Functional and transcriptomic analysis of extracellular vesicles identifies calprotectin as a new prognostic marker in peripheral arterial disease (PAD)

Figures & data

Table 1. Demographic and clinical parameters in controls (Ctrl, n = 100) and PAD patients (n = 317) before, and after classifying PAD by disease severity in intermittent claudication (IC, n = 188) and critical limb ischaemia (CLI, n = 129).

	Ctrl n = 100	PAD n = 317	p vs. Ctrl	IC n = 188	CLI n = 129	p vs. IC
Demographic and clinical data
Sex (male, %)	61	88	<0.001	89	86	0.459
Age (years)	71(8)	70(11)	0.233	68(10)	73(11)	<0.001
Smokers (%)
Never	51	19	<0.001	13	29	0.002
Current	4	33		37	28
Former	45	48		50	43
Diabetes mellitus (%)	36	53	0.004	38	74	<0.001
Hypertension (%)	73	75	0.725	72	78	0.231
Dyslipidemia (%)	76	67	0.108	71	62	0.084
Treatment (%)
Anticoagulants	11	13	0.667	7	20	0.001
Antiplatelets	20	77	<0.001	81	70	0.033
ACE inhibitors	16	34	<0.001	33	36	0.692
ARA-2	42	27	0.003	24	30	0.212
Calcium antagonists	11	22	0.017	18	28	0.028
Vasodilators	0	6	0.247	6	7	0.685
β-Blockers	17	26	0.079	25	26	0.786
Statins	58	69	0.041	72	64	0.130
Laboratory data
Total cholesterol (mg/mL)	179(44)	172(45)	0.260	184(42)	156(43)	<0.001
LDL-C (mg/dL)	99(38)	107(83)	0.317	111(76)	103(94)	0.426
HDL-C (mg/dL)	87(38)	44(15)	<0.001	48(14)	37(13)	<0.001
Triglycerides (mg/dL)	118(57)	147(83)	0.001	152(91)	141(71)	0.279
hs-CRP (mg/L)^a	1.8(3)	4.6(10)	<0.001	3.2(4.7)	9.7(20)	<0.001

Mean (SD) is shown. ^aLogarithmically transformed variables are presented as median (Interquartile range). ACE: angiotensin-converting enzyme, ARA-2: angiotensin II receptor antagonist, LDL: low-density lipoprotein, HDL: high-density lipoprotein, hs-CRP: high-sensitivity C reactive protein.

Figure 1. Platelet-derived EVs are most abundant in PAD patients. (a) Gate definition for flow cytometry analysis on platelet free-plasma using the violet side scatter (Violet-SSC) against the regular SSC. The gate was established using calibrated beads ranging from 250 nm to 1.34 µm. (b) The number and cellular origin of EVs was measured in platelet-free plasma of PAD patients by flow cytometry (n = 45). Specific antibodies for: platelets (PEVs, anti-CD41/CD61 in grey), erythrocytes (EryEVs, anti-CD235a in red), endothelial cells (EndEVs, anti-CD62E in green), and leukocytes (LeuEVs, anti-CD11b in blue) were used. Platelet derived EVs were most abundant followed by erythrocyte, endothelial and leukocyte derived EVs. Data are presented as EVs/µL. (c) Combined flow cytometry analysis for EVs cellular origin and Annexin V staining in platelet-free plasma of PAD patients. Annexin V percentage was calculated for each EVs subpopulation based on the total number of platelet, erythrocyte, endothelial and leukocyte EVs numbers respectively. (d) Correlation between the clotting time of platelet-free plasma, measured by the Procoag-PPL kit, and the number of platelet-derived EVs (PEVs, log transformed) in PAD patients (n = 43). *p < 0.01 vs. PEVs. #p < 0.01 vs. EryEVs. †p < 0.01 vs. EndEVs.

Figure 2. Characterization of isolated EVs. (a) Representative size distribution histogram for platelet-free plasma derived EVs. The NTA analysis shows a polydisperse heterogeneous vesicle population, the vast majority of them ranging from 100 to 400 nm. (b) Western blot for EVs (Alix and EMMPRIN) and non-EVs markers (ApoB100 and ApoA1) on EVs samples isolated from platelet-free plasma (n = 2). The first line on the left corresponds to the molecular weight marker (MW, kDa) of each detected protein. (c) Representative cryo-electron micrographs of EVs isolated from platelet-free plasma. Panels II and IV correspond to the insets drawn in panels I and II, respectively. EVs are round shaped and delimited by a lipid bilayer. Moreover, smaller electron dense particles (~25 nm) lacking a visible lipid membrane are observed (black arrows), indicating the presence of contaminants such as VLDL or LDL. Scale bar denotes 100 nm. (d) Representative flow cytometry dot-plot for unstained medium/large size EVs isolated from platelet-free plasma within the working gate (in blue). The gate was defined using the violet side scatter (Violet-SSC) against the regular SSC, using calibrated beads of sizes ranging from 0.25 to 1.34 µm as explained in Figure 1(a). (e) Representative dot-plots for gated EVs confronting the fluorescence intensity for FITC vs. APC. Processing of CFSE in EVs gives a positive signal in the FITC channel. Left panel shows no fluorescent signal in unstained EVs, while 80% of CFSE stained EVs are FITC positive (right panel).

