Peritoneal resident macrophages constitute an immunosuppressive environment in peritoneal metastasized colorectal cancer

Saris, J.; Li Yim, A. Y. F.; Bootsma, S.; Lenos, K. J.; Franco Fernandez, R.; Khan, H. N.; Verhoeff, J.; Poel, D.; Mrzlikar, N. M.; Xiong, L.; Schijven, M. P.; van Grieken, N. C. T.; Kranenburg, O.; Wildenberg, M. E.; Logiantara, A.; Jongerius, C.; Garcia Vallejo, J. J.; Gisbertz, S. S.; Derks, S.; Tuynman, J. B.; D’Haens, G. R. A. M.; Vermeulen, L.; Grootjans, J.

doi:10.1038/s41467-025-58999-6

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Published: 17 April 2025

Peritoneal resident macrophages constitute an immunosuppressive environment in peritoneal metastasized colorectal cancer

Nature Communications volume 16, Article number: 3669 (2025) Cite this article

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Abstract

Patients with peritoneal metastasized colorectal cancer (PM-CRC) have a dismal prognosis. We hypothesized that an immunosuppressive environment in the peritoneal cavity underlies poor prognosis. We define the composition of the human peritoneal immune system (PerIS) using single-cell technologies in 18 patients with- and without PM-CRC, as well as in matched peritoneal metastases (n = 8). Here we show that the PerIS contains abundant immunosuppressive C1Q⁺VSIG4⁺ and SPP1⁺VSIG4⁺ peritoneal-resident macrophages (PRMs), as well as monocyte-like cavity macrophages (mono-CMs), which share features with tumor-associated macrophages, even in homeostasis. In PM-CRC, expression of immunosuppressive cytokines IL10 and VEGF increases, while simultaneously expression of antigen-presenting molecules decreases in PRMs. These intratumoral suppressive PRMs originate from the PerIS, and intraperitoneal depletion of PRMs in vivo using anti-CSF1R combined with anti-PD1 significantly reduces tumor burden and improves survival. Thus, PRMs define a metastatic site-specific immunosuppressive niche, and targeting PRMs is a promising treatment strategy for PM-CRC.

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Introduction

Metastases to the peritoneal cavity are commonly observed in gastrointestinal malignancies, such as in patients with colorectal cancer (CRC)¹. Peritoneal metastases (PMs) are associated with a dismal prognosis, as evidenced by a median overall survival of less than 12 months². Only in a minority of patients, cytoreductive surgery (CRS) combined with heated intraperitoneal chemotherapy (HIPEC) is a treatment option, but recurrence rates are high, and so is associated morbidity³. Improving survival and reducing disease burden in patients with peritoneal metastasized CRC (PM-CRC) is a large unmet clinical need.

Immune checkpoint blockade (ICB) has gained much excitement over the past decade because of its unparalleled potential to treat certain types of metastasized cancer, like melanoma and non-small-cell lung cancer^4,5, however, only a minority of patients with CRC respond adequately to ICB. Much attention in the field goes to dissecting the biology underlying therapy resistance, including tumor-intrinsic mechanisms such as lack of neoantigen presentation as observed in microsatellite stable (MSS) cancers. Interestingly, however, peritoneal metastasized cancer is often associated with therapy resistance to ICB, even in CRC with microsatellite instability (MSI)^6,7, and irrespective of the origin of the primary tumor site (e.g., colon, stomach, pancreas)^1,6,8. This suggests that in peritoneal metastases, tumor cell-extrinsic mechanisms, such as the presence of an immunosuppressive niche, play a role in therapy resistance. Interestingly, it was recently demonstrated that PM-CRC is highly enriched for the MSS CMS4 subtype^9,10; a molecular subtype of CRC that is typically characterized by infiltration of tumor-associated macrophages (TAMs) and Tregs and exclusion of cytotoxic T cells^11,12.

To define whether peritoneal cavity immune cells contribute to a site-specific immunosuppressive niche that underlies therapy resistance in PM-CRC, we studied the composition of the human peritoneal immune system (PerIS) and its role in defining the immune environment in PM-CRC, using Cellular Indexing of Transcriptomes and Epitopes sequencing (CITE-seq) and single-cell RNA-sequencing (scRNA-seq). We investigated peritoneal fluid (PF) immune cells in a series of both healthy subjects and in patients with PM-CRC, as well as the tumor immune microenvironment (TIME) in PM. We found that even in homeostatic conditions, the PerIS is defined by high abundance of immunosuppressive C1QA⁺VSIG4⁺ and SPP1⁺VSIG4⁺ peritoneal resident macrophages (PRMs), which share features with TAMs. In PM-CRC, these PRMs increased expression of immunosuppressive cytokines, including IL10 and VEGF, while expression of antigen-presenting molecules was downregulated. Intriguingly, SPP1⁺VSIG4⁺ PRMs, with features of lipid-associated and regulatory TAMs, were the most abundant TAMs in peritoneal metastases. In a separate patient cohort, macrophage immunosuppressive gene set scores showed increased infiltration of macrophages in PM as compared to matched liver metastases and primary CRC. Finally, we demonstrate in pre-clinical in vivo studies that simultaneous depletion of macrophages using anti-CSF1R with anti-PD1 therapy significantly reduced disease burden and improved survival. Therefore, targeting peritoneal macrophages is a promising strategy to enhance ICB therapy in PM-CRC.

Results

The peritoneal immune system is defined by immunosuppressive peritoneal cavity resident macrophages

To provide a comprehensive and unbiased characterization of the human PerIS, we employed CITE-seq to simultaneously quantify the transcriptome as well as the protein expression of 134 cell-surface exposed proteins on live cells from PF obtained from patients that underwent surgery for achalasia, a disorder of the esophagus in which no immunological alterations of the peritoneal cavity are expected. These PF samples are therefore considered healthy controls (HC, n = 5; 14,441 cells). To confirm tissue-specificity, we compared PF CITE-seq transcriptome data with scRNA-seq data on peripheral blood mononuclear cells (PBMCs; 13,242 cells) from matched patients (Supplementary Fig. 1a and Supplementary Table 1). Although some overlap of PF-derived immune cells was observed with PBMC-derived immune cell subsets, we also observed PF-unique clusters demonstrating that these peritoneal immune cells have a distinct transcriptional profile (Fig. 1A and Supplementary Fig. 1B). By harnessing both the gene and protein expression profiles, we successfully resolved the 4 main immune lineages using markers CD3/CD3D, CD11b/ITGAM, CD56/NCAM1 and CD20/MS4A1, thereby identifying T cells, myeloid cells, natural killer (NK)/innate lymphoid cells, and B cells respectively (Supplementary Fig. 1C). Overall, there was a good correlation detected between protein and gene expression for the canonical markers (Supplementary Fig. 1D). Clusters represented tissue-specific immune cells with no patient specific subsets (Supplementary Fig. 1E). This multi-omic single-cell approach was particularly useful for immunophenotyping rare immune cell subsets that displayed more optimal resolution at either protein level, such as CD141 (THBD) for conventional dendritic cells 1 (cDC1), or gene level, such as IL7R (CD127) and CTLA4 (CTLA4) for innate lymphoid cells and CD4 regulatory T cells, respectively (Supplementary Fig. 1F, G).

