Introduction

Atherosclerosis and its complications, myocardial infarction and stroke, represent the leading cause of mortality worldwide.¹ Atherosclerosis is characterized by the build-up of leukocyte-rich plaques in the intimal layer of large and medium-sized arteries. Experimental laboratory research has established that atherosclerosis is an inflammatory disease of the arterial wall, initiated and maintained by the influx, proliferation, and activation of immune cells.² Atherogenesis is shaped by the interplay of pro- and antiatherogenic leukocytes both within the atherosclerotic aorta and systemically.³ The principal hematopoietic lineages found in the atherosclerotic aorta of rodents were discovered by fluorescence-based flow cytometry in 2006.⁴ With the development and validation of state-of-the-art, high-dimensional parameter analysis tools—such as mass cytometry and single-cell transcriptomics^5,6—it is now increasingly appreciated that flow cytometry is unable to fully elucidate the actual complexity of immune cell subsets. Specialized leukocyte subsets may be distinct in function although they express the same principal lineage markers.⁷ In atherosclerosis, the diversity of plaque leukocytes on a single-cell level and their relative frequencies are incompletely defined. Here, we applied 2 novel approaches—single-cell RNA-sequencing (scRNAseq) and mass cytometry (CyTOF)—to precisely define leukocytes in mouse and human atherosclerosis. Our results indicate an unexpected heterogeneity: We found that aortic leukocytes have a complexity similar to that of leukocytes in lymphoid organs and identified several novel leukocyte subpopulations that are differentially regulated in disease and may predict cardiovascular events in humans.

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Methods

Data Availability

All data and methods used in the analysis and materials used to conduct the research will be made available to any researcher for the purpose of reproducing the results or replicating the procedures. All data, methods, and materials are available on personal request at the La Jolla Institute for Allergy and Immunology, CA (contact: [email protected]).

A Material and Methods section is available in the Online Data Supplement.

Results

Single-Cell Transcriptomes Identify 11 Distinct Leukocyte Populations in the Atherosclerotic Aorta

Flow cytometry of aortic leukocytes has emerged as an accepted technique in atherosclerosis research.^4,8 However, the phenotypes and transcriptomes of aortic leukocytes have remained on an incomplete level. Here, we hypothesized that a screening approach by single-cell transcriptomics (scRNAseq) to detect clusters of cells that share phenotypic and transcriptional similarity may be superior to conventional, preselected marker-driven flow cytometry. Therefore, viable aortic CD45⁺ leukocytes were flow sorted from the thoracic and abdominal aorta of chow diet (CD)– or Western diet (WD)–fed female Apoe^−/− mice (Figure 1A; Online Figure IA). We confirmed larger and more complicated atherosclerotic lesions in cross-sections of the aorta from WD- compared with CD-fed Apoe^−/− mice (Figure 1B; Online Figure IB and IC). After sequencing, we detected transcripts from 1138 different genes with >257 000 sequencing reads per cell in the merged data set of aortas from CD- and WD-fed mice (Online Figure II). To group cells with similar gene expression, we applied an unsupervised cluster detection algorithm (SEURAT) and detected 11 leukocyte clusters that were mostly distinguishable by hematopoietic lineage-defining genes, such as Itgam, Cd19, and Cd3e (Figure 1B and IC; Online Figure III). We found that Cd3e⁺ T-cells accounted for 49%, Cd19⁺ B-cells for 27%, Itgam⁺ (CD11b⁺) myeloid cells for 22%, and Klrb1c⁺ Natural killer (Nk)–cells for 2% of leukocytes. To validate the exact leukocyte cluster identities in an unsupervised approach, we compared their transcriptomes to published transcriptomes from mouse peripheral blood mononuclear cells.⁹ Based on the genetic overlap between clusters and reference transcriptomes (Online Figure IV), we found 5× T-cell (CD4⁺/CD8⁺ T-cells), 2× B-cell, 1× macrophage, 2× monocyte, and 1× Nk-cell populations. This lineage commitment was also reflected by differentially expressed (DE) genes for each of the clusters against all remaining (Figure 1E; Online Table I), including Cd79b, Ebf1, Mzb1 (B-cells), Cd4, Cd8 (T-cells), Klrb1a (Nk-cells), or Csf1r, Adgre1, Cd68 (macrophages). In addition, we applied conventional multicolor flow cytometry with a 14-marker panel (Online Table II) on the input cells for scRNAseq and applied an accepted gating strategy (Online Figure VA and VB). We validate that the frequencies of B-, myeloid, and Nk-cells were comparable to those defined by protein-based flow cytometry with the exception of some T-cell subsets that remained undefined by flow cytometry (Online Figure VA and VC).

