, LYVE1, FOLR2 and COLEC12 (Fig 6B, C). Consistently, enriched GO terms for “proinflammatory” MM5 were cellular response to LPS and chemokine-mediated signaling pathway, whereas top GO terms for “homeostatic” MM11 were receptor-mediated endocytosis, synaptic pruning and apoptotic cell clearance (Figure 4). Interestingly, high expression of PMP22 and EMP1 genes were observed in MM8 and MM11 (Figures S6 and S8). These genes are mainly expressed in Schwann cells (Taylor et al., 1995). PMP22 protein is part of the myelin sheath that protects neurons. High expression of PMP22 and EMP1 genes suggests that MM are phagocytosing Schwann cells and thus are in intimate contact with enteric neurons. Cluster 9, similar to LpM8 in mucosa, expressed high levels of heat shock protein genes (HSP) (Figure S6) with enriched GO terms such as unfolded protein responses (Figure 4). HSP protects against cellular stress and it was recently shown that increased expression of HSP in macrophages concomitant with downregulation of IL-1 had anti-inflammatory effects in response to change of diet in experimental mice (Brykczynska et al., 2020). In agreement, HSP+ MM expressed low levels of proinflammatory cytokines and chemokines (Figure 6B).
Pseudotime trajectory analysis, using random cells in MM0 as root, showed a trajectory following two distinct branches (Figure 6D). One branch followed through MM1, MM5, and MM6; clusters expressing many proinflammatory genes. Interestingly, all three subsets shared the GO term positive regulation of angiogenesis (Figure 4). Conversely, “homeostatic” branch expressed lower levels of proinflammatory genes (Figure 6B) and ended in MM11; the cluster most strongly associated with enteric neurons. As for LpM, typical monocyte-related genes were rapidly downregulated along the trajectory, whereas TRM genes (e.g. LYVE1, C1QC and APOE) were rapidly upregulated and found to be more broadly expressed by MM (Figure 6E) than by LpM (Figure 2E). To determine the anatomical localization of MM we performed multicolor immunostaining in situ. As in the submucosa, most MM expressed both Lyve-1 and Colec12.
They were distributed throughout the muscularis tissue but were enriched adjacent to neurons and vessels (Figure 6A-D). In addition, a minor fraction of calprotectin+ MM were found scattered throughout the tissue (Figure S5B).
Together, we found that the MM population was very heterogeneous, consisting of multiple functionally distinct subsets. Transcriptomic profiling and pseudotime trajectory analysis revealed significant differences between MM and their LpM counterparts, indicating that tissue-specific signals from the local microenvironment are important for macrophage differentiation and diversity.
A subpopulation of mucosal and muscularis macrophages express genes compatible with an embryonic origin
Studies in mice have reported that gut macrophages are composed of both monocyte-derived and embryonic-derived macrophages (De Schepper et al., 2018; Shaw et al., 2018). To analyze whether embryonic-derived macrophages also occurred in adult human colon we examined genes reported to be differentially expressed between lineages. Interestingly, MM4 and LpM9 (and MM10) showed higher expression of several genes related to embryonic ontogeny, such as CD63, ADAMDEC1, and DNASE1L3 (Figure S9A, B) (De Schepper et al., 2018; Shaw et al., 2018). Moreover, using ClusterMap (Gao et al., 2019), a method to determine cluster similarity across biological samples, we found that MM4 and LpM9 showed the highest similarity index (0.21) when all clusters were compared to each other. To further investigate the possibility that these clusters contained embryonic-derived macrophages we examined the transcriptomic profile of intestinal macrophages in human embryos. Fawkner-Corbett et al. recently published a study analyzing the human intestinal development using scRNA-seq (Fawkner-Corbett et al., 2021). Reexamining the immune cell data of embryos at post conception week 12-22 we found that intestinal macrophages contained three distinct subpopulations (Figure S9C). Cluster 3 expressed high levels of monocyte-related genes such as S100A8, S100A9, VCAN, and FCN1, whereas cluster 0 and 4 expressed higher levels of genes associated with TRM such as C1QA and SELENOP (Figure 9D). Interestingly, only cluster 4 expressed high levels of DNASE1L3 and ADAMDEC1, similar to embryonic-derived macrophages in mouse colon (Figure 9D). Together, these results may suggest that a minor population of gut macrophages in human adults are of embryonic ontogeny.