**Fig. 1: The homeostatic peritoneal immune system is dominated by peritoneal-resident cavity macrophages.**

Compared to PBMCs, the PerIS harbored fewer T and B cells, similar numbers of NK/innate lymphoid cells, and a larger proportion of myeloid cells (Supplementary Fig. 2A, B). In total, we identified 27 different immune cell types in the human PerIS (Fig. 1B and Supplementary Fig. 2C). Monocytes-macrophages were the most abundant immune cells of the PerIS accounting for ~30% of all immune cells (Supplementary Fig. 2D). Given the high abundance of macrophages within the PerIS and their putative role in shaping the immune environment in cancer¹³, we further phenotyped this immune subset by reclustering only mononuclear phagocytes (MNPs) (Fig. 1C). As all MNPs express ITGAM, dendritic cells (DCs) were separated from monocytes and macrophages based on CLEC4C, XCR1 and CD1C/CD1C, which were subsequently used to identify the plasmacytoid dendritic cells (PDCs) and conventional dendritic cells type 1 (CDC1s) and 2 (CDC2s), respectively (Fig. 1C). Virtually all monocyte and macrophage subpopulations were defined based on the expression of CD14, CD163, MARCO and CCR2 (Fig. 1C). Unsupervised clustering of the PF-derived monocyte and macrophage populations resulted in four clusters (Fig. 1D). Interestingly, all clusters showed expression of V-set and immunoglobulin domain-containing 4 (VSIG4)¹⁴ (Fig. 1E), a complement receptor of the immunoglobulin superfamily (CRIg) that is specifically expressed in resting tissue-resident macrophages^15,16. Cluster 1 expressed both monocyte-like markers CD14 and CCR2 on both transcriptional and protein level (Supplementary Fig. 2E), macrophage marker VCAN, and tissue resident marker VSIG4 on transcriptional level (Fig. 1E, F), suggesting that cluster 1 represented peritoneal monocyte-like cavity macrophages (mono-CMs) as described recently¹⁷. Indeed, CITE-seq demonstrated that this VCAN⁺CCR2⁺ cluster was CD163^dim at protein level, confirming that MARCO⁺CD163^cite-dimVCAN⁺VSIG4⁺CCR2⁺ were mono-CMs¹⁷ (mean: 28.1%; cluster 1). In addition, higher entropy was observed for cluster 1 relative to the other clusters, suggesting higher differentiation potential¹⁸ compared to clusters 2 and 4, which represented more terminally-differentiated macrophages (Supplementary Fig. 2F). Unlike cluster 1, cluster 3 displayed lower entropy and was annotated as a second subset of mono-CMs expressing C1Q (MARCO⁺CD163^cite-dimVCAN⁺C1QA⁺VSIG4⁺CCR2⁻ C1Q⁺ mono-CMs; mean: 4.7%; cluster 3). Cluster 2 presented a more pronounced expression of CD163 (CITE-seq) and C1QA, but lacked the overt expression of VCAN and CCR2, thereby distinguishing it from the mono-CMs, which we therefore named C1Q⁺ cavity macrophages (CMs) MARCO⁺CD163^cite-brightVCAN⁻C1QA⁺VSIG4⁺CCR2⁻. These C1Q⁺ CMs were the most abundant macrophages (mean: 30.9%). Lastly, we identified a subset of macrophages co-expressing SPP1 (MARCO⁺CD163^cite-brightSPP1⁺VSIG4⁺CCR2^- SPP1⁺ CMs; mean: 10.5%; cluster 4) (Fig. 1F, G). While the macrophage expression of SPP1 and C1QA was described to be mutually exclusive¹⁹, co-expression was reported in TAMs and correlates with worse overall survival in breast cancer patients²⁰ and resistance to ICB in melanoma patients²¹. Furthermore, C1QA, combined with co-expression of TREM2, a gene that was expressed in the majority of PRMs (Supplementary Fig. 2G), has also been associated with immunosuppression^22,23. Having established the presence of immunosuppressive PRMs in the homeostatic PerIS and given the fact that macrophages lack unifying nomenclature, we employed marker gene analyzes to identify differential expressed genes (DEG) analyzes to further characterize these macrophage subsets. As expected, we found high expression of marker genes used for subset classification (SPP1, C1QA, VCAN, and CCR2). SPP1⁺ CMs expressed NRP1 and LGMN, while C1Q⁺ mono-CMs expressed FLT1 and EZR. C1Q⁺ CMs are characterized by FN1 and LYVE1 expression, and mono-CMs expressed FGL2 and MNDA, all of which genes are involved in promoting immunosuppression, tumor growth, and migration (Supplementary Fig. 2H and Supplementary Data 1)^{24,25,26,27,28,29,30,31}. Moreover, gene ontology (GO) analyzes^32,33,34 found no major differences between the subsets apart from enhanced cell-cell signaling and regulation of lipid transport in SPP1⁺ CMs (Supplementary Data 2). However, using a recently published proposed model on TAM signatures, we classified all of our identified macrophage subsets in the PF of HCs accordingly³⁵. Most subsets were found to match the lipid-associated (LA), resident-tissue macrophage like (RTM), regulatory (Reg) and proangiogenic (Angio) TAMs signatures, while inflammatory-cytokine enriched (INFLAM) or IFN-primed (IFN), associated with pro-inflammatory characteristics, were underrepresented in PRMs in healthy controls (Fig. 2A). Indeed, genes associated with pro-inflammatory functions including IL1B and IDO1 were downregulated in bona fide CMs^36,37,38. By contrast, anti-inflammatory genes FOLR2, MRC1, TREM2, and MERTK were highly expressed in both C1Q⁺ CMs and SPP1⁺ CMs^23,38,39,40 (Fig. 2B). The four macrophage subsets could be further characterized using our 134-cell-surface CITE-seq panel, showing unique protein expression profiles. Interestingly, especially the CD163^cite-bright C1Q⁺ CMs and SPP1⁺ CMs show marked expression of CD169, CD304, HLA-DR, and CD112, respectively. This is associated with IL10 production, stimulating angiogenesis while functioning as the VEGFR2 co-receptor, increased antigen presentation, and regulatory T cell differentiation via binding of TIGIT, and may thereby contribute to the immunosuppressive peritoneal niche^41,42,43 (Fig. 2C).

**Fig. 2: Peritoneal-resident cavity macrophages are immunosuppressive by nature.**

To understand whether the abundance of macrophages could provide a tumor-promoting niche that contributes to poor prognosis and therapy resistance in peritoneal metastasized cancer, we compared abundance of macrophages in PF with macrophage abundance in both colon- (Gut Cell Atlas)⁴⁴ and liver tissue (Liver Cell Atlas; GSE192742)⁴⁵, being the primary cancer site in CRC and the most common site of disseminated CRC, respectively (Supplementary Fig. 2I and Supplementary Fig. 3A–D). Interrogating healthy, non-cancer bearing donors, the relative abundance of macrophages in the PF was approximately 9 and 18 times higher than in colon and liver, respectively (Fig. 2D, E). Interestingly, while C1Q⁺ macrophages were present in healthy colon- and liver tissue albeit in lower abundance (8x and 5x respectively, compared to peritoneal fluid), SPP1 expression was not found in homeostatic colon- and liver-derived macrophages from the selected donors (Fig. 2E).

Taken together, the homeostatic PerIS is characterized by a large number of PRMs that harbor anti-inflammatory characteristics. In addition, the homeostatic peritoneal cavity also harbors a subset of SPP1⁺ macrophages, which shares features that are typically attributed to TAMs and which we did not detect in normal colon and liver tissue (Gut Cell Atlas⁴⁴ and Liver Cell Atlas⁴⁵).

