Lyve-1 expressing perivascular macrophages orchestrate pericyte expansion to sustain angiogenesis in cancer

Tumor associated macrophages (TAMs) are a highly plastic stromal cell type which are exquisitely polarized by the tumor microenvironment to support cancer progression1, 2. Single-cell RNA-sequencing (scRNA-seq) of TAMs from a spontaneous murine model of mammary adenocarcinoma (MMTV-PyMT) identified three distinct polarization trajectories for these cells within the tumor microenvironment. We reveal sub-divisions within the pro-tumoral TAM population with one subset expressing Lyve-1 and residing in a spatial niche proximal to blood vasculature within the tumor. We demonstrate that selective depletion of the Lyve-1+ TAM population significantly slows tumor growth because of a non-redundant role of these cells in orchestrating the platelet derived growth factor-CC (PDGF-CC)-dependent expansion of tumor-resident pericytes which underpins vasculature growth and development. This study uncovers that local pericyte expansion in cancer is not an autonomous event but tightly regulated by the perivascular Lyve-1+ TAM population, which ultimately govern the success of angiogenesis in cancer.

appeared to be the least polarized (TAM01 and 02), with few enriched GO terms, represented almost a quarter of TAMs within the tumor (23.3% ± 3.4 of all TAMs analyzed), suggesting that a significant proportion of TAMs remain relatively unspecialized in their role ( Fig. 1e,f and Supplementary Fig. 1e). Trajectory analysis using Slingshot 23 and diffusion maps was able to align the 8 identified clusters into a three trajectory polarization model with TAM04, 06 and 07 clusters representing predicted polarization extremes (Fig. 1g,h and Supplementary Fig. 2). Analysis of the three developmental pathways for their enrichment of M1/M2 24 programs using the marker gene list of Orecchioni et al 25 highlighted TAM04 to be skewed towards an inflammatory (M1-like) transcriptome (Fig. 2a,b) which were more enriched for expression of inflammatory genes representative of a cellular response to type-1 interferons such as Irf7 and Isg15. TAM06 and TAM07 possessed a more pro-tumoral (M2-like) transcriptome (Fig. 2a,b). TAM06 was more enriched for anti-inflammatory genes such as Il10, whereas both TAM06 and TAM07 were enriched in Ccl2, Mmp19 and Hb-egf.
TAM06 and TAM07, the two pro-tumoral TAM programs, both also commonly expressed Mrc1 (the gene for MRC1/CD206) 26 , but were functionally distinct in many of their enriched GO biological pathways, with a preferential skewing of TAM06 towards angiogenic processes and TAM07 towards immune regulation, highlighting a specialized sub-division of roles within the tumor (Fig. 2c,d). Using flow cytometry analysis of gated CD206 + F4/80 hi TAMs stained for markers identified within the scRNA-seq analysis, confirmed that similar TAM sub-populations could be distinguished using the predicted protein markers in MMTV-PyMT tumors. The CD206 expressing pro-tumoral TAMs could be differentiated based on their expression level of CD206, MHCII, and the lymphatic vessel endothelial hyaluronic acid receptor 1 (Lyve-1) (Fig. 2e,f), into CD206 lo MHCII lo Lyve-1 -(TAM05) and the predicted polarization extremes of CD206 hi MHCII lo Lyve-1 + (TAM06) and CD206 int MHCII hi Lyve-1 -(TAM07) population. Lyve-1 has traditionally been considered a marker of lymphatic endothelium 27 , but has also been utilized as a marker on tissue-resident macrophages 28,29,30,31,32 and TAMs 33 . The Lyve-1 + subset (TAM06) accounted for 10.7±3.5% of TAMs and 1.4±0.4% of live cells within the tumor (Fig. 2g).
To validate that the populations identified in the scRNA-seq and flow cytometry data were equivalent, the FACs-gated populations were subjected to bulk population RNA-seq alongside CD206 -MHC lo F4/80 hi TAMs as a comparator group. Principal component (PC) analysis confirmed these populations to be transcriptionally distinct (Fig. 2h,i). Comparing the bulk population RNA-seq to that of the scRNA-seq populations validated close concordance between the identified populations across a range of predicted marker genes ( Fig. 2j). CD206 hi MHCII lo Lyve-1 + (TAM06) also selectively expressed the transcription factor Maf ( Supplementary Fig. 2d) and CD206 int MHCII hi Lyve-1 -(TAM07) the transcription factor Retnla (Fig. 1d), which may indicate that these transcription factors play a role in polarization identity. A monocyte-derived macrophage with a similar MHCII lo Lyve-1 hi surface phenotype has been demonstrated to reside proximal to vasculature in a variety of healthy tissues 32 .
