Perivascular mesenchymal cells instruct ST2+ reparative macrophages to promote endovascular injury-induced neointimal hyperplasia in mice

Domain: chemistry
Published: 2026-03-07
Editorial outcome: major_revision

Abstract
Tissue-resident mesenchymal stromal cells (MSC) instruct immune cell activation at injury
sites, a key event in tissue repair. However, the full array of immunomodulatory
mechanisms driving injury-responsive immune-stromal cell interactions is underexplored.
Here, using an endovascular injury mouse model, we demonstrate that perivascular
MSCs enhance arterial immune response by facilitating recruitment of ST2-expressing
reparative macrophages in male mice. Time-resolved single-cell sequencing reveals an
MSC-mediated, macrophage phenotypic switch, essential for vascular regeneration.
Mechanistically, injury activates NFB-dependent IL-33 production in MSCs, which acts
as a paracrine signal that drives Osteopontin (OPN/SPP1) production in ST2+
macrophages and stimulates vascular smooth muscle cell (VSMC) proliferation and
neointima formation. Local hydrogel-mediated delivery of siRNAs targeting Il33 or Spp1
effectively alleviates injury-induced neointimal hyperplasia, suggesting a potential
therapeutic strategy to prevent restenosis and other vascular diseases. Our findings
define an IL33-ST2-OPN axis that mediates functional crosstalk between perivascular
MSCs and reparative macrophages, orchestrating immune-mediated reparative
responses.

Introduction
Tissue repair can occur through tissue regeneration, which restores normal tissue
structure, or through healing, which results in scar formation. A well-balanced tissue repair
process is crucial for reinstalling tissue homeostasis and determines the pathogenesis of
diseases1, 2, 3, 4. Immune cells play a critical role in the regulation of regenerative and
inflammatory responses essential for tissue repair5. Following tissue injury, a large
number of macrophages are mobilized and activated, contributing to tissue regeneration,
inflammation and fibrosis1, 5, 6. Notably, macrophages act in a sequential and coordinated
cascade to remove pathogens or necrotic debris, and resolve inflammation during tissue
injury and repair. The prevailing paradigm suggests that macrophages initially recruited
to the injured sites are pro-inflammatory, while those involved in the subsequent
regeneration phase transition to an anti-inflammatory and reparative phenotype2, 5, 7, 8.
This phenotypic transition ensures the precisely timed progression of tissue repair,
preventing asynchrony which could lead to impaired regeneration, increased fibrosis, and
prolonged inflammation1, 2, 5. Despite their critical functions, the precise identities and
lineage origins of these reparative macrophages, as well as the mechanisms governing
their acquisition of pro-repair phenotype, remain largely unclear.

Vascular repair is a tightly regulated process whose progression determines the
delicate balance between proper regeneration of endothelial monolayer and the
undesired formation of vascular lesions following vessel damage. Upon endovascular
injury, vascular repair process includes endothelial cell (EC) dysfunction, vascular smooth
muscle cell (VSMC) proliferation as well as active immune response within blood vessel
walls9, 10, 11. In response to endothelial injury, VSMCs are stimulated to proliferate and
migrate from the tunica media into the tunica intima, driving a vascular remodeling
process called neointima hyperplasia. This process leads to vascular wall thickening and
lumen narrowing and is associated with a variety of cardiovascular diseases, such as
atherosclerosis, restenosis, hypertension, and arterial aneurysm9, 10, 12, 13. However, the
mechanisms underlying the injury-responsive immune response and their implications in
VSMCs proliferation and neointima formation remain poorly understood.

A complex dialogue between immune system and other microenvironmental
components is crucial for tissue repair and regeneration1, 2, 3. Injury triggers the
recruitment, proliferation, and activation of various immune and non-immune cells which
together make up an intercellular communication network to orchestrate tissue repair1, 3,
4. Recent efforts have focused on clarifying how reparative macrophages act in a
paracrine and/or autocrine manner to contribute to the repair cell milieu via the production
of bioactive factors, such as cytokines, growth factors and metabolites1, 6, 7, 8, 14, 15, 16. A
panel of cytokines (e.g., IL-4, IL-13, IL-10, TGFβ), growth factors (e.g., CSF-1, IGF-1) and
signaling pathways (e.g., STAT6, NFκB, PPARγ, AMPK) have been reported to regulate
macrophage phenotypic transitions17, 18. Notably, IL-33/ST2 signaling, through
downstream effectors such as AKT, ERK or GATA3, governs the phenotypes of
macropahges and other immune cells and has been implicated in various contexts19, 20,
21. However, its role in macrophage phenotypic switching during tissue injury and repair
remains unclear.

Mesenchymal stromal cells (MSC), a major stromal fraction localized in perivascular
regions, have recently been identified as a primary cellular source of IL-33 in peripheral
tissue 22, 23, 24, 25, 26. MSCs are recognized for their fundamental role in providing structural
support to tissues and organs27, 28. It has now become evident that they have additional
functions in tissue repair, particularly by activating immune system following injury28, 29. In
the vasculature, adventitial MSCs residing in the tunica adventitia have been implicated
in pathways in the contexts of vascular remodeling and disease development27, 30, 31, 32.
Recent studies have revealed that MSCs could respond to injury-associated signals
through reciprocal interactions with other immune cells to mobilize various
microenvironmental components critical for tissue regeneration and wound healing28, 33,
34, 35. Furthermore, studies employing cell fate mapping and single-cell sequencing have
underscored the tunica adventitia as a regulatory center for vascular remodeling,
influencing the functions of medial and intimal cells31, 32, 36, 37, 38. Notably,
PDGFRβ+SCA1+ adventitial MSCs do not directly contribute to new VSMCs in the
context of wire-induced artery injury30. Instead, they are likely to function in a regulatory
capacity, supporting the artery repair process rather than serving as a cellular source of
new VSMCs30.

In this study, we integrate time-resolved single cell RNA sequencing and multi-color
flow cytometry to comprehensively analyze the dynamic changes in cell-cell
communication circuits within the artery wall and define how MSC coordinate immune
responses following endovascular injury. We find that mesenchymal IL-33 drives the
activation of ST2+ macrophages, which promote smooth muscle cell proliferation and
neointimal growth through Osteopontin (OPN/SPP1), and that distupting this IL-33-ST2-
OPN axis limits neointimal hyperplasia. These results establolish a mechanistic
framework for immune-stromal crosstalk in arterial repair and highlight a therapeutically
targetable pathway for preventing restenosis and related vascular diseases.

Results
Arterial recruitment of CX3CR1+ macrophages is essential for endovascular injury-
induced neointimal hyperplasia.
Emerging evidence highlights the critical role of vascular immune cells in coordinating
blood vessel repair11. Using the endovascular injury mouse model where a wire is inserted
into the murine femoral artery to mimic clinical coronary stent implantation39, we
monitored the temporal recruitment of CD45+ immune cells in the injured artery wall over
a 4-week period of vascular injury and repair (Fig. 1a, and Supplementary Fig.1a).
Quantification of CD45+ immune cells, together with functional activity score analysis of
immune cell populations, revealed that endovascular injury induced a rapid immune
response, with immune cell infiltration peaking around 2 days post-injury (Fig. 1b and
Supplementary Fig.1b-d).

The abundance of immune cells in the artery began to decline around 4-7 days post-
injury (Fig. 1b and Supplementary Fig.1b,d), coinciding with the inflammatory-to-
reparative phase transition in the repair process40, 41. To characterize the temporal
changes in the immune landscape during artery injury and repair at single cell resolution,
we collected femoral arteries at 0, 2, and 7 days post-injury for single-cell RNA
sequencing (scRNA-seq) (Fig. 1a). A total of 27,515 high-quality cells were captured and
sequenced across the 3 time points (0 days [D0 or Sham], 5,960 cells; 2 days [D2],
11,016 cells; 7 days [D7], 10,539 cells). After combining the data from all 3 time points,
clustering and visualization using Uniform Manifold Approximation and Projection (UMAP) revealed
4 major vascular cell populations, each expressing specific markers: VSMC (Myh11,
Acta2, Tagln), MSC (Pdgfrb, Pdgfra, Dcn), EC (Pecam1, Cdh5), and Immune cell (Ptprc)
(Supplementary Fig.1e,f, and Supplementary Data 1).

Endovascular injury induced a pronounced recruitment of various arterial immune cell
types, including B cells, macrophages, mast cells, neutrophils, NK/T cells, dendritic cells
(DC), monocytes, and group 2 innate lymphoid cells (ILC2) (Fig. 1c, Supplementary
Fig.1c,d, and and Supplementary Data 1). Consistent with histological observations,
these immune cell populations exhibited distinct temporal dynamics throughout the
vascular repair progress (Fig. 1d,e, and Supplementary Fig.1b-d, S1i-k). Notably, a
substantial increase in macrophages residing within the arterial wall (i.e., arterial
macrophages) were detected at 7 days and 14 days post femoral artery injury (FAI) (Fig.
1d,e, and Supplementary Fig.1c,d), coinciding with the period of active VSMC
proliferation38. Their abundance eventually declined to baseline levels as vascular
regeneration progressed by 28 days post-injury (Supplementary Fig.b,S1i-k). In addition
to the single-cell transcriptomics, we also employed multi-color flow cytometry to
comprehensively analyze vascular immune cells at the time points of 0, 2, and 7 days
post-injury (Fig. 1a). Consistent with the histological and scRNA-seq data, the abundance
of CD45+ immune cell increased ~16-fold in the injured artery immediately post-injury and
remained 8-fold higher at 7 days post-injury compared to uninjured controls (Fig. 1f). t-
SNE analysis of the multi-color flow cytometry data identified 11 distinct immune cell
populations, including macrophages, monocytes, neutrophils, ILC2s, mast cells,
plasmacytoid DCs (pDCs), conventional DCs (cDCs), NK cells, B cells, CD8+ T cells, and
CD4+ T cells (Fig. 1g, Supplementary Fig.1l, and Supplementary Data 1). The distinct
immune landscapes at each time point were reflected by the altered proportions and
abundance of these immune subsets (Fig. 1h-j).

As the most abundant immune cell population in the injured arteries, macrophages
showed robust changes after injury, with ~60-fold increase at 2-days and over a 20-fold
increase at 7 days post-FAI (Fig. 1h,j, and Supplementary Fig.2a). Both flow-cytometric
analyses using Cx3cr1GFP reporter mice and anti-CX3CR1 antibodies, as well as in
combination with scRNA-seq analysis, confirmed that the increased macrophages in the
injured arterial wall were predominantly CX3CR1+ cells (Fig. 1k,l, and Supplementary
Fig.2b-f). To evaluate the role of macrophages in neointimal formation, we generated an
inducible macrophage depletion mouse model (Cx3cr1CreER, Rosa26-iDTR, denoted as
Mac△ mice), in which tamoxifen administration sensitized CX3CR1+ cells to diphtheria
toxin (DT) by activating diphtheria toxin receptor (DTR) expression (Fig. 1m). DT-induced
depletion effectively eliminated vascular arterial macrophages without affecting the
abundance of other immune cell populations within the arterial wall (Supplementary
Fig.2g). Consequently, neointimal formation was markedly reduced in Mac△ mice
compared with control animals at 4 weeks post-injury (Fig. 1n). Collectively, these results
demonstrate that arterial macrophages are essential for the neointimal hyperplasia
following endovascular injury.

Identification of a subset of reparative macrophages that facilitates VSMC
proliferation upon endovascular injury.
Following endovascular injury, artery repair begins with an initial inflammatory phase that
predominates during the first 3 days post-injury, before transitioning into a reparative
phase to complete the repair process40, 41 (Fig. 2a). Next, we examined whether arterial
macrophages displayed distinct activation states at the 2 days (D2, inflammatory phase)
and 7 days (D7, reparative phase) post-FAI. Compared to the baseline state (D0), genes
upregulated in D2 macrophages were mainly associated with inflammation-related
pathways, such as Innate immune response, Cell chemotaxis, and Phagocytosis (Fig. 2b,
left). In contrast, D7 arterial macrophages showed elevated expression of genes
associated with vascular regeneration, including those related to VSMC proliferation,
Regeneration, and Collagen metabolism (Fig. 2b, right). These findings suggested distinct
functional states of arterial macrophages across different phases of the repair process.
Further unsupervised clustering identified 4 macrophage subsets (Cluster 1-4) within the
arterial wall during vascular repair, each showing substantial temporal changes in
abundance and transcriptional profiles (Fig. 2c,d, and Supplementary Fig.3a). Clusters 1,
2, and 3 predominantly emerged in the arterial wall after injury, whereas Cluster 4
represented homeostatic resident macrophages as being observed only in arteries under
normal condition (Fig. 2d). High Cx3cr1 expression in Clusters 1, 2, and 3, together with
the absence of Adgre1 in Cluster 3 (Supplementary Fig.3b), identified Cluster 3 as
monocytes and suggested that Clusters 1 and 2, which were sequentially recruited during
the reparative phase, originate from these infiltrated monocytes (Cluster 3). Pseudo-
temporal ordering and differentiation analyses supported this, showing that the Clusters
1 and 2 derived from Cluster 3 monocytes (Fig. 2e,f, and Supplementary Fig.3c-e). To
validate their cellular origin, GFP-labeled monocytes were i.v. injected into wild-type mice
at D4 (see Methods for experimental details). By D7, ~50% of arterial macrophages were
GFP+ cells by flow cytometry analysis, indicating that newly infiltrated monocytes are the
primary source of Cluster 2 macrophages during the reparative phase of vascular repair
(Fig. 2g).

In addition to their distinct cellular origins, Cluster 1 macrophages expressed higher
levels of inflammatory genes, while Cluster 2 macrophages showed relative upregulation
of genes associated with vascular repair and regeneration (Supplementary Fig.3f,g). To
develop a flow cytometry protocol capable of distinguishing inflammatory from reparative
macrophages, we examined top-ranked differentially expressed genes (DEGs) in each
subcluster to identify suitable cell-surface markers. Ly6c2 (encoding LY6C protein) was
dominantly expressed in Cluster 1, while Cd63 and Cd72 (encoding CD63 and CD72
proteins, respectively) were expressed relatively higher among surface markers in Cluster
2 (Supplementary Fig.3h, and Supplementary Table 2). Notably, LY6C was more
abundant in D2 macrophages, whereas CD63 and CD72 were predominant in D7
macrophages at protein levels (Supplementary Fig.3i). In agreement, over 90% of D2
macrophages were LY6C positive, but this proportion decreased to ~35% at D7 (Fig. 2h,i).
In contrast, a LY6C-CD63+CD72+ subset of arterial macrophages, which was rarely
detected at the onset of injury, was abundantly observed at D7 (Fig. 2h,j). This inverse
occurrence of these 2 macrophage clusters prompted us to investigate whether LY6C+
macrophages and the LY6C-CD63+CD72+ counterparts function differently during the
artery repair process. First, we used the CSF1R inhibitor Ki20227 to temporarily deplete
vascular monocytes/macrophages at different time points after injury42. Notably, Ki20227
treatment administered immediately after injury (D0-D3) resulted in markedly reduced
immune cell infiltration, particularly macrophages and downregulated inflammation genes
(e.g., Ccl2, Cxcl1, Cxcl10, Il6, Nos2, Il1b), without affecting cell proliferation-related genes
(e.g., Ccna2, Ccnb1, Ccnd1, Ccne1, Mki67, Pcna) (Fig. 2k,l, and Supplementary Fig.4a,b).
In contrast, macrophage depletion between D4-D7 resulted in decreased expression of
genes related to cell proliferation, but not those associated with inflammation (Fig. 2m,n,
and Supplementary Fig.4c). In addition, 5-ethynyl-2’-deoxyuridine (EdU) staining showed
there was approximately 50% fewer proliferating VSMCs (Fig. 2o) as well as nearly 80%
reduction in neointimal formation (Fig. 2p) in animals treated with Ki20227 between D4-
D7. These results collectively demonstrate that LY6C+ macrophages represent
inflammatory macrophages (herein referred to as InfMAC) that mediate vascular
inflammatory response following injury, while the LY6C-CD63+CD72+ subset functions
as reparative macrophages (referred to as RepMAC) that promote VSMC proliferation
and neointimal hyperplasia.

Perivascular PDPNhi MSCs exhibit heightened immunoregulatory activity in artery
wall upon endovascular injury.
In addition to the remarkable changes in immune cell abundance and frequency, our
scRNA-seq dataset uncovered extensive interactions among MSCs, VSMCs, ECs, and
immune cells, which were significantly intensified following endovascular injury (Fig. 3a,
b, and Supplementary Fig.5a). Notably, the intensity of these cell-cell interactions was
found to be both cell type-dependent and highly dynamic throughout the repair process
(Fig. 3c, Fig. S5a,b). Among all detected cell types, MSCs exhibited the most pronounced
interactions with immune cell populations, particularly macrophages. These interactions
became progressively strengthened during tissue repair (Fig. 3c, and Supplementary
Fig.5b). Further analysis identified 2 distinct subpopulations of MSCs localized in arteries,
designated as MSC1 and MSC2 (Fig. 3d). In contrast to MSC1 which represented the
major MSC population in the normal state, MSC2 was predominantly detected at 7 days
post-injury and exhibited elevated expression of genes involved in extracellular matrix
deposition and immune cell regulation (Fig. 3f,g), indicating their specific recruitment
associated endovascular injury. As a recognized marker of MSCs, Podoplanin (PDPN)
expression is upregulated in response to various contexts including infection, tissue injury,
chronic inflammation and cancer, and is implicated in the migration and activation of
MSCs24, 43. Notably, the injury-associated MSC2 cells expressed higher levels of PDPN
(PDPNhi) compared to the MSC1 counterparts (Fig. 3h-k), which was consistent with the
increased presence of PDPN+ MSCs in injured arteries, as quantified by flow cytometry
analysis (Fig. 3l). Bulk RNA sequencing experiments using isolated PDGFR+PDPN+
MSCs from femoral arteries after wire injury or sham operations (Fig. 3m) revealed that
endovascular injury caused substantial alterations in the transcriptomes of arterial MSCs
(Fig. 3n, and Supplementary Fig.5c). Notably, the 1796 upregulated genes in MSCs from
the injury group were predominantly enriched in immune response-related pathways (Fig.
3o), confirming the heightened injury-induced immunoactivation of these MSCs. These
new findings suggest that arterial PDPNhi MSCs with immunomodulatory features are
involved in the immune remodeling and tissue repair processes following endovascular
injury.