Peritoneal resident macrophages shape an immunosuppressive niche for PM-CRC

Given the TAM-like phenotype of PRMs in homeostasis, we next defined whether PRMs retained their immunosuppressive phenotype in the presence of peritoneal metastases in thirteen PM-CRC patients, using scRNA-seq of PF-immune cells (Supplementary Table 1). Patients were prospectively included upon macroscopically visible PM during diagnostic laparoscopy and had a median Peritoneal Carcinomatosis Index (PCI) score of 13. The majority of PM was mismatch repair proficient (92.3%), as expected¹². Only two (15.4%) patients received prior systemic chemotherapy 3 months before the sample was obtained. All four macrophage subsets were identified in the PerIS of PM-CRC patients but clustered differently from their HC counterparts, indicating PM-specific transcriptional differences (Fig. 3A). No patient specific clusters were found (Supplementary Fig. 4A). Although no significant differences in abundance were observed in macrophage subsets between HC and PM-CRC patients (Fig. 3B), differential gene expression analysis demonstrated that PRMs maintained their polarized immunosuppressive nature. In fact, anti-inflammatory macrophage markers such as VEGFA and IL10, both of which have been associated with a pro-tumorigenic environment^46,47,48,49, were significantly higher expressed in PM-CRC as compared to HC, in mono-CMs, C1Q⁺ CMs, and SPP1⁺ CMs. By contrast, PF from PM-CRC patients presented lower expression of genes encoding proteins involved in antigen presentation, such as HLA-A, HLA-C and HLA-DRB1, in all but the C1Q⁺ CMs (Fig. 3C, D, Supplementary Fig. 4B). KEGG pathway analysis confirmed reduced antigen processing and presentation, as well as cytokine-cytokine receptor interaction pathways in PM-CRC macrophages (Supplementary Fig. 4C and Supplementary Data 3). In addition to transcriptional changes in PRMs, we also observed significantly increased protein levels of the immunosuppressive cytokines IL10 and VEGFA (Fig. 3E), as well as another important anti-inflammatory cytokine TGF-β⁵⁰ (Supplementary Fig. 4D) in the PF of PM-CRC patients compared to HC. The significance of this observation was demonstrated by the fact that protein levels of VEGFA and IL10 levels, as well as TGF-β, positively correlated with PCI score (Fig. 3F and Supplementary Fig. 4E). In addition, both IL10 and VEGFA were predominantly expressed in macrophages in the PF of HC and PM-CRC patients (Fig. 3G, H), although VEGFA was also slightly expressed by DCs, granulocytes and non-immune populations (Fig. 3G, H). These findings demonstrate a highly immunosuppressive tumor niche in the peritoneal cavity, with abundance of PRMs that define an immune-privileged phenotype in PM-CRC.

Peritoneal-resident immunosuppressive macrophages infiltrate PMs

To determine whether PRMs constitute the tumor immune microenvironment in PM-CRC, we analyzed PF and matched PMs of eight patients with isolated PM-CRC (Supplementary Fig. 1a). In line with our recent studies, the majority (7/8; 87.5%) had microsatellite stable (MSS) CRC¹² (Supplementary Table 1). Phenotyping identified 34 immune subsets (6459 cells) within PM-CRC (Supplementary Fig. 5A–C), of which the top three most abundant subsets were the CD8 T effector memory cells (mean: 13.1%), SPP1⁺ CMs (mean: 8.5%) and CD4 regulatory T cells (mean: 8.0%) (Supplementary Fig. 5D). Phenotyping of the monocyte and macrophage subsets in PMs at single cell level demonstrated that PMs harbored similar mono-CM and CM subsets as detected in the PF, as well as both classical (cluster 1) and non-classical (cluster 2) monocytes (Fig. 4A and Supplementary Fig. 5A, C). Interestingly, the SPP1⁺ CMs, which we identified as a peritoneal macrophage subset, were the most abundant macrophage within the TIME of peritoneal metastases (mean 47.7%, Fig. 4B), although also other mono-CMs and C1Q⁺ CMs were found within PM-CRC (Fig. 4B). To visualize macrophages within PM, we performed IHC for CD163, which was uniformly expressed on peritoneal macrophages and mono-CMs (Supplementary Fig. 5E). We demonstrated that CD163⁺ macrophages infiltrated both invasive margin and tumor centers (Supplementary Fig. 5F), while CD8⁺ T cells were excluded from the PM tumor centers and instead accumulated at the invasive margins (Supplementary Fig. 5G).

To address whether PM macrophage infiltration was an environment- rather than a tumor-specific feature, we assessed PF macrophage abundance as well as PM macrophage infiltration in 6 gastric cancer patients with PMs (PM-GC). All macrophage subsets found in the PerIS of PM-CRC patients were also identified in the PerIS of PM-GC patients (Supplementary Fig. 6A–F). In addition, we confirmed that also in PM-GC, SPP1⁺ CMs as well as C1Q⁺ CMs were highly abundant in tumors (Supplementary Fig. 6G, H).

To define whether immune cells in PM were PerIS-derived, we first assessed expression of tissue resident marker VSIG4 within the myeloid subsets, which showed that the majority of myeloid cells express VSIG4, except for classical and non-classical monocytes, which are likely blood-derived (Fig. 4C). Reclustering monocytes and macrophages from PBMCs, PF and PM from both HCs and PM-CRC patients together indicated separate clusters for monocytes and macrophages with notable separation between PF- and PM-derived macrophages, which is to be expected as macrophages undergo differentiation within the TIME⁵¹ (Fig. 4D, E). Indeed, through unsupervised clustering analysis, we demonstrated that the PM-derived macrophages are more similar to the PF-derived macrophages compared to PBMC-, PF-, or PM-derived monocytes (Fig. 4F). We therefore hypothesized that PM-derived macrophages originate from the PF. To prove this, we performed an in vivo mouse macrophage labeling experiment using the PKH26 linker, which enabled us to trace PRMs. We found that 2 weeks after CT26 colorectal cancer cell injection, up to 61% of F4/80⁺ macrophages in PM-CRC were PKH26⁺, and thus directly peritoneal-derived (Fig. 4G–I). Collectively, this demonstrates that PRMs actively infiltrate peritoneal metastases^52,53.

Peritoneal-resident immunosuppressive macrophages contribute to the peritoneal metastatic TIME

We next investigated potential mechanisms by which PRMs were attracted into PM by investigating expression levels of macrophage receptors that have been shown to be critical for macrophage recruitment into cancers⁵⁴. In human PM, we observed expression of CCR1, CCR5, CSF1R, and CXCR3, on SPP1⁺ CMs and C1Q⁺ CMs in PMs, as well as on mono-CMs (Fig. 5A). In addition to cell surface receptors on macrophages, we analyzed the presence of ligands in the PF, and identified increased levels of the key macrophage chemoattractants CCL3 (MCP-1α), CSF-1 and CXCL9 in PM-CRC patients compared to HC, suggestive for the involvement of the CCL3-CCR1/CCR5, the CSF1-CSF1R and the CXCL9-CXCR3 axis in PF PRMs (Fig. 5B).

Macrophages in PM showed similar transcriptional TAM signatures as in PF³⁵ and could mainly be characterized as lipid-associated and regulatory-like TAMs (Fig. 5C). Interestingly, both C1Q⁺ mono-CMs and SPP1⁺ CMs, accounting for the vast majority (mean: 78.7%) of TAMs in PM, had the most immunosuppressive phenotype and particularly showed increased expression of FN1 and CD74, respectively, both genes associated with cancer progression and dampened anti-tumor immune responses^55,56 (Fig. 5D).

Lastly, we set out to define whether infiltration of PRMs shapes a peritoneal-unique TIME, which is distinct from the TIME in other metastases or primary tumors. In an independent metastatic CRC cohort, we compared the expression of a macrophage immunosuppressive gene signature (MARCO, C1QA, VSIG4, CD163, and SPP1) in PMs with matched primary CRC and liver metastases¹⁰. In line with our results, immunosuppressive gene set scores were significantly higher in PM as compared to matched primary CRC and matched liver metastases¹⁰ (Fig. 5E, F), demonstrating that PM are characterized by increased influx of immunosuppressive macrophages. Thus, blocking influx of suppressive macrophages by targeting these macrophage receptors could be promising treatment strategy for PM-CRC.