The GO pathway analysis also suggested that CD206 hi MHCII lo Lyve-1 + TAMs were highly endocytic (Fig. 2c). Liposomes containing the fluorescent lipophilic dye 1'-dioctadecyl-3,3,3',3''tetramethylindocarbocyanine perchlorate (Dil) have previously been used to study perivascular TAM (PvTAM) development 13 and we predicted they could represent a tool to specifically label the CD206 hi MHCII lo Lyve-1 + TAM subset. We developed a labelling protocol that could selectively mark PvTAMs, rather than a monocytic progenitor ( Supplementary Fig.   3a). Confocal analysis of the tumors demonstrated that Dil-liposomes labelled a population of PvTAMs (Fig. 2k-m) and ex vivo characterization of the Dil-labelled cells in enzymedispersed tumors confirmed their phenotype to indeed be that of the CD206 hi MHCII lo Lyve-1 + TAM (TAM06, Fig. 2n).
As the liposome labeling protocol preferentially labelled CD206 hi MHCII lo Lyve-1 + TAMs (  Fig. 3c). However, even over the long-term administration of clodronate-filled liposomes, the selective nature of the depletion of CD206 hi MHCII lo Lyve-1 + (TAM06) TAMs was maintained, sparing the CD206 int MHCII hi Lyve-1 -(TAM07) subset of specialized pro-tumoral TAMs (Fig. 3c-e), CD206 -TAMs, (Fig. 3f) and CD11b + Ly6C + monocytes (Fig. 3g). Furthermore, using immunofluorescence imaging there was an observable selective spatial loss of PvTAMs within the clodronate-filled liposome treated mice (Fig. 3h), where clusters of TAMs surrounding blood vessels were no longer observable. To understand the mechanism through which CD206 hi MHCII lo Lyve-1 + TAMs promote tumor progression (Fig. 3b), we first phenotyped the immune-infiltrate of the tumors, however there was no associated change in abundance of any immune cell population analyzed within the tumor microenvironment, other than a statistically significant increase in the abundance of the migratory CD11c + CD103 + dendritic cells (DCs) (Fig. 3i), which contribute to cytotoxic T-lymphocyte recruitment in the tumor 35 and priming of the anti-tumor immune response 36 . However, there was no increase in CD8 + or CD4 + T-cell recruitment in the absence of CD206 hi MHCII lo Lyve-1 + TAMs (Fig. 3i). Due to the Pv location of CD206 hi MHCII lo Lyve-1 + TAMs, it was possible that these cells may play a central role in promoting neo-angiogenesis to sustain tumor growth 37,38 . Immunofluorescence analysis of sections from MMTV-PyMT tumors stained for CD31 + endothelial cells and perivascular α-smooth muscle actin (αSMA) expressing stromal cells (Fig. 3j), revealed no significant change in the overall proportion of endothelial cells (Fig. 3k) and a small, but significant, increase in vessel density ( Supplementary Fig. 3d) in the tumor. However, there was a striking loss of αSMA + stromal cells proximal to vasculature (Fig. 3j,l). Although there is evidence that mesenchymal populations can be phagocytic 39 , neither CD45 -CD31 + endothelial cells nor CD45 -CD90 + mesenchymal stromal cells 40 had up-taken the liposomes ( Supplementary Fig. 3e), excluding any direct killing effect of the clodronate on these populations. This however, highlighted a potential role of CD206 hi MHCII lo Lyve-1 + TAMs in maintenance of the αSMA + stromal population.
Staining tissue sections from MMTV-PyMT tumors for αSMA + cells and F4/80 + TAMs placed these populations in the same perivascular 'niche' with a close spatial arrangement providing opportunity for interactions (Fig. 4a). This colocalization was also evident in human invasive breast carcinomas, where CD68 + TAMs and αSMA + cells could be found in close proximity adjacent to CD31 + endothelial cells lining blood vessels (Fig. 4b). Interestingly, this relationship was not observed in ductal carcinoma in situ (DCIS), suggesting that the spatial arrangement could be associated with progressive disease where there is ongoing neoangiogenesis (Fig. 4b). To further investigate these perivascular αSMA + cells, we characterized the heterogeneity of a broad pool of tumor-associated mesenchymal stromal cells (collectively termed cancer