IL-33 expression in artery adventitial MSCs is activated by NFB signaling
pathway upon endovascular injury.
Growing evidence highlights the critical role of MSCs in modulating immune cell functions
and remodeling microenvironment via the release of secreted factors43, 44. To identify
MSC-derived factors regulating artery repair, we analyzed genes that encoded secreted
proteins whose expression was altered by FAI in bulk RNA-seq data (MSC_FAI vs. Sham).
Among the 50 DEGs, Il33 is the top1 upregulated gene and predominantly expressed in
MSCs (Supplementary Fig.5d,e). Notably, Il33, encoding the alarmin cytokine IL-33 that
orchestrates reparative type 2 immune responses in multiple settings19, 45, 46, was among
the most strongly upregulated genes in the FAI group compared to Sham counterparts
(Fig. 4a). Akin to findings in white adipose tissue, pancreas and lung22, 23, 24, 25, 26, murine
artery IL-33 expression was predominantly detected in PDGFR+ or PDGFR+ MSC
subsets located in the adventitial layer, as evidenced by scRNA-seq analysis of FAI model
shown in Figure 1, immunofluorescence staining of injured artery sections with IL-33
antibodies, and flow cytometry using the Il33GFP reporter mouse model (Fig. 4b-c, and
Supplementary Fig.5e, S6a-c). Moreover, the upregulation of Il33 gene expression, rather
than the changes in the abundance of Il33-expressing cells, appeared to contribute to the
higher IL-33 levels observed in injured arteries (Fig. 4d-f, and Supplementary Fig.6a-c).
Notably, plasma IL-33 levels were significantly higher in patients with in-stent restenosis
(ISR) compared with those without ISR (Fig. 4g). Endothelial cell denudation is a key
initiating event in multiple vascular pathologies, including stent-induced artery injury9, 39.
To model this process in vitro, we co-cultured Il33-expressing PDGFR+ MSCs isolated
from inguinal adipose tissue22 with conditioned media (CM) derived from mechanically
injured cultured human and mouse endothelial cell lines, respectively (Fig. 4h, and
Supplementary Fig.6d). As expected, exposure to the CM from both injured HUVECs and
b
bEND.3 cells (CM_Inj) induced a 2-3-fold increase in Il33 expression compared to CM
from normal ECs (CM_Nor) (Fig. 4i,j, and Supplementary Fig.6e). To identify the secreted
factor(s) mediating IL-33 expression, primary MSCs were treated with the CM which was
subjected to 95°C boiling or filtration through a 10-kDa-cutoff filter (Frac<10K). The results
pointed to the presence of heat-sensitive macromolecules derived from injured ECs
contributed to the regulation of IL-33, as the effect of CM_Inj on Il33 induction was
abolished only when boiled CM was applied, but not with the filtered CM (Fig. 4k-l). Hence,
we further analyzed the bulk RNA-seq dataset of MSCs shown in Figure 3 using Gene
Set Enrichment Analysis (GSEA) and found that the NFB signaling pathway, along with
related pathways such as the TNF signaling pathway and IL-17 signaling pathway, were
highly enriched in the FAI group (Fig. 4m). Indeed, exposure to CM_Inj or the fractions
containing materials with molecular weight of 10 kDa or above (Frac>10K) resulted in
robust activation of the IKK-NFB signaling pathway in MSCs, as evidenced by the
elevated levels of phosphorylated IKK/ and p65 (Fig. 4n). In agreement, the effects of
CM_Inj and Frac>10K on Il33 expression were largely blocked by inhibition of the IKK-NFB
signaling pathway using p65 inhibitor JSH23 or through Rela knockdown via siRNA (Fig.
4o-q). Moreover, several secreted proteins were identified to be upregulated in the
conditioned media derived from injured ECs, including B2M, MIF, CXCL5 and ITGB1
which are known to activate NFB signaling pathway (Supplementary Fig.6d,f, and
Supplementary Data 2). In addition, sequence analysis of the promoter region of murine
Il33 gene identified 3 putative p65 binding sites (B.S.-1~3) (Fig. 4r). Chromatin
Immunoprecipitation (ChIP) assays demonstrated that the addition of CM_Inj significantly
enhanced the direct binding of p65 proteins to chromatin regions of B.S-2 and -3 (Fig. 4r),
supporting the direct regulation of NFB signaling pathway on Il33 transcription in MSCs.

IL-33 ablation in MSCs impedes the recruitment of reparative macrophages and
limits neointimal hyperplasia following endovascular injury.
To determine the role of IL-33 in MSC-mediated immune remodeling in the injured arterial
wall, we developed a mouse model in which Il33 was selectively inactivated in PDGFR+
cells using a tamoxifen-inducible Cre system (PdgfrbCreER, Il33loxp/loxp; denoted as Pb-
Il33KO mice) (Fig. 5a). In this model, tamoxifen administration resulted in a >80% reduction
in Il33 expression within the arterial wall of Pb-Il33KO mice (Fig. 5b), confirming that
PDGFR+ cells serve as the primary source of IL-33 in the artery. While there were no
significant effects on the total number of CD45+ immune cells (Fig. 5c), the absence of
IL-33 in MSCs led to notable changes in the composition of vascular immune cells, as
revealed by immune cell profiling using multi-color flow cytometry (Fig. 5d,e). Interestingly,
similar frequencies of type 2 immune cells, including ILC2s, mast cells, and CD4+ T cells,
along with comparable mRNA levels of type 2 cytokines (Il4, Il5 and Il13) and inflammatory
genes (Ccl2, Cxcl1, Cxcl2, Il1b, Tnf) were detected in the arteries of Pb-Il33KO mice and
their control counterparts (Fig. 5d-f, and Supplementary Fig.7a,b). These findings
suggested that elevated IL-33 did not activate conventional type 2 immune responses in
the injured arteries. Nevertheless, a ~36% reduction of total macrophages was observed
in the arteries of Pb-Il33KO mice (Fig. 5f), accompanied by a trend towards higher
neutrophil accumulation. Neutrophils primarily act during the acute phase of vascular
injury, when the immune response peaks47. Their contribution to late-stage neointimal
hyperplasia is likely less significant compared with macrophages, which at this stage have
largely acquired a reparative phenotype. Moreover, immunofluorescence staining of
artery sections showed that IL33-expressing MSCs were localized in close proximity to
macrophages in the adventitial layer of the injured artery (Fig. 5g). Meanwhile, the number
of LY6C-CD63+CD72+ macrophages was significantly decreased in the injured arteries
of Pb-Il33KO mice compared to their control counterparts (Fig. 5h,i), indicating that
mesenchymal IL-33 was driving the recruitment of reparative macrophages.
Next, we asked whether inactivation of IL-33 in MSCs affected injury-induced
neointimal formation. In Pb-Il33KO mice subjected to endovascular injury, the pathological
thickening of the arterial SMC layer was reduced by ~70% (Fig. 5j). Compared to the
control group, there was a remarkable decrease in VSMC proliferation in injured arteries
with Il33 deletion, as evidenced by an ~51% reduction of Ki67+ VSMCs (Fig. 5k,l) and an
~72% decrease in EdU+ VSMCs (Fig. 5m). To confirm that these effects were due to the
loss of local IL-33 signal, we delivered a siIl33-containing hydrogel to selectively silence
Il33 expression at the femoral artery injury sites (Fig. 5n). As a result, the locally applied
siIl33-containg hydrogel effectively suppressed both IL-33 mRNA and protein levels in
injured arteries (Fig. 5o,p), which was associated with the reduced endovascular injury-
induced thickening of SMC layers (Fig. 5q). These results confirm the local effect of IL-33
in promoting neointimal hyperplasia.

IL-33-ST2 signaling-activated reparative macrophages drive the neointimal
hyperplasia in response to endovascular injury.
To determine whether IL-33 directly influences VSMC proliferation, we supplemented
VSMC cell line MOVAS cultures with recombinant IL-33 proteins and assessed the impact
on cell proliferation. VSMCs treated with IL-33 showed comparable growth rates and
proliferative capacities to those treated with vehicle (Supplementary Fig.7c-e),
suggesting the involvement of indirect IL-33-dependent mechanism(s) by which IL-33 stimulates
VSMC proliferation. Therefore, we hypothesized that IL-33’s effect on facilitating VSMC
proliferation require the presence of other cell types that express ST2, the obligate IL-33
receptor19. Indeed, the expression levels of Il1rl1 gene (encoding ST2 protein) in the
artery were remarkably increased immediately following endovascular injury, peaking at
1-2 weeks post-injury when extensive VSMC proliferation occurred (Fig. 6a). Importantly,
these observations were in line with the results showing that IL1RL1 gene expression
was also notably upregulated in human arteries following stent surgery (Fig. 6b).
Moreover, the endovascular injury-induced ST2 expression coincided with the recruitment
of ST2-expressing immune cells (Fig. 6c-e). Supporting our hypothesis that MSC-derived
IL-33 was required for the recruitment of ST2+ immune cells to the injured sites, the
number of ST2+ immune cells was decreased by ~45% in the injury arteries of Pb-Il33KO
mice (Fig. 6f-h). To further investigate the immune cells regulated by local IL-33-ST2
signaling within injured arteries, multi-color flow cytometry analysis identified 7 distinct
types of ST2+ immune cells, including macrophages, monocytes, neutrophils, ILC2s,
mast cells, B and T cells (Fig. 6i). Notably, ~58% of ST2+ immune cells in the injured
arteries were macrophages, and the abundance of these ST2+ macrophages were
reduced by ~55% in injured arteries when IL-33 was inactivated in MSCs (Fig. 6j).
Transcriptomic datasets of IL33-treated BMDM (IL-33UP) and arterial macrophages
after injury (D7_MacUP) revealed that 451 common genes were upregulated (Fig. 6k).
These upregulated genes were significantly enriched in pathways related to wound
healing and VSMC proliferation (Fig. 6k), which implied that IL-33-activated ST2+
macrophages acquired the reparative phenotype. In agreement, ST2+ macrophages
were predominantly found within the RepMAC population in the arteries at D7 (Fig. 6l),
with nearly 60% of RepMACs identified as ST2+ cells (Fig. 6m). Thus, we generated a
mouse model in which Il1rl1 was specifically knocked out in myeloid cells (Lyz2Cre,
Il1rl1loxP/loxP; denoted as Mac-Il1rl1KO mice) to determine the role of macrophage ST2 in
artery injury and repair. Macrophage ST2 deficiency substantially restrained the neointima
formation in response to endovascular injury (Fig. 6n-p). Mac-Il1rl1KO mice exhibited
significantly reduced VSMC proliferation, evidenced by ~60% reduction in Ki67+ VSMCs
and ~78% decrease in the number of EdU-incorporated VSMCs in injured arteries (Fig.
6q-s). Taken together, these new results demonstrate that IL-33-activated ST2+
reparative macrophages are crucial for facilitating VSMC proliferation and promoting
neointimal hyperplasia.

ST2+ macrophage-derived Osteopontin stimulates VSMC proliferation upon artery
injury.
To elucidate how ST2+ macrophages promote VSMC proliferation, cultured VSMCs were
subjected to the CM derived from IL33-treated macrophages (Fig. 7a). The CM exerted
enhanced capacity in promoting VSMC growth, an effect which was blocked in the
absence of ST2 (Fig. 7b, and Supplementary Fig.8a,b). These observations led to the
hypothesis that IL-33-induced secreted factor(s) from macrophages contribute to the
stimulation of VSMC proliferation. Therefore, we profiled the putative ligand-receptor
interactions between macrophages and VSMCs, and identified several macrophage-
derived ligands—Spp1, Tgfb1, Osm, Tnf, Lgals9, and Pdgfb—whose interactions with
their receptors on VSMCs were enhanced following endovascular injury (Fig. 7c). Among
these ligands, Spp1 (which encodes Osteopontin, OPN) appeared to be a promising
candidate for the following reasons. First, Spp1 gene expression and OPN proteins were
predominantly detected in macrophages in the context of artery injury (Fig. 7e-g).
Secondly, Spp1 expression was markedly upregulated in macrophages within injured
arteries (Fig. 7d-g). In addition, the enhanced macrophage-VSMC interactions mediated
by Spp1 were evident at D14 (Supplementary Fig.8c,d). Consistently, arterial Spp1 mRNA
levels were markedly elevated after endovascular injury (Fig. 7h), following a pattern
similar to Il33 (Fig. 4f). Moreover, plasma samples from IRS patients also showed
increased OPN protein levels (Fig.7i).
Vascular Spp1 expression levels in injured arteries were reduced by ~50% in Pb-Il33KO
mice in comparison with control littermates (Fig. 7j). In addition, the absence of ST2 also
resulted in nearly 50% reduction of Spp1 expression in the injured arteries (Fig. 7k).
Therefore, we set to determine whether the IL-33 receptor signaling pathway promotes
OPN production in macrophages. As expected, IL-33 treatment led to over 4-fold
upregulation of Spp1 gene expression in the macrophages, while this induction was
abolished in ST2-deficient cells (Fig. 7l). In agreement, siRNA-mediated silencing Spp1
in macrophages using siRNA significantly reduced the expression levels of proliferation-
related genes in VSMCs exposed to the aforementioned IL-33-treated macrophage-
derived CM (Fig. 7m,n, and Supplementary Fig.8e). In vivo, hydrogel-embedded siSpp1
effectively inhibited the progression of injury-induced neointima formation (Fig. 7o).
Together, these results demonstrate that macrophage-derived OPN, driven by paracrine
IL-33 signaling, promotes VSMC proliferation and neointimal hyperplasia following
endovascular injury.

Methods
Animals
All animal experiments were performed according to protocols approved by the
Institutional Animal Care and Use Committee at Zhejiang University. All mice were from
C57BL/6 background in this study. Male C57BL/6 mice (7-8 weeks old) were obtained
from Hangzhou Ziyuan Laboratory Animal Technology Co., Ltd. Cx3cr1GFP mice (Strain#:
005582), Cx3cr1CreER mice (Strain#: 020940), Rosa26-iDTR mice (Strain#: 007900) were
obtained from The Jackson Laboratory and kindly provided by Dr. Peng Shi (Zhejiang
University). PdgfrbCreER mice (Strain#: 029684), Il33flox/flox-eGFP mice (Strain#: 030619)
were obtained from The Jackson Laboratory. Lyz2Cre mice (Strain#: T003822), Il1rl1flox/flox
mice (Strain#: T005741) were generated by GemPharmatech. Macrophage specific Il1rl1
gene knockout mice (Mac-Il1rl1KO) were generated by intercrossing Il1rl1flox/flox mice with
Lyz2Cre mice. Inducible macrophage ablation mice (Mac) were generated by crossing
Cx3cr1CreER mice with Rosa26-iDTR mice. The mice were co-housed, breeding and
maintained in at 23  3 °C with a humidity of 35  5% under a 12-hour dark-light cycle,
with free access to water and food in the SPF facilities of the Animal Facility of Zhejiang
University. For the animal study, the number of animals in each experiment has been
indicated in the Figure legnds or Supplementary Data 3. In this study, male mice were
used to avoid estrogen-related variability that could confound the mechanistic
interpretation of vascular injury responses.