Improved overall survival in mice after macrophage-depleting therapy combined with immune checkpoint blockade

Given the potential role of PRMs in poor outcome of PM-CRC, we next studied whether peritoneal macrophage depletion would be a therapeutic strategy to overcome resistance to ICB in a pre-clinical PM-CRC model, in which CT26 colorectal cancer cells were intraperitoneally injected in BALB/c mice⁵⁷. As (1) we demonstrated that CSF1R is broadly expressed in human PRMs, and csf1r expression has been demonstrated on mouse peritoneal macrophages^17,58, and (2) we observed increased levels of CSF1 in PF of PM-CRC patients as compared to controls (Fig. 5B), we used anti-CSF1R as a macrophage depleting strategy, and anti-PD1 to activate suppressed anti-tumor T cells. Effective depletion of peritoneal macrophages was confirmed in anti-CSF1R-treated groups by flow cytometry in both PMs (Fig. 6A, B) and PF (Supplementary Fig. 7A) and in PMs by immunofluorescence for CD206 (Fig. 6C, D). Anti-CSF1R/anti-PD1 treatment was accompanied by increased intratumor levels of activated FSC^high CD8⁺ T cells (Supplementary Fig. 7B) and increased levels of intratumor CD8⁺ T cells as demonstrated by flow cytometry (Supplementary Fig. 7C, D) and CD8 IF (Fig. 6E, F). Subgroup analysis of mice that either reached their humane endpoint or were sacrificed after 21 days showed that anti-CSF1R/anti-PD1 combination therapy, but not anti-PD1 alone, resulted in a reduction of tumor burden, including significantly decreased levels of ascites and peritoneal tumor burden as measured by the modified PCI score for murine studies (mPCI⁵⁷) (Fig. 6G–I and Supplementary Fig. 7E). Most importantly, these improved clinical parameters were accompanied by improved survival rates in both a prevention (Fig. 6J, K) and a treatment (Fig. 5J, L) model, in anti-CSF1R/anti-PD1-treated mice as compared to mice that received anti-CSF1R, anti-PD1 monotherapy or vehicle controls (Fig. 6J–L). Interestingly, depletion of macrophages combined with anti-PD1 resulted in complete remission with absence of ascites in almost half of the mice (3/7) compared to anti-PD1 monotherapy (1/7) and anti-CSF1R monotherapy or vehicle control (both 0/7) (Fig. 6M, N and Supplementary Fig. 7F). Together, these data demonstrate that the combination of macrophage depleting therapy with anti-PD1 immune checkpoint blockade is an effective treatment strategy for PM-CRC in these models.

Discussion

In the current study, we determined the phenotypical characteristics of the human peritoneal immune system to find potential immunomodulatory treatment strategies for PM-CRC.

First, our characterization of the PerIS in healthy controls demonstrated that, under homeostatic conditions, the peritoneal cavity contains abundant levels of resident macrophages, T cells, and NK cells. Neutrophils appeared to be underrepresented in peritoneal fluid, but this may be a technical issue of the single-cell platform that was used, rather than a biological phenomenon, as previously also reported⁵⁹. While it should be noted that we focus on immune cells in this study, we acknowledge the importance of non-immune cells like tumor-associated fibroblasts or other stromal cells as well^60,61.

Given the high abundance of macrophages in the PF, we investigated their phenotype using scRNA-seq and CITE-seq and demonstrated that the majority of PF macrophages have an immunosuppressive phenotype based on their expression level of MARCO⁺CD163^cite-brightC1QA⁺VSIG4⁺CCR2^-, with high expression of immunosuppressive genes APOE, FOLR2, MRC1, TREM2, and MERTK^23,38,39,40. Interestingly, the peritoneal cavity in healthy controls also contained a subset of macrophages expressing SPP1, a subset that is typically restricted to tumor-associated macrophages of various cancers^19,22. SPP1 encodes phosphoprotein 1, also referred to as osteopontin, a secreted phosphorylated glycoprotein that characterizes a subset of TAMs whose intratumoral presence is associated with poor clinical outcome in CRC and lung carcinomas. SPP1⁺ macrophages in tumors negatively correlate with tumor lymphocyte infiltration and are associated with CD8⁺ T cell suppression as well as evasion of anti-tumor immune responses^62,63,64. Indeed, SPP1⁺ macrophages interact with cancer-associated FAP⁺ fibroblasts, possibly through TGF-β and interleukin-1, increasing fibrosis, which further limits T cell infiltration⁶². In addition, SPP1-expressing TAMs facilitate chemo- and radiation resistance by activating the CD44 receptor, PI3K/AKT signaling, and induction of epithelial-mesenchymal transition^64,65,66,67. Lastly, SPP1⁺ macrophages in peritoneal fluid showed high expression of CD112, a protein that links to TIGIT and thereby induces immunosuppressive CD4⁺ regulatory T cell differentiation⁴³. Taken together, these functions of SPP1⁺ macrophages facilitate tumor growth and prevent effective anti-tumor immune responses⁶⁸.

Next to SPP1⁺ macrophages, we observed high abundance of C1Q⁺ CMs and C1Q⁺ mono-CMs in the homeostatic PerIS. C1Q⁺ macrophages have also been linked to poor prognosis. For example, recent studies in human and mouse malignant pleural effusion indicate that C1Q-expressing TAMs undermine effective anti-tumor effects of CD8⁺ T cells by enhancing fatty acid metabolism, thereby activating peroxisome proliferator-activated receptor gamma, which ultimately leads to transcription of immune inhibitory molecules as TREM2, Tim-3, SIRPα, PD-1, and PD-L1⁶⁹.

Second, we demonstrated that PRMs in the setting of PM-CRC show increased immunosuppressive nature, as demonstrated by downregulation of antigen presentation machinery particularly in C1Q⁺ and SPP1⁺ macrophages. This is important as human leukocyte antigen-DR (HLA-DR) molecules are essential to generate targeted immune responses⁷⁰. Indeed, presence of MHC II^low TAMs is correlated with disease progression in hepatocellular carcinoma murine model⁷¹. Apart from decreased antigen presentation, macrophages can promote cancer cell growth by secreting factors that promote angiogenesis, such as VEGFA, or factors that inhibit T cell activity, including IL10 and TGFB^50,72,73. While TGFB was not differentially expressed in macrophages, we observed higher expression of VEGFA and VEGFR1 on C1Q⁺ macrophages, and increased IL10 expression in C1Q⁺ and SPP1⁺ macrophages, which was accompanied by increased levels of VEGF and IL10 in peritoneal fluid of PM-CRC patients, cytokines that have been associated with poor prognosis in patients with peritoneal metastasized gastric cancer⁷⁴.

Third, we have shown a direct link between the local immune compartment in the peritoneal cavity and the TIME in peritoneal metastases. While an outstanding question in the field is to what extend tissue-resident immune cells dictate the tumor immune infiltrate, we here demonstrate that intratumoral macrophages share features with PF macrophages and also express tissue-resident marker VSIG4, suggesting peritoneal origin. The hypothesis that local peritoneal macrophages define the tumor environment was further strengthened by the observation that PM-derived macrophages are transcriptionally more similar to PF-derived macrophages than blood-derived monocytes. Indeed, SPP1⁺ CMs, C1Q⁺ CMs, and C1Q⁺ mono-CMs subsets that were observed in PF were also found in PM of PM-CRC patients, as well as in both PF and PM in an independent cohort of PM-GC patients. Lastly, presence of peritoneal-labeled macrophages within peritoneal tumors in mice suggests that these PRMs actively shape the TIME of PM. In line, this has previously also been demonstrated in the setting of liver metastases, in which circulating PKH-labeled macrophages infiltrated liver metastases and promoted tumor growth⁵². Interestingly, comparing immunosuppressive macrophage gene sets scores from a separate series of patients with matched PM, liver metastases, and primary colon cancers, we show that peritoneal cancers are enriched for immunosuppressive macrophages. Taken together, this demonstrates that peritoneal macrophages likely shape a distinct metastatic niche, which may be involved in poor survival of peritoneal metastatic disease.