associated fibroblasts; CAFs) that were CD45 -CD31 -CD90 + using flow cytometry within enzyme-dispersed MMTV-PyMT tumors. The CD45 -CD90 + CAF population accounted for 4.0±1.6% of total live cells within 350mm 3 tumors and their abundance increased as tumors progressed (Fig. 4c). We screened the CD45 -CD90 + CAFs 40 for cell surface markers identified as being associated with CAFs 40, 41 including; Ly6a, CD34, PDGFRα, FAP and CD29 42,43 . Clustering of the multi-parametric flow cytometry data using UMAP 22 and FlowSOM 44 distinguished two distinct subsets (Fig. 4d) and these two populations could be separated based on their expression of CD34 (Fig. 4e). To identify the αSMA-expressing population and further characterize the functionality of these cells, we subjected CD34 + and CD34 -CAFs to bulk RNA-seq. This analysis demonstrated clear transcriptional differences in these CAF subsets ( Supplementary Fig. 4a-c), with the CD34 -CAFs, in addition to expressing αSMA (Acta2), also expressing Des, Pdgfrb and Cspg4 (Fig.   4f) which are genes associated with pericytes, a population of specialized vessel associated mesenchymal cells 45 . To confirm the presence of Desmin (Des) and NG2 (Cspg4) at the protein level in these cells, immunofluorescence staining of tissues sections from MMTV-PyMT mice confirmed that the Pv αSMA + CAFs were also Desmin + ( Supplementary Fig. 4d), and ex vivo flow cytometry confirmed the presence of surface NG2 ( Supplementary Fig. 4e).
Alongside their proximity to vasculature, we elected to refer to these cells as 'pericytes'.
Analyzing the abundance of pericytes over the different stages of tumor progression from the healthy mammary gland, hyperplasia and the growing tumor revealed a relative increase in the abundance of the population within the CAF population over tumor progression, suggesting a selection of this subset within the tumor microenvironment (Fig. 4g). Analyzing the identified CAF populations across different ectopic tumor models including B16, LL2 and orthotopic 4T1, demonstrated these cell populations to be present in all tumor models analyzed ( Supplementary Fig. 4f,g). The progenitor of pericytes/CAF populations still remain debated in cancer, and may originate from different sources 46 . To elucidate the route through which these cells were accumulating in the tumor, we first explored local proliferation and pulsed mice bearing MMTV-PyMT tumors with 5-ethynyl-2'-deoxyuridine (Edu) to label actively proliferating cells. Although both CD34 + CAFs and pericyte populations displayed evidence of proliferation by comparison with healthy mammary gland, the pericytes were proliferating at a significantly faster rate (Fig. 4h). To exclude recruitment and address whether the proliferation of the pericytes was sufficient to account for their preferential expansion with tumor growth we utilized the Kaede mouse 47 crossed to the MMTV-PyMT model. Using this approach, we were able to photoconvert all tumor and stromal cells within a 100mm 3 tumor from Kaede-green to Kaede-red (Fig. 4i). Analyzing tumors 72h after photoconversion demonstrated that CD45 + stromal cells predominantly displayed Kaede-green, highlighting the continual recruitment of hematopoietic stromal cells to the tumor from the periphery 13,48,49,50 . In contrast, both pericyte and CD34 + CAFs remained Kaede red, which indicated that the expansion of this population was solely from tumor-resident cells and not dependent on a peripheral source (Fig. 4i). The rapid proliferation of the pericytes relative to the CD34 + CAF also explains the dynamics within the CD90 + mesenchymal stromal compartment over tumor growth (Fig. 4g).
Immunofluorescence analysis for Ki67, a marker of proliferation 51 , on αSMA + cells confirmed a close spatial relationship between proliferating Ki67 + αSMA + pericytes and F4/80 + PvTAMs ( Fig. 4j). Pericytes have previously been shown to sense inflammation and attract and interact with macrophages 52, 53 . However, the biological implications of this interaction are largely unknown. To investigate whether CD206 hi MHCII lo Lyve-1 + PvTAMs might be implicated in the expansion of pericytes to sustain the angiogenic requirements of the tumor, we analyzed the incorporation of Edu in the presence and absence of CD206 hi MHCII lo Lyve-1 + PvTAMs (depleted using clodronate-filled liposomes) (Fig. 4k,l). Despite no observable drop in the proportion of pericytes within the tumor over the short-term acute treatment regimen (Fig. 4m), proliferation of the pericyte population was significantly diminished in the absence of CD206 hi MHCII lo Lyve-1 + PvTAMs, with no change in the proportion of cells which had incorporated Edu + cells within the CD34 + CAF or tumor cell compartments (Fig. 4n).