Mucosal and muscularis macrophages interact extensively with resident tissue cells and immune cells
Cell differentiation in tissues is triggered by contacts with other neighboring cells through receptor-ligand interactions. Thus, we interrogated microenvironmental signals that could be involved in the transcriptional reconfiguration process observed in both compartments. We determined such interactions applying CellPhoneDB2.0 (Efremova et al., 2020) combining our scRNA-seq datasets of LpM and a published scRNA-seq dataset covering all stromal and immune cells from normal colonic mucosa (Smillie et al., 2019). Numerous statistically significant interactions were found between LpM and subtypes of fibroblasts, endothelial cells and epithelial cells (Figure 8A). Among receptor-ligand pairs that are particularly important for macrophage survival and differentiation we found that the CSF1R-ligand cytokines CSF1, CSF3 and IL34 (Lavin et al., 2015), were expressed by postcapillary venules and activated fibroblasts, whereas genes involved in the Notch signalling pathway (e.g. DLL4/JAG1:NOTCH2) were expressed by epithelial and stromal cells (Figure S10). Endothelial cells and fibroblasts expressed many adhesion molecule and chemokine genes that interacted primarily with LpM0-LpM8. This is in agreement with the concept that endothelial cells and fibroblasts are involved in the recruitment, migration and localization of LpM (Figure S10). LpM9 and LpM10, on the other hand, displayed interactions with epithelial cells (CDH1:aEb7, DSG2:DSC2) (Figure S10) consistent with their subepithelial localization (Figure 5C). Importantly, stromal and epithelial cells showed several interactions with LpM that regulate macrophage functions. This included interactions associated with negative regulation of macrophages activation (LGALS9:HAVCR2, HLA−G:LILRB2, HLA−G:LILRB1, TNFSF10:RIPK1) (Chen et al., 2018; Hartwig et al., 2017; Ocana-Guzman et al., 2016), M2-like polarization (GAS6/PROS1:AXL, GAS6:MERTK) (Myers et al., 2019), and “don’t eat me” signals (CD47:SIRPA, CD52:SIGLEC10) (Barkal et al., 2019; Li et al., 2021). On the other hand, LpM expressed ligands (EGFR and ERRB3) for receptor tyrosine kinases expressed by epithelial cells and activated fibroblasts, suggesting a bidirectional regulation of survival and function between LpM and tissue resident cells. LpM also showed numerous interactions with immune cell subtypes (Figure 8B). In particular, LpM6, LpM7, LpM9, (as well as DC3-like LpM8), which expressed high levels of MCH class II genes (Figure 3B), showed interactions with cycling, memory and regulatory T cells (e.g. CD28:CD80, CLTA4:CD80, CD28:CD86, CLTA4:CD86) (Figure S11), suggesting that LpM subsets play a role as regulators of T cell responses in the colonic mucosa. Moreover, selective expression of several CXCR3- binding chemokines were expressed by LpM6 (Figures 3B and S11), indicating a role in the recruitment of CD8 T cells.
Studies in mice have shown that MM crosstalk with enteric neurons (De Schepper et al., 2018; Muller et al., 2014). To interrogate macrophage-neuron interactions in muscularis propria we analyzed our MM scRNA-seq dataset together with a published scRNA-seq dataset of the human enteric neuron system (Drokhlyansky et al., 2020). A very high number of interactions between multiple neuron subtypes and MM was found (Figure 8C). Central to MM survival and differentiation, several neuron subtypes expressed Notch ligands (DDL1, DDL3, and JAG2) and IL34 interacting NOTCH2 and CSF1R on MM, respectively. Furthermore, numerous receptor-ligand pairs were involved in macrophage migration, localization and activation/regulation (Figure S12). Finally, MM-neuron interactions were associated with synapse pruning (C3:C3AR1, C5:C5AR2) (Hong et al., 2016) and neuron stimulation (BMP2:BMPR2), strengthening the notion that there is an extensive crosstalk between MM and enteric neurons in the human colon that controls gastrointestinal motility (De Schepper et al., 2018; Muller et al., 2014).