Mouse studies
For the establishment of the wire-induced femoral artery injury (FAI) model 38, 8-week-old
male mice were anesthetized via intraperitoneal injection of avertin (#75-80-9, Sigma)
and the femoral artery was isolated by carefully removing the surrounding vein and
connective tissues using microsurgical forceps. Then, a 0.25 mm flexible 21-gauge wire
(tips of a CROSS-IT 200XT guide wire, #1003313H, Abbott Laboratories) was inserted
into the femoral artery and advanced 5–10 mm towards the iliac artery, where it was left
in place for 3 minutes. Blood flow was restored following ligation of the profunda femoris
branch. Mice were placed on a warming pad until fully awake post-surgery and were
euthanized via intraperitoneal injection of avertin (#75-80-9, Sigma) at the indicated time
points for further analysis.
For pharmacological depletion of macrophages, 8-week-old male mice were
administered Ki20227 (#7688, Selleck) orally at a dosage of 40 mg/kg body weight, or
vehicle (0.5% methylcellulose) at the indicated time points post-FAI. To inhibit
inflammatory macrophages, administration was conducted once a day for 2 consecutive
days right after surgery. To inhibit reparative macrophages, daily administration
commenced at 4 days post-FAI and continued for 3 consecutive days. Mice were
euthanized and then sacrificed at 4 days, 7 days, or 28 days post-FAI, and femoral artery
samples were collected for further analysis.
For tamoxifen-induced gene knockout in mice, tamoxifen (#10540-29-1, Sigma) was
dissolved in corn oil (#8001-30-7, Aladdin) at a concentration of 20 mg/mL. Mice carrying
PdgfrbCreER allele or control animals, received intraperitoneal (i.p.) injections of tamoxifen
at a dosage of 50 mg/kg body weight for 5 consecutive days to induce Cre-mediated
recombination. For the mice carrying Cx3cr1CreER allele or control animals, tamoxifen
injection at a dosage of 50 mg/kg body weight were performed for 2 consecutive days
prior to FAI surgery and at 1, 7 and 14 days post-FAI.
To selectively deplete monocyte-derived macrophages by diphtherial toxin injection, 8-
week-old male Mac mice (Cx3cr1CreER; Rosa26-iDTR) and control animals were treated
with tamoxifen as described above, followed by intraperitoneal (i.p.) injections of
diphtheria toxin (DT, #D381867, Aladdin) at a dosage of 8 μg/kg body weight at 1, 7, and
14 days post-FAI. Mice were euthanized and then sacrificed at indicated time points post-
FAI.
For in vivo siRNA delivery via hydrogel, a 20% (w/v) Pluronic F127 (#P2443, Sigma)
solution was prepared by dissolving the polymer in sterile PBS and stirring overnight at
4 °C to ensure complete dissolution. siRNA (siIl33, siSpp1, or scramble control siRNA)
was incorporated at a final concentration of 30 µg per 50 µL of gel, and all procedures
were performed on ice to maintain the liquid state prior to application. 50 μL of 20%
Pluronic gel F127 containing 30 μg siIl33, siSpp1, or scramble siRNA (siNC) was applied
to the adventitial surfaces of injured arteries right after surgery, respectively. Femoral
artery samples were harvested at indicated time for further analysis. Small interfering
RNAs were purchased from GenePharma: siNC (sense 5’-
UUCUCCGAACGUGUCACGUTT-3’ and antisense 5’-ACGUGACACGUUCGGAGAATT-
3’), siIl33 (sense 5’-AGACUCCGUUCUGGCCUCACCAUAA-3’ and antisense 5’-
UUAUGGUGAGGCCAGAACGGAGUCU-3’), siSpp1 (sense 5’-
GCCAUGACCACAUGGACGATT-3’ and antisense 5’-
UCGUCCAUGUGGUCAUGGCTT-3’).
For in vivo evaluation of VSMC proliferation using EdU incorporation, mice were i.p.
administered with EdU (#ST067, Beyotime) at a dosage of 50 mg/kg body weight at
indicated time points. EdU incorporation evaluation of the cells in the femoral artery
samples collected at the indicated time was performed with immunofluorescence staining
using the BeyoClick™ EdU Cell Proliferation Kit (#C0075, Beyotime). For in vitro
proliferative cell detection, VSMC cell line MOVAS were cultured and incubated with 10
μM EdU for 2 hours. Cells were then washed with PBS for three times and fixed with 4%
PFA. Incorporated EdU was detected and evaluated using the BeyoClick™ EdU Cell
Proliferation Kit (#C0075, Beyotime). MOVAS cell line was obtained from ATCC and
cultured in DMEM supplemented with 10% FBS and 1% penicillin/streptomycin.
For in vivo monocyte infiltration assay44, leukocyte pools from the blood of 8-week-old
Cx3cr1GFP mice were subjected to erythrocyte lysis prior to the incubation of indicated
flow antibodies. Then, CD45+CD11b+CD115+ circulating monocytes were isolated from
the leukocytes by FACS. For each mouse, 1×106 viable monocytes were resuspended in
100 μL PBS and injected into the mouse via tail vein at 4 days post-FAI. Three days post
injection, GFP-expressing cells in the femoral artery were evaluated using flow cytometry.

Arterial cells preparation from murine femoral artery and flow-cytometric assays
For single arterial cells preparation, fresh femoral arteries were harvested and placed in
a petri dish containing pre-cooled PBS. Perivascular adipose tissues and connective
tissues were carefully removed, and the arteries were cut into fine pieces before
incubation in a digestion solution containing 1.5 mg/mL Collagenase I (#17018029, Gibco)
and 0.25 mg/mL bovine serum albumin (BSA, #A8020, Solarbio) in Hank’s balanced salt
solution (#BL559A, Biosharp). The digestion was performed at 37°C with shaking at 120
rpm. Detached cells in the supernatant were collected every 10 minutes and suspended
in pre-cooled FACS buffer (Ca2+/Mg2+ free PBS supplemented with 2% FBS). Then, fresh
digestion solution was added into the remaining undigested tissue for 3-4 rounds more
digestion processes. All collected cells from each artery sample were pooled, filtered
through a 70-μm cell strainer, and centrifuged at 600 g for 5 minutes. The cell pellets were
resuspended in 100 μL RBC Lysis Buffer (#420301, Biolegend) and incubated on ice for
1 minute for erythrocyte lysis. Then, the cells were harvested and resuspended in 1 mL
pre-cooled FACS buffer for further analysis.
For flow-cytometric analysis, isolated cells were incubated with fluorescence-
conjugated flow antibodies at 4°C for 30 minutes. The following antibodies and working
concentrations were used: CD45-BV510 (#103138E, Biolegend, 1:400), CD3-AF488
(#100210, Biolegend, 1:200), CD4-eF450 (#48-0041-82, Invitrogen, 1:400), CD8a-AF700
(#557959, BD Biosciences, 1:100), B220-BV605 (#103244, Biolegend, 1:400), NK1.1-
APC (#108710, Biolegend, 1:400), CD11b-PerCP/Cyanine5.5 (#101228, Biolegend,
1:400), F4/80-APC/Cy7 (#123118, Biolegend, 1:400), CD11c-Dazzle594 (#117348,
Biolegend, 1:800), I-A/I-E-BV711 (#107643, Biolegend, 1:800), LY6C-PE (#128008,
Biolegend, 1:2000), LY6G-PerCP (#127653, Biolegend, 1:200), ST2-PE-Cyanine7 (#25-
9335-80, Invitrogen, 1:400), CD117-BV785 (#105841, Biolegend, 1:400), CD127-BV650
(#135043, Biolegend, 1:400), CD206-BV421 (#141717, Biolegend, 1:400), CD11b-BV421
(#101236, Biolegend, 1:400), F4/80-APC (#123116, Biolegend, 1:400), CX3CR1-
PerCP/Cyanine5.5 (#19010, Biolegend, 1:400), CD45-PerCP/Cyanine5.5 (#103132,
Biolegend, 1:400), CD31-PerCP/ Cyanine5.5 (#102420, Biolegend, 1:400), PDGFRβ-PE
(#13600, Biolegend, 1:100), PDGFRβ-PE-Cyanine7 (#25-1402-8, Invitrogen, 1:100),
SCA1-PE-Cyanine7 (#108113, Biolegend, 1:2000), OPN-PE (#IC808P, R&D, 1:100), and
Ki67-FITC (#652410, Biolegend, 1:400) (Supplementary table 5). For intracellular Ki67
staining, cells post the staining of cell membrane markers were further fixed,
permeabilized and stained with Ki67-FITC antibodies using the Foxp3 Staining Set (#00-
5523-00, eBioscience) according to the manufacturer’s instructions. For the evaluation of
intracellular lipid contents, the cells were further incubated with 2 M BODIPY™ 493/503
(#D3922, Thermo Fisher) for an additional 20 minutes following cell membrane protein
staining. For intracellular OPN staining, cell suspensions were incubated with Brefeldin A
(3ug/mL; #HY-16592, MCE) for 6 hours prior to being fixed, permeabilized and stained
with OPN-PE antibodies using the Foxp3 Staining Set (#00-5523-00, eBioscience).
Immune cell populations were identified by flow cytometry based on the following surface
marker expression profiles: Macrophage (Mac) as CD45+CD11b+CD3-B220-NK1.1-LY6G-
F4/80+; Neutrophils (Neu) as CD45⁺CD11b⁺F4/80⁻LY6G⁺; Monocyte (Mon) as
CD45⁺CD11b⁺F4/80⁻LY6G⁻LY6C+; B cells as CD45+CD11b-CD3-B220+; Natural Killer cell
(NK) as CD45⁺CD11b⁻B220⁻CD3⁻NK1.1⁺; CD4⁺ T cell as
CD45⁺CD11b⁻B220⁻CD3⁺CD4⁺; CD8⁺ T cell as CD45⁺CD11b⁻B220⁻CD3⁺CD8⁺; Classic
Dendritic cell (cDC) as CD45⁺CD11b⁺CD11c⁺MHCII⁺; plasma DC(pDC) as CD45+ CD11b-
LY6G-F4/80-CD3-B220-NK1.1-CD11c+; ILC2 as CD45+CD11b-CD3-B220-NK1.1-CD11c-
CD117+; Mast cells as CD45+CD11b-CD3-B220-NK1.1-CD11c-CD117hiMHCIIlow
(Supplementary Data 1). Flow cytometric analyses were performed using Aurora CS
(Cytek) or CytoFLEX LX (Beckman). Flow data were analyzed with FlowJo V10 (version
10.8.1). All gating strategies are provided in Supplementary Figure 9.

RNA extraction and qRT-PCR
Total RNA was isolated using TRIzol reagent (#15596018CN, Thermo Fisher) from
indicated cells or tissues. cDNA was generated by PrimeScript RT reagent Kit with gDNA
Eraser (Perfect Real Time; #RR047A, Takara). Real-time quantitative PCR was
performed using TB Green PCR reagents (#RR420A, Takara) and Roche LightCycler 480
II. Actb or Rps18 were used as internal controls for normalization. Oligonucleotide primer
pairs used in this study are listed in Supplementary Table 1.

Immunoblot analysis and antibodies
Protein extracts from cells were prepared by homogenization in RIPA lysis buffer (150
mM NaCl, 1% NP-40, 0.5% sodium deoxycholate, 0.1% SDS and 50 mM Tris-HCl, pH
7.4) containing complete protease-inhibitor cocktail (#P8340, Sigma). Protein extracts
were separated by SDS-PAGE and then transferred onto a PVDF membrane
(#IPVH00010, Millipore). After incubation with the indicated antibodies, blots were
developed with SuperSignal West Pico Chemiluminescent substrate (#34580, Thermo
Fisher) or Immobilon Western Chemiluminescent HRP substrate (#WBKLS0100,
Millipore). The primary antibodies against P65 (#A22676, 1:1000), Phos-P65 (#AP1294,
1:1000) were purchased from Aclonal, and β-Tubulin (#2128S, 1:1000), IKK/ (#96794,
1:1000), Phos-IKK/ (#2697, 1:1000) were purchased from Cell Signaling Technology
(Supplementary Table 5). Validation information is available on the manufacturers'
websites. Unprocessed western blots were presented in the Source Data File.

Histology and immunofluorescence staining
For histological analysis, femoral artery samples were fixed in 10% formalin for 2 days
and then embedded in paraffin. The paraffin-embedded samples were sectioned into 5
μm thick slices and mounted on adhesion microscope slides for hematoxylin-eosin (H&E)
staining. All images were acquired using an EVOS AMF7000 microscope.
For immunofluorescence staining, antigen retrieval of the rehydrated sections was
performed using Antigen Retriever Tris-EDTA (#G1R206, Servicebio). Sections were then
blocked with PBS supplemented with 3% goat serum (#16210064, Thermo Fisher) and
0.1% Triton X-100 (#9002-93-1, Solarbio) at room temperature for 1 hour, followed by
incubation with primary antibodies at 4°C overnight. The sections were washed three
times with PBS, and incubated with Alexa Fluor-conjugated secondary antibodies at room
temperature for 1 hour, and then mounted with DAPI-containing antifade mounting
medium (#S2110, Solarbio). The primary antibodies and their working concentrations
were as follows: αSMA (#ab7817, Abcam, 1:400), CD45 (#ab208022, Abcam, 1:400), IL-
33 (#ab187060, Abcam, 1:400), and MAC2 (#CL8942AP, Cedarlane, 1:800)
(Supplementary Table 5). EdU click staining was performed using the BeyoClick™ EdU
Cell Proliferation Kit (#C0075, Beyotime) according to the manufacturer’s instruction.
Images were acquired using a Zeiss LSM800 confocal microscope and analyzed with
ImageJ (NIH, https://imagej.nih.gov/ij/) or Photoshop software.

Cellular assays
For bone marrow-derived macrophages (BMDMs) preparation64, femurs and tibias from
8~12-week-old male mice were isolated to extract bone marrow. Macrophage precursors
were isolated using a Ficoll-Paque PLUS Media (#17144003, Cytiva) gradient and
cultured in BMDM growth medium, which consisted of DMEM containing 10% FBS, 10
ng/ml M-CSF (#315-02, PeproTech), and 1% penicillin/streptomycin, on bacteriological
petri dishes (#351029, Corning) for 7 days. The culture medium was refreshed every two
days. Mature BMDMs were then maintained in DMEM supplemented with 10% FBS and
1% penicillin/streptomycin for subsequent experiments.
For primary mesenchymal stromal cells (MSCs) preparation, MSCs were retrieved from
stromal vascular fractions of murine inguinal white adipose tissue (WAT) using FACS22.
In brief, depots of inguinal WAT were minced thoroughly before being incubated for 80
minutes in 10 mL digestion buffer (1XHBSS, 1.5%BSA and 1mg/ml Collagenase D
(#11088882001, Roche)) at 37°C in a shaking water bath. The digested mixture was
placed on the ice immediately after digestion and then filtered through a 100-μm cell
strainer into 20 mL pre-chilled 2% FBS/PBS. After centrifugation (600g for 5 minutes at
4°C), the stromal pellet was resuspended and subjected to erythrocyte lysis. Cells were
then filtered again through a 40-μm cell strainer before centrifugation at 600g for 5
minutes at 4°C. After centrifugation, cells were resuspended in pre-cooled FACS buffer
and incubation on ice before flow antibodies incubation. For sorting PDGFR+ MSCs,
primary antibodies were added to the cells and incubated at 4°C for 15 minutes. The cells
then were resuspended in 2% FBS/PBS for sorting using a BD Bioscience FACSAria
cytometer at Zhejiang University School of Medicine. The primary antibodies and the
dilution were as follows: CD45-PerCP/Cyanine5.5 (#103132, Biolegend, 1:400); CD31-
PerCP/Cyanine5.5 (#102420, Biolegend, 1:400); PDGFR-PE (#136006, Biolegend,
1:75).
For siRNA transfection, primary MSCs and BMDMs were transfected with siRNAs
using BEX CUY21EDIT II Electroporator with the pulse conditions of 240V, 4ms, 50Ω. All
siRNAs in this study were synthesized at GenePharma Co. Ltd and their sequences are
listed above or siRela (sense 5’-GCGACAAGGUGCAGAAAGATT-3’ and antisense 5’-
UCUUUCUGCACCUUGUCGCTT-3’),
For the preparation of conditioned media, The cell lines of human umbilical vein
endothelial cells (HUVECs) and mouse brain endothelial cells (bEND.3) were obtained
from the National Collection of Authenticated Cell Cultures and kindly provided by Dr.
Yong Liu (Wuhan University), and cultured in DMEM supplemented with 10% FBS and 1%
penicillin/streptomycin. For the preparation of conditioned media (CM), HUVECs were
subjected to mechanical injury through being scraped for several times. The remained
cells were washed with PBS, and the fresh media were added for an additional incubation
for 24 hours. The culture media were then collected and diluted with an equal volume of
fresh media to generate the conditioned media (CM). For coculture assays, primary MSCs
were incubated in the indicated CMs for 24 hours before further analyses. The CM were
also subjected to be boiled at 95°C for 5 minutes, or filtered using Vivaspin protein
concentrator spin columns (#11668019, Cytiva) for the fractions of Frac>10K and Frac<10K.
For secretome study, conditioned medium (CM) derived from the bEND.3 cells were
collected, concentrated, and processed for protein profiling via high-resolution liquid
chromatography-tandem mass spectrometry (LC−MS/MS). The quantified data of detected
proteins were listed in Supplementary Data 2.
For Chromatin Immunoprecipitation (ChIP) assays44, primary MSCs were cultured in
10 cm dishes until reaching 90% confluency. The cells were cross-linked by incubating in
1% formaldehyde at room temperature for 10 minutes, followed by the addition of 125
mM glycine and incubation for 2 minutes at 4°C to quench the reaction. Cells were
resuspended in 1 mL of Farnham lysis buffer (5 mM PIPES pH 8.0, 85 mM KCl, 0.5% NP-
40, 1 mM DTT, and Protease inhibitor cocktail (#P8340, Sigma]) for 10 minutes to isolate
nuclear material. Crude nuclear pellets were re-suspended with lysis buffer (50 mM Tris-
HCl pH 7.9, 1% SDS, 10 mM EDTA, 1mM DTT, and Protease inhibitor cocktail). Lysates
were sonicated into 150–300 bp fragments using the setting of 6 cycles of 30s on/60s off
with a Bioruptor (Diagenode) at maximum power. Lysates were centrifuged at 13,000 x g
for 10 min at 4°C to remove cell debris, then the supernatant were collected and diluted
1:10 with dilution buffer (20 mM Tris-HCl pH 7.9, 0.5% Triton X-100, 2 mM EDTA, 150
mM NaCl, 1 mM DTT, and Protease inhibitor cocktail). Chromatin was pre-cleared by
adding 20 μL per mL Protein G Sepharose™ 4 Fast Flow (#17-0618-01, GE Healthcare
Bio-sciences) for 1h at 4°C and then incubated with anti-p65 antibody overnight (#8242,
Cell Signaling technology) at 4°C. Following overnight incubation, 80 μL washed Pierce
protein G beads were added and incubated for 2h at RT. Beads were captured by
centrifugation and then washed with low salt wash buffer (20 mM Tris-HCl pH 7.9, 2 mM
EDTA, 125 mM NaCl, 0.05% SDS, 1% Triton X-100, and Protease inhibitor cocktail),
high salt wash buffer (20 mM Tris-HCl pH 7.9, 2mM EDTA, 500 mM NaCl, 0.05% SDS,
1% Triton X-100, and Protease inhibitor cocktail), LiCl wash buffer (10 mM Tris-HCl pH 7.9,
1 mM EDTA, 250 mM LiCl, 1% NP-40, 1% sodium deoxycholate, and Protease inhibitor
cocktail), and 1x Tris-EDTA (TE). Chromatin was eluted by elution buffer (100 mM sodium
bicarbonate, 1% SDS) for 15 min at RT. Crosslinks were reversed through addition of 5
M NaCl and incubation at 65°C overnight. RNA and protein were removed by incubation
with RNase (#11119915001, Roche) and proteinase K (#EO0491, ThermoFisher
Scientific). DNA was purified through Phenol:Chloroform:IsoAmyl (25:24:1) extraction and
Isopropanol precipitation. Purified DNA was resuspended in 20 μL ddH2O and subjected
to qPCR (ChIP-qPCR). The amount of each DNA sequence in ChIP samples was
calculated relative to input using the ΔCt method. The sequences of primers for ChIP-
qPCR are B.S._1 (Forward 5’-GGCTTGGAGCTCTCATTATCT-3’ and Reverse 5’-
GACTAGTAAGAGCATCATGCAC-3’), B.S._2 (Forward 5’-
TTCAACCTAGAGTCAGGCTG-3’ and Reverse 5’-TGCCTGCAACTTTATCGC-3’),
B.S._3 (Forward 5’-GCCTCACCTGATATTTTATGTGG-3’ and Reverse 5’-
AAAGCTGAAAATGGGGCTC-3’).