Finally, we tested this hypothesis in vivo in pre-clinical mouse models for PM-CRC. A unique opportunity in peritoneal metastasized disease is the ability to directly interfere with the local immune microenvironment by administration of intraperitoneal immunomodulatory treatments, which is fundamentally different from metastases to other organs. Using anti-CSF1R, we effectively depleted peritoneal fluid macrophages, which in the human PF broadly expressed CSF1 receptor. It is likely that treatment with anti-CSF1R antibody also depleted macrophages that line the peritoneal surface^75,76,77, and these have been shown to be tumor-promoting^75,76. Future studies will be needed to disentangle the distinct role of the different macrophages^75,76,77. Macrophage depleting strategies have previously been shown to be effective in peritoneal metastasized ovarian and gastric cancer^78,79,80. Interestingly, several clinical trials are currently ongoing, studying the use of TAM targeting agents like anti-CSF1R, in combination with either ICB or backbone chemotherapy, and show tolerable safety profiles in humans¹³. Treatment with FDA-approved CSF1R activation inhibitor emactuzumab depletes TAMs, increases lymphocyte infiltration, and improves patient outcome in patients suffering diffuse-type giant cell tumors^81,82. Interestingly, CSF1R⁺ TAMs were reduced upon emactuzumab treatment in multiple solid tumors, including colorectal cancer⁸². We utilized in vivo immunocompetent mouse models to validate and test our findings. Successful macrophage depletion resulted in increased levels of activated FSC^high CD8T cells and an increased number of intratumor CD8⁺ T cells, corroborating findings of macrophage depleting strategies in mouse cancer models^53,83,84. More importantly, targeting macrophages either before or after tumor cell implantation, combined with immune checkpoint blockade, significantly reduced disease severity and improved survival in a pre-clinical mouse model for PM-CRC. Although survival data based on mouse discomfort remains difficult to directly link to tumor burden, we feel that mostly, albeit not exclusively, mouse discomfort is correlated to tumor burden as mice treated with anti-CSF1R/anti-PD1 have significant lower ascites formation (Fig. 6g) and tumor burden (Fig. 6h). As anti-CSF1R potentially depletes all macrophages, including pro-inflammatory anti-tumor macrophages, it would be interesting to investigate in future studies whether anti-inflammatory PRMs can be selectively targeted, for example through metabolic reprogramming⁸⁵.

In conclusion, the PerIS is a unique immune compartment containing high abundance of immunosuppressive resident macrophages, and while this is likely an evolutionary important feature in the setting of for example, peritonitis⁸⁶, this can be deleterious in the setting of peritoneal metastases, as cancer cells may hijack this feature to favor their growth. Targeting PRMs is a potential weak spot of peritoneal metastases, and is thus an exciting strategy to combat peritoneal metastasized cancers and improve outcomes for patients with this devastating condition.

Methods

Patients

Patients were enrolled in the study according to Dutch research guidelines of the Federation of Dutch Medical Scientific Societies (FMDSS), as described in “Human Tissue and Medical Research: Code of Conduct for Responsible Use”. The Medical Ethical Committee (METC) of the Amsterdam UMC approved the study protocol: 2019_224. Patients were informed and provided informed consent prior to sampling. Patients did not receive any form of compensation.

Peritoneal flushes were obtained perioperatively. Participating patients were subject to either laparoscopic or laparotomic abdominal surgery, depending on the surgical indication of the procedure. Between 2019 and 2024, a total of 23 patients (5 achalasia (HC), 13 PM-CRC, and 6 PM-GC) were enrolled in this study at the Amsterdam UMC. Patients who underwent surgery for achalasia (n = 5), a rare disorder of the esophagus, in which no immunological alterations of the peritoneal cavity are to be expected, were included to study the peritoneal immune system in homeostasis. For PM-CRC and PM-GC, adult patients (>18 years old) with histologically-proven PM (n = 13 and n = 6, respectively) were included. Exclusion criteria were presence of other intra-abdominal diseases (i.e., endometriosis, pancreatitis, appendicitis, cholecystitis, cholangitis, perforation of the GI tract, bleeding of the GI tract or corpus alienum), or abdominal surgery shorter than 6 months ago. The sex of participants was considered in the study design and was determined based on self-report.

Sample collection

Peritoneal flushes were obtained perioperatively during abdominal surgery by flushing milky spot-enriched areas like the diaphragm and omentum, similar as what has been done in mice^87,88. Peritoneal immune cells within the mesothelial lining may have been missed, and additionally, it should be noted that differences in previous reported human studies on the peritoneal immune system may differ because in these studies, single cell suspensions were usually obtained from peritoneal fluid, such as peritoneal dialysis effluent or ascites^89,90,91. Potential blood contamination was minimized while gaining access to the abdominal cavity, after which the peritoneal cavity was flushed with 1 L of 0.9% NaCl saline solution at body temperature. The omentum and paracolic gutters (1/3) as well as both sides of the diaphragm (1/3 right and 1/3 left) were flushed. The peritoneal flush was removed by suction 2 min after infusion and stored in clean plastic canisters. Afterwards, the peritoneal fluid was quickly transferred to a glass canister and stored on ice. Periprocedural, peripheral blood (6 mL) was drawn from the patient and collected in EDTA tubes and kept on room temperature. Peritoneal tumor samples were taken perioperatively at discretion of the operating surgeon and transferred to transferring medium (RPMI 1640 + 10% FCS) for processing into single cell suspensions or 4% PFA/PBS for tissue embedding.

PBMC preparation for scRNA-seq

Whole blood was transferred to an empty 50 mL tube, and the EDTA tube was flushed with PBS (RT) and added to a total of 17 mL. Then, a total of 13 mL Ficoll-Paque® PLUS (GE/VWR) was added gently, and the sample was centrifuged at 876 × g for 20 min (RT). The PBMC layer was collected, transferred to a new tube, and PBS (RT) was added to a total of 30 mL, after which the sample was centrifuged at 493 × g for 7 min (RT). The supernatant was discarded, and the sample was washed with ice-cold PBS 0.1% BSA and centrifuged at 493 × g for 7 min (4 °C). Ten mL of erythrocyte lysis buffer (self-made) was added and kept on ice for 10 min after the sample was washed with 10 mL ice-cold PBS 0.1% BSA, forced through a 70 µm cell strainer (Greiner), and centrifuged at 493 × g for 7 min (4 °C). The sample was washed again with 10 mL ice-cold PBS 0.1% BSA and centrifuged at 493 × g for 7 min (4 °C). Finally, the sample was resuspended in 1 mL of ice-cold PBS 0.1% BSA and counted using the hemocytometer trypan blue exclusion method.

Peritoneal fluid preparation for scRNA-seq

Tubes (50 mL) were coated with FCS, after which the total volume of peritoneal fluid was transferred into these tubes, forced through a 100 µm cell strainer (Greiner), and centrifuged at 493 × g for 7 min (4 °C). Supernatant was discarded, and pellets of all 50 mL tubes were pooled with ice-cold PBS 0.1% BSA, and the sample was centrifuged at 493 × g for 7 min (4 °C).

Ten mL of erythrocyte lysis buffer (self-made) was added and kept on ice for 10 min after the sample was washed with 10 mL ice-cold PBS 0.1% BSA and the suspension was forced through a 70 µm cell strainer (Greiner) and centrifuged at 493 × g for 7 min (4 °C). The sample was washed again with 10 mL ice-cold PBS 0.1% BSA and centrifuged at 493 × g for 7 min (4 °C). Finally, the sample was resuspended in 1 mL of ice-cold PBS 0.1% BSA and counted using the hemocytometer trypan blue exclusion method.