Together, our results indicated that macrophages interact extensively with tissue resident cells in both compartments, compatible with the concept that the local microenvironment is important for imprinting of macrophage specialization and niche-specific localization.
Gene regulatory network analysis indicates that a limited number of transcription factors control macrophage differentiation and diversity
To identify transcriptional factors (TF) that may control transcriptional programming of LpM and MM we used single-cell regulatory network inference and clustering (SCENIC) (Van de Sande et al., 2020) to determine sets of genes co-expressed with their associated TF (regulons). We found a total of 185 and 187 regulons (active in more than 1% of the cells) for LpM and MM, respectively, with significantly enriched motifs for the corresponding TF. By ordering macrophage clusters along the pseudotime developmental trajectory, we identified a restricted number of regulons corresponding to specific clusters. LpM0 showed increased regulon activity corresponding to RXRA and IRF7, whereas LpM2 was associated with several NFKB family members (NFKB1, NFKB2, and REL) (Figure 9A). LpM6 was associated with STAT1, whereas LpM8-LpM11 and LYVE1+ SmM showed regulon activity driven by TF such as MAF, MAFB, HES1, and EGR1. MAF and MAFB are known for their role in driving terminal macrophage differentiation (Aziz et al., 2009).
The gene regulatory network in MM showed similarities with LpM (Figure 9B). MM0 showed regulon activity corresponding to NFKB family members (NFKB1, RELB and BCL11A), whereas regulons corresponding to MAF and MAFB were upregulated in MM11. On the other hand, MM5 (and to a lesser extent MM8) expressed regulons corresponding to TF such as MAFF, RARA, and BHLHE40, not found in the mucosal compartment. Interestingly, regulon activity with corresponding HES1 was broadly expressed by MM and by subsets of LpM (Figure 9A, B). HES1 is a classic TF downstream of the Notch signalling pathway (Sharma et al., 2012). Together with our cell-cell interaction data (Figures S10 and S12), expression of HES1 suggests that the Notch signalling pathway is involved in reprogramming of LpM and MM, as shown for monocyte-derived macrophages in the liver (Ramachandran et al., 2019; Sharma et al., 2020).
In summary, we find that regulon activity corresponds to distinct macrophage clusters following the pseudotime trajectory, indicating that a limited number of key TF control the process of macrophage reprogramming observed in both compartments.
Discussion
Using high-resolution spatiotemporal single-cell analysis we show that macrophage populations in mucosa and muscularis propria of the human colon contain multiple transcriptionally distinct subsets that display niche-specific localizations and functional properties. Our results also reveal tissue-specific cell-cell interactions and a limited number of TF that may be key players in the extensive monocyte-to-macrophage reprogramming and sub-tissular-specific localization observed.
Studies in mice show that macrophages in different organs display functional differences imprinted by tissue-specific cues from the local microenvironment (Guilliams et al., 2020; Okabe and Medzhitov, 2014). Moreover, it was recently shown that heterogeneous macrophages with different transcriptional programs are governed by sub-tissular niches (Chakarov et al., 2019). Therefore, to understand the functional diversity of macrophages within a tissue detailed spatiotemporal characterization of the macrophage population and their interactions with neighboring cells is needed. However, the nature of molecular signals that drive macrophage differentiation in the human gut is poorly characterized. Studies in humans and mice have clearly shown that mucosal macrophages in the intestine largely originate from bone marrow-derived monocytes (Bain et al., 2014; Bujko et al., 2018). Thus, probing colonic mucosal macrophages in histologically normal tissue is a unique possibility to study the monocyte-to-macrophage differentiation process in situ under steady state conditions.