Bulk RNA sequencing and analysis
Total RNA was extracted using TRIzol reagent (#15596018CN, Thermo Fisher) from
indicated cells isolated from the femoral arteries using FACS, and was eluted in 10 µL of
sterile water. Bulk RNA sequencing was performed by Novogene Co., Ltd. (Beijing, China).
Briefly, SMARTer Ultra RNA Kit (#634936, Takara) was used to synthesize cDNA.
After library amplification, cDNA was tagmented and bar coded using TruePrep DNA
Library Prep Kit V2 (#TD503-02, Vazyme). The quantity and quality of final libraries were
evaluated using the Qubit 2.0 Fluorometer and Agilent 2100 bioanalyzer. Then cDNA
libraries were pooled and sequenced on the Illumina Novaseq 6000 platform using a 150
bp paired-end modality. The sequencing process utilized the Sequencing by Synthesis
(SBS) method, employing four fluorescently labeled dNTPs, DNA polymerase, and
sequencing primers.
The process of raw data was performed on Galaxy platform (https://usegalaxy.org/).
Quality Control was performed with FastQC (Version 0.12.1). After trimming low quality
sequences using Cutadapt (Version 4.9), RNA STAR (Version 2.7.11a) was used to map
reads to the mouse reference genome (GRCm38/mm10). Gene expression quantification
was performed using featureCounts (Version 2.0.3) against mouse Gencode version 20
annotations. Differential expression analysis was conducted with the DESeq2 package
(version 1.42.0) in R, and p-values were adjusted using the Benjamini-Hochberg method
to manage the false discovery rate, ensuring statistically rigorous detection of differentially
expressed genes. The differentially expressed genes (DEGs) identified between different
groups were analyzed for pathway enrichment. Gene Ontology (GO), Kyoto Encyclopedia
of Genes and Genomes (KEGG) pathway enrichment analyses, and Gene Set
Enrichment Analysis (GSEA) were performed using the ClusterProfiler package (version
4.10.1) in R. All bulk RNA-seq data have been deposited to GEO (GSE281009).

Single-cell RNA sequencing and analyses
For single-cell RNA sequencing (scRNA-seq), arterial cell isolation from murine femoral
arteries at 2 days (D2) and 7 days (D7) post-surgery or Sham operations was performed
as described above. The resuspended cells were stained with 10 μg/mL DAPI (#BL105A,
Biosharp, 1:1000), filtered through a 40-μm cell strainer, and subjected to fluorescence-
activated cell sorting (FACS) using a Cytek Aurora CS to isolate singlets. For each
experimental condition, alive cells from 6 mice of the FAI or Sham group were pooled to
create one sample per group. Single-cell RNA sequencing (scRNA-seq) was conducted
by Berry Genomics (Beijing, China) using the Chromium Single Cell 3’ Reagent Kits v3
(10X Genomics) in accordance with the manufacturer’s protocol. Briefly, cells with a
viability of over 85% were washed with 0.04% BSA in DPBS and resuspended to a
concentration of 700–1200 cells/μL. The cells were then encapsulated in droplets, each
containing a uniquely barcoded bead, enabling the reverse transcription of mRNA into
cDNA. The cDNA libraries were subsequently amplified and sequenced on the Illumina
NovaSeq platform. Raw sequencing data were processed using Illumina’s bcl2fastq
converter to generate FASTQ files for further analysis. All scRNA-seq data have been
deposited to GEO (GSE281008).
For scRNA-seq data analysis and visualization, the sequencing data were processed
into gene expression matrix with the CellRanger (version 7.2.0, 10X Genomics).
Downstream analysis of the data was performed with the R package Seurat (Version 5.0.1)
65. Genes expressed in fewer than three cells were removed. Cells with >300 expressed
genes, <6,000 expressed genes, mitochondrial gene content <10%, and unique
molecular identifier (UMI) counts <40,000 were retained for further analysis, yielding to
27,515 cells. Samples of the Sham, FAI_D2, and FAI_D7 conditions were integrated using
R package harmony (Version 1.2.0) 66. Gene expression counts were normalized using
Log-normalization with a scale factor of 10,000, and the top 2,000 genes were selected
for individual sample through the variance stabilizing transformation method. Principal
components analysis (PCA) was performed and unsupervised clustering was visualized
in Uniform Manifold Approximation and Projection (UMAP) plots with a resolution of 0.1,
based on the top ten principal components. Cell clusters were then annotated based on
the well-established markers. Differentially expressed genes among the identified cell
clusters were determined by FindMarkers function from the Seurat package. For pseudo-
temporal single-cell trajectory analysis, Monocle (Version 2.30.1) and Monocle3 (Version
1.3.7) was utilized to investigate developmental trajectories. CellChat (Version 2.1.2) 67
was used to identify cell-cell communications, and cell differentiation states were
predicted by CytoTRACE package (Version 0.3.3) 68. For cell-cell communication analysis,
the numbers, strengths, and probabilities of cell-cell interactions across cell types were
quantitatively computed using CellChat (Version 2.1.2) based on the curated ligand-
receptor interaction database (CellChatDB). The information on the analysis results was
provided in the Supplementary Data 4 and 5. Lipid metabolism signature score was
computed using the function of AddModuleScore. The gene sets used for the pathway
enrichment analyses in this study were listed in the Supplementary Data 6 and 7. A QC
summary of the single-cell RNA sequencing was provided in Supplementary table 4.

Human study
This study enrolled patients who underwent one-year angiographic follow-up after
second-generation DES implantation at the Department of Cardiology, Affiliated
Hangzhou First People’s Hospital of Westlake University between July and October 2025.
The cohort consisted of 25 patients with in-stent restenosis (ISR), define as ≥50%
diameter stenosis in the stented segment or its immediate 5 mm margins, and 25 control
patients without ISR. Patients with hepatic, renal disease, malignancy, acute inflammation,
or age<18 or >80 years were excluded. All samples were obtained from patients after
receipt of written informed consent in accordance with the Declaration of Helsinki. The
study protocol was approved by the ethics committee of Affiliated Hangzhou First People’s
Hospital of Westlake University (approval ID-2025ZN282-1). The basic demographic
information of all participants enrolled in this study is presented in Supplementary Table
3. Plasma IL-33 and OPN protein levels were determined by a commercially available
human IL33 ELISA Kit (#EK133, Multi sciences) and human OPN ELISA Kit (#EK1135,
Multi Sciences) according to the manufacturer’s instructions.