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Because we observed a significant overlap in global DE genes between the 2 B-cell–, and between the myeloid cell clusters, we next identified DE genes within the principal leukocyte lineages. Between the 2 monocyte subsets (Itgam⁺Adgre1^low), we found an enhanced expression of the inflammatory genes S100a8, S100a9, Cxcl2, Ccl2, Ccl3, F10, Tnf, and Ly6c2 in cluster 9, suggestive of Ly-6C⁺ inflammatory monocytes. T cell clusters mainly varied in the expression of the transcription factors Rorc, coding for the T-helper 17 cell (T_H17)–defining transcription factor RORγT (retinoic acid receptor-related orphan receptor; cluster 5), for the T_H2-defining transcription factor Gata3, and for the T_H2 cytokines IL (interleukin)-4 and IL-13 (cluster 6). In cluster 3, we did not detect differentially expressed transcription factors, but a predominant expression of the genes Sell and Il7r (coding for memory markers CD62L and IL-7R), suggestive of a population of memory T-cells. One T-cell cluster contained Cd8a⁺ cytotoxic T-cells (cluster 10) while T-cell cluster 1 contained transcripts coding for both CD8 and CD4, therefore likely reflecting a mixed cell population. B-cell clusters 1 and 2 did not differ relevantly in known B-cell lineage markers. Collectively, these data establish the proportions and selective gene expression patterns of aortic leukocytes.

Spatial and Numeric Differences in the Aortic Immune Cell Repertoire in Atherosclerosis

Because the detected leukocyte clusters in whole-atherosclerotic aortas likely contained tissue-resident vascular leukocytes, we applied scRNAseq on aortic leukocytes from 8-week-old, healthy Apoe^−/− mice and observed only 5 principal leukocyte clusters (Online Figure VI): 2× macrophage, 1× T-cell, 1× B-cell, and 1× monocyte cluster, suggesting that leukocyte heterogeneity is lower in healthy aortas. For instance, we observed only 1 T-cell cluster that was transcriptionally similar to the memory T-cell containing cluster in atherosclerotic mice. Both macrophage clusters expressed Cx3cr1, the marker for tissue resident, self-renewing arterial macrophages, but differed in the expression of Lyve1 (tissue-resident) and Ly6c (recruited): Lyve1^lowLy6c1^neg (cluster 4) versus Lyve1^negLy6c1⁺ (cluster 5). Monocytes showed an overlapping gene signature with published genomes of Ly-6C⁺ inflammatory monocytes. Next, we asked how the relative frequencies of leukocyte subsets change in Apoe^−/− mice fed with a WD compared with a CD for 12 weeks. Because input cell numbers differed by >2-fold in WD, we followed an iterative approach by downsampling both input cell populations repetitively to increase the statistical robustness. Among a total of 11 leukocyte clusters in the CD and WD groups, we observed 4 populations that increased while 5 populations decreased (Figure 2B). For instance, the macrophage-containing cluster increased by 110% while the T_H2-cell containing cluster contracted by 60%.