Integration of our data suggests that circulating monocytes are elicited through post-capillary venules in the crypt area, after which some cells rapidly differentiate into different types of proinflammatory macrophages, whereas others migrate to the subepithelial region where they upregulate expression of genes related to endocytosis, wound healing and antigen presentation, while concomitantly downregulate proinflammatory genes. This latter subpopulation is thus ideally equipped to maintain mucosal barrier integrity with minimal collateral damage. Our cell-cell communication analysis suggested that the survival, migration and differentiation of LpM are controlled by an extensive interaction with tissue resident cells (endothelial cells, fibroblasts and epithelial cells) and immune cells. Stromal cells may provide a supply of macrophage-trophic factors (e.g. CSF-1, CSF3, IL-34), which are crucial for macrophage development and survival (Lavin et al., 2015), whereas several cell types expressed Notch ligands suggesting that the Notch-signaling pathway plays a role in monocyte-to-macrophage reprogramming (Sharma et al., 2020). Several receptor-ligand pairs have been shown to have inhibitory or anti-inflammatory effects on macrophages, in line with the idea that most tissue macrophages are tolerogenic under steady state conditions. Interestingly, however, many of these macrophage genes have been shown to promote tumor growth and may be targets for immunotherapy (e.g. SIRPA, SIGLEC10, AXL, MERTK, RIPK1) (Barkal et al., 2019; Li et al., 2021; Myers et al., 2019; Wang et al., 2018). Thus, strategies to target these genes to treat cancer should take into account possible side effects, such as drug-induced colitis, as observed by current checkpoint inhibition (Luoma et al., 2020).
Our gene regulatory network analysis identified cluster-specific TF that are likely to control subset-specific transcriptional programs, which strengthen the idea that the LpM population contains multiple transcriptionally stable macrophage subsets that coexist in the tissue. Interestingly, the transcriptomic profile of several LpM subsets is similar to macrophages associated with various inflammatory and fibrotic diseases (Martin et al., 2019; Qu et al., 2020; Ramachandran et al., 2019). This suggests that these disease-promoting macrophages also play a role under steady state conditions, but that the relative contribution of functionally different LpM subsets is important for maintenance of homeostasis.
Analysis of muscularis propria showed that many MM were positioned in close contact with nerves and cell-cell interaction analysis displayed numerous MM-neuron interactions. Such interactions included BMP2:BMP2R and CSF1R:IL34. As previously shown in mouse models (Gabanyi et al., 2016), this finding suggests that MM expressing BMP2 regulate peristaltic motility in the colon by activating BMP2R on enteric neurons, whereas neurons expressing IL34 feedback on CSF1R+ MM by stimulating their survival and differentiation. Most MM expressed high levels of C1Q genes and we identified interactions between genes of the complement system (C3:C3AR1, C5:C5AR2), suggesting that MM are involved in synapse pruning (Stephan et al., 2012). Finally, MM contained transcripts specific for Schwann cells indicating that MM phagocytose cellular material from such nerve-protecting cells. Together, these findings are in agreement with the concept that MM-neuron crosstalk play a pivotal role for gut homeostasis (De Schepper et al., 2018; Muller et al., 2014). MM were also positioned adjacent to vessels and the GO term positive regulation of angiogenesis was enriched in MM1, MM5 and MM6. Pseudotime trajectory analysis showed that these subsets were found along the “proinflammatory” branch, whereas neuron-associated MM subsets (first of all MM11) were linked to the “homeostatic” branch. This suggests that functionally specialized neuron- and blood vessel-associated MM coexist in the human colon to ensure proper functioning of enteric neurons and blood vessels, respectively (De Schepper et al., 2018).