Pericytes are important to angiogenesis, supporting vessel stabilization and endothelial cell survival 37 . The endothelium's expression of PDGF-β which signals on PDGF-Rβ expressed on pericytes has been demonstrated as a vital chemo-attractant axis for localizing pericyte progenitors to the vasculature 54 55, 56, 57 . In the absence of pericytes, as is observed in Pdgfb and Pdgfrb knockout mice, the vasculature displays systemic defects 58 . Our data suggest that despite endothelial cells playing a well-established role in maintaining pericytes in the perivascular space 55, 56 , the role of directing expansion of this population during pathological angiogenesis is divested to the immune system, specifically the CD206 hi MHCII lo Lyve-1 + PvTAMs.
To unravel the underlying mechanism for how CD206 hi MHCII lo Lyve-1 + PvTAMs were orchestrating pericyte expansion, we utilized CellPhoneDB, a manually curated repository and computational framework to map the possible biological ligand:receptor interactions within RNA-seq datasets 59 between the CD206 hi MHCII lo Lyve-1 + PvTAMs, pericytes and CD31 + endothelial cells (which were bulk-population RNA-sequenced) to construct an interactome of the major cell types in the perivascular niche (Fig. 5a). There were a total of 788 possible unique interactions between these three cell types, highlighting the range of potential crosstalk between these populations in constructing the angiogenic niche ( Supplementary Fig. 5a). To refine this list, we selected for non-integrin mediated ligands which were enriched in CD206 hi MHCII lo Lyve-1 + PvTAMs compared to other TAM populations and could interact with receptors specifically expressed on pericytes and not endothelial cells (Fig. 5b,c). This highlighted the selective expression of Pdgfc 60 by the CD206 hi MHCII lo Lyve-1 + PvTAMs which could signal through Pdgfra expressed selectively on the pericytes (Fig. 5c). More broadly, the CD206 hi MHCII lo Lyve-1 + TAM subset was a major source of Pdgfc in the tumor ( Fig. 5d and Supplementary Fig. 5b).
PDGFRs form either homo-or hetero-dimers between the α and β receptor subunit (αα, αβ and ββ) and a homodimer of PDGF-C (PDGF-CC) selectively signals through PDGFRα 61 which has been demonstrated to be a mitogenic and migratory factor for human dermal fibroblasts 62,63 . PDGF-CC is a prognostic factor for poor survival in breast cancer 64 and has been demonstrated to be important to angiogenesis 65,66 . The bulk population RNA-seq demonstrated that pericytes selectively expressed Pdgfra (Fig. 5e), and the protein was also detectable ( Fig. 5f), emphasizing the selectivity of the PDGF-CC:PDGFRα axis within the perivascular niche. Tumors grow slower in MMTV-PyMT Pdfgc -/mice and display increased necrotic areas and evidence of hemorrhage 64 . To assess whether PDGF-CC may play a role in directing the proliferation of the pericyte population within the perivascular niche we administered neutralizing antibodies to PDGF-CC, within an acute treatment regimen, in tumor bearing MMTV-PyMT mice (Fig. 5g). Neutralization of PDGF-CC did not affect the abundance of the cell populations at the acute timepoint ( Fig. 5h) but did diminish Edu + incorporation selectively of the pericytes, but not in the tumor or CD45 -CD31 + endothelial cells (Fig. 5i). This highlighted that the expansion of pericytes was PDGF-CC dependent and could account for the role of CD206 hi MHCII lo Lyve-1 + PvTAMs in expanding the pericyte population to support angiogenesis in cancer. In accordance with our observations in murine models, using The Cancer Genome Atlas (TCGA) we revealed an enrichment for a SMA + pericyte signature (using genes identified in the murine population) above that of healthy tissue in human breast cancer ( Supplementary Fig. 5c) and interestingly the SMA + pericyte signature also positively correlated with PDGFC expression within the tumor ( Supplementary   Fig. 5d).

