Our population included 30 chronic coronary syndrome (CCS) patients and 74 patients with non-ST-elevation acute coronary syndromes (NSTE-ACS) (Knuuti et al., 2020; Collet et al., 2021). Characteristics of the study population are presented in Table 1, while patient allocation in each experimental condition is schematized in Figure 1. All patients gave their written informed consent. The local Ethics Committee approved the study. Exclusion criteria were patient age over 80 years; heart failure with reduced ejection fraction (HFrEF) [left ventricular ejection fraction (EF) <40%; New York Heart Association (NYHA) class III–IV] (Ponikowski et al., 2016); moderate to severe heart valve disease, both stenosis and regurgitation (inferred according to the ESC Guidelines, Baumgartner et al., 2017); in-stent restenosis cases or stent thrombosis; culprit saphenous vein graft lesions and more generally by-pass graft lesions (including left internal mammary artery, LIMA) due to the different pathogenic mechanisms with respect to native coronary lesions; anti-inflammatory or immunosuppressive therapies other than low-dose aspirin; autoimmune diseases; evidence of immunologic disorders or chronic infectious diseases; liver diseases (class A–C of CHILD-PUGH), malignancies; chronic kidney disease at stage 4; and recent major surgical procedures or trauma.

Eighteen milliliters of venous blood were obtained at 9±3h from hospital admission and collected in ethylenediaminetetraacetic acid (EDTA) tubes. Peripheral blood mononuclear cells (PBMCs) were isolated at 2,200 × g and 25°C for 25 min through density gradient centrifugation methods (Lympholyte^®-H Cell Separation Media, CEDARLANE). PBMC pellets were washed and suspended in Dulbecco’s phosphate-buffered saline (DPBS) (GIBCO; Invitrogen, Carlsbad, CA, United States) and used according to scheduled protocol steps (see sections “Flow Cytometry Analysis” and “Cell Cultures and Multiplex Assay Analysis”).

Optical Coherence Tomography images were obtained using a frequency domain OCT system (C7-XR FD-OCT; St. Jude Medical, St. Paul, MN, United States). OCT image analysis was performed using an offline review workstation (Ilumien Optis; St Jude Medical) by two expert investigators who were blinded to patient data. In presence of any diagnostic discordance between the two readers, a consensus was achieved taking into account the opinion of a third investigator. Plaque erosion has been classified by the presence of intra-luminal thrombus overlying a plaque presenting with intact fibrous cap (IFC), otherwise the presence of an irregular luminal surface at the site of the culprit lesion in the absence of thrombus. RFC has been defined by the existence of fibrous cap discontinuity with a cavity formation inside the plaque, or with a direct communication between the lumen and inner core of the lesion (Prati et al., 2010; Tearney et al., 2012; Johnson et al., 2019). Furthermore, the presence of macrophage infiltrations (MØI) at the culprit sites was assessed by OCT imaging. OCT-MØI appeared as “signal-rich, distinct, or confluent punctate regions that exceed the intensity of background speckle noise and generate a backward shadowing” (Phipps et al., 2015; Montone et al., 2020).

Among our NSTE-ACS patients who underwent OCT investigation of culprit coronary lesions before stent procedure for clinical reasons and in whom it was possible to clearly identify the features of the culprit plaques, eight presenting with IFC and 13 with RFC have been identified. Patients were then divided into four groups according to the presence or absence of MØI as follows: four IFC without MØI, four IFC with MØI, seven RFC without MØI, and six RFC with MØI.