Several studies in mice have suggested that macrophages important for vascular integrity selectively express Lyve-1 (Chakarov et al., 2019; Lim et al., 2018). However, we found that the vast majority of MM, both those associated with vessels and nerves, expressed Lyve-1 and Colec12, and we were not able to distinguish these subsets by in situ staining. We also identified Calprotectin+ MM scattered throughout the muscularis propria. Clustering and pseudotime trajectory analysis strongly suggested that the vast majority of TRM found in the muscularis propria originated from these incoming monocytes. Macrophages are the dominating leukocyte population in muscularis propria and submucosa. Interestingly to this end, we found that MM subsets and SmM expressed high levels of several monocyte-attracting chemokines (e.g. CCL3, CCL4, CCL3L1, CCL4L2), suggesting that TRM in these compartments are important for continuous recruitment of monocytes.
Somewhat surprisingly, we found that subpopulations of LpM and MM showed phenotypic similarities with embryonic-derived macrophages in mouse colon (De Schepper et al., 2018) and with colonic macrophages in human fetuses (Fawkner-Corbett et al., 2021). As far as we know there are no established markers to separate embryonic-derived and from bone marrow monocyte-derived macrophages in humans, and evidence to suggest that human macrophages in adult life originate from embryonic precursors is sparse. We and others have shown that macrophages in various tissues may live for several years (Eguiluz-Gracia et al., 2016; Patel et al., 2021). However, the origin of these long-lived cells has not been determined. Although the fraction of macrophages with an “embryonic” signature in our study was minor, it is possible that embryonic-derived macrophages play a more prominent role other settings, in particular during infancy. Thus, it should be studied further whether genes such as DNASE1L3 and ADAMDEC1 could be useful markers to distinguish macrophage lineages.
Together, we find that LpM and MM are extremely heterogeneous and consist of several subsets with distinct functional properties. It appears that maintenance of homeostasis depends on coexistence of both proinflammatory and protective/homeostatic subtypes. Our data also give insights into cell-cell interactions and key TF that are likely to control tissue-specific macrophage reprogramming. This work constitutes an important framework to understand the complexity of macrophage biology in the human gut and to identify potential targets to better treat inflammatory disorders and cancer in the future.
STAR METHODS
KEY RESOURCES TABLE
Materials Availability
This work did not generate new unique reagents.
Data and Code Availability
Single cell RNA-Seq datasets generated in this study are deposited in the Genome Expression Omnibus under the following accession numbers: to be updated.
Reference datasets used in this study:
Human UC scRNA-seq dataset (Smillie et al., 2019) SCP:SCP259
Human ENS scRNAseq dataset (Drokhlyansky et al., 2020) SCP: SCP1038
Human fetal intestinal scRNA-seq dataset (Fawkner-Corbett et al., 2021) GEO:GSE158702
EXPERIMENTAL MODEL AND SUBJECT DETAILS
Patients and tissue samples
Colonic resections were obtained from patients operated for sigmoid colon cancer at Akershus University Hospital (Ahus, 1478 Lørenskog, Norway). The resected colon was immediately examined by an experienced pathologist and macroscopically normal colon, at least 10 cm from the tumor, were placed in vials with RPMI 1640 and put on ice for transport. The study was performed in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants, and the study was approved by the Regional Committee for Medical Research Ethics (REK, 2018/703, Health Region South-East, Norway). For scRNA-seq, 4 colon specimens from sigmoid or ascending colon were obtained (age 62-78, 3 males). Immunofluorescence stainings were done on samples from 8 patients (age 62-78, 4 males) from ascending, transverse, descending and sigmoid colon. None of the patients had autoimmune, infectious or inflammatory diseases, nor received neoadjuvant chemotherapy or radiation therapy before the operation. All patients were operated following the national guidelines.