Peritoneal tumor dissociation and preparation for scRNA-seq

To isolate single cells for scRNA-seq, tumors were transferred to a 6-well plate and washed with ice-cold PBS. Tumors were cut into small pieces using a scalpel blade and transferred to a 50 mL tube and 10 mL warm digestion medium (RPMI 1640, 1.5 mg/ml Collagenase (Sigma-Aldrich), 20 µg/ml DNAse I (Roche)) was added and the sample was placed at 37 °C for 30 min (mice) or 45 min (human) in a prewarmed VWR® Incubating Mini Shaker (400 rpm). To stop the digestion process, 10 mL of cold wash medium (RPMI 1640 + 10% FCS) was added, the tube was shaken vigorously, and the suspension was forced through a 70 µm cell strainer (Greiner) and centrifuged at 493 × g for 7 min. The pellet was washed in PBS 0.1% BSA and centrifuged again at 493 × g for 7 min (4 °C). The pellet was then resuspended in 10 mL red blood cell lysis buffer (self-made) and incubated for 10 min on ice. Ten mL cold PBS 0.1% BSA wash medium was added, and the suspension was centrifuged at 493 × g for 7 min (4 °C) and washed once more, and finally resuspended in 1 mL PBS 0.1% BSA. Cell numbers and viability were assessed using hemocytometer trypan blue exclusion, and brought to a concentration of 25 × 106 cells/mL for subsequent sorting and kept on ice.

scRNA-seq library preparation

Sorted DAPI- live cells were centrifuged at 493 × g for 5 min (4 °C), and supernatant was removed. Cells were diluted in 0.1% BSA/PBS to 1000 cells/μL and kept on ice until further processing. Directly after this, libraries (loading of 7200 cells) were prepared according to Chromium Next GEM Single Cell 3ʹ Reagent Kits v3.1 (PN-1000121). Briefly, Gel Beads-in-emulsion (GEMs) were generated by combining barcoded Single Cell 3′ v3.1 Gel Beads, a Master Mix containing cells, and Partitioning Oil onto Chromium Next GEM Chip. Immediately following GEM generation, the Gel Bead was dissolved, primers were released, and any co-partitioned cell was lysed. Primers were mixed with the cell lysate and a mastermix containing reverse transcription (RT) reagents. Next, Silane magnetic beads were used to purify the first-strand cDNA from the post GEM-RT reaction mixture. The full-length cDNA was amplified via PCR to generate sufficient mass for library construction. After end repair, A-tailing, adaptor ligation, and PCR amplification, the final mRNA libraries were sequenced on the Illumina HiSeq4000 and NovaSeq6000 at the Core Facility Genomics, Amsterdam UMC. The mRNA libraries were sequenced to 150 M per GEM-well.

CITE-seq library preparation

Sorted DAPI- live cells were centrifuged at 493 × g for 5 min (4 °C), and supernatant was removed. Cells were transferred to an Eppendorf in 50 μL of 0.1% BSA/PBS and incubated with Human TruStain FcXTM Fc blocking reagent (Biolegend) on ice for 10 min. Subsequently, cells were transferred to the TotalSeqTM—B Human Universal Cocktail, V1.0 (BioLegend) and incubated on ice for 30 min. The cells were washed twice with 0.1% BSA/PBS and centrifuged at 493 × g for 5 min (4 °C). Cell suspensions and library preparation were performed as described previously under “scRNA-seq library preparation” (loading of 7200 cells).

Statistics and reproducibility

The scRNA-seq experiments were performed on 5 patients with achalasia (HC), 13 patients with PM-CRC, and 6 patients with PM-GC. No statistical method was used to predetermine sample size, and no data were excluded from the analyzes. The Investigators were blinded to mouse allocation during experiments and outcome assessment.

scRNA-seq data analysis

Analyzes were orchestrated using Snakemake⁹² version 7.21. Raw reads were aligned to the human reference genome GRCh38 and processed using the CellRanger 7.0.0 software from 10X Genomics to generate unique molecular identifier (UMI) counts. The filtered gene expression matrices were imported into R version 4.1.1 and processed using the Seurat R package⁹³ version 4.1.1. Gene counts were normalized by SCTransform⁹⁴, while CITE-seq counts were normalized using CLR. Comparisons where PBMC, PF, and/or PM were combined were processed using Harmony to remove batch effects⁹⁵ for subsequent unsupervised clustering analyzes. Unsupervised clustering analyzes were performed using the Louvain method on either the gene expression alone (PBMCs) or on the integrated gene and protein expression (PF) using the weighted nearest neighbor approach. Subsequent visualizations were conducted using the uniform manifold approximation projection (UMAP) algorithm calculated on the principal components⁹⁶ derived from gene expression data, or the weighted nearest neighbor graph calculated from both gene and protein expression when interrogating PF from HC. Cells were initially annotated by lifting over annotations from a public PBMC dataset⁹⁷. A subsequent curation was conducted by manually checking canonical markers. Debris and dead cells were identified based on a high percentage of mitochondrial reads in combination with a low number of unique genes identified. Since we were interested in the immune composition of the PerIS and TIME, all CD45- non-immune cells, apoptotic, proliferating, dead/dying cells, multiplets, and neutrophils were removed from the dataset. Neutrophils were excluded from subsequent analysis because (i) comparison with PBMCs was difficult since PBMCs, but not PF, had undergone Ficoll treatment, and (ii) the 10X genomics platform generally yields low transcripts due to low RNA content and high amounts of RNases.

Differential abundance analyzes were conducted by performing pairwise t-tests as implemented in the propeller function from speckle⁹⁸ version 0.99.7 when comparing between samples, whereas Mann-Whitney tests were implemented to compare proportions between cell types. Multi-sample differential expression analyzes were conducted using the pseudobulk method⁹⁹, where we summed reads per cell type per sample and conducted differential expression analyzes using DESeq2¹⁰⁰ package version 1.34. Gene set enrichment analyzes were conducted using the fgsea package version 1.26. Cellular classification of TAMs was conducted using UCell¹⁰¹ version 2.4, where comparative analyzes were conducted through a mixed model approach with a random effect per donor using the lme4 package for general omnibus testing and nlme package for pairwise analyzes.

Public scRNA-seq datasets

The Colon Immune atlas of the publicly available dataset “Gut Cell Survey” (gutcellatlas.org)⁴⁴ was used to compare the colonic immune compartment to our data. In total, 5 healthy patients (2 males and 3 females; patients IDs: 290b, 298c, 302c, 390c, and 417c; regions: cecum, sigmoid colon, and transverse colon) were included. In total, 27,619 cells were used in our comparative analysis. The mRNA libraries in this dataset were sequenced using only Chromium Next GEM Single Cell 3ʹ Reagent Kits v2. The cecum, sigmoid and transverse colon were included in the analysis, and the mesenteric lymph nodes were excluded. Further analysis of the data was performed as described previously under “scRNA-seq data analysis”. Cell subset annotations were manually curated and matched to our own dataset for comparability purposes.

The Human Liver atlas of the publicly available dataset “Liver Cell Atlas” (GSE192742; https://www.livercellatlas.org)⁴⁵ was used to compare the liver immune compartment to our data. In total, 5 selected histologically healthy (absence of fibrosis), lean patients (5 females; patient IDs: H14, H16, H21, H22, and H25) were included. In total, 29,037 cells were used in our comparative analysis. The mRNA libraries in this dataset were sequenced using only Chromium Next GEM Single Cell 3ʹ Reagent Kits v3.1. Further analysis of the data was performed as described previously under “scRNA-seq data analysis”. Cell subset annotations were manually curated and matched to our own dataset for comparability purposes.

Immune subsets and subsequent macrophages were annotated using the same genes as per Fig. 1C, F. Dotplots and feature plots presenting the expression of several marker genes for the public datasets are included in the manuscript (Supplementary Fig. 3A–D).

Cell culture

Cell line CT26 was cultured in Dulbecco’s modified Eagle’s medium with L-glutamine, 15 mM HEPES (Thermo Fisher Scientific), supplemented with 8% fetal bovine serum (Life Technologies), penicillin, and streptomycin. The cell line was authenticated and regularly tested for mycoplasma infection.

Animal experiments

All in vivo experiments were approved by the animal experimentation committee at the Amsterdam UMC (in Amsterdam under the nationally registered license number AVD11800202013801) and performed according to national guidelines. Female BALB/cOlaHsd mice (6–12 weeks old) were purchased from Envigo. The mice were housed on a 12 h light-dark cycle at 20–26 °C with 30–70% humidity. Animals were randomly assigned to experimental groups.