Statistical analysis
Statistical analysis was carried out as indicated in the figure legends. All data are
presented as the mean ± s.e.m. unless otherwise indicated in the figure legends. Data
variance was examined by F test or Bartlett’s test. The data meet the assumptions of the
indicated statistical analysis. All tests were performed as two sided. A p value less than
0.05 was considered statistically significant. All statistical analyses were performed using
Microsoft Excel or GraphPad Prism 8.0 (GraphPad Software). All statistical information,
including p values, samples sizes and repetitions, are provided in Supplementary Data 3.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this manuscript, the authors report on the role of fibroblast-macrophage interaction following femoral artery endovascular injury in mice. The authors identify a pathway involving IL33-ST2-OPN in the regulation of macrophage activity that promotes neointimal hyperplasia after injury. This study is data intensive and experiments were generally well performed and well described. However, the scRNAseq data are superficially analyzed/described, additional data are needed to verify that perturbations have intended effects, gating strategies for flow cytometry need to be reported and quantification strategies justified, some data are overinterpreted and data presentation could be improved.\nMajor comments\n1. In the Introduction, authors claim that factors regulating macrophage phenotype switching remain unclear. However, numerous studies implicate many potential candidates including cytokines, signaling pathways, growth factors. Please acknowledge this previous work and summarize current understanding.\n2. Several datasets, including published scRNAseq data, were used in this study. It is difficult to follow which dataset was used for analysis in several figures. Please clarify the source of data used to generate each graph in each Figure.\n3. Flow cytometry data and presentation should be improved. First, enzymatic tissue digestion causes significant cell death. Therefore, live/dead cell staining should be used in all flow analysis to allow gating out of dead cells from analysis. In addition, complete gating strategies for all flow cytometry analyses should be provided either in main or supplementary Figures. When presenting flow data as cell numbers, authors chose to normalize to per cm artery. Please justify this choice, as the obvious change in artery thickness could be expected to influence cell numbers. When presenting flow data as percentages, please clarify the denominator cell population in the calculation (e.g. total cells or immediate parent cell gate). Finally, please clarify which flow cytometry antibodies were used to identify immune cell populations in Figure 1 and how these correspond to the populations identified in the scRNAseq data.\n4. scRNAseq analysis is overall superficial and/or incomplete. For data in Figure 1 and Extended Figure 1, please provide a more complete list of marker genes that differentiate both major cell clusters and immune cell subclusters for all datasets analyzed. Along these lines, please discuss how CD72 and CD63 were chosen as markers to discriminate macrophage clusters; these markers do not appear to be differentially expressed to a substantial level in the single cell data.\nFor all pathway analyses, please provide complete gene list as supportive data for each pathway identified.\nFor pseudotime analysis, cluster 1 macrophages are distributed across all pseudotime branches. Please discuss the potential significance of the presence of this apparently pro-inflammatory population over all pseudotime states. Since cluster 2 macrophages tend to have higher pseudotimes, one could infer that there may be a transition from cluster 1 to 2 macrophages. Please discuss. For Monocle 3 analyses, please indicate which cluster was selected as starting cluster. In addition to the pseudotime analysis, scRNA velocity analysis would provide additional insight into potential cell state transitions.\nFor the CellChat analysis, only numbers of interactions between cell clusters is presented. The strength of these interactions should also be presented as is likely less prone to false positives. In general, this analysis should be discussed more fully. As is, it is not clear whether this analysis was used to guide downstream experiments and a full appreciation of potential signaling networks involved is missing. Along these lines, please provide rationale to support choosing IL33 as a primary target. A discussion of other possible candidates from the scRNAseq data is warranted\nIn Extended Figure 3C, Trem2 expression appears to be high in both cluster 1 and 2 macrophages, which is inconsistent with flow data. Please discuss this discrepancy\n5. For cell tracking and perturbation experiments, additional data are needed to verify effects on target cells and their specificity. For experiments with CX3CR1-GFP mice and CX3CR1-DTR mice, specificity of targeting macrophages should be demonstrated. CX3CR1 is expressed on many cell types, including other immune cells and fibroblasts, which could confound interpretation of these experiments. In addition, for the Ki20227 CSF1R inhibitor experiments, please show impact on monocyte/macrophage accumulation via flow cytometry. For Pdgfrb-CreER-IL33 mice, please demonstrate specificity of knockout to fibroblasts and lack of leakiness of knockout phenotype. For LysM-IL1RL1 mice, please demonstrate specificity of knockout to macrophages; neutrophils also express ST2 and osteopontin.\n6. For the lineage tracing experiment using GFP mice (Figure 2g),it appears that the gating of GFP+ cells in the right panel should be moved to the right to the point showing clear separation between GFP+ vs GFP- cells. Negative cell fluorescence levels can be shifted between control and treated groups due to differing effects of compensation (due to lack of one color in control). Therefore, it is possible that the actual GFP+ cell proportion was less than 80% and a significant part of cluster 2 macrophages could come from transition from resident or cluster 1 cells. Additionally, please provide evidence(markers) to identify GFP+ cells as cluster 2 macrophages.\n7. For the co-culture experiments, authors should justify use of HUVEC instead of primary mouse endothelial cells, and PDGFRb+ cells isolated from inguinal adipose tissue instead of from vessels. Also, authors should identify source of VSMC.\n8. A paragraph on the limitations of the study is missing. Please add.\nMinor comments\n9. In Extended Figure 1a, please identify whether there are any significant differences between time points. In the first paragraph of the Results, please clarify what is meant by “significant temporal changes”. Also, please either show VSMCs proliferate extensively or properly cite previous studies.\n10. Extended Figures 3 and 4 do not add to the primary focus of the manuscript and could be deleted.\n11. For Figure 5, additional data on the impact of loss of IL33 in PDGFRb+ cells on levels of cytokines in the vessel would provide insight into changes in the inflammatory environment. For Figure 5e, please present as bar graphs similar to Figure 5f, as it is currently difficult to assess differences between conditions. In Figure 5f, there is a trend towards higher neutrophil accumulation in response to loss of IL33 while macrophage numbers were downregulated. Please discuss potential impact on neointimal hyperplasia.\n12. In Figure 7, additional flow cytometry data assessing OPN protein levels directly in macrophages and neutrophils isolated from arteries would strengthen the manuscript.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_3": "In this manuscript the relationship between ST2 (IL1RL1)+ macrophages attracted by tissue resident stromal cells and vascular smooth muscle cells (VSMC) in an endovascular injury model in male mice was demonstrated. The authors persuasively show that IL33 produced by the stromal cells induce a repair associated macrophage type that express Osteopontin that promotes VSMC proliferation and neointima formation. This manuscript shows convincing in vivo data with well executed transcriptomic and flow cytometry data.\nThe translational impact of these finding is a challenge since although the authors describe that these findings are relevant for coronary artery disease it is well known that cells in peripheral vascular tissues behave differently than in the heart. Furthermore, this model lacks any atherosclerotic background resulting in very suboptimal reflections. The presented research is thorough but for me unclear how relevant this is for human coronary PCA. Thorough additional research on the key factors in human disease samples is necessary to show the translational concept.\nThe concept of the ST2 macrophage/IL33 axes is well know and the link to osteopontin is described in other disease such as arthritis limiting the novelty of these findings.\nA good explanation why only male mice are used should be clearly discussed.\nAlthough the single cell sequencing data on these small samples is impressive I have some remarks; less than 10.000 cells for a heterogenous tissue such as the vessel wall (t0 timepoint) is affecting the reliability of these analysis. Interpretations of this datapoint should be very cautious. Why is only one gene (Ptprc) selected to represent the immune cells and why only use the Go pathway analysis and not other source such as KEGG or MSIG/DB?\nLy6c is a very general marker as the authors also describe, is this really the best marker for these inflammatory associated macrophages?\nFigure 2P why is the vehicle completely occluded? This results in totally different processes than neointima formation and should be excluded from the analysis. How often is this observed in the experiments?\nIn some of the experiments it is not clearly described (for instance the histology in figure 1) which background and how many mice are used for the analysis.", "reviewer_4": "The study by Ping et al; investigates the of immunomodulatory mechanisms driving injury-responsive immune-stromal cell interactions in the realm of endovascular femoral artery injury. The study integrates time-resolved single cell RNA sequencing and multi-color flow cytometry to analyze the alterations in cellular components and cell-cell communication circuits within the injured femoral artery. The findings revealed a subset of ST2-expressing macrophages whose recruitment is fostered by injury-activated adventitial MSCs and plays a critical role in regulating VSMC proliferation. Also, MSC-derived IL-33 has been found to trigger the reparative phenotype of these ST2+ macrophages, that stimulate VSMC proliferation via OPN. Manipulating the IL33-ST2-OPN regulatory axis between MSCs and macrophages prevents NIH in the injured artery. Overall, the study is very interesting and is significantly supported by ample data and findings. However, several issues need to be addressed for considering for publication in Nat Comm. Nonetheless, the results reveal a novel mechanism addressing the wound healing process and post-injury neointima formation, targeting the MSC-macrophage interactions to prevent restenosis and other vascular diseases.\nComments\n1. The introduction is very generic. A comprehensive survey of ST2-expressing macrophages and associated cells of interest emphasizing the immunomodulatory communications with resident MSCs along with underlying mechanisms are required to understand the rigor in this field. The introduction does not sufficiently highlight the study’s novelty or define the critical knowledge gap it intends to fill, limiting the reader’s understanding regarding the significance.\n2. Density count of CD45 cells from multiple sections of each time frame would be appreciated for assessing the extend of WBC infiltration. Differential count and expression of WBC subsets are required to determine the nature/status of NIH. Also, measuring the difference in lumen diameter/aspect ratio will be helpful to quantify the extent of NIH.\n3. In the results the authors mentioned that ‘Notably, a large number of vascular macrophages were detected at 7 days and 14 days post femoral artery injury……’. How are the vascular macrophages defined? What is their phenotypic signature? The tissue level characterization of these macrophages is crucial to understand their progression during the progression of pathology.\n4. Similarly, ‘Their abundance eventually declined to baseline levels as vascular regeneration progressed by 28 days post-injury’. How was the vascular regeneration quantified? Tissue level data is required.\n5. TREM2+ LLM are promising in RepMAC; however, the information regarding the pro-inflammatory counter part TREM1 would provide additional confirmation regarding the inflammatory/reparative status. TREM2-TREM1 ratio will be highly appreciated.\n6. The figures and extended data are mixed up and not in the proper order which makes hard to follow the figures.\n7. Upregulation of Il33 gene expression, rather than the changes in the abundance of Il33-expressing cells, appeared to contribute to the higher IL-33 levels observed in injured arteries. Explanation regarding the cell types and the underlying mechanisms for IL33 expression in the context of FAI.\n8. Do inguinal adipose MSCs typically replicate the pathophysiology of MSCs from injured vascular tissue? Does the embryonic nature of HUVEC cells simulate the secretome content of injured ECs associated with mature blood vessels? How accurate and reproducible is the mechanical injury model? These models require further rationale and validations to correlate with the in vivo pathophysiology.\n9. Screening data to quantify the content of CM is unavailable. The secretome analysis is required to provide mechanistic insights for CM-driven Il33 action.\n10. Experimental details and data regarding the hydrogel composition and compatibility are missing.\n11. The authors discussed the findings related to CAD; however, the model used was FA injury which would be a representation of PAD. Justification for the animal model is required. How the findings relate to clinical CVDs? What is the translational significance? What are the limitations of the study?\n12. A section concluding the significance of key findings will be appreciated."}, "author_response": "POINT-BY-POINT RESPONSE TO REVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nIn this manuscript, the authors report on the role of fibroblast-macrophage\ninteraction following femoral artery endovascular injury in mice. The authors\nidentify a pathway involving IL33-ST2-OPN in the regulation of macrophage\nactivity that promotes neointimal hyperplasia after injury. This study is data\nintensive and experiments were generally well performed and well described.\nHowever, the scRNAseq data are superficially analyzed/described, additional\ndata are needed to verify that perturbations have intended effects, gating\nstrategies for flow cytometry need to be reported and quantification strategies\njustified, some data are overinterpreted and data presentation could be\nimproved.\nMajor comments\n1. In the Introduction, authors claim that factors regulating macrophage\nphenotype switching remain unclear. However, numerous studies implicate\nmany potential candidates including cytokines, signaling pathways, growth\nfactors. Please acknowledge this previous work and summarize current\nunderstanding.\nResponse: We thank the reviewer for raising this important point and for\nreminding us to reference the known roles of macrophages in this context. As\nnoted by the reviewer, previous studies have highlighted the functions of\ncytokines and growth factors (e.g., IL-4, IL-13, IL-10, CSF-1, TGFβ, IGF-1) as\nwell as key downstream signaling mediators (e.g., STAT6, NFκB, PPARγ,\nAMPK). We appreciate the comment that our initial description may have\noversimplified the current understanding of macrophage biology in this setting.\nWhile these regulators and mechanisms have been implicated previously, the\ndetailed processes underlying macrophage “phenotypic switching” and its\nregulatory control still remain poorly defined. In response, we have revised the\ntext to explicitly acknowledge prior work and clarify how our findings provide\nnew insights into how macrophages acquire a reparative phenotype in this\ncontext. (Page 4, line 84-97)\n2. Several datasets, including published scRNAseq data, were used in this\nstudy. It is difficult to follow which dataset was used for analysis in several\nfigures. Please clarify the source of data used to generate each graph in each\nFigure.\nResponse: We thank the reviewer for the careful reading of our manuscript and\nfor bringing this to our attention. We apologize for the omission of the relevant\nscRNA-seq dataset information in the original version. We have now updated\nthe figures and the revised manuscript to include these details.\n3. Flow cytometry data and presentation should be improved. First, enzymatic\ntissue digestion causes significant cell death. Therefore, live/dead cell staining\nshould be used in all flow analysis to allow gating out of dead cells from analysis.\nIn addition, complete gating strategies for all flow cytometry analyses should be\nprovided either in main or supplementary Figures. When presenting flow data\nas cell numbers, authors chose to normalize to per cm artery. Please justify this\nchoice, as the obvious change in artery thickness could be expected to\ninfluence cell numbers. When presenting flow data as percentages, please\nclarify the denominator cell population in the calculation (e.g. total cells or\nimmediate parent cell gate). Finally, please clarify which flow cytometry\nantibodies were used to identify immune cell populations in Figure 1 and how\nthese correspond to the populations identified in the scRNAseq data.\nResponse: We thank the reviewer for the helpful suggestions regarding\nmethodologic information and data analysis, which provides an opportunity to\nimprove the clarity of our Methods section and to offer additional details.\nIn the flow assays in this work, we have gated out of dead cells based on\nlive/dead cell staining from analyses. With respect to methodological\ninformation, the gating of live cells based on live/dead cell staining and\ncomplete gating strategies for the flow cytometry experiments have been\nincluded in the revised manuscript (see the Source Data Figure 1-3), as\nsuggested by the reviewer. We also have explicitly clarified the denominator\ncell population used for each percentage calculation in the panels, figure\nlegends and the Methods section, thereby enhancing the clarity and\ninterpretability of our analyses. In addition, the complete information on flow\ncytometry antibodies used for immune cell identification has been added to the\nMethods section (Page 30, line 935-943, also see Table S6), and the cell type-\ndefining markers used in both flow cytometry and scRNA-seq analyses have\nbeen specified in the revised manuscript (Table S6).\nRegarding the reviewer’s comment on artery thickness and our choice to\nnormalize flow cytometry data per centimeter (cm) artery, our decision was\nbased on the unique characteristics of the mouse femoral artery and the\nrequirements of flow cytometry analysis, while also considering reported\napproaches in the literature. Multiple normalization strategies have been\ndescribed, including normalization to artery number for analysis (PMID:\n36899827), each with inherent limitations. Given the very small size of the\nmouse femoral artery, normalization to tissue weight (per gram) is impractical\nand introduces significant variability due to subtle differences in dissection or\nsurrounding adventitial fat. It is worthy to note that our samples were collected\nat the early phase (D0, D2, and D7) post-injury when neointimal thickening is\nnot obvious, thereby minimizing the biological need to account for thickness as\na major factor for normalization. Moreover, enzymatic digestion of vascular\ntissue into a single-cell suspension eliminates the tissue’s three-dimensional\nstructure, making post-digestion measurements of thickness or volume\nunfeasible.\nTo address these challenges and ensure uniformity, we employed a\nconsistent sampling methodology. In the femoral artery wire-injury model, a\nguide wire is inserted via the superficial femoral artery branch and advanced\ndistally to induce injury, after which the branch is ligated. For flow cytometry,\nwe consistently harvested a precisely defined 1 cm segment of the femoral\nartery, measured proximally from the ligated branch junction. This approach\nprovided high reproducibility and ensured uniform sample collection across all\nexperiments.\n4. scRNAseq analysis is overall superficial and/or incomplete. For data in\nFigure 1 and Extended Figure 1, please provide a more complete list of marker\ngenes that differentiate both major cell clusters and immune cell subclusters for\nall datasets analyzed. Along these lines, please discuss how CD72 and CD63\nwere chosen as markers to discriminate macrophage clusters; these markers\ndo not appear to be differentially expressed to a substantial level in the single\ncell data. For all pathway analyses, please provide complete gene list as\nsupportive data for each pathway identified.\nResponse: We thank for the reviewer for these constructive comments and\nsuggestions. In response, we have now provided a complete list of marker\ngenes used to identify the major cell clusters and immune cell subclusters in\nour scRNA-seq datasets (Table S6). For pathway analyses, the complete gene\nlists for each pathway identified has been provided in Table S8.\nRegarding our rationale for selecting CD63 and CD72 to differentiate\nmacrophage subclusters in this study, our selection was based on two major\nconsiderations:\n1) The primary reason was the need to identify robust cell surface markers that\ncould be used to establish a flow cytometry protocol for the analysis and\nisolation of macrophage subclusters. Our scRNA-seq analysis revealed\nCD63 and CD72 were among the top selectively expressed markers in the\ninjury-associated reparative macrophage subcluster. A full list of cell\nmembrane marker genes supporting the selection of CD63 and CD72 is\nprovided in Table S7. Leveraging their selective expression, we developed\na flow cytometry strategy using commercially available antibodies, enabling\nboth the analysis and isolation of these macrophage subclusters from\ninjured arteries. This approach was further validated by our observation that\nthe abundance of CD63⁺CD72⁺ macrophages significantly increased during\nthe repair stages post artery injury (Fig. 2h-j), coinciding with the time when\nreparative macrophages are expected to predominate.