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Because leukocyte clusters in our approach reflect a mix of adventitial, lesional, and leukocytes in the media of the artery, we addressed the spatial distribution of these cell populations. Because scRNAseq and flow cytometry require a relatively large input of cell numbers and because the digestion of aortas may affect the survival of particular leukocyte clusters, we applied a genetic deconvolution (GD) method,¹⁰ which allows to robustly calculate the relative frequency of specific cellular leukocyte signatures in bulk mRNA expression data sets also containing mRNA from non-CD45⁺ cells. Based on the expression of cluster DE genes of scRNAseq-defined aortic leukocyte clusters, we constructed gene signatures, which were highly specific for the input cluster gene expression data sets (Online Figure VIIB) and which detected frequencies of principal hematopoietic lineages in bulk transcriptomes of blood, spleen, and lymph nodes in the expected and flow cytometry–validated range (Online Figure VIIC and VIID). We applied these aortic signatures to a GD within whole-tissue mRNA expression data sets of whole aortas from Apoe^−/− mice and found a cellular composition predominated by myeloid cells, including macrophages and monocytes (Online Figure VIID), indicating these lineages are under-represented in digestion-based scRNAseq and flow cytometry. At the level of major lineages, this composition was also confirmed in a GD of published mouse peripheral blood mononuclear cell signature.¹¹ Next, we applied the aortic leukocyte signatures to a GD within bulk mRNA expression of laser-microdissected tissue specimen from atherosclerotic lesions, the aortic media, adventitia without tertiary lymphoid organs (ATLO), and adventitia with ATLOs in aged, WD-fed Apoe^−/− mice (>60 weeks).¹² We found that atherosclerotic lesions were predominantly populated by macrophages, monocytes, and T-cells while ATLO-rich adventitial tissue was dominated by B-cells. The overall leukocyte signal was relatively higher in ATLO-rich adventitial tissue, rendering this compartment one of the major immune cell reservoirs in late atherosclerosis. Finally, we asked to which extent the 2 commonly used atherosclerosis models, Apoe- and Ldlr-deficiency, impact on the cellular composition in aortas. Therefore, we directly compared the frequency of principal leukocyte lineages by scRNAseq from CD- and WD-fed Ldlr^−/− mice (Figure 2D; Online Table III). The content of B-cells (higher in Apoe^−/− mice) and macrophages (higher in Ldlr^−/− mice) differed in aortas from both genotypes after a WD while on a CD the cellular composition was comparable in both genotypes and relatively dominated by T-cells. These data establish the spatial and temporal dynamics of leukocyte composition of atherosclerotic aortas.

Cell-Type–Specific Genetic Pathways in Aortic Leukocytes

In contrast to bulk tissue gene expression, scRNAseq enables a selective analysis of gene expression at the single-cell level. To identify cell- or lineage-specific gene expression, we tested the enrichment of curated pathways at the single-cell level by calculating gene expression scores (Figure 3A–3C; Online Figure VIII). Interestingly, most of the tested pathways overlaid on specific cell types: myeloid cells (macrophages and monocytes) overexpressed genes implicated in lipid- and cholesterol metabolism (GSEA-M5892, including Abca1, Abcg1, Ldlr), genes associated with apoptosis (GSEA-M8492, including caspases and TNF [tumor necrosis factor] signaling), and genes required for the activation of the inflammasome (GSEA-M1063, including Nlrp3, P2rx7, Txn). Genes correlating with acute myocardial infarction (GSEA-M15394, including genes coding for collagens, tissue factor, and fibrinogen) were enriched in Ly-6C⁺ monocytes. On the contrary, the T_H2 cell containing cluster was enriched for genes implicated in cytokine secretion (GSEA-M6910), proliferation, and chemokine secretion (GSEA-M4844). In addition, we performed Ingenuity Pathway Analysis by testing cluster genes increased and decreased in WD-fed compared with CD-fed Apoe^−/− mice (Online Table IV). This strategy allowed us to establish a map of disease-related genetic programs in aortic leukocytes: For instance, macrophages upregulated signaling events associated with iNOS (inducible nitric oxide synthase), an enzyme expressed in the proinflammatory M1 phenotype,¹³ and the group of the proatherogenic IRF (interferon regulatory factor) transcription factors.¹⁴ However, the observed higher expression of iNOS did not induce a M1-like transcriptome in macrophages in WD-fed mice (Online Figure VIII). In contrast, TGF-β, a driver of plaque stabilization,¹⁵ decreased in plaque macrophages from WD-fed animals. In addition, we observed a decrease of anti-inflammatory IL-10 transcripts events in the Ly-6C⁻ monocyte-containing cluster. In accord with the concept of enhanced proinflammatory pathways in atherosclerosis, CD4⁺/CD8⁺ T-cells, memory T-cells, and CD8⁺ T-cells switched their transcriptional profile from a recruitment-phenotype in CD-fed mice (homing receptors and genes required for cellular motility) to a more proinflammatory phenotype, including an upregulation of IFN-γ (interferon-gamma) signaling in WD-fed mice. Furthermore, some pathways were regulated diversely in clusters (Online Table V): hypoxia-induced pathways were downregulated in macrophages under a WD, but upregulated in T_H2-cells, while TGF-β was downregulated in macrophages and upregulated in Ly-6C⁺ monocytes. These data emphasize the necessity to test gene expression on a single-cell level for an unbiased approach and suggest scRNAseq as powerful tool to combine cell cluster detection and lineage-specific gene expression at the same time.

