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Aptamer-Based Proteomic Profiling Reveals Novel Candidate Biomarkers and Pathways in Cardiovascular Disease

Originally published 2016;134:270–285



Single-stranded DNA aptamers are oligonucleotides of ≈50 base pairs in length selected for their ability to bind proteins with high specificity and affinity. Emerging DNA aptamer-based technologies may address limitations of existing proteomic techniques, including low sample throughput, which have hindered proteomic analyses of large cohorts.


To identify early biomarkers of myocardial injury, we applied an aptamer-based proteomic platform that measures 1129 proteins to a clinically relevant perturbational model of planned myocardial infarction (PMI), patients undergoing septal ablation for hypertrophic cardiomyopathy. Blood samples were obtained before and at 10 and 60 minutes after PMI, and protein changes were assessed by repeated-measures analysis of variance. The generalizability of our PMI findings was evaluated in a spontaneous myocardial infarction cohort (Wilcoxon rank-sum). We then tested the platform’s ability to detect associations between proteins and Framingham Risk Score components in the Framingham Heart Study, performing regression analyses for each protein versus each clinical trait.


We found 217 proteins that significantly changed in the peripheral vein blood after PMI in a derivation cohort (n=15; P<5.70E-5). Seventy-nine of these proteins were validated in an independent PMI cohort (n=15; P<2.30E-4); >85% were directionally consistent and reached nominal significance. We detected many protein changes that are novel in the context of myocardial injury, including Dickkopf-related protein 4, a WNT pathway inhibitor (peak increase 124%, P=1.29E-15) and cripto, a growth factor important in cardiac development (peak increase 64%, P=1.74E-4). Among the 40 validated proteins that increased within 1 hour after PMI, 23 were also elevated in patients with spontaneous myocardial infarction (n=46; P<0.05). Framingham Heart Study analyses revealed 156 significant protein associations with the Framingham Risk Score (n=899), including aminoacylase 1 (β=0.3386, P=2.54E-22) and trigger factor 2 (β=0.2846, P=5.71E-17). Furthermore, we developed a novel workflow integrating DNA-based immunoaffinity with mass spectrometry to analytically validate aptamer specificity.


Our results highlight an emerging proteomics tool capable of profiling >1000 low-abundance analytes with high sensitivity and high precision, applicable both to well-phenotyped perturbational studies and large human cohorts, as well.


Editorial, see p 286

Emerging proteomic technologies are beginning to permit the systematic characterization of human plasma samples. Protocols for deep (ie, low abundance) unbiased biomarker discovery, predominantly focusing on liquid chromatography-tandem mass spectrometry (LC-MS/MS), have been applied to multiple disease states.18 However, formidable, interrelated obstacles remain. These include the wide dynamic range of plasma protein concentrations necessitating multiple upfront enrichment and separation techniques before mass spectrometry analysis and the paucity of rigorously characterized antibodies for follow-up studies. Together, these challenges contribute to diminished sample throughput and have precluded proteomic discovery profiling of low-abundance analytes in large numbers of plasma samples.

Single-stranded DNA aptamers are oligonucleotides of ≈50 base pairs in length that are selected for their ability to bind target proteins or peptides with high specificity and affinity. They can be immobilized to streptavidin-coated beads and incubated with samples to assay analytes in a highly multiplexed manner. Emerging DNA aptamer-based proteomic technologies may address important limitations of existing proteomic techniques,911 but data in humans are still limited, particularly studies integrating independent validation strategies. Applications to archived samples from population-based cohorts are also lacking. Here, we applied an aptamer-based proteomic technology that measures 1129 proteins12 to identify potential novel biomarkers of cardiovascular disease.

As proof of principle, we first applied the platform to humans subjected to a robust, clinically relevant perturbation. We studied patients undergoing alcohol septal ablation for hypertrophic cardiomyopathy, a model of planned myocardial injury in which each individual serves as his or her own biological control. The planned myocardial infarction (PMI) reproduces key clinical features of spontaneous myocardial infarction (SMI), including chest pain, electrocardiographic changes, and wall motion abnormalities, as well as the release of established markers of myocardial injury.13,14 Leveraging the timed nature of the human model, we tested whether the platform could identify very early markers of myocardial injury.

We then applied the aptamer proteomics platform to samples from participants of the Framingham Heart Study (FHS) Offspring Study, enrolled between 1991 and 1995.15 In this community-based cohort, we tested whether the technology could identify novel proteins associated with cardiovascular risk factors in participants without overt cardiovascular disease. Finally, we developed a novel analytic workflow integrating DNA-based immunoaffinity and mass spectrometry to begin to examine the specificity of aptamer-based techniques. These studies document a reproducible aptamer-based protein-profiling platform with far better throughput than LC-MS/MS–based plasma proteomic techniques. Furthermore, studies from both populations highlight many novel candidate biomarkers of cardiovascular disease.


Study Patients

Patients With Hypertrophic Cardiomyopathy Undergoing Septal Ablation

A total of 30 patients undergoing PMI using alcohol septal ablation for the treatment of symptomatic hypertrophic cardiomyopathy were included in this study (15 in the derivation cohort; 15 in the validation cohort). Inclusion criteria for this cohort and the procedure were performed as previously described.1,2,16 Blood was drawn at baseline, 10 minutes, 1 hour, and 24 hours after injury. An additional 6 patients consented to the placement of a catheter into the coronary sinus during the ablation, allowing for the simultaneous sampling of blood from the coronary sinus and femoral catheters at baseline, 10 minutes, and 1 hour. Four patients undergoing elective, diagnostic cardiac catheterization for cardiovascular disease, but not acute myocardial ischemia, were used as controls for the PMI patients. Blood was drawn before the onset of cardiac catheterization and at 10