This study characterizes a biologically important subset of TAMs selectively expressing
Lyve-1 that are associated with angiogenesis through a mechanism independent of VEGF.
We demonstrate that the Lyve-1 + PvTAM subset, which only accounts for 1.4±0.4% of live tumoral cells, is pivotal to tumor growth. We define a new role for PvTAMs in directing the expansion of the pericyte population to support angiogenesis and disease progression,

Competing interests
Authors declare no competing interests relating to this work.

Tumor studies
Murine 4T1 mammary adenocarcinoma, Lewis lung carcinoma (LL2) and B16-F10 melanoma cells were obtained from ATCC. 2.5 x 10 5 cells in 100μl RPMI and were injected by subcutaneous (s.c.) injection into the mammary fat pad of syngeneic Balb/c (4T1) or C57Bl/6 (B16-F10 and LL2) female mice that were six to eight weeks of age. In studies using MMTV-PyMT mice tumors arose spontaneously. When tumors became palpable, volumes were measured every 2 days using digital caliper measurements of the long (L) and short (S) dimensions of the tumor. Tumor volume was established using the following equation: Volume= (S 2 xL)/2. PyMT/Kaede mice were photo-labelled under anesthesia, individual tumors mice were exposed to a violet light (405nm wavelength) through the skin for nine 20 second exposure cycles with a short 5 second break interval between each cycle. Black cardboard was used to shield the rest of the mouse throughout the photoconversion procedure. Mice for 0 h time points were culled immediately after photoconversion. This photoconversion approach was adapted from that used to label peripheral lymph nodes 68 .
Tumor tissue for flow cytometry analyses were enzyme-digested to release single cells as previously described 40 . In brief, tissues were minced using scalpels, and then single cells were liberated by incubation for 60 mins at 37°C with 1 mg/ml Collagenase I from

Murine Tissue Staining
Mouse mammary tumors were fixed overnight (O.N.) in 4% paraformaldehyde, followed by O.N. dehydration in 30% sucrose prior to embedding in OCT and snap freezing in liquid nitrogen. Frozen sections from these tumors were fixed in 4% paraformaldehyde in PBS for 10 mins at RT and were washed in Tris Buffered Saline (100mM Tris, 140mM NaCl), 0.05%, Tween 20, pH7.4 (TBST) and blocked with TBST, 10% donkey serum (Sigma-Aldrich), 0.2% Triton X-100. Immunofluorescence was performed as previously described 1  Nuclei were stained using 1.25 μg/ml 4',6-diamidino-2-phenylindole,dihydrochloride (DAPI) (Thermo Fisher Scientific). Images were acquired using a Nikon Eclipse Ti-E Inverted spinning disk confocal with associated NIS Elements software. Quantitative data was acquired from the images using NIS Elements software.

Human Tissue Staining
FFPE human breast adenocarcinoma tissue sections of 4 µm were incubated at 60°C for 1 h, before being deparaffinized with Tissue-Tek® DRS™2000, Sakura. Heat-induced antigen retrieval was performed using a pressure cooker (MenaPath Access Retrieval Unit, PASCAL). The slides were immersed in modified citrate buffer pH 6 and gradually heated to 125°C. Excess of antigen retrieval buffer was washed firstly with distilled water followed by PBS, before incubation of the slides in blocking buffer containing 0.5% Triton and 5% Mounting medium (Fluorsave, Millipore) was applied to the slides. Images were acquired using a Nikon Eclipse Ti-E Inverted spinning disk confocal with associated NIS Elements software.