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Mass Cytometry (CyTOF) Confirms the Phenotypic Heterogeneity of Aortic Leukocytes

Because leukocyte phenotyping is traditionally based on the expression of surface protein markers, we applied mass cytometry (CyTOF) and tested a panel of 35 metal-labeled pan-leukocyte antibodies (Figure 4A; Online Table VI). To optimize the detection of the small number of aortic leukocytes, we spiked cell suspensions with splenocytes from age-matched CD45.1 Apoe^−/− mice that were later excluded from analysis (Figure 4B and 4C). To establish cell clustering and lineage detection by protein markers, aortic leukocytes from Apoe^−/− mice fed a CD or WD for 12 weeks were acquired on CyTOF and separated into cell clusters by a CyTOF-adapted dimensionality reduction and cluster detection algorithm (PhenoGraph, Figure 4D). Of 28 clusters from aortas from CD- and WD-fed mice, we found 23 unique cell clusters above the selected frequency threshold of 1% (Figure 4D; Online Table VII). This heterogeneity was comparable to the spleen, where we detected 22 unique clusters (Online Figures IX and X). In aortas, 21 clusters (≈97% of all leukocytes) could be assigned to principal hematopoietic lineages (Figure 4E): We detected 10× myeloid cell clusters, thereof 3× F4/80-expressing macrophage clusters, 3× F4/80^negCD11c^high dendritic cell clusters, 2× monocyte-clusters (F4/80^negCD11b^medLy-6C^high/low), and 2× granulocyte-clusters (Ly-6G^high or Siglec-F^high). In addition, 7× T-cell clusters could be identified by the expression of the lineage markers T-cell receptor (TCR)-β, TCR-γ/δ, CD4, and CD8. We identified 1 cluster of γ/δ-T-cells, 2× CD8⁺ T-cell clusters, 1 mixed CD4⁺/CD8⁺ T-cell cluster, and 1 unusual TCR-β^low cluster (20). Within the CD4⁺ T-helper cell clusters, CD5 and Ly-6C were the main differentiating markers that allowed us to subseparate CD4⁺ T-cells into CD5^medLy-6C^high and CD5^highLy-6C^neg cells. Also, we found 4 B-cell clusters, of which 3 were exclusively present in WD-fed mice and 1 Nk-cell cluster. The relative frequency of clusters differed extensively between aorta and splenic tissue, suggesting a tissue-specific response. We next examined diet-induced changes of cluster frequencies in CD- versus WD-induced disease. Although the frequency of the most parental leukocyte lineages (T-, B-, myeloid cells) did not change, we found 2 clusters only present in CD-fed mice (1, 20), and 4 clusters only in WD-fed mice (4, 9, 19, 22; Figure 4F). Three clusters were detectable in CD-fed mice but increased in WD-induced disease (3, 6, 11), whereas the frequency of 1 cluster (26) decreased (Online Table VII). These data demonstrate a distinct regulation of leukocyte frequencies within, but not of, leukocyte lineages.

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We next asked how these protein-defined clusters corresponded to transcriptionally defined clusters. Therefore, we built a Pearson correlation between normalized mRNA expression and mean-signal intensity in CyTOF for the same cellular markers. We used 3 panels of targets: classical lineage markers defining B-cells (Figure 5A), T-cells (Figure 5B), and myeloid cells (Figure 5C). The resulting correlation matrices revealed a high phenotypic overlap between scRNAseq and CyTOF clusters, for instance cluster 7 in scRNAseq (macrophages) was highly correlated with CyTOF macrophage cluster 3 (F4/80^medCD64^highMHC-II^med; R=0.91) and cluster 9 (F4/80^highCD64^medMHC-II^med; R=0.91), but not with cluster 22 (dendritic cells, F4/80^negCD64^lowMHC-II^highCD11b^med; R=−0.11), suggesting the macrophage clusters in CyTOF, 3 and 9, may be included in 1 scRNAseq macrophage cluster. Vice versa, CyTOF cluster 22 showed a correlation of R=0.72 and 0.77 with the scRNAseq clusters containing monocytes and Ly-6C^high monocytes, identifying these cells as myeloid-derived DCs. These data demonstrate the feasibility and superiority of higher multiplexed immuno-phenotyping in estimating the diversity and diet-induced changes of aortic leukocytes. Together with mRNA-based definition of clusters, our data help to build a cellular immune cell atlas in atherosclerosis.