\n2) In addition to their suitability as cell surface markers, CD63 and CD72 are\nfunctionally relevant to macrophage biology. CD63 is broadly involved in\nextracellular vesicle formation and secretion, which are critical for tissue\nrepair and immune modulation (PMID: 40537510). Emerging evidence also\nimplicates CD72 in promoting macrophage polarization toward an anti-\ninflammatory phenotype，which is one of the key features for reparative\nmacrophages (PMID: 34100920). Thus, CD63 and CD72 may serve as\n“functional” markers of this newly identified injury-associated macrophage\nsubcluster, providing a promising direction for future mechanistic studies.\nIn summary, CD63 and CD72 rank among the top differentially expressed\ncell surface makers between macrophage subclusters, and their selective and\nconsistent expression at both the mRNA level (scRNA-seq) and protein level\n(flow cytometry) makes them highly useful markers across a broad range of\nexperiments conducted in this study.\nFor pseudotime analysis, cluster 1 macrophages are distributed across all\npseudotime branches. Please discuss the potential significance of the\npresence of this apparently pro-inflammatory population over all pseudotime\nstates. Since cluster 2 macrophages tend to have higher pseudotimes, one\ncould infer that there may be a transition from cluster 1 to 2 macrophages.\nPlease discuss. For Monocle 3 analyses, please indicate which cluster was\nselected as starting cluster. In addition to the pseudotime analysis, scRNA\nvelocity analysis would provide additional insight into potential cell state\ntransitions.\nResponse: We thank the reviewer for these constructive suggestions regarding\nour scRNA-seq analysis and data presentation. In response, we have revised\nthese sections as follows:\n1) We agree that the observation of Cluster 1 macrophages persisting across\nall pseudotime states is noteworthy, as it challenges a strictly linear\n'inflammatory to reparative' transition model. This distribution likely reflects\na more dynamic and complex process. We hypothesize that Cluster 1\nrepresent a continuously replenished population of pro-inflammatory\nmacrophages, either newly recruited to the injury site or maintained in a\nlow-level activated state. The coexistence of inflammatory and reparative\nmacrophage populations suggests that macrophage phenotypic transitions\nduring tissue repair do not occur as a simple binary switch but instead\nreflect a dynamic equilibrium in which both macrophage subsets coexist\nand remain functionally active. As suggested, we have expanded the\nDiscussion to include this interpretation (Page 17, line 503-505).\n2) We agree with the reviewer that the higher pseudotimes of Cluster 2\nmacrophages strongly suggest a transition from Cluster 1 to Cluster 2.\nConceptually, pseudotime trajectory analysis is designed to model the\ncontinuous progression of cells along a differentiation or activation pathway.\nAccordingly, we have revised the Discussion to include this interpretation\n(Page 17, line 503-505).\n3) For the Monocle 3 analyses, Cluster 3 (monocytes) was selected as the\nstarting cluster, which is now indicated in the revised Extended Data Fig.\n3c.\n4) As suggested, scRNA velocity analysis has been performed to provide\nadditional insights into potential cell state transitions. Specifically, the\nresults support a directional flow from the pro-inflammatory cluster (Cluster\n1) toward the reparative cluster (Cluster 2) (Revision Fig.1). Furthermore,\nthe velocity analysis revealed more complex dynamics: monocytes\nprimarily differentiate into the pro-inflammatory macrophage cluster\n(Cluster 1); there is bidirectional interconversion between the pro-\ninflammatory (Cluster 1) and reparative (Cluster 2) clusters, indicating that\nthese phenotypes are not fixed but rather represent reversible states\nmodulated by the tissue microenvironment; and the homeostatic\nmacrophage cluster primarily undergoes self-renewal, with a minor\npotential contribution from the reparative cluster. These new findings have\nbeen included in the revised Fig. 2e.\nRevision Figure 1. RNA velocity analysis of the macrophage\nsubpopulations in arteries following femoral artery injury (FAI). (also see\nthe Fig. 2e in the revised manuscript)\nDifferentiation trajectory of the four macrophage subclusters were inferred\nusing RNA velocity based on the scRNA-seq dataset described in Figure 1.\nVelocity vectors are indicated as streamlines.\nFor the CellChat analysis, only numbers of interactions between cell clusters is\npresented. The strength of these interactions should also be presented as is\nlikely less prone to false positives. In general, this analysis should be discussed\nmore fully. As is, it is not clear whether this analysis was used to guide\ndownstream experiments and a full appreciation of potential signaling networks\ninvolved is missing. Along these lines, please provide rationale to support\nchoosing IL33 as a primary target. A discussion of other possible candidates\nfrom the scRNAseq data is warranted. In Extended Figure 3C, Trem2\nexpression appears to be high in both cluster 1 and 2 macrophages, which is\ninconsistent with flow data. Please discuss this discrepancy.\nResponse: We thank the reviewer for the constructive comments on our\nCellChat analysis and agree with the suggestion to provide a more detailed\npresentation and discussion of these findings. Accordingly, we have revised the\nmanuscript to include a discussion of potential signaling networks and a\ndetailed rationale for selecting IL-33 as a primary target. The revisions are\nsummarized as follows:\n1. With respect to CellChat analysis, we have re-analyzed our CellChat data\nto include both the number and the strength of interactions between cell\nclusters. The heatmaps of interaction strength revealed robust interactions\nbetween macrophages and monocytes across the examined time points.\nNotably, substantial changes were also observed in interactions between\nmacrophages and MSCs, neutrophils, as well as within macrophages\nthemselves (Revision Figure 2). These findings are, in principle, consistent\nwith the original analysis based on interaction numbers and suggest a\npotential functional link between MSCs and macrophage phenotypic\ntransitions during tissue injury and repair in injured arteries.\nRevision Figure 2. Interaction strength of identified ligand-receptor pairs\namong eleven cell populations in injured arteries.\nHeat map showing the interaction strength between each pair of cell\npopulations in injured arteries, as inferred by CellChat based on the scRNA-\nseq dataset described in Figure 1.\n2. Regarding the rationale for choosing IL-33 as a primary target, to explore\nthe potential secreted factors derived from perivascular MSCs, we analyzed\nour bulk RNA-seq dataset and identified the top 50 genes encoding secreted\nproteins in MSCs whose expression levels were upregulated in response to\ninjury. Among these genes, Il33 exhibited most prominent upregulation, and\nacts as the top1 upregulated and predominantly expressed in MSCs\n(Revision Fig. 3a-b). IL-33 was ultimately selected as the primary target due\nto its strong literature (PMID:36520518, 39617790, 26872699) support for\na role in tissue repair and its ability to modulate macrophage function. This\nrationale was fully validated by following functional experiments using\ngenetic inactivation of Il33 and hydrogel-mediated siRNA silencing Il33,\nwhich demonstrated its importance in the context of injury-induced\nneointimal hyperplasia.\nRevision Figure 3. Multi-omics analyses of secreted proteins identify\ncandidate mediators of MSC-Macrophage crosstalk in injured arteries.\n(also see the Extended Data Fig. 4d,e in the revised paper)\na, Heatmap of top 50 genes encoding secreted proteins upregulated in MSCs\nof FAI mice relative to Sham group which were identified based on bulk RNA-\nseq dataset described in Fig. 3m.\nb, Violin plot showing the expression patterns of the top 10 genes presented in\nPanel a across immune cells (Immune), endothelial cells (EC), smooth muscle\ncells (SMC) and MSC, based on our scRNA-seq datasets described in Fig. 1c.\nWe thank the reviewer for the helpful suggestion to discuss other potential\ncandidates identified from the scRNA-seq data. In response, we have\nexpanded the Discussion to include a more detailed analysis of the top\ninteraction pairs between MSCs and macrophages identified by CellChat.\n(Revision Figure 4). Our analysis highlights several key interaction pairs,\nincluding Tgfb1/2/3 -- Acvr1+Tgfbr1, Mif -- Cd74+Cxcr4/Cd44, and Ccl2/7/8/12\n-- Ccr1/2/4/5. The interactions of these pairs were enhanced between MSC and\nmacrophages post injury, particularly at D2, suggesting that the role of MSCs\nin mediating the recruitment of macrophages in response to injury.\n(Page 16, line 461-465).\nRevision Figure 4. Temporal analysis of MSC-macrophage ligand-\nreceptor interactions during arterial repair. Analysis of the putative ligand-\nreceptor pairs in mediating interactions between MSCs and macrophages at\nD0, D2 and D7. Interactions were inferred using CellChat based on the scRNA-\nseq dataset presented in Figure 1.\nMoreover, we appreciate the reviewer for raising the important point\nregarding Trem2 expression in macrophage subpopulations. Indeed, the high\nTrem2 mRNA levels observed in Cluster 1 and Cluster 2 were not accompanied\nby consistently high TREM2 protein expression, as indicated by our flow\ncytometry analysis. Such discrepancies between mRNA and protein\nabundance are not uncommon and are often attributed to post-transcriptional\nand post-translational regulation. Moreover, flow cytometry relies on antibody\nbinding to surface proteins, with a detection threshold and specificity that differ\nfrom mRNA-based methods. It is possible that TREM2 protein levels in Cluster\n1 macrophages fall below the detection limit of flow cytometry or that the protein\nis insufficiently exposed on the cell surface. Importantly, protein levels\nmeasured by flow cytometry are thought to more directly reflect cellular\nphenotype. Following the reviewer’s suggestion (Minor Point 10), original\nExtended Data Figures 3 and 4 are removed in the revised manuscript due to\nthat these data do not add to the primary focus of the manuscript.\n5. For cell tracking and perturbation experiments, additional data are needed to\nverify effects on target cells and their specificity. For experiments with CX3CR1-\nGFP mice and CX3CR1-DTR mice, specificity of targeting macrophages should\nbe demonstrated. CX3CR1 is expressed on many cell types, including other\nimmune cells and fibroblasts, which could confound interpretation of these\nexperiments. In addition, for the Ki20227 CSF1R inhibitor experiments, please\nshow impact on monocyte/macrophage accumulation via flow cytometry. For\nPdgfrb-CreER-IL33 mice, please demonstrate specificity of knockout to\nfibroblasts and lack of leakiness of knockout phenotype. For LysM-IL1RL1 mice,\nplease demonstrate specificity of knockout to macrophages; neutrophils also\nexpress ST2 and osteopontin.\nResponse: We thank the Reviewer for raising these questions regarding the\nspecificity of the systems used in the cell tracking and perturbation experiments.\nFor Cx3cr1-expression based experimental systems, we examined the scRNA-\nseq data for Cx3cr1 expression in our scRNA-seq dataset across cell types and\nfound that the immune cells are the primary Cx3cr1-expressing population\n(Revision Fig. 5a). We then analyzed Cx3cr1GFP mice after FAI operation and\nobserved that GFP+ cells are mainly immune cells, rather than non-immune\ncells like fibroblasts (Revision Fig. 5b). Further analysis of scRNA-seq dataset\nrevealed that macrophages expressed particularly high Cx3cr1 expression\namong immune cells (Revision Fig. 5c) . We separately presented the\npercentages of GFP+ cells across the eleven cell populations using multi-color\nflow cytometry, and found that in line with this expression pattern, >70% of\nGFP+ cells are macrophages (Revision Fig. 5d). Importantly, significant\nreductions in GFP+ cells following FAI in Mac mice were primarily detected\nwithin the macrophage population (Revision Fig. 5e). It is noteworthy that the\nreduction of GFP+ cell proportion in DCs and Mast cells was also detected.\nAccordingly, we have revised the relevant text, and these new findings have\nbeen included in the revised Extended Data Figure 2.\nRevision Figure 5. Cx3cr1 expression identifies macrophages as the\ndominant CX3CR1+ cells in injured arteries. (also see the Extended Data\nFigure 2 in the revised manuscript)\na, Violin plot showing Cx3cr1 expression across cell types in the artery wall,\nincluding immune cells (Immune), endothelial cells (EC), smooth muscle cells\n(SMC) and mesenchymal stromal cells (MSC).\nb, Flow cytometry analysis of cells isolated from artery walls of Cx3cr1GFP mice\nat 2 days post-injury. GFP+ cells and CD45+ immune cells were quantified.\nWildtype mice served as controls.\nc, Violin plot showing Cx3cr1 expression across eight immune cell populations.\nd, (Left) The eleven immune cell subpopulations (Mac, cDC, NK, ILC2, pDC,\nNeu, Mast, Mon, B, CD4+ and CD8+ T) were annotated and color-coded. (Right)\nGFP+ cells are shown in red, with quantification of GFP+ cell percentages\nwithin each subpopulation. n=5 mice.\ne, (Left) The experimental design showing intraperitoneal (i.p.) injections of DT\n(40g/kg body weight; orange arrows) and tamoxifen (blue arrows) in Mac\nmice and littermate controls at the indicated time points. Quantification of the\neleven cell populations in the artery wall at 2 days post-FAI by flow cytometry\n(n=5 mice each group). Data are shown as the mean ± s.e.m. * p < 0.05, or ***\np < 0.001 by unpaired two-tailed Student’s t-test. ns, not significant.\nWith the reviewer’s question regarding the Ki20227 CSF1R inhibitor\nexperiments, we performed flow cytometry analysis of macrophages in injured\narteries from Ki20227-treated mice (Revision Fig. 6a). The results revealed\nsubstantial decrease observed in macrophage frequencies and numbers\n(Revision Fig. 6b-d). These new results have been incorporated into the\nExtended Data Figure 3 in the revised manuscript.\nRevision Figure 6. Effects of Ki20227 administration on macrophage\nabundance in injured arteries. (also see the Extended Data Figure 3 in the\nrevised manuscript)\na, Experimental design for pharmacological macrophage depletion. 8-week-old\nmale mice were orally administrated with CSFR1 inhibitor Ki20227 (40 mg/kg\nbody weight) for 3 consecutive days immediately following FAI (red arrows).\nn=6 mice per group.\nb, Quantification of CD45+ cells in the indicated groups. n=6 mice per group.\nc-d, (c) Frequencies and (d) numbers of macrophages in arteries from the\nindicated groups. n=6 mice per group. *** p < 0.001 by unpaired two-tailed\nStudent’s t-test.\nFor the knockout specificity of Pdgfrb-CreER-Il33 mice (referred as Pb-\nIl33KO mice in our manuscript), it is supported by the previous study report\ndemonstrating the highly restricted activity of Cre recombinase in Pdgfrb-\nexpressing cells (PMID: 26398496), as well as by our scRNA-seq analysis\nshowing that Il33 expression is strictly confined to Pdgfrb-expressing cells (Fig.\n4b in the revised manuscript). Consistent with this, qPCR analysis revealed that\nPb-Il33KO mice displayed a robust reduction of Il33 mRNA levels in isolated\narterial PDGFRβ+ cells, which are the exclusive source of Il33 in the context of\nvascular injury, thereby validating this cell type-specific knockout system (Fig.\n5b in the revised manuscript).\nTo address the reviewer’s concern regarding the specificity of the LysM-\nIL1RL1 (referred as Mac-Il1rl1KO mice in our manuscript), we examined both\nthe knockout efficiency and functional effects of the LysM-Cre system in primary\nbone marrow-derived macrophages (BM-Mac) and neutrophils (BM-Neu). The\nresults revealed clear differences in both knockout efficiency of Il1rl1 gene and\ncellular responses to IL-33. Il1rl1 expression was nearly completely ablated in\nBM-Mac, whereas the efficiency was only ~60–70% in BM-Neu (Revision Fig.\n7b). Upon IL-33 stimulation, macrophages showed a robust increase in Spp1\nexpression, whereas neutrophils exhibited no such induction. In addition,\nbaseline Spp1 expression in BM-Neu was ~10-fold lower than in BM-Mac,\nsuggesting a minimal role for neutrophils in osteopontin-mediated signaling in\nthis context (Revision Fig. 7a). Together, these data confirm that the LysM-Cre\nsystem preferentially targets the IL-33/ST2 signaling pathway in macrophages\nof Mac-Il1rl1KO mice, establishing them as the dominant IL-33 responders and\nkey mediators of Spp1-dependent signaling in our study.\nRevision Figure 7. Comparison of Il1rl1 knockout efficacy and Spp1\nexpression in macrophages and neutrophils from Mac-Il1rl1KO mice.\na, Il1rl1 mRNA levels in cultured bone marrow-derived macrophages (BM-Mac)\nand bone marrow-derived neutrophils (BM-Neu) from Control and Mac-Il1rl1KO\nmice.\nb, Spp1 mRNA levels in cultured BM-Mac and BM-Neu exposed to 20 ng/mL\nIL-33 for 24 hours.\nData are shown as the mean ± s.e.m.. * p < 0.05, or *** p < 0.001 by two-way\nANOVA.\n6. For the lineage tracing experiment using GFP mice (Figure 2g), it appears\nthat the gating of GFP+ cells in the right panel should be moved to the right to\nthe point showing clear separation between GFP+ vs GFP- cells. Negative cell\nfluorescence levels can be shifted between control and treated groups due to\ndiffering effects of compensation (due to lack of one color in control). Therefore,\nit is possible that the actual GFP+ cell proportion was less than 80% and a\nsignificant part of cluster 2 macrophages could come from transition from\nresident or cluster 1 cells. Additionally, please provide evidence(markers) to\nidentify GFP+ cells as cluster 2 macrophages.\nResponse: We thank for the reviewer for carefully reviewing the flow cytometry\nanalysis. In response, the gating strategy has been adjusted in revised\nmanuscript following the reviewer’s suggestion (Fig. 2g in the revised\nmanuscript). After applying the new gating method, the GFP+ cell proportion\nwas 40-55%. In our study, we retrieved GFP+ circulating monocytes were\nretrieved from Cx3cr1GFP mice and i.v. injected into recipient wildtype mice\n(Revision Fig. 8a). It is worthy to note that the recipient mice comprised both\nGFP negative and GFP-expressing monocytes in the blood, which suggest that\nmore than 50% of the macrophages in the injured artery wall are derived from\nnewly recruited monocytes. Nevertheless, based on our current data, we\nacknowledge that a part of Cluster 2 macrophages may originate from resident\nmacrophages or Cluster 1 counterparts.\nThen, we used LY6C, CD63, CD72 as markers to discrete Cluster 1 and 2\nmacrophages using flow cytometry. Among GFP+ cells, nearly 50% were\nLY6C+ Cluster1 pro-inflammatory macrophages, while nearly 40% were\nCluster 2 reparative macrophages (Revision Fig. 8a-b). Together with the\npseudotime analysis described in the response to point 4 (Revision Figure 1),\nthese data suggested a bidirectional interconversion between pro-inflammatory\n(Cluster 1) and reparative (Cluster 2) clusters. These new results have been\nincluded in the Figure 2 of the revised manuscript.\nRevision Figure 8. Contribution of circulating monocytes to Cluster 2\nmacrophages. (also see the Figure 2 in the revised manuscript)\na, Experimental design for GFP-labeled monocyte transplantation\nexperiments in FAI mice (see Methods for details).\nb, Flow cytometry analyses of GFP+ macrophages and their expression of\nLY6C, CD72 and CD63 in arteries of the recipient mice at 7 days post-FAI.\nc, Percentage of GFP+ macrophages among CD45+ immune cells in injured\narteries at 7 days post-FAI.\n7. For the co-culture experiments, authors should justify use of HUVEC instead\nof primary mouse endothelial cells, and PDGFRb+ cells isolated from inguinal\nadipose tissue instead of from vessels. Also, authors should identify source of\nVSMC.\nResponse：We thank the reviewer for raising this question. Due to the limited\nrecovery of endothelial cells and PDGFR+ perivascular MSCs from arteries\nafter cell sorting, we instead used HUVECs and PDGFR+ perivascular cells\nfrom mouse adipose tissue for the in vitro co-culture experiments, as done in\nprevious studies (PMID: 30054204). We also repeated these coculture assays\nusing mouse brain endothelial cell line bEND.3 in the revised manuscript\n(Extended Data Fig. 6d,e). The source of VSMC have been added to Methods\n(Page 29, line 882-885).\n8. A paragraph on the limitations of the study is missing. Please add.\nResponse: As suggested, we have added a paragraph discussing the\nlimitations of the current study in the revised manuscript (Page 19, line 545-\n549).\n“A key limitation of our current study is lack of in vivo validation in humans to\nconfirm the translational relevance of our findings from mice. Future\ninvestigations involving human subjects will be essential to elucidate the role of\nMSC-instructed reparative macrophages in vascular repair after injury and to\nevaluate their potential as a therapeutic target for preventing neointimal\nhyperplasia”.\nMinor comments\n9. In Extended Figure 1a, please identify whether there are any significant\ndifferences between time points. In the first paragraph of the Results, please\nclarify what is meant by “significant temporal changes”. Also, please either\nshow VSMCs proliferate extensively or properly cite previous studies.\nResponse: Done. The statistical significance is now indicated in the revised\nExtended Data Fig. 1d. We also included the statistical notation to clearly\nindicate the significant differences in immune cell subset abundance between\nthe analyzed time points. Furthermore, we have incorporated an analysis of\nimmune cell-specific functional activity scores, which are based on the\nexpression of classical activity markers, to further corroborate the significant\ntemporal changes of immune cells in the revised Extended Data Fig. 1c.