METHOD DETAILS
Single cell dissociation
Resected colonic tissues were processed within 2 h after removal from the patient. Single cell suspensions of colonic resections were obtained using a modified version of a previously published protocol (Bujko et al., 2018). The intestinal specimens were opened longitudinally and washed in Dulbecco’s phosphate-buffered saline (PBS). The muscularis propria was first removed with scissors, after which the mucosa was dissected in narrow strips. The mucosal fragments were then incubated with shaking in PBS with 2 mM EDTA (Sigma-Aldrich) and 1% FCS (Sigma-Aldrich) three times for 15 min at 37°C. The remaining tissue was minced and digested with stirring for 60 min in complete RPMI (RPMI1640; Lonza; supplemented with 10% fetal calf serum (FCS, 1% Pen/Strep; Lonza) containing 0.25 mg/ml Liberase TL (Roche) and 20 U/ml DNase I (Sigma). Digested cell suspension was passed through a 100-μm filter and washed.
Flow cytometry and cell sorting
Released tissue cells were stained in aliquots of 1 ×106 cells/100 μL of PBS with 2% fetal calf serum (FCS, GIBCO) and 0.1% NaN3 for 30min on ice. Non-specific staining was blocked with 10 μL FcR Blocking Reagent (Miltenyi Biotec) prior to staining. Dead cells were excluded by TO-PRO™-1 Iodide (ThermoFisher) or Fixable Viability Dye eFluor 780 (eBioscience) staining. Analysis was performed with a BD LSRFortessa X20 and sorting with a FACS Aria IIIu (BD Biosciences) running BD FACSDIVA 9.0 software. Purity of > 98% was achieved in sorted populations. Data were processed with FlowJo 10.6.1 (Tree Star, Inc). Intracellular staining was performed after surface staining, using a Fixation & Permeabilization Buffer Set (eBioscience) according to manufacturer’s instructions. A full list of antibodies is provided in the Key Resources Table.
Single Cell RNA-sequencing RNaseq libraries preparation
Cellular suspensions (~15000 cells, with expected recovery of ~7500 cells) of sorted CD45+ HLA-DR+ CD14+ macrophages from colonic mucosa and muscularis propria were loaded on the 10X Chromium Controller instrument (10X Genomics) according to the manufacturer’s protocol using the 10X GEMCode proprietary technology. All samples from individual patients were loaded in one batch. The Chromium Single Cell 3’ v2 Reagent kit (10X Genomics) was used to generate the cDNA and prepare the libraries, according to the manufacturer’s protocol. The libraries were then equimolarly pooled and sequenced on an Illumina NextSeq500 using HighOutput flow cells v2.5. A coverage of 400M reads per sample was targeted, in order to obtain 50 000 reads per cell. The raw data were then demultiplexed and processed with the Cell Ranger software (10X Genomics) v2.1.1.
Pre-processing scRNA-seq data
In total, we analyzed 63917 human cells from donors (n = 4). We aligned the reads of the input dataset to the GRCh38 reference genomes, and estimated cell-containing partitions and associated unique molecular identifiers (UMIs) using the Cell Ranger Chromium Single Cell RNA-seq version 3.0.2. We performed data preparation using Seurat R packages. Genes expressed in fewer than 3 cells in a sample were excluded, as well as cells that expressed fewer than 200 genes and mitochondrial gene content > 5% of the total UMI count. We normalized data by using gene counts for each cell that were divided by the total counts for that cell and multiplied by 10000 and then log-transforming. Subsequently, we identify genes that are outliers on a ‘mean variability plot’ using the vst method with 2000 genes. For mucosa and muscularis data, we separately found integration anchors and then performed data integration using a pre-computed anchorset with default parameters. Finally, we scaled data and centered genes in the dataset using linear model.
Dimensionality reduction, clustering and differential expression analysis
We ran PCA dimensionality reduction with 30 PCs to compute and store (on 2000 variable genes). We estimated dimensions of reduction parameter (for LpM equal 13 and for MM type equal 20) and constructed a Shared Nearest Neighbor (SNN) Graph for given datasets. We first determined the k-nearest neighbors of each cell. We used this k-Nearest Neighbour graph to construct the SNN graph by calculating the neighborhood overlap (Jaccard index) between every cell and its 20 nearest neighbors. To obtain the resolution parameter we used clustree from the clustree R package with resolution varying from 0.1 to 2.0. We got resolution parameter for LpM equal 0.7 and for MM 0.6.