The frequency of circulating monocyte subsets was assessed on basal PBMCs (30 CCS and 69 NSTE-ACS, of which 8 IFC and 13 RFC) by flow cytometry (Cytomics FC-500; Beckman Coulter, Brea, CA, United States). Monocyte staining with fluorochrome-labeled monoclonal antibodies (mAbs) was performed for 15 min at room temperature (RT) and under dark condition using anti-CD14 electron coupled dye (ECD)-conjugated and anti-CD16 phycoerythrin cyanin 7 (PC7)-conjugated (both from Beckman Coulter). Monocytes were gated using a forward/sideward scatter (FS/SS) plot, and monocyte subsets were defined according to their cluster of differentiations (CD) CD14 and CD16 surface expression (Figure 1A). Furthermore, to distinguish the monocyte populations, additional cell surface expressions of CD14, CD16, CD45, CD69, matrix metalloproteinase 9 (MMP-9), C-C chemokine receptor 2 (CCR2), and C-X-C motif chemokine receptor 4 (CXCR4) were simultaneously evaluated in five patients presenting with cardiovascular diseases using a multi-color detector configuration set up (CytoFlex S; Beckman Coulter). The relative median fluorescence intensity (MFI) was assessed, and monocyte subset distribution was shown as frequency (%) of each subset among the monocyte group according to gating strategy. Non-specific staining with isotype-matched control mAb was <1%. For each acquisition, 50,000 events were captured, and they were adequate for rare population analysis. Acquired data were analyzed using the Kaluza analysis software (Beckman Coulter).

PBMCs were incubated at a density of 2 × 10⁶/ml for 16 h at 37°C under 5% CO₂ and 20% O₂ in cell culture medium (RPMI 1640, LONZA) supplemented with 100 U/ml penicillin, 0.1 mg/ml streptomycin, 2 mmol glutamine and 10% fetal bovine serum (FBS; Thermo Fisher Scientific, Carlsbad, CA, United States) and treated (or not) with 1 μg/ml Escherichia coli lipopolysaccharide (LPS, Sigma-Aldrich, S. Louis, MO, United States), as CD14 ligand as well as a known pro-inflammatory stimulus. We then analyzed the concentrations of six cytokines (interleukin-1β, IL-1β; interleukin-1rα, IL-1rα; interleukin-6, IL-6; interleukin-8, IL-8; interleukin-10, IL-10; interferon-γ, IFN-γ) in collected supernatants from non-treated (NT) and LPS-treated PBMCs (n = 5 CCS and n = 10 NSTE-ACS, of which n = 4 IFC and n = 6 RFC) by using Bio-Plex^TM Pro human 6-plex for group I cytokine (Bio-Rad Laboratories, Hercules, CA, United States) according to the protocol provided by the manufacturer. Data were collected and analyzed using a Bio-Rad BioPlex 200 instrument equipped with Bio-Plex Manager software version 6.0 (Bio-Rad Laboratories, Hercules, CA, United States). The precision based on both intra- and inter-assay variations were <10% within the detection limits provided by the manufacturer.

Due to the pioneering nature of the study, it was not possible to make a formal estimate of the power and sample size. Therefore, the enrollment of 30 patients in the CCS group and 74 patients in the NSTE-ACS group was arbitrary. However, based on very recent data (Merah-Mourah et al., 2020), the differences we observed should maintain statistical significance even with increasing sample size.

Variable distribution was assessed by Shapiro–Wilk test. For data normally distributed, we used unpaired t-test for statistics between two groups and one-way ANOVA for repeated measures followed by Holm–Sidak’s multiple comparison test. Data that did not follow a normal distribution were analyzed using non-parametric tests, as follows: Mann–Whitney U test was performed for statistics between two groups, and Wilcoxon and Friedman or Kruskal–Wallis test followed by Dunn’s post hoc test for multiple comparisons. Mean ± standard deviation (SD) and median and interquartile range (IQR) have been used for description of normally and non-normally distributed data, respectively. For all the experimental assays, a two-tailed p ≤ 0.05 was considered statistically significant. The PRISM software (GraphPad 8.02; GraphPad Software Inc., San Diego, CA, United States) was used as statistics tool.

Analysis of the distribution of circulating monocyte subsets according to their CD14 and CD16 surface expression led to the identification of a fourth monocyte population other than the three already known subsets (classical, CM: CD14⁺⁺CD16^–; intermediate, IM: CD14⁺⁺CD16⁺; non-classical, NCM: CD14⁺CD16⁺⁺). In line with CD14 and CD16 surface staining, the newly identified cells were CD14⁺CD16^– (Figure 2 and Table 2). We decided to label this novel subset as “pre-classical monocytes” (PCM) because of their proximity to the CM while observing the flow-cytometry acquired event distribution on dot plots.