\nMoreover, previous studies have been cited in the revised manuscript to\nsubstantiate this claim.\n10. Extended Figures 3 and 4 do not add to the primary focus of the manuscript\nand could be deleted.\nResponse: In accordance with the reviewer’s suggestion, we have removed\nExtended Data Figures 3 and 4, and streamlined the manuscript accordingly.\n11. For Figure 5, additional data on the impact of loss of IL33 in PDGFRb+ cells\non levels of cytokines in the vessel would provide insight into changes in the\ninflammatory environment. For Figure 5e, please present as bar graphs similar\nto Figure 5f, as it is currently difficult to assess differences between conditions.\nIn Figure 5f, there is a trend towards higher neutrophil accumulation in response\nto loss of IL33 while macrophage numbers were downregulated. Please discuss\npotential impact on neointimal hyperplasia.\nResponse: As suggested, we examined the expression of several pro-\ninflammatory cytokines (Ccl2, Cxcl1, Cxcl2, Il1b, Tnf) in PDGFRβ+ cells\nisolated from FAI- or Sham-operated Pb-Il33KO and control mice. The results\ndid not reveal any apparent changes in their expression levels (Revision Figure\n9). These new data have been included in the revised Extended Data Fig. 7b.\nRevision Figure 9. IL-33 inactivation in PDGFR+ cells does not affect pro-\ninflammatory gene expression in injured arteries. (also see the Extended\nData Fig. 7b in the revised manuscript)\nmRNA levels of indicated pro-inflammatory genes in femoral arteries from the\nindicated groups. n=6 per group. ns, not significant.\nThe data originally shown in original Fig. 5e has been reformatted as a bar\ngraph and are now presented in the revised manuscript, as suggested by the\nreviewer.\nWe thank the reviewer for this comment. We interpret the increase in\nneutrophils observed following IL-33 deficiency as a potential compensatory\ninflammatory response. Neutrophils, as key pro-inflammatory cells, are\npredominantly involved in the early phase of vascular injury, when the\ninflammatory response is at its peak (PMID: 11864917, 40492585, 10669628).\nTogether with the data regarding the compassion of macrophages and\nneutrophils, we think that the contribution of neutrophils to late-stage neointimal\nhyperplasia is likely less significant than that of macrophages, which at this\nstage have largely acquired a reparative phenotype. As suggested, we have\nexpanded the Discussion to address this point (Page 12, line 350-353).\n12. In Figure 7, additional flow cytometry data assessing OPN protein levels\ndirectly in macrophages and neutrophils isolated from arteries would strengthen\nthe manuscript.\nResponse: We thank for this helpful suggestion. In response, we used\ncommercial OPN flow antibodies to assess intracellular OPN protein levels in\nmacrophages and neutrophils at 7 days post injury. Flow cytometry analysis\nrevealed that 60-80% of macrophages expressed OPN, whereas only 10-20%\nof neutrophils are OPN-expressing cells (Revision Fig. 7a,b). Meanwhile, the\nintensity of OPN proteins is much higher in macrophages than neutrophils\n(Revision Fig. 7b). These data are consistent with scRNA-seq analysis in Fig.\n7f of the revised manuscript and Revision Fig. 7b, supporting that macrophages\nare the main source of OPN in injured arteries.\nRevision Figure 10. Comparison of OPN expression in macrophages and\nneutrophils in injured arteries. (also see the Fig. 7f in the revised manuscript)\na, Flow cytometry analysis of OPN protein expression in macrophages and\nneutrophils in injured arteries at 7 days post-FAI. Fluorescence-minus-one\n(FMO) was used for the gating control.\nb, Quantification by flow cytometry of the OPN+ cell frequency and OPN protein\nmean fluorescence intensity (MFI) in macrophages and neutrophils. n=6 per\ngroup. Data are shown as the mean ± s.e.m. ***, p < 0.001 by unpaired two-\ntailed Student’s t-test.\nReviewer #2 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed\nreports. This is part of the Nature Communications initiative to facilitate training\nin peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.\nReviewer #3 (Remarks to the Author):\nIn this manuscript the relationship between ST2 (IL1RL1)+ macrophages\nattracted by tissue resident stromal cells and vascular smooth muscle cells\n(VSMC) in an endovascular injury model in male mice was demonstrated. The\nauthors persuasively show that IL33 produced by the stromal cells induce a\nrepair associated macrophage type that express Osteopontin that promotes\nVSMC proliferation and neointima formation. This manuscript shows convincing\nin vivo data with well executed transcriptomic and flow cytometry data.\n1. The translational impact of these finding is a challenge since although the\nauthors describe that these findings are relevant for coronary artery disease it\nis well known that cells in peripheral vascular tissues behave differently than\nin the heart. Furthermore, this model lacks any atherosclerotic background\nresulting in very suboptimal reflections. The presented research is thorough\nbut for me unclear how relevant this is for human coronary PCA. Thorough\nadditional research on the key factors in human disease samples is necessary\nto show the translational concept.\nResponse: We thank the reviewer for the insightful comments regarding the\ntranslational impact of our study. We fully agree that biological differences exist\nbetween peripheral and coronary arteries, and acknowledge that our model\nlacks an atherosclerotic background.\nHowever, we would like to clarify the primary objective of this study and the\nrelevance of our model. The focus of this work is to investigate the specific\npathological process of neointimal hyperplasia-related restenosis, which is a\nmajor long-term complication following PCI, rather than to model the entire\ncourse of coronary artery disease. PCI procedures, including balloon\nangioplasty and stent implantation widely adopted clinical treatments of\ncoronary atherosclerosis, inevitably cause mechanical injury to the vessel wall,\nleading to endothelial denudation and the aberrant proliferation and migration\nof vascular smooth muscle cells, the key pathological events underlying\nrestenosis. Mouse arteries, however, unlike those of larger animals such as\npigs, rabbits and rats, are too small for transluminal injury with a balloon. The\nfemoral artery wire-injury model faithfully recapitulates this mechanical injury\nprocess. Although femoral arteries differ from coronary arteries, femoral artery\ninjury model which is conducted by dilation of the mouse femoral artery by\nintraluminal insertion of a larger wire, is widely accepted and reliable for\nstudying critical aspects of restenosis, including endothelial repair, VSMC\nproliferation, and neointimal formation. Its major advantage lies in its ability to\nisolate the mechanical injury-induced restenosis process from confounding\nfactors like an atherosclerotic background, thereby enabling precise\ninvestigation of the underlying cellular and molecular mechanisms. This model\nis widely used to analyze genetically modified mice in studies on the\npathophysiology of post-angioplasty restenosis since it was established at 2000\n(PMID: 11040113). We have also added supporting literature to further\nsubstantiate the validity of this model for restenosis research (PMID: 39873228).\nNevertheless, we recognize the biological differences between peripheral and\ncoronary arteries and have now added a discussion of these limitations in the\nrevised manuscript.\nDue to the limitation in accessing human artery samples post PCI\ntreatments, we alternatively collected plasma samples from patients with or\nwithout in-stent restenosis (ISR) after coronary stent implantation to validate\nthe clinical relevance of our findings. Plasma protein levels of IL-33 and\nosteopontin (OPN) were then correlated with the patients’ angiographic\noutcomes. ELISA results revealed that both IL-33 and OPN levels were\nsignificantly higher in patients who developed restenosis (ISR) compared to\nthose without restenosis (Non-ISR, Revision Figure 11). These new human\ndata provide important clinical evidence, suggesting that the IL-33–ST2–OPN\naxis is not only important in our mouse model but also relevant in the\ncomplication following PCI in patients.\nRevision Figure 11. Plasma IL-33 and OPN protein levels in patients with\nor without in-stent restenosis (ISR). (also see the Fig. 4g and Fig. 7i in the\nrevised manuscript)\na-b, Plasma IL-33 and OPN protein levels were measured by enzyme-linked\nimmunosorbent assay (ELISA) in patients with or without in-stent restenosis\n(ISR). n = 25 individuals each group. Detailed patient information is provided in\nTable S9. Data are shown as mean ± s.e.m.. ***p < 0.001 by unpaired two-\ntailed Student’s t-test.\n2.The concept of the ST2 macrophage/IL33 axes is well know and the link to\nosteopontin is described in other disease such as arthritis limiting the novelty\nof these findings.\nResponse: We thank the reviewer for raising this question and for providing the\nopportunity to highlight the novelty of our findings. We agree that IL-33 signaling\nhas been reported to regulate macrophage functions (PMID: 32644975,\n32272082, 29420261). Likewise, OPN has been implicated in various\npathological conditions, including neointimal hyperplasia of arteriovenous\nfistulae (PMID: 36646166). Previous studies have also described an IL-33–\nST2–Amphiregulin axis in Th2 cells, which then promotes the OPN production\nof eosinophils in the context of allergic airway inflammation and fibrosis (PMID:\n29958800). However, to our knowledge, no prior work has demonstrated a\ndirect relationship between the IL-33–ST2 axis and OPN expression in\nmacrophages. In this study, we show that IL-33/ST2 signaling are crucial in the\nrecruitment of reparative macrophages and directly regulates OPN expression\nin this macrophage subset, a key cell type driving vascular repair and\nremodeling. This direct cellular and molecular connection defines a previously\nunrecognized mechanism underlying neointimal hyperplasia, thereby\ndistinguishing our work from earlier studies.\n3.A good explanation why only male mice are used should be clearly discussed.\nResponse：We appreciate the reviewer’s comment regarding the use of only\nmale mice in this study. This decision was based on the well-documented\ninfluence of sex hormones, particularly estrogen, on vascular physiology and\npathology. Estrogen exerts protective effects on the vasculature, including\nmodulation of vascular injury responses and inhibition of vascular smooth\nmuscle cell proliferation. Including female mice, with their fluctuating estrogen\nlevels, would introduce additional biological variability and potentially confound\nthe interpretation of our mechanistic findings, particularly with respect to the\nspecific molecular pathways under investigation.\n4.Although the single cell sequencing data on these small samples is\nimpressive I have some remarks; less than 10.000 cells for a heterogenous\ntissue such as the vessel wall (t0 timepoint) is affecting the reliability of these\nanalysis. Interpretations of this datapoint should be very cautious. Why is only\none gene (Ptprc) selected to represent the immune cells. why only use the Go\npathway analysis and not other source such as KEGG or MSIG/DB?\nResponse: We appreciate the reviewer’s constructive comments on our\nscRNA-seq analysis and data presentation. In response to the most important\npoint raised, although the Sham group (D0) contained fewer cells (5,960)\ncompared to D2 (11,016) and D7 (10,539), all quality control metrics confirm\nexcellent data quality, and the biological conclusions remain robust. Our\ndetailed responses to this point are as follows:\n1. Regarding cell number and sample derivation, each sample was derived\nfrom a pool of multiple mice (D0, 12 mice; D2 and D7, 6 mice each group).\nThe lower cell number in the Sham group reflects the biological context:\nblood vessels in the normal condition remain intact with minimal immune\ncell infiltration, naturally yielding fewer isolated cells compared with injured\nstates, which show strong immune responses.\n2. With respect to quality control metrics, the Sham group showed\ncomparable genome alignment rate (91.3%) and transcriptome alignment\nrate (77.7%) to the other two groups (92.9%/79.2% and 92.7%/79.5%),\nindicating no significant difference in data validity. Q30 base quality\n(UMI >97.5%, RNA reads >94.6%) and valid barcode ratio (95.4%) meet\nthe widely-accepted standards for single-cell sequencing and alignment.\nFor cell data integrity, the higher sequencing saturation (82.4%) in Sham\ngroup demonstrates sufficient transcriptome coverage, with minimal benefit\nfrom additional sequencing depth, and the significantly higher mean reads\nper cell (45,728 vs ~28,000 in the other two groups) ensures robust\ntranscript abundance per cell. The slightly lower median genes per cell\n(1,660 vs ~2,300 in the other two groups) likely reflects the quiescent\ntranscriptional state of control cells rather than technical artifacts.\nImportantly, the total genes detected (22,850) were comparable to FAI\ngroups (23,000–24,000), confirming comprehensive capture of the gene\nrepertoire.\nIn conclusion, the Sham group data fully meet single-cell research\nstandards, and the difference in cell numbers does not compromise our core\nfindings. Full QC details (UMI counts, alignment rates, and other metrics) are\nprovided in the Table S10 of the revised manuscript for verification.\nTo clarify our use of Ptprc gene for immune cell identification, Ptprc gene\nencodes CD45 protein, a classical and one of the most widely used marker for\nhematopoietic immune cells. In addition, we employed multiple lineage-specific\nmarkers to define distinct immune cell types, as detailed in the Table S6.\nWe thank the reviewer for the helpful suggestion regarding pathway\nenrichment. In response, we have updated our pathway analysis to include\nKEGG and MSIG/DB pathway analyses. The results are consistent with our\noriginal GO pathway analysis, again highlighting the relative enrichment of\ngenes involved in collagen degradation, extracellular matrix organization\npathways in Cluster 2 macrophages. These new results have been organized\nand incorporated into the Table S11.\n5.Ly6c is a very general marker as the authors also describe, is this really the\nbest marker for these inflammatory associated macrophages?\nResponse: We thank the reviewer for this comment, which provided an\nopportunity to clarify our rationale for selecting LY6C to distinguish pro-\ninflammatory macrophage subcluster in this study. Our selection was guided by\ntwo main considerations. First, LY6C is a widely-used surface marker that\nenables the development of a flow cytometry protocol for both the analysis and\nisolation of macrophage subclusters. Second, LY6C is reported as a marker for\npro-inflammatory macrophages (PMID: 38698086), aligning with the molecular\nand cellular characteristics of LY6C+ macrophage subcluster identified in our\ndata. Taken together, these features make LY6C a highly practical and\nfunctionally relevant marker for a broad range of analyses in this study.\n6.Figure 2P why is the vehicle completely occluded? This results in totally\ndifferent processes than neointima formation and should be excluded from the\nanalysis. How often is this observed in the experiments?\nResponse: We thank the reviewer for the careful evaluation of the histological\nresults presented in original Figure 2P. We agree that the image originally\nshown for the Vehicle control displays near-complete, though not total (the\nremaining lumen area is now indicated in Revision Fig. 12a) vascular occlusion.\nThis represents an extreme pathological manifestation and deviates from the\ntypical neointimal hyperplasia process that is the focus of our study.\nWe initially selected this image for its visual clarity in illustrating severe\nvascular pathology; however, we acknowledge that it may be misleading. To\nenhance data transparency, we now provide all six images corresponding to\nthe Vehicle samples from six mice used for the statistical analysis, thereby\nillustrating the average pathological status across all examined animals.\n(Revision Fig. 12b). In addition, to more accurately represent the average\npathological state of the Vehicle control group, we have replaced the image in\nFigure 2P with one that is representative of neointimal hyperplasia observed in\nthis group.\nRevision Figure 12. H&E staining of femoral artery sections from vehicle\n-treated samples used for statistical analysis in the original Fig. 2p. (also\nsee the Fig. 2p in the revised manuscript)\na, The remaining lumen area is now indicated by the arrow in the\nrepresentative image shown in original Fig. 2p.\nb, Additional H&E staining of the other 5 vehicle-treated samples used for\nstatistical analysis in the original Fig. 2p. Revised quantification of neointima-\nto-media layer thickness (Intima/Media ratio) in femoral artery sections from\nvehicle and Ki20227 group. n=5-6 mice per group. Data are shown as the mean\n± s.e.m.. ***p < 0.001 by unpaired two-tailed Student’s t-test . Scale bars, 50\nμm.\n7.In some of the experiments it is not clearly described (for instance the\nhistology in figure 1) which background and how many mice are used for the\nanalysis.\nResponse: We thank the reviewer for pointing this out and apologize for the\nomission of this information in the original manuscript. Details regarding the\nbackground and number of mice used in this study have now been included in\nthe figure legends of the revised figures throughout the manuscript.\nReviewer #4 (Remarks to the Author):\nThe study by Ping et al; investigates the of immunomodulatory mechanisms\ndriving injury-responsive immune-stromal cell interactions in the realm of\nendovascular femoral artery injury. The study integrates time-resolved single\ncell RNA sequencing and multi-color flow cytometry to analyze the alterations\nin cellular components and cell-cell communication circuits within the injured\nfemoral artery. The findings revealed a subset of ST2-expressing macrophages\nwhose recruitment is fostered by injury-activated adventitial MSCs and plays a\ncritical role in regulating VSMC proliferation. Also, MSC-derived IL-33 has been\nfound to trigger the reparative phenotype of these ST2+ macrophages, that\nstimulate VSMC proliferation via OPN. Manipulating the IL33-ST2-OPN\nregulatory axis between MSCs and macrophages prevents NIH in the injured\nartery. Overall, the study is very interesting and is significantly supported by\nample data and findings. However, several issues need to be addressed for\nconsidering for publication in Nat Comm. Nonetheless, the results reveal a\nnovel mechanism addressing the wound healing process and post-injury\nneointima formation, targeting the MSC-macrophage interactions to prevent\nrestenosis and other vascular diseases.\nComments\n1. The introduction is very generic. A comprehensive survey of ST2-expressing\nmacrophages and associated cells of interest emphasizing the\nimmunomodulatory communications with resident MSCs along with underlying\nmechanisms are required to understand the rigor in this field. The introduction\ndoes not sufficiently highlight the study’s novelty or define the critical knowledge\ngap it intends to fill, limiting the reader’s understanding regarding the\nsignificance.\nResponse: We appreciate the reviewer’s constructive comments on the\nIntroduction. To better highlight the contribution of our findings to the current\nunderstanding of MSC-Immune cell immunomodulatory interactions, we have\nadded a few lines summarizing reported studies on ST2-expressing\nmacrophages and other immune cells in their communication with resident\nMSCs. These additions, included in the revised manuscript (Page 4, line 93-\n97), further emphasize the involvement of AKT (PMID: 34982959), ERK1/2\n(PMID: 32272082), and GATA3 (PMID: 34644537) signaling pathways. Our\nfindings highlight a newly identified IL-33–ST2–OPN regulatory axis, revealing\na key communication mechanism by which MSCs modulate pro-repair immune\nresponses in macrophages.\n2. Density count of CD45 cells from multiple sections of each time frame would\nbe appreciated for assessing the extend of WBC infiltration. Differential count\nand expression of WBC subsets are required to determine the nature/status of\nNIH. Also, measuring the difference in lumen diameter/aspect ratio will be\nhelpful to quantify the extent of NIH.\nResponse: We thank the reviewer for the helpful suggestions. To better\ncharacterize neointimal hyperplasia (NIH), we quantified CD45+ cells and\nlumen diameter/aspect ratio across the time course. Our results showed that\nday 2 post-FAI (D2) had the most abundant infiltrated immune cells and largest\nlumen diameter/aspect ratio, identifying it as the most active inflammatory stage,\nwhile both parameters gradually returned to baseline by day 28 post-FAI (D28),\nindicating completion of repair (Revision Fig. 13a-b). Using scRNA-seq data,\nwe further evaluated functional activity scores of eight main leukocyte subsets,\nwhich revealed dynamic changes in specific immune cell populations during the\nprocess of neointimal hyperplasia (Revision Fig. 13c). In addition, we provided\nlumen diameter/aspect ratio data for each animal assays as suggested\n(Revision Fig. 13d-i).\nRevision Figure 13. Temporal dynamics of immune cell populations in\narteries following endovascular Injury. (also see the Figures 1, 2, 5, 7 and\nExtended Data Figure 1 in the revised manuscript)\na, Lumen diameter/aspect ratio in femoral artery sections from the indicated\ngroups. n=5 mice for each group.\nb, CD45+ immune cell counts in femoral artery sections from the indicated\ngroups. n=5 mice for each group.\nc, Function activity scores of the indicated immune cells, calculated based on\nthe average expression of classical activity markers.\nd-i, Lumen diameter/aspect ratios in femoral artery sections from the indicated\nmice following endovascular injury.