Intraperitoneal tumor growth and treatment

To generate intraperitoneal tumors, 1 × 10⁵ CT26 cells in medium were mixed at a 1:1 ratio with Matrigel (Corning) and injected intraperitoneally in BALB/c (CT26) mice. Anti-mouse CSF1R (BioXCell, clone AFS98) or vehicle control (phosphate-buffered saline, Fresenius Kabi) treatment was started at the same day as tumor cell injection and administered 3x week intraperitoneally for 21 days, 400 μg/injection. Anti-mouse PD1 (BioXCell, clone J43) or vehicle control (phosphate-buffered saline, Fresenius Kabi) treatment was started 7 days after tumor cell injection and administered 3x week intraperitoneally for 16 days, 100 μg/injection. In the second model, treatment initiation of all groups was only started 7 days after tumor cell injection. Mice were euthanized after 3 weeks for immune cell analysis, or when a humane endpoint was met for survival analysis, with a maximum of 16 weeks. Humane end points were determined as more than 15% weight gain within 2 days or signs of discomfort (e.g., decreased activity, reduced self-grooming, hunched posture, isolation from the group). The predefined humane endpoints were not exceeded. Tumors were harvested and scored according to the modified Peritoneal Carcinomatosis Index (mPCI), which evaluates tumor burden across seven anatomical regions of the murine peritoneal cavity: right and left subphrenic, (sub)hepatic, subgastric, small bowel/mesentery, pelvic, and back area. Each region was assigned a score from 0 to 3 based on the number and size of tumor nodules (0 = no macroscopic tumors, 1 = tumor nodules ≤2 mm, 2 = tumor nodules between 2 and 5 mm or >5 tumor nodules, 3 = tumor nodules ≥5 mm or >10 tumor nodules present), with a total possible score of 21⁵⁷.

Peritoneal macrophage labeling

PKH26 Red Fluorescent Cell Linker Kit for Phagocytic Cell Labeling (Sigma–Aldrich; 500 µl of 5 µM solution) was used to selectively label cells with phagocytic capabilities, and in the peritoneal cavity, selectively stains peritoneal macrophages¹⁰². BALB/c mice were injected intraperitoneally with PKH26 or PBS control, after which, 2 days later CT26 cells were injected intraperitoneally, as described under “Intraperitoneal tumor growth and treatment”. Mice were sacrificed 16 days after initial PKH26 dye injection and PF, spleen, and peritoneal tumors were processed and analyzed using flow cytometry, as described in their respective parts, “Mouse peritoneal fluid, spleen, and tumor preparation for flow cytometry and Flow cytometry”, respectively.

Mouse peritoneal fluid, spleen, and tumor preparation for flow cytometry

Directly after euthanasia using CO₂ (no cervical dislocation to prevent intra-abdominal blood contamination), peritoneal lavage with 5 mL ice-cold PBS 0.1% BSA was performed. The suspension was kept on ice, forced through a 100 µm cell strainer (Greiner), and centrifuged at 493 × g for 7 min (4 °C). Peritoneal tumors were dissociated as described in “Peritoneal tumor dissociation and preparation for scRNA-seq”. Spleens were dissociated when actively forced through a 100 µm cell strainer (Greiner) and centrifuged at 493 × g for 7 min (4 °C). Subsequent processing was the same for peritoneal fluid, spleen, and tumor samples.

Supernatant was discarded, and 10 mL RBC lysis buffer (Thermo Fisher Scientific) was added for 5 min, after which the sample was washed with 10 mL ice-cold PBS 0.1% BSA, forced through a 70 µm cell strainer (Greiner), and centrifuged at 493 × g for 7 min (4 °C). The sample was washed again with 10 mL ice-cold PBS 0.1% BSA and centrifuged at 493 × g for 7 min (4 °C). Finally, the sample was resuspended in 1 mL ice-cold PBS 0.1% BSA, counted using the hemocytometer trypan blue exclusion method, and brought to a concentration of a maximum of 25 × 106 cells/mL for subsequent flow cytometry analysis.

Flow cytometry

Murine PF, spleen, and PM were included for flow cytometry analysis. After successful isolation of single cell suspension, cells were washed in PBS and stained with LIVE/DEADTM Fixable Blue Dead Cell Stain Kit (Invitrogen, 1:1000). Cells were then centrifuged at 493 × g for 7 min (4 °C) and washed in PBS 0.1% BSA and incubated with TruStain FcX™ (anti-mouse CD16/32; clone 93, Biolegend, 1:100) for Fc receptor blocking. Afterwards, cells were centrifuged at 493 × g for 7 min (4 °C) and washed in PBS 0.1% BSA and subsequently stained for surface markers belonging to either a T-cell panel or myeloid panel using the following antibodies: CD8a; PerCP/Cy5 (53–6.7, Biolegend, 1:100), CD3; PE/Dazzle594 (17A2, Biolegend, 1:200), CD4; PE (GK1.5, Biolegend, 1:100); CD11b; Pacific blue (M1/70, Biolegend, 1:100), F4/80; PE/Cy7 (BM8, eBioscience, 1:100), CD19; 6D5 (APC/Cy7, Biolegend, 1:100), CD45; AF647 (30-F11, Biolegend, 1:200), CD45; AF700 (30-F11, Biolegend, 1:800), CSF1R; APC (AFS98, Biolegend, 1:200), F4/80; APC (BM8, Biolegend, 1:100). Acquisition was performed on the BD LSR Fortessa™, where large peritoneal macrophages (LPMs) were identified as live/dead-CD45⁺CD3⁻CD19⁻CD11b⁺F4/80⁺. Analysis was performed using FlowJo (Treestar) and R. The gating strategy of Figs. 4H, 6A and Supplementary Fig. 7C can be found in Supplementary Fig. 8A, B, C. For the PKH26 dye, laser was excited at 561 nm and detected using 585/15 band pass, as recommended by the manufacturer.

Immunohistochemistry (human peritoneal tumors)

Immunohistochemical stainings were performed on 5 μm-thick sections of paraffin-embedded tissues with the Ventana Benchmark Ultra immunostainer (Roche/Ventana, Tucson, USA). For detection and visualization of the proteins, the Optiview detection kit, including DAB and CuSO4 (Roche, Basel, Switzerland), was used. After Immunohistochemical staining, the sections were dehydrated with baths of ethanol and cleared with xylene. All sections were mounted with Tissue Tek ® coverslipping film (Sakura Finetek Europe B.V., Alphen aan den Rijn, The Netherlands). The following primary antibodies were used: CD8 (Agilent, 1:50) and CD163 (Cell Marque, 1:25). Pictures were made using Leica DM6 (Leica Microsystems), using the N PLAN 40x/0.65 objective, PCO edge 4.2 camera, and LEICA LAS-X acquisition software was used. Adobe Illustrator (Adobe) was used as processing software to crop images.

Immunofluorescence

Directly after isolation, tumors from mice sacrificed 19–23 days after tumor cell injection were fixed overnight in 4% formaldehyde, embedded in paraffin, and sectioned. Immunofluorescence stainings were performed on 5 μm-thick sections of paraffin-embedded tissues. Sections were deparaffinized with xylene and gradually rehydrated in ethanol. Slides were boiled in 0.01 mol/L sodium citrate buffer (pH 6) for 10 min at 120 °C in an autoclave for antigen retrieval. Slides were blocked for 30 min at room temperature in PBT. Then, slides were incubated overnight with primary antibody diluted in the blocking buffer. Slides were washed with PBS, and secondary antibody diluted in blocking buffer was added for 1 h at room temperature. Slides were mounted using Vectashield antifade mounting medium with DAPI (Vector Laboratories). The following primary antibodies were used: CD8 (ThermoFischer, 1:100) and CD206 (Abcam, 1:400). For both, donkey anti-rabbit AF568 (Invitrogen, 1:200) was used as secondary antibody. Pictures were made using Leica DM6 (Leica Microsystems), using the N PLAN 40x/0.65 objective, PCO edge 4.2 camera, the DAP and Y3 filter blocks, and Leica LAS-X acquisition software was used. Adobe Illustrator (Adobe) was used as processing software to crop images.