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Transcriptional, Phenotypic, and Functional Profiles Identify 3 Principal Aortic B-Cell Subpopulations

Leukocyte heterogeneity has consequences for atherosclerosis. For instance, B-cells can be proatherogenic (B2-like, B220⁺CD23⁺CD11b⁻)^16,17 or atheroprotective (B1-like, B220^lowCD11b⁺CD23⁻CD5⁺).¹⁸ To test whether highly multiplexed approaches can detect such intra–B-cell heterogeneity in an unsupervised manner, we filtered cellular Cd19-mRNA⁺ events from aortas with WD-induced atherosclerosis (Figure 6A). The filtered cells specifically expressed transcripts of B-cell markers (Figure 6B). The top 250 variable genes were used as an input for dimensionality reduction, which retrieved 3 B-cell populations (Figure 6C). Pathway analysis of cluster DE genes (Figure 6D) retrieved an increase of genes involved in antigen presentation, costimulation, antibody generation (immunity), and cellular- adhesion and cross-talk (cell adhesion) in B-cell cluster 1. The smallest cluster 2 expressed cell division genes while cluster 3 upregulated apoptosis-related and proinflammatory TNF-signaling genes. These scRNAseq-defined B-cell clusters were distinguishable by the markers CD43 and B220 (CD43^highB220^neg, CD43^negB220^high, CD43^lowB220^high). Notably, these markers also separated B-cells in fluorescence-activated cell sorting (Online Table VIII) and CyTOF (Figure 6E). Based on their transcriptome, the 3 B-cell clusters showed differential expression of CCL5 and GM-CSF (granulocyte-macrophage colony stimulating factor), 2 important mediators of atherosclerosis. We, therefore, quantified in vitro cytokine production (Figure 6F–6H): the B-cell cluster CD43^highB220^neg showed higher expression of the proatherogenic chemokine CCL5 (RANTES) while CD43^negB220^high expressed more of the proinflammatory cytokine IFN-γ. Both B220^neg clusters showed higher expression of GM-CSF compared with B220^high B-cells. Taken together, these data suggest a distinct functional role of cell clusters identified by scRNAseq/CyTOF.

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Frequency of Aortic Leukocyte Populations Predicts Clinical Events in Patients With Atherosclerosis

We next aimed to clarify the composition of human atherosclerotic plaques with an anti-human antibody panel for CyTOF (Online Table IX) tested on leukocytes isolated from a human carotid endarterectomy specimen (Figure 7A). In contrast to histology, which has established a predominance of macrophages in human plaques, we found only a small percentage of cells assigned to the myeloid cell lineages (CD11b⁺). Contrastingly, the heterogeneity of leukocyte populations was comparable to CyTOF of mouse aortic leukocytes with 19 clusters in total (Figure 7B). To exclude false-negative results caused by lack of specific macrophages markers in humans or selective death of macrophages during tissue digestion, we applied the mRNA deconvolution strategy to gene expression arrays of 126 carotid plaques from the Biobank of Karolinska Endarterectomies¹¹ to enumerate the relative abundance of leukocyte populations (Figure 7C; Online Figure XI). This approach revealed that 51.1%±0.9% of all leukocytes were macrophages, 19.3%±0.6% T-cells, and 13.9%±0.4% monocytes. The fraction of most leukocyte populations (except for the CD4⁺/CD8⁺ T-cell– and the Nk-cell–containing clusters) was relatively enriched in plaques when compared with their relative frequencies in peripheral blood mononuclear cells from the same patients, suggesting a nonrandom enrichment (Figure 7D). Finally, we correlated cluster frequencies in human plaques and the rate of ischemic events post-endarterectomy in the same patient cohort. We found that of all tested clusters, the memory T-cell–containing cluster negatively predicted cardiovascular events (Figure 7E) while the frequency of macrophages only showed a weak (positive) tendency (not shown). These results help to clarify leukocyte diversity in human plaques and suggest leukocyte composition as potential driver of adverse clinical events.

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Discussion

Here, we applied novel high-dimensional methods, using 35 parameters (CyTOF) and ≈1100 parameters (scRNAseq) and an unsupervised subset detection strategy to define the diversity, phenotypes, and transcriptomes of leukocytes in atherosclerotic aortas. Our study demonstrates several fundamental discoveries: We resolved the relative leukocyte composition in murine atherosclerosis in a more accurate way than previously reported. Reports on conventional flow cytometry of aortic leukocytes were limited by predefined leukocyte panels, which only examined parental hematopoietic lineages or were focused on the heterogeneity within distinct lineages.^4,8,19 The limited number of markers in flow cytometry is not sufficient to deeply characterize >1 lineage at a time.⁷ Using CyTOF, we assigned almost all aortic leukocytes to hematopoietic cell identities while 17% of leukocytes remained unidentifiable by flow cytometry. We confirmed that B-cells, myeloid cells, T-cells, and others—including rare (TCR-γ/δ⁺) and atypical T-cell clusters—each account for ≈¼ of all leukocytes. These results are comparable to previously published frequencies of myeloid cells (≈25%–42%), T-cells (≈21%–35%), and B-cells (≈13%–19%) in Apoe^−/− mice.^4,8 These numbers seem to be robust despite substantial differences in feeding, tissue digestion protocols, and leukocyte panels used in earlier studies.^4,19 CyTOF was superior in detecting smaller populations, such as TCRγ/δ T-cells that accounted for only ≈1% of leukocytes. Sublineage heterogeneity, that is, the subseparation of lineages in distinct phenotypes, was surprisingly high in aortic leukocytes (23 CyTOF-defined clusters) and equivalent to the spleen (22 clusters). Although B-cells were the dominant leukocyte subset in the spleen (8 cluster accounting for ≈42% of leukocytes), T-cell and myeloid cell clusters dominated aortic leukocytes in mouse and human atherosclerotic lesions based on GD. The higher number of clusters retrieved by CyTOF was likely caused by the predefined panel of surface markers with a known high variability within lineages, whereas scRNAseq algorithms aim to detect differences in the whole transcriptome and thereby may neglect cellular entities with a few distinct protein markers only. Based on our approach to integrate CyTOF and scRNAseq, we were able to define some hallmarks of leukocyte diversity and dynamics in atherosclerosis (Online Figure XII):

T-Cells

T-cells represented the second most frequent population in healthy arterial tissue. Of the 3 pure CD4⁺ T-cell clusters in scRNAseq, 1 cluster was transcriptionally similar to memory T-cells and showed a WD-induced enrichment of cell cycle genes, suggestive of a tissue-specific proliferative response. Recently, tissue-resident memory T cells within nonlymphoid organs have been identified. These persist in tissue after viral or bacterial infection and show a strong tissue-restricted response after reactivation with their cognate antigen.²⁰ Notably, we observed that the absence of this T-cell subset significantly correlated with increased risk of developing an ischemic event in patients, suggesting an overall protective function. The frequency of this memory-like T-cell population was outnumbered by the T_H17-cell cluster. T_H17 cells have mostly been attributed to a proatherogenic phenotype^21–23 and seem to represent the predominant T_H-lineage induced by atherosclerosis-related antigens besides T_H1.² Some reports also suggested a regulatory, atheroprotective role.²⁴ Whether T_H2-polarized T-cells, which we detected in the third CD4⁺ T-cell cluster, are important in atherosclerosis is controversial.² CyTOF of aortic leukocytes detected 2 CD4⁺ T-cell clusters: CD5^medLy-6C^high (15) and CD5^highLy-6C^neg (17). Interestingly, we observed the same pattern of low Cd5 and high Ly6c1 gene expression in the memory-like RNAseq cluster while the T_H17 cluster had an inverted pattern. Notably, Ly-6C can be a marker of memory T-cells. During viral infection, memory T-cells stem from a Ly-6C⁺ T-helper cell subset.²⁵ Contrastingly, Ly-6C was also described as marker of a smaller subset of T-regulatory cells (T_regs) with a weakened immunosuppressive capacity.²⁶ In both subsets, in scRNAseq and CyTOF, CD5 expression was inversely correlated to Ly-6C. The expression level of CD5 is known to correlate with TCR signaling events and antigen-induced proliferation.²⁷ CD5 is also highly expressed on induced T_regs that are less suppressive and prone to turn into a T-effector cell.²⁸ In addition, CD5 is known as survival factor for T cells. For instance, tumor infiltrating CD5⁺ T-cells downregulate the apoptosis-inducing factor FasL^29,30 and induce CK2-dependent survival pathways.³¹ Both T-helper cell subsets also expressed folate receptor 4, a marker of protective T_regs.³² The absence of the T_reg-defining marker CD25 (IL-2 receptor)³³ suggests that both clusters, however, may be central memory or effector-memory T-cells.³⁴ This is also supported by the observation that antigen-stimulated CD4⁺ T-cells upregulate folate receptor 4 and downregulate CD25.³² Collectively, these findings render the main T-cell subsets in the atherosclerotic aorta as effector or central memory and likely not regulatory T-cells, which is consistent with a loss of T_regs in the plaque³⁵ likely caused by a phenotypic conversion into effector T-cells.³⁶ Whether the memory-like T-cell cluster found in healthy aortas represents a type of tissue-resident T-cells cannot be answered by our data set. As well, the functional contribution of CD4⁺ T-cell expressed CD5, folate receptor 4, or Ly-6C to atherosclerosis is unknown and will require studies with T-helper cell–specific knockouts.

B-Cells

CD19⁺ B-cells were already detectable in healthy arteries, likely in the adventitia, where we detected a robust B-cell–specific gene signal with our GD approach while in late atherosclerosis, B-cells predominantly populated ATLOs as suggested before.³⁷ Contrastingly, in atherosclerotic lesions, both in mice and humans, we found B-cells only in minor frequencies, thus, indicating that conventional flow cytometry from whole-atherosclerotic aortas overestimates the actual B-cell content. CyTOF of atherosclerotic aortas identified a B2-like cell cluster that expanded in WD-fed mice and may therefore reflect a proatherosclerotic subset while the B1-like cell clusters 1 and 13 were found in lower quantities in atherosclerotic lesions, consistent with the proposed atheroprotective function of B1-cells.¹⁸ However, the marker-based categorization in known subsets with established functions may not be entirely appropriate: In a subclustering approach of Cd19-mRNA⁺ cells in scRNAseq, we found 3 distinct clusters. One cluster that matched the core marker profile of B1-cells (CD43^highB220^negCD11b^high) showed enrichment for TNF signaling and cell adhesion pathways, validated by a strong secretion of the chemokine CCL5. Notably, B-cell–specific deletion of TNF-α reduced atherosclerosis and plaque vulnerability, rendering these cells as likely proatherogenic.³⁸ The other 2 B-cell clusters with a surface marker profile of proatherogenic B2-cells³⁹ secreted the known proatherogenic mediators IFN-γ and GM-CSF.

Myeloid Cells

In scRNAseq, healthy arterial tissue was predominantly populated by macrophages that coexpressed markers of the previously reported arterial, tissue-resident Cx3cr1⁺ macrophage subset. Interestingly, the expression of Cx3cr1 and Lyve1 on aortic macrophages was maintained throughout atherosclerosis, raising the general question to which extent the macrophage pool is replenished from monocytes or depending on in situ proliferation as previously suggested.⁴⁰ Interestingly, the fraction of monocytes in the aorta was relatively higher in healthy tissue, comprised mainly of Ly-6C⁺ inflammatory monocytes, and decreased in disease. Among all monocytes in the atherosclerotic aorta, Ly-6C^low monocytes overwhelmed their Ly-6C^high counterparts. The latter, however, was significantly enriched in genes associated with acute myocardial infarction, which suggests a role of these cells in clinical disease.