\nData are shown as the mean ± s.e.m.. *p < 0.05 or ***p < 0.001 by one-way\nANOVA or unpaired two-tailed Student’s t-test.\n3. In the results the authors mentioned that ‘Notably, a large number of vascular\nmacrophages were detected at 7 days and 14 days post femoral artery\ninjury……’. How are the vascular macrophages defined? What is their\nphenotypic signature? The tissue level characterization of these macrophages\nis crucial to understand their progression during the progression of pathology.\nResponse: We appreciate the reviewer’s attention to the definition of vascular\nmacrophages. In this study, the term “vascular macrophages” referred to\nmacrophages residing within the vasculature, which were the primary target cell\npopulation examined. Consistent with conventional definitions, these cells were\nidentified as CD45+CD11b+F4/80+ using flow cytometry. To prevent potential\nmisunderstanding, we have revised the manuscript by replacing “vascular\nmacrophages” with a more precise description throughout the text.\n4. Similarly, ‘Their abundance eventually declined to baseline levels as vascular\nregeneration progressed by 28 days post-injury’. How was the vascular\nregeneration quantified? Tissue level data is required.\nResponse: We thank for the reviewer for bringing this to our attention. To\nquantify vascular regeneration, we utilized established histological metrics of\nneointimal hyperplasia (NIH) and including vessel lumen diameter-to-aspect\nratio, and immune cell infiltration (Revision Fig. 13a,b), thereby addressing the\nreviewer’s request for tissue-level data.\nConsistent with prior studies (PMID: 25867187), our data showed that\nimmune cell infiltration peaks by 2 days post-injury then declines to basal levels\nat 28 days post-injury (Revision Fig. 13b), with proliferation of resident cells\nsuch as smooth muscle cells and endothelial cells, following a similar timeline\n(PMID: 34474592). These results have been included in the revised manuscript\n(Extended Data Fig. 1a-c in the revised manuscript).\n5.TREM2+ LLM are promising in RepMAC; however, the information regarding\nthe pro-inflammatory counter part TREM1 would provide additional\nconfirmation regarding the inflammatory/reparative status. TREM2-TREM1\nratio will be highly appreciated.\nResponse: We thank the reviewer for bringing this to our attention. As\nsuggested, we performed an analysis of Trem1 and Trem2 expression in\nmacrophages at different time points based on our scRNA-seq datasets, and\nthe results showed that Trem1-expressing macrophages continuously\nincreased post-injury, rising from 8.3% to 27% at 2 days post-injury (D2), and\nreaching 29.7% at 7 days post-injury (D7). This suggests that a pro-\ninflammatory macrophage population persists in the early phase of the vascular\nrepair process. While Trem2-expressing macrophages increased sharply post-\ninjury, rising from 17.7% to a peak of 66.4% at D2, before decreasing to 47%\nat D7 (Revision Figure 14). These results described the distinct dynamic\nchanges of these two macrophage populations during vascular injury and repair,\nsuggesting potential different functional roles of these Trem1+ and Trem2+\nmacrophages in this setting.\nRevision Figure 14. Temporal dynamics of Trem1+ and Trem2+\nmacrophages following FAI. (also see the Extended Data Fig. 3j in the\nrevised manuscript)\nFrequencies of the Trem1-expressing and Trem2-expressing macrophages at\ndifferent time points following FAI, based on the scRNA-seq dataset as\ndescribed in Figure 1.\n6. The figures and extended data are mixed up and not in the proper order\nwhich makes hard to follow the figures.\nResponse: We thank the reviewer for pointing out the confusion regarding the\nfigure and extended data order. We have carefully reorganized all main figures\nand extended data figures in the revised manuscript.\n7. Upregulation of Il33 gene expression, rather than the changes in the\nabundance of Il33-expressing cells, appeared to contribute to the higher IL-33\nlevels observed in injured arteries. Explanation regarding the cell types and the\nunderlying mechanisms for IL33 expression in the context of FAI.\nResponse: In response, we performed scRNA-seq analysis,\nimmunofluorescence staining with anti-IL-33 antibodies and flow cytometry\nusing the Il33GFP reporter mouse model to examine Il33 expression pattern in\ninjured arteries (Fig. 4b-d and Extended Data Fig. 6a in the revised manuscript).\nThese analyses showed that Il33 expression was predominantly detected in\nPDGFR+ MSC subsets located in the adventitial layer.\nTo investigate the regulatory mechanisms of Il33 expression upon FAI, we\nco-cultured Il33-expressing PDGFR+ MSCs isolated from inguinal adipose\ntissue with conditioned media (CM) derived from mechanically injured cultured\nendothelial cells (HUVECs). CM from injured HUVECs (CM_Inj) induced a 2-3-\nfold increase in Il33 expression compared with CM from uninjured HUVECs\n(CM_Nor) (Fig. 4i). Bulk RNA-seq analysis of MSCs after FAI using Gene Set\nEnrichment Analysis (GSEA) revealed enrichment of the “NFB signaling\npathway” and related pathways, including “TNF signaling pathway” and “IL-17\nsignaling pathway”. Consistently, CM_Inj activated IKK-NFB signaling\npathway in MSCs, as evidenced by the elevated levels of phosphorylated\nIKK/ and p65, while Il33 induction was blocked by the p65 inhibitor JSH23 or\nRela knockdown (Fig. 4o,q). Sequence analysis of murine Il33 promoter\nidentified 3 putative p65 binding sites (B.S.-1~3). Chromatin\nImmunoprecipitation (ChIP) assays confirmed that CM_Inj enhanced p65\nbinding at B.S-2 and -3, suggesting that NFB signaling directly regulates Il33\ntranscription in MSCs (Fig. 4r). Together, we found that MSCs serve as the\nmain source of IL-33 in arteries and injury-upregulated Il33 transcription in\nMSCs are driven by the activated NFB signaling pathway during FAI.\n8. Do inguinal adipose MSCs typically replicate the pathophysiology of MSCs\nfrom injured vascular tissue? Does the embryonic nature of HUVEC cells\nsimulate the secretome content of injured ECs associated with mature blood\nvessels? How accurate and reproducible is the mechanical injury model? These\nmodels require further rationale and validations to correlate with the in vivo\npathophysiology.\nResponse: We thank for the reviewer’s comment regarding the relevance of our\nin vitro models to in vivo pathophysiology, particularly their use in uncovering\nthe underlying mechanisms. To address this, we compared the top 10\nupregulated genes identified in vivo from scRNA-seq of endothelial cells and\nbulk RNA-seq of MSCs with gene expression in the cells using in our in vitro\nmodels. We found that these genes were also significantly upregulated in\ninjured HUVECs and MSCs treated by CM_injured (Revision Fig. 15a,b).\nThese results indicate that our in vitro models faithfully recapitulate key transcriptional\nresponses observed in vivo, thereby validating their use for dissecting the\ncellular and molecular mechanisms of vascular repair.\nRevision Figure 15. In vitro cell line models recapitulate key\ntranscriptional changes observed in vivo.\na, Top 10 genes upregulated in injured endothelial cells (ECs) identified from\nscRNA-seq analysis as described in Figure 1. mRNA levels of these genes\nwere measured by qPCR in cultured HUVECs 24 hours after mechanical injury.\nb, Top 10 genes upregulated in mesenchymal stromal cells (MSCs) in FAI\ngroup identified from bulk RNA-seq analysis as described in Figure 3. mRNA\nlevels of these genes were measured by qPCR in cultured MSCs exposed to\nthe indicated conditioned media.\nData are shown as the mean ± s.e.m.. * p < 0.05, ** p < 0.01 or *** p < 0.001\nby unpaired two-tailed Student’s t-test.\n9. Screening data to quantify the content of CM is unavailable. The secretome\nanalysis is required to provide mechanistic insights for CM-driven Il33 action.\nResponse: We thank the reviewer for this constructive suggestion. To\ninvestigate the mechanisms underlying the transcriptional activation of Il33, we\nperformed a comprehensive secretome analysis of CM derived from endothelial\ncell lines using mass spectrum. The full list of identified proteins is provided in\nTable S12. Our mechanistic model proposes that CM from inured endothelial\ncells actives Il33 expression in MSCs through the NFκB pathway. Consistent\nwith this hypothesis, our analysis revealed increased levels of four secreted\nproteins known to activate NFκB signaling, including the immunoregulator β2-\nmicroglobulin (B2M), macrophage migration inhibitory factor (MIF), the\nchemokine CXCL5, and integrin β1 (ITGB1) (Revision Fig.16a-c) (PMID:\n32059688, 34758833, 34242901, 30792394). These results suggest that these\nfactors may mediate the activation of NFκB-IL-33 axis in MSCs in response to\nendothelial injury.\nRevision Figure 16. Proteomics revealed NFκB related proteins in\nconditioned medium regulated Il33 expression in MSCs. (also see the\nExtended Data Fig. 6d-f in the revised manuscript)\na, The schematic illustration showing the experimental design for panels e-f.\nMouse brain endothelial cell line (bEND.3) were mechanically injured, and\nincubated in fresh media. The conditioned media (CM) was collected after 24\nhours of culture and for mass spectrum or mixed with fresh media for MSCs\ntreatment. CM from normal or injured bEND.3 were denoted as CM_Normal or\nCM_Injured, respectively.\n42\nb, mRNA levels of Il33 gene in MSCs exposed to CM_Normal and CM_Injured\nfor the 24 hours.\nc, Proteomic Analysis of the secreted factors derived from ECs in CM.\nData are all shown as the mean ± s.e.m.. **p < 0.01 or ***p < 0.001 by unpaired\ntwo-tailed Student’s t-test.\n10. Experimental details and data regarding the hydrogel composition and\ncompatibility are missing.\nResponse: We thank the reviewer for highlighting the importance of hydrogel\ncomposition and compatibility details. In this study, we used a 20% (w/v)\nPluronic F127 hydrogel, a well-established thermosensitive triblock copolymer\nthat remains liquid at 4 °C and rapidly gels at 37 °C, enabling precise local\ndelivery after surgery. The solution was prepared in sterile PBS, stirred\novernight at 4 °C, and mixed with siRNA (30 μg) on ice to maintain its liquid\nstate until application. Macroscopic and histological analyses confirmed\neffective gel formation and local retention at the injured artery, with no apparent\ndiffusion. Pluronic F127 is widely validated as a biocompatible, low-toxicity\npolymer, supporting the safety of our delivery approach. These methodological\ndetails have been added to the Methods section in the revised manuscript.\n11. The authors discussed the findings related to CAD; however, the model\nused was FA injury which would be a representation of PAD. Justification for\nthe animal model is required. How the findings relate to clinical CVDs? What is\nthe translational significance? What are the limitations of the study?\nResponse: We thank the reviewer for the comments regarding the translational\nimpact of our study, which were also raised by Reviewer 3. We would like to\nclarify that the primary objective of this work was to investigate the pathological\nprocess of neointimal hyperplasia-related restenosis, a major complication\nfollowing PAD, rather than to model the entire course of CAD. The femoral\nartery wire-injury model is widely accepted for this purpose, as it faithfully\nrecapitulates the mechanical injury process and allows focused investigation of\nkey events such as SMC proliferation, endothelial repair, and neointimal\nformation. Mouse arteries, however, unlike those of larger animals such as pigs,\nrabbits and rats, are too small for transluminal injury with a balloon. Although\nfemoral arteries differ from coronary arteries, femoral artery injury model which\nis conducted by dilation of the mouse femoral artery by intraluminal insertion of\na larger wire, is widely accepted and reliable for studying critical aspects of\nrestenosis, including endothelial repair, VSMC proliferation, and neointimal\nformation. This model is widely used to analyze genetically modified mice in\nstudies on the pathophysiology of post-angioplasty restenosis since it was\ncreated at 2000 (PMID: 11040113). Its strength lies in isolating mechanical\ninjury–induced restenosis from confounding factors such as atherosclerosis,\nthereby enabling precise dissection of the underlying cellular and molecular\nmechanisms with translational implications. Moreover, our findings on the IL-\n33–ST2–OPN axis, together with the demonstrated efficacy of hydrogel-\nmediated siRNA silencing of Il33 or Spp1 in preventing restenosis, conceptually\nsuggest the potential of this approach for clinical translation.\nIn addition, a paragraph outlining the limitations of the study has been added\nto the revised manuscript (Page 19, line 545-549).\n12. A section concluding the significance of key findings will be appreciated.\nResponse: We thank the reviewer for this helpful suggestion. We have added\na section in the revised manuscript highlighting the significance of our findings\n(Page 19, Line 550-557).\n“We reveal a previously unrecognized MSC–macrophage signaling axis that\nshapes vascular repair after injury. Injury-activated adventitial MSCs release IL-\n33 to recruit and activate reparative ST2+ macrophages, which drive VSMC\nproliferation via OPN secretion. Targeting this IL-33–ST2–OPN pathway offers\na promising strategy to limit neointimal hyperplasia and prevent restenosis.\nThese findings significantly advance our understanding of how immune–\nstromal interactions regulate tissue repair, particularly in the vascular context,\nand uncover MSC–macrophage crosstalk as a potential therapeutic target for\npreventing restenosis and related vascular diseases.”\nReviewer #1 (Remarks to the Author):\nThe authors have satisfactorily addressed most of my concerns and the paper is\nimproved. I have a few remaining minor concerns:\n1. I may be missing them, but I am not able to find complete gating strategies for each\nflow cytometry assay in either the main or supplementary figures. These must be\nincluded to allow readers to judge the quality of the data.\nResponse: We thank the reviewer for brining this to our attention. The gating strategies\nwere presented in the Source data file in last revision. To make it more convient for\naccessible, we have added the gating strategies into Supplemental Figure 9 for the\nreviewer’s reference.\n2. In response to my comments on the pseudotime analysis, the authors state “The\ncoexistence of inflammatory and reparative macrophage populations suggests that\nmacrophage phenotypic transitions during tissue repair do not occur as a simple binary\nswitch but instead reflect a dynamic equilibrium in which both macrophage subsets\ncoexist and remain functionally active. As suggested, we have expanded the Discussion\nto include this interpretation (Page 17, line 503-505)”, on page 6 of their rebuttal letter.\nThe discussion added does adequately address this issue. Please add verbiage similar\nto what was provided in the rebuttal letter.\nResponse: We thank the reviewer for this constructive suggestion. In response, we have\nrevised the text to include this discussion as suggested (Page 17, line 484-491).\n3. Although the authors have added a limitations paragraph, potential differences in\nphenotypes of cells used for in vitro experiments compared to arterial cells and\nincomplete understanding of the regulation of macrophage phenotype shifts observed\nshould be added to this paragraph.\nResponse: In response to the reviewer’s suggestion, we have expanded the limitations\nparagraph to include the highlighted points (Page 18, line 533-536).\n“In addition, the endothelial cell lines used in our in vitro experiments may not fully\nrecapitulate the phenotypic features of arterial endothelial cells. Finally, the mechanisms\ngoverning macrophage phenotypic transtition in this context remain incompletey defined\nand warrant future investigation.”\nReviewer #2 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports.\nThis is part of the Nature Communications initiative to facilitate training in peer review\nand to provide appropriate recognition for Early Career Researchers who co-review\nmanuscripts.\nResponse: Many thanks.\nReviewer #3 (Remarks to the Author):\nThe authors have clearly performed a comprehensive and thoughtful revision that has\nconsiderably strengthened the manuscript. Most reviewer concerns have been\naddressed in a satisfactory and convincing manner. I therefore support publication of this\nrevised version.\nResponse: Many thanks.\nReviewer #4 (Remarks to the Author):\nThe revised manuscript has successfully addressed the comments, recommendations\nand suggestions provided during initial review. The revised manuscript has been included\nwith conceptual clarity, additional data, methodological details and interpretations. The\nrevised manuscript can be accepted for publication in Nature Communications.\nResponse: Many thanks."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have satisfactorily addressed most of my concerns and the paper is improved. I have a few remaining minor concerns:\n1. I may be missing them, but I am not able to find complete gating strategies for each flow cytometry assay in either the main or supplementary figures. These must be included to allow readers to judge the quality of the data.\n2. In response to my comments on the pseudotime analysis, the authors state “The coexistence of inflammatory and reparative macrophage populations suggests that macrophage phenotypic transitions during tissue repair do not occur as a simple binary switch but instead reflect a dynamic equilibrium in which both macrophage subsets coexist and remain functionally active. As suggested, we have expanded the Discussion to include this interpretation (Page 17, line 503-505)”, on page 6 of their rebuttal letter. The discussion added does adequately address this issue. Please add verbiage similar to what was provided in the rebuttal letter.\n3. Although the authors have added a limitations paragraph, potential differences in phenotypes of cells used for in vitro experiments compared to arterial cells and incomplete understanding of the regulation of macrophage phenotype shifts observed should be added to this paragraph.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_3": "The authors have clearly performed a comprehensive and thoughtful revision that has considerably strengthened the manuscript. Most reviewer concerns have been addressed in a satisfactory and convincing manner. I therefore support publication of this revised version.", "reviewer_4": "The revised manuscript has successfully addressed the comments, recommendations and suggestions provided during initial review. The revised manuscript has been included with conceptual clarity, additional data, methodological details and interpretations. The revised manuscript can be accepted for publication in Nature Communications."}, "author_response": "Point-by-point response to the REVIEWERS' COMMENTS\nReviewer #1 (Remarks to the Author):\nThe authors have satisfactorily addressed most of my concerns and the paper is\nimproved. I have a few remaining minor concerns:\n1. I may be missing them, but I am not able to find complete gating strategies for each\nflow cytometry assay in either the main or supplementary figures. These must be\nincluded to allow readers to judge the quality of the data.\nResponse: We thank the reviewer for brining this to our attention. The gating strategies\nwere presented in the Source data file in last revision. To make it more convient for\naccessible, we have added the gating strategies into Supplemental Figure 9 for the\nreviewer’s reference.\n2. In response to my comments on the pseudotime analysis, the authors state “The\ncoexistence of inflammatory and reparative macrophage populations suggests that\nmacrophage phenotypic transitions during tissue repair do not occur as a simple binary\nswitch but instead reflect a dynamic equilibrium in which both macrophage subsets\ncoexist and remain functionally active. As suggested, we have expanded the Discussion\nto include this interpretation (Page 17, line 503-505)”, on page 6 of their rebuttal letter.\nThe discussion added does adequately address this issue. Please add verbiage similar\nto what was provided in the rebuttal letter.\nResponse: We thank the reviewer for this constructive suggestion. In response, we have\nrevised the text to include this discussion as suggested (Page 17, line 484-491).\n3. Although the authors have added a limitations paragraph, potential differences in\nphenotypes of cells used for in vitro experiments compared to arterial cells and\nincomplete understanding of the regulation of macrophage phenotype shifts observed\nshould be added to this paragraph.\nResponse: In response to the reviewer’s suggestion, we have expanded the limitations\nparagraph to include the highlighted points (Page 18, line 533-536).\n“In addition, the endothelial cell lines used in our in vitro experiments may not fully\nrecapitulate the phenotypic features of arterial endothelial cells. Finally, the mechanisms\ngoverning macrophage phenotypic transtition in this context remain incompletey defined\nand warrant future investigation.”\nReviewer #2 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports.\nThis is part of the Nature Communications initiative to facilitate training in peer review\nand to provide appropriate recognition for Early Career Researchers who co-review\nmanuscripts.\nResponse: Many thanks.\nReviewer #3 (Remarks to the Author):\nThe authors have clearly performed a comprehensive and thoughtful revision that has\nconsiderably strengthened the manuscript. Most reviewer concerns have been\naddressed in a satisfactory and convincing manner. I therefore support publication of this\nrevised version.\nResponse: Many thanks.\nReviewer #4 (Remarks to the Author):\nThe revised manuscript has successfully addressed the comments, recommendations\nand suggestions provided during initial review. The revised manuscript has been included\nwith conceptual clarity, additional data, methodological details and interpretations. The\nrevised manuscript can be accepted for publication in Nature Communications.\nResponse: Many thanks."}]

Decision letter