Olink

In peritoneal fluid of both healthy controls and patients suffering PM-CRC, the abundance of 92 immuno-oncology proteins was analyzed using the OLINK Target 96 Immuno-Oncology assay. An additional cohort (n = 9; total n = 21) was used for validation and correlation analysis. In short, a unique pair of oligonucleotide-labeled antibody probes binds to the targeted protein, which results in proximity-dependent DNA polymerization. The subsequent sequence is detected and quantified by real-time PCR. Protein concentrations are normalized and transformed using internal extension controls to adjust for intra-run variation. Data are provided as Normalized Protein Expression (NPX), with higher values indicating higher protein expression. Samples for HC and PM-CRC were randomized on the plate to avoid batch effects.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

All raw sequencing data generated during this study have been made deposited in the European Genome-phenome Archive (EGA) under controlled access to comply with the local privacy laws. PBMC and PF scRNA-seq and CITE-seq (PF) data from healthy controls can be found under accession codes: EGAD50000000250, EGAD50000000251, and EGAD50000000252, respectively [https://www.ega-archive.org/studies/EGAS50000000174]. PF and PM scRNA-seq data from PM-CRC patients can be found under accession codes: EGAD50000000248 and EGAD50000000249, respectively [https://ega-archive.org/studies/EGAS50000000173]. Researchers are allowed to apply for access to the data access committee governing this data under the condition that the data will be used for research purposes only and that no attempts will be made to identify the participants. Source data are provided as a Source Data file. Data from the Colon Immune atlas were obtained from the “Gut Cell Survey’ (gutcellatlas.org). Data from the Liver Cell Atlas was obtained from GSE192742. [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE192742]. Source data are provided with this paper.

Code availability

All computational scripts used to prepare and analyze the data can be found on https://github.com/ND91/TIPRJ0000015_MAPS_CRC (https://doi.org/10.5281/zenodo.15064487).

Change history

05 May 2025
In the version of the article initially published, the reference 103 was incorrectly cited. It has been removed from the HTML and PDF versions of the article.

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Acknowledgements

We wish to thank all the patients and staff from all the units that participated in the study. In particular, Susan Kenter, Core facility Genomics. Noortje van Gils, CITE-seq protocol. Pim Koelink, Jacqueline Vermeulen, Stephanie de Wit, Sofía Frigerio, and Lisanne Nijman support with laboratory experiments. Toni van Capel, Berend Hooibrink, Kim Brandwijk-Paarlberg and Daisy Picavet-Havik, Core facility Advanced Microscopy and Flow Cytometry and Wim Vos, Pathology. Figures 4g, 6j and Supplementary Fig. 1a were created with BioRender.com. This work is supported by The New York Stem Cell Foundation (LV), grants from the European Research Council (ERC-CoG 101045612 - NIMICRY) (LV), ZonMw (Vici 09-15018-21-10029) (LV) and Dutch Cancer Society grant (KWF 13435 / 2021-1) (LV), KWF YIG 13915 (JG), NWO ZonMw Veni Grant 09150161810115 (JG), NWO ZonMw Vidi Grant 09150172210058 (JG), Top Institute for Knowledge and Innovation grant ImPACT (JG), donation by mr H.J.M. Roels through Oncode Institute (JG). LV is a New York Stem Cell Foundation—Robertson Investigator.

Author information

These authors contributed equally: J. Saris, A. Y. F. Li Yim, S. Bootsma.
These authors jointly supervised this work: L. Vermeulen, J. Grootjans.

Authors and Affiliations

Department of Gastroenterology and Hepatology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
J. Saris, A. Y. F. Li Yim, M. E. Wildenberg, G. R. A. M. D’Haens & J. Grootjans
Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam, The Netherlands
J. Saris, A. Y. F. Li Yim, S. Bootsma, K. J. Lenos, R. Franco Fernandez, H. N. Khan, J. Verhoeff, D. Poel, N. M. Mrzlikar, M. P. Schijven, M. E. Wildenberg, C. Jongerius, G. R. A. M. D’Haens, L. Vermeulen & J. Grootjans
Cancer Center Amsterdam, Amsterdam, The Netherlands
J. Saris, S. Bootsma, K. J. Lenos, R. Franco Fernandez, J. Verhoeff, N. C. T. van Grieken, C. Jongerius, J. J. Garcia Vallejo, S. S. Gisbertz, S. Derks, J. B. Tuynman, L. Vermeulen & J. Grootjans
Tytgat Institute for Liver and Intestinal Research, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
J. Saris, A. Y. F. Li Yim, R. Franco Fernandez, H. N. Khan, J. Verhoeff, D. Poel, N. M. Mrzlikar, L. Xiong, M. E. Wildenberg & J. Grootjans
Laboratory for Experimental Oncology and Radiobiology, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
J. Saris, S. Bootsma, K. J. Lenos, R. Franco Fernandez, H. N. Khan, D. Poel, N. M. Mrzlikar, A. Logiantara, C. Jongerius, L. Vermeulen & J. Grootjans
Oncode Institute, Amsterdam, The Netherlands
J. Saris, S. Bootsma, K. J. Lenos, R. Franco Fernandez, H. N. Khan, D. Poel, N. M. Mrzlikar, A. Logiantara, C. Jongerius, S. Derks, L. Vermeulen & J. Grootjans
Amsterdam Infection & Immunity Institute, Amsterdam, The Netherlands
A. Y. F. Li Yim, J. Verhoeff, J. J. Garcia Vallejo & J. Grootjans
Molecular Cell Biology & Immunology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
J. Verhoeff & J. J. Garcia Vallejo
Department of Surgery, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
M. P. Schijven
Amsterdam Public Health, Digital Health, Amsterdam, The Netherlands
M. P. Schijven
Department of Pathology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
N. C. T. van Grieken
Laboratory Translational Oncology, Division of Imaging and Cancer, University Medical Center Utrecht, Utrecht, The Netherlands
O. Kranenburg
Utrecht Platform for Organoid Technology, Utrecht University, Utrecht, The Netherlands
O. Kranenburg
Department of Surgery, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands
S. S. Gisbertz
Department of Medical Oncology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
S. Derks
Department of Surgery, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
J. B. Tuynman
Discovery Oncology, Genentech Inc., South San Francisco, CA, USA
L. Vermeulen

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Contributions

J.S., S.B., J.V., R.F., H.K., D.P., N.M., L.X., M.S., C.J., K.L., and J.T. performed experiments. J.S. and R.F. collected patient material and clinical data. J.S., A.L.Y., S.B., J.V., R.F., H.K., and K.L. analyzed data. A.L.Y., J.V., K.L., and J.V.G. provided bioinformatics input. J.S., A.L.Y., S.B., K.L., L.V., and J.G. conceived, designed, and directed the research. J.S., A.L.Y., S.B., J.V., R.F., H.K., L.X., M.S., N.v.G., O.K., M.W., C.J., K.L., J.G.V., S.G., S.D., J.T., G.D., L.V., and J.G. provided scientific input. J.S., A.L.Y., S.B., L.V., and J.G. wrote the manuscript. All authors approved the content of the manuscript.

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Correspondence to J. Grootjans.

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Competing interests

A.L.Y. received consultancy fees from Janssen, Johnson & Johnson, DeciBio, and was previously employed by GSK. None of the aforementioned entities had relation to the content of this publication. L.V. received consultancy fees from Bayer, MSD, Genentech, Servier, and Pierre Fabre, but these had no relation to the content of this publication. L.V. is an employee of Genentech Inc. J.G. has a collaboration with Roche. The other authors declare no competing interests.

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Saris, J., Li Yim, A.Y.F., Bootsma, S. et al. Peritoneal resident macrophages constitute an immunosuppressive environment in peritoneal metastasized colorectal cancer. Nat Commun 16, 3669 (2025). https://doi.org/10.1038/s41467-025-58999-6

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Received: 22 April 2024
Accepted: 09 April 2025
Published: 17 April 2025
DOI: https://doi.org/10.1038/s41467-025-58999-6