Flow cytometry
Flow cytometry was performed as previously described 6

Single-cell RNA-sequencing data processing and analysis
The raw sequenced data was processed with the Cell Ranger analysis pipeline version 3.0.2 by 10x Genomics (http://10xgenomics.com/). Briefly, sequencing reads were aligned to the mouse transcriptome mm10 using the STAR aligner 72 . Subsequently, cell barcodes and unique molecular identifiers underwent Cell Ranger filtering and correction. Reads associated with the retained barcodes were quantified and used to build a transcript count tables for each sample. Downstream analysis was performed using the Seurat v3 R package 73 . Before analysis, we first performed quality control filtering with the following parameters: cells were discarded on the following criteria: where fewer than 800 unique genes detected, reads composed greater than 12% mitochondrial-associated gene transcripts and cells whose number of reads detected per cell was greater than 65k for sample 1 and 2, 60k for sample 3. All genes that were not detected in at least ten single cells were excluded. Based on these criteria the final dataset contained 9,615 TAMs with 25,142 detected genes. The data was first normalized using the LogNormalize function and a scale factor of 10,000. The

Bulk RNA-sequencing
Cells were sorted directly into RLT plus buffer (Qiagen) supplemented with 2-βmercaptoethanol (BME) (Gibco) and lysates were immediately stored at -80°C until used.
RNA was extracted with the RNeasy Plus Micro kit (Qiagen) as per the manufacturers' protocol, in addition to on-column DNase digestions specified by the manufacturer (Qiagen).
cDNA was generated and amplified using the SMARTseq v4 Ultra Low Input RNA Kit (Clontech) on the contactless Labcyte liquid handling system (Beckman Coulter Life Sciences). Two hundred ng of amplified cDNA was used from each sample where possible to generate libraries using the Ovation Ultralow Library System V2 kit (NuGEN). In brief, cDNA was fragmented through sonication on Covaris E220 (Covaris Inc.), repaired, and polished followed by ligation of indexed adapters. Adapter-ligated cDNA was pooled before final amplification to add flow cell primers. Libraries were sequenced on HiSeq 2500 (Illumina) for 100 paired-end cycles in rapid mode.

Bulk RNA-sequencing data processing and analysis
Pre-alignment QC for each sample, independently for forward and reverse reads, was performed using the standalone tool FastQC. Reads were trimmed using Trimmomatic 77 and aligned to the reference genome (mm10) using HISAT2 78 . PCR duplicates were removed using SAMtools 79 . Counts were generated using the GenomicAlignment 80 package using mm10. Prior to performing differential gene expression analysis, genes with very low expression were discarded. Differential expression analysis was performed with DESeq2 81 package in R. The test statistics' p-values were adjusted for multiple testing using the procedure of Benjamini and Hochberg. Genes with adjusted p-values lower than 0.05 and absolute log2 fold change greater than 1 were considered significant.

Gene Ontology pathway enrichment analysis
Enriched pathways were identified based on differentially expressed genes using gProfiler 82 (http://www.biit.cs.ut.ee/gprofiler/). We used pathways gene sets from the 'biological processes' (GO:BP) of Gene Ontology (http://www.geneontology.org/). All p-values were adjusted for multiple testing using the procedure of Benjamini and Hochberg.

Ligand:receptor mapping analysis
Ligand:receptor mapping was performed with the local python implementation of the CellPhoneDB v2.0 tool (https://github.com/Teichlab/cellphonedb) 83 run without the statistical method. Cell type ligand receptome data was generated with scRNA-seq TAM06 differentially expressed genes and CD34 -CAF1 and endothelial cells bulk-sequencing data as input, selecting genes with expression of 16 normalized counts or greater as input. The resulting interaction list was filtered on the ligand:receptor pairs selecting for ligands present in the GO term 'growth factor activity' that were investigated further as potential candidates.

Statistics
Normality and homogeneity of variance were determined using a Shapiro-Wilk normality test and an F-test respectively. Statistical significance was then determined using a two-sided unpaired Students t test for parametric, or Mann-Whitney test for nonparametric data using GraphPad Prism 8 software. A Welch's correction was applied when comparing groups with unequal variances. Statistical analysis of tumor growth curves was performed using the "compareGrowthCurves" function of the statmod software package 84 . No outliers were excluded from any data presented.

Study approval
All experiments involving animals were approved by the Animal and Welfare and Ethical