Figure 3. Downstream analysis of differential expression results in EVs. (a) Schematic overview of EVs isolation by high-speed centrifugation from platelet-free plasma. Poly-A transcripts were captured using magnetic beads and scRNA-Seq libraries were generated following an adapted protocol from Jaitin et al, [16]. Libraries were sequenced (Illumina NextSeq 500) and data questioned with an optimized bioinformatics workflow for data pre-processing and analysis. (b) Volcano plots showing the differentially expressed genes of the contrasts performed by LimmaVoom. In red, genes with a fold-change (log2) and p-value higher than 1.5 and 0.01, respectively. In green, genes that show differential expression in the CLI vs control contrast. (c) Hierarchical clustering (Euclidean distance) and heatmap imaging of the 15 differentially expressed genes in control and PAD patients (n = 12/group). Samples are arranged in columns (control in green, IC in orange and CLI in pink) and genes in rows. Up-regulated expression is shown in red and downregulated expression in blue. The heatmap was generated using counts per million expression values (CPM, logarithmically transformed) after adjustment for batch, sex and age. (d) mdGSA function enrichment network visualization by Cytoscape. Upregulated gene-sets (Gene Ontologies) for two contrasts are shown; IC vs. control in orange, and CLI vs. control in blue. Nodes size is proportional to number of genes, and edge thickness to degree of similarity between nodes.

Table 2. Differentially expressed genes after transcriptomic analysis of circulating EVs. Controls (Ctrl, n = 12), intermittent claudication (IC, n = 12) and critical limb ischaemia (CLI, n = 12).

		Fold-Change (Log2)				p
Ensembl ID	Gene name	IC vs. Ctrl	CLI vs. Ctrl	CLI vs. IC	AveExpr (Log2-CPM)	Limma Voom	Kruskal–Wallis
ENSG00000104964	AES	−1.28	−1.06	0.22	9.00	0.0020	0.0023
ENSG00000129255	MPDU1	−0.39	2.76	3.16	3.65	0.0003	0.0002
ENSG00000172349	IL16	−3.54	−1.14	2.39	4.86	0.0000	0.0001
ENSG00000126698	DNAJC8	0.90	3.08	2.18	4.99	0.0023	0.0032
ENSG00000167644	C19orf33	1.48	2.65	1.17	4.65	0.0028	0.0034
ENSG00000108829	LRRC59	1.18	3.17	1.99	3.72	0.0011	0.0044
ENSG00000163736	PPBP	1.99	0.89	−1.10	10.18	0.0009	0.0012
ENSG00000169727	GPS1	−0.53	−3.66	−3.13	5.05	0.0001	0.0001
ENSG00000161920	MED11	−3.54	−3.64	−0.10	3.70	0.0001	0.0004
ENSG00000163220	S100A9	0.70	1.71	1.01	13.43	0.0000	0.0000
ENSG00000101335	MYL9	1.89	2.03	0.13	8.16	0.0004	0.0004
ENSG00000128245	YWHAH	0.80	3.36	2.56	4.13	0.0007	0.0014
ENSG00000107521	HPS1	−0.15	1.82	1.97	5.68	0.0096	0.0034
ENSG00000159496	RGL4	−0.58	2.40	2.98	4.83	0.0022	0.0050
ENSG00000148346	LCN2	1.68	3.25	1.57	5.61	0.0009	0.0018

Genes with p < 0.01 obtained by LimmaVoom and Kruskal–Wallis were considered as differentially expressed. Fold changes (Log2) between the different contrasts is shown (IC vs. control; CLI vs. control; CLI vs. IC), as well as the average gene expression in all the samples (AveExpr).

Figure 4. S100A9 mRNA expression and calprotectin levels in circulating EVs and serum of control and PAD patients. (a) The association between the mRNA levels of S100A9 in EVs measured by RNA-Seq (Y axis, log-transformed), and the serum levels of calprotectin (X axis, log-transformed) determined by ELISA in the same subjects, showed a positive significant correlation between them (n = 35, r = 0.337, p = 0.048). (b, c) Serum calprotectin levels were measured by ELISA in the complete PAD population (n = 317). Increased levels of calprotectin were observed in PAD patients with amputation (b) and MACE1 (c) in the follow-up (mean follow-up 3.6 years). *p < 0.05 and **p < 0.001 vs. no event. (d, e) Kaplan-Meier curves for the incidence of amputation (d) and MACE1 (e) in the follow-up. PAD patients were categorized according to the combination of calprotectin (≥7.4 µg/mL) and hs-CRP levels (≥13 mg/L). Group 1: low calprotectin and low hs-CRP; Group 2: either high calprotectin or high hs-CRP, and Group 3: high calprotectin & high hs-CRP. Patients with high levels of calprotectin and hs-CRP (group 3) presented increased risk for amputation and MACE1 than those within groups 1 and 2.

Table 3. Competing risk analyses (Fine-Gray model) for calprotectin and hs-CRP, and amputation and MACE1 in PAD patients (n = 317).