We then ran the Uniform Manifold Approximation and Projection (UMAP) dimensional reduction technique with PCA dimension reduction and we found differentially expressed genes for each of the clusters in the datasets.
We identified non-macrophage cell types such as B-cells and T-cells and these were filtered out. We got 5263 cells from mucosa and 14649 cells for MM. Then we ran UMAP and we found differentially expressed genes for each of the clusters in the datasets.
All heat maps, UMAP visualizations, violin plots and dot plots were produced using Seurat functions in conjunction with the ggplot2, pheatmap and grid R packages. ClusterMap (Gao et al., 2019) was used to compute a similarity metric for subclusters between LpM and MM.
Developmental trajectory inference and transcriptional regulation
To generate pseudotemporal dynamics we used the Monocle R package. We ordered cells in a semi-supervised manner on the basis of their Seurat clustering, scaled the resulting pseudotime values from 0 to 1, and mapped them onto UMAP visualizations generated by Seurat. Differentially expressed genes along this trajectory were identified using the Moran’s I test. For transcription factor analysis, we obtained a list of all genes identified as human transcription factors. To analyze transcription factor regulons further, we adopted the Single Cell Regulatory Network Inference and Clustering, (SCENIC) (https://aertslab.org/#scenic), using default parameters and the normalized data matrices from Seurat as input. SCENIC is a combination of 3 packages (GENIE3, RcisTarget and AUCell). For motif visualization we obtained the highest normalized enrichment score of the motif in the gene-set.
Identification of significant ligand-receptor pairs
For comprehensive systematic analysis of inter-lineage interactions, we used CellPhoneDB2.0 (https://www.cellphonedb.org). CellPhoneDB2.0 is a manually curated repository of ligands, receptors and their interactions, integrated with a statistical package for inferring cell–cell communication networks from single-cell transcriptomic data. This package searches for ligand-receptor interactions and outputs multiple result files based on curated databases such as UniProt, IUPHAR, and Ensembl.
Each dataset was analyzed using matrices from Seurat and datasets covering epithelial, endothelial, fibroblast, immune cells (Smillie et al., 2019) subsets and enteric neurons (Drokhlyansky et al., 2020). Significant ligand-receptor pairs identified from datasets, with adjusted p value < 0.05 were extracted, requiring the ligand and receptor to be expressed in at least 10% of the cells.
Immunofluorescence stainings
Sections of formalin-fixed and paraffin-embedded tissue were cut in series at 4 μm, mounted on Superfrost Plus object glasses (Thermo Fisher Scientific), and washed sequentially in xylene, ethanol, and PBS. Heat-induced epitope retrieval was performed by boiling sections for 20 min in citrate buffer (pH 6.0) and cooled to room temperature before staining. Sections were incubated with mixtures of primary antibodies for 1 h at 37°C, rinsed in PBS, and incubated with secondary antibodies for 1.5h at RT. Sections were then incubated for 5 min at RT in Hoechst 33342 nucleic acid stain, and stained sections were mounted with ProLong Glass Antifade mountant (Molecular Probes). Laser scanning confocal microscopy was performed by acquiring tile scans on an Andor Dragonfly equipped with a fusion stitcher. The Andor Dragonfly was built on a Nikon TiE inverted microscope equipped a 60x/1.40 NA oil immersion objective.
Acknowledgments
We thank all patients and staff at Akershus University Hospital for providing tissue samples. Dr Susanne Lorenz at the Genomics Core Facility at Oslo University Hospital is greatly acknowledged for supervising the sequencing. We also thank Dr Frode Skjeldal at the Oslo NorMIC imaging platform at the Department of Biosciences, University of Oslo, for expert help with confocal microscopy analysis. Kjersti Thorvaldsen Hagen is greatly acknowledged for technical help with tissue processing and immunostainings. This work was supported by the Norwegian Cancer Society and Southern and Eastern Norway Regional Health Authority.