Limitations

One limitation of the current study and of previous reports is that we assessed the leukocyte distribution in whole aortas, which represents a mixture of phenotypically and likely biologically distinct lesions from atherosclerotic plaques, plaque-free parts of the aorta, developing atheroma, and fatty streaks, and may affect leukocyte composition, in particular in comparison to human carotid or coronary atherosclerotic lesions. Currently, the only technique to isolate plaque-specific areas is laser capture microdissection,⁴¹ which yields insufficient cells for fluorescence-activated cell sorting, CyTOF, and scRNASeq. It is likely that leukocyte phenotypes may depend on their temporal and spatial distribution. For instance, it is known that leukocytes accumulate at different sites in healthy and atherosclerotic aortas. Although macrophages and macrophage-derived foam cells are predominantly found in atherosclerotic plaques,² B-cells are thought to primarily reside in the adventitia or at later stages, in tertiary lymphoid organs.³⁷ However, we have circumvented this limitation by using a novel gene expression deconvolution method with gene signatures specific for aortic leukocytes. These data establish a predominant accumulation of myeloid cells and T-cells in microdissected mouse plaques and human carotid plaques while B cells predominate adventitial tissue and are only found in minor frequencies in plaques. In this regard, flow cytometry of whole-mouse aortas is incapable of estimating the actual cellular composition of the plaque.

To define specific markers is a prerequisite of CyTOF and flow cytometry, which may limit the detection of some human lesional leukocytes. For instance, some well-established macrophage markers in the mouse are either not translatable (F4/80) or less specific for human plaque macrophages (CD69, CD68, MERTK). This is consistent with our observation that in CyTOF of leukocytes from human lesions, only a minor fraction was identified as CD11b⁺ myeloid cells, which is in contrast to their high frequencies reported in histology. Also, the prolonged digestion and mechanical disruption of often calcified human plaques may contribute to a selective loss of some more fragile leukocyte subsets.

scRNAseq can bypass some of these limitations for CyTOF but has an inferior sensitivity to detect cellular clusters: cells with a similar transcriptional program as consequence of a tissue wide biological response may be grouped together in dimensionality reduction plots and thereby falsely regarded as the same population. Complementary strategies by integrating marker expression (protein), marker gene transcripts (mRNA), and pathways (gene set enrichment of differentially expressed genes) as reported here, however, may limit such effects.

Translational Aspects

The identification of aortic/lesional and specialized leukocyte subpopulations has greatly advanced atherosclerosis research in the last decades, but the relevance of these findings for human disease remains puzzling.² Conventional flow cytometry of whole-atherosclerotic mouse aortas has yielded inconsistent results compared with the leukocyte composition of human plaques. Although the partial lack of translatable cellular markers and the use of the likely biased Apoe^−/− and Ldlr^−/− mouse models may explain some of this inconsistency, site-specific accumulation of leukocytes and the inability to isolate sufficient numbers of leukocytes from mouse plaques for immune-phenotyping represents another important limitation. By using a novel deconvolution strategy based on single-cell transcriptomes, we show that the cellular composition of mouse and human plaques is almost identical and relatively dominated by macrophages and myeloid cells (≈60%–75%) and T-cells (≈25%). In addition, our study proposes that frequencies and the gene expression of lesional leukocyte may have the potential to serve as supplementary risk stratification tool. Gene expression deconvolution methods have opened the avenue to virtually construct the leukocyte repertoire in gene expression data from biopsies in cancer¹⁰ and specific changes in gene expression sets from carotid plaque specimen associate with clinical outcomes.¹¹ Although coronary plaque samples are not available on a routine basis, such cellular biomarkers could improve risk prediction tools, which will be of great value for emerging personalized therapies in future cardiovascular medicine.

Our findings show that the immune cell landscape in atherosclerosis is unexpectedly diverse, relatively dominated by T-cell and myeloid cells, and highly selectively regulated. Besides the methodological novelty of our work, our data directly propose the existence of several novel leukocyte subsets that were not detected by traditional technology. In addition, our data represent a useful resource for future functional work in which subsets described herein are manipulated by Cre-lox, adoptive transfers, or depletion approaches. The design of specific surface marker panels to define aortic leukocyte subsets may yield useful biomarkers to assess progression and regression of atherosclerosis, and to predict plaque vulnerability, which correlates with clinical events.

Acknowledgments

We thank Cheryl Kim, Lara Boggeman, Denise Hinz, and Robin Simmons for help with flow sorting and Kristen Jepsen for the help with single-cell RNA-sequencing. We thank Angela Denn for histology and microscopy.

Footnotes