	Calprotectin^a (µg/mL)			hs-CRP^a (mg/L)
	SHR	95% CI	p	SHR	95% CI	p
Amputation
Unadjusted	2.49	1.54–4.04	<0.001	1.76	1.48–2.09	<0.001
Model 1	2.56	1.56–4.19	<0.001	1.82	1.52–2.20	<0.001
Model 2	2.62	1.58–4.34	<0.001	1.70	1.42–2.05	<0.001
Model 3	2.57	1.58–4.17	<0.001	1.74	1.47–2.07	<0.001
MACE1
Unadjusted	1.56	1.03–2.35	0.034	1.53	1.35–1.75	<0.001
Model 1	1.70	1.14–2.54	0.009	1.48	1.28–1.70	<0.001
Model 2	1.74	1.17–2.58	0.006	1.46	1.28–1.66	<0.001

Sub-Hazard ratios (SHR) are effect sizes for a doubling of serum calprotectin and hs-CRP. Amputation models were adjusted as follows; Model 1: sex, age. Model 2: diabetes mellitus, dyslipidemia and Model 3: hypertension and estimated glomerular filtration rate (eGFR). Major adverse cardiovascular events 1 (MACE1, including amputation and CV-death) models were adjusted as follows; Model 1: sex, age. Model 2: diabetes mellitus, hypertension, dyslipidemia, eGFR. ^aLogarithmically transformed variable.

Table 4. Competing risk analyses (Fine-Gray model) for categorized calprotectin and hs-CRP, and amputation and MACE1 in PAD patients (n = 317).

	Calprotectin ≥7.4 µg/mL			hs-CRP >13 mg/L			Combination
	SHR	95% CI	p	SHR	95% CI	p	SHR	95% CI	p
Amputation
No Adj	7.81	3.51–17.4	<0.001	10.5	4.21–26.3	<0.001
G1: Low Calp & CRP							1(ref)
G2: High Calp or CRP							5.42	1.79–16.5	0.003
G3: High Calp & CRP							26.9	9.53–76.0	<0.001
Model 1	8.12	3.57–18.5	<0.001	12.0	4.58–31.1	<0.001
G1: Low Calp & CRP							1(ref)
G2: High Calp or CRP							5.52	1.81–16.8	0.003
G3: High Calp & CRP							28.9	10.4–80.7	<0.001
Model 2	8.22	3.68–18.3	<0.001	9.69	3.79–24.8	<0.001
G1: Low Calp & CRP							1(ref)
G2: High Calp or CRP							5.46	1.78–16.7	0.003
G3: High Calp & CRP							25.6	8.71–75.3	<0.001
Model 3	8.26	3.75–18.2	<0.001	11.0	4.44–27.3	<0.001
G1: Low Calp & CRP							1(ref)
G2: High Calp or CRP							5.55	1.84–16.8	0.002
G3: High Calp & CRP							29.6	10.6–82.4	<0.001
MACE1
No Adj	4.13	2.32–7.34	<0.001	4.91	2.96–8.15	<0.001
G1: Low Calp & CRP							1(ref)
G2: High Calp or CRP							2.86	1.57–5.21	0.001
G3: High Calp & CRP							12.0	6.19–23.4	<0.001
Model 1	5.12	2.82–9.31	<0.001	4.42	2.54–7.69	<0.001
G1: Low Calp & CRP							1(ref)
G2: High Calp or CRP							2.70	1.45–5.01	0.002
G3: High Calp & CRP							11.7	5.86–23.5	<0.001
Model 2	5.06	2.83–9.04	<0.001	4.43	2.63–7.47	<0.001
G1: Low Calp & CRP							1(ref)
G2: High Calp or CRP							2.90	1.63–5.18	<0.001
G3: High Calp & CRP							11.6	5.74–23.3	<0.001

Sub-Hazard ratios (SHR). Amputation models were adjusted as follows; Model 1: sex, age. Model 2: diabetes mellitus, dyslipidemia and Model 3: hypertension and estimated glomerular filtration rate (eGFR). Major adverse cardiovascular events 1 (MACE1, including amputation and CV-death) models were adjusted as follows; Model 1: sex, age. Model 2: diabetes mellitus, hypertension, dyslipidemia, eGFR.

Figure 5. S100A9 expression on human femoral EVs and arterial tissue. (a) S100A9 mRNA was detected by RT-qPCR in EVs from conditioned medium of human femoral atherosclerotic plaques ex vivo (n = 3), and locally in femoral atherosclerotic tissues (n = 3). EVs were pretreated with proteinase K and RNase before RNA isolation to eliminate possible RNA contaminants from non-EVs sources. Data are presented as Ct values. (b, c) Representative western blots for S100A9 in EVs from femoral plaques (panel b, n = 2) and in atherosclerotic femoral tissue (panel c, n = 3). We were able to detect the monomers of S100A9 (≈14 kDa, black arrow), and also found bands at higher sizes (≈24 to 70 kDa) corresponding to the heterodimeric, trimeric or tetrameric forms of S100A9.

Jaitin DA, Kenigsberg E, Keren-Shaul H, et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science. 2014;343(6172):776–779.

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