Moreover, as shown in Supplementary Figure 1, to better distinguish the novel monocyte population of PCM from the known classification of CM, IM, and NCM, flow cytometry characterization of this subset was performed in patients presenting with NSTE-ACS (n = 5), in reliance on the expression of the following surface markers: CD14, CD16, CD45, CD69, MMP-9, CCR2, and CXCR4 (Table 2).

Interestingly, when monocyte subset distribution was evaluated in the whole study population, the newly identified PCM subset was more frequent in NSTE-ACS rather than in CCS patients (median, IQR: 5.2, 6.2 vs. 2.8, 3.9; p = 0.005). NCM was more frequent in CCS rather than in NSTE-ACS patients (median, IQR: 5.2, 3.6 vs. 3.3, 4.3; p = 0.006), while no significant differences were recorded comparing the CM (median, IQR: 67.0, 22.7 vs. 64.6, 19.6; p = ns) and IM (median, IQR: 6.1, 4.9 vs. 6.5, 4.8; p = ns) frequency distributions between CCS and NSTE-ACS patients (p > 0.999 for both). Nevertheless, CM was the most frequent monocyte subgroup in both CCS and NSTE-ACS, as expected according to the known monocyte subset distribution (Figure 3A).

When plaque phenotype was analyzed by OCT, NSTE-ACS patients with RFC plaques (n = 13) compared to patients with IFC plaque (n = 8) displayed a decreased frequency of CM (mean ± SD: 60.1 ± 13.4 vs. 74.5 ± 7.8, p = 0.013) and an increased frequency of NCM (median, IQR: 6.9, 7 vs. 2.0 2.0, p = 0.008). IM frequencies were similar between erosion and ruptured plaques (p = 0.886). The same analysis showed an increasing frequency of PCM in NSTE-ACS patients presenting with RFC as compared with IFC lesions (median, IQR: 5.0, 10.1 vs. 3.2, 3.4; p = 0.054) (Figure 3B). Representative dot plots of monocyte subset distribution in CCS and NSTE-ACS patients (Figure 3C) and in NSTE-ACS according to plaque phenotype (Figure 3D), and representative OCT images (Figure 3E) are shown.

Higher frequency of PCM was recorded in NSTE-ACS patients presenting plaque MØI as compared to patients without local MØI (median, IQR: 7, 10.1 vs. 2.5, 3.3 p = 0.013), and specifically in NSTE-ACS patients with RFC lesions and MØI as compared to those with RFC lesions without MØI (median, IQR: 12.6, 15.1 vs. 2.5, 3.3; p = 0.011), while no significant differences were recorded comparing the other three monocyte subsets (Figures 4A–C).

The assessment of a cytokine panel by multiplex ELISA revealed that cultured PBMCs from NSTE-ACS patients respond to LPS treatment by releasing significant amounts of IL-1β, IL-6, and IL-10 in the cell culture medium, as compared with non-treated cells (NT) (p = 0.008; p = 0.016; p = 0.008, respectively), while no differences have been recorded for IL-1rα, IL-8, and INF-γ (Figure 5A). Although showing a release of IL-1β, IL-6, and IL-10 in response to LPS stimulation, no significant differences has been recorded between NT and LPS-treated PBMCs from CCS patients for all the six cytokines studied. According to OCT analysis, the magnitude of LPS-mediated effects on IL1-β, IL-6, and IL-10 release in cell culture medium appeared to be greater in patients with RFC (n = 6), when compared to patients with IFC (n = 4), hence showing that these data have plaque specificity. Indeed, supernatants from PBMCs of RFC patients released the highest levels of IL-1β (p = 0.035), IL-6 (p = 0.012), and IL-10 (p = 0.014) after LPS treatment, while no significant differences have been recorded within the IFC group (Figure 5B). No significant differences were found in high-sensitivity C-reactive protein (hs-CRP) levels comparing IFC and RFC patient samples (see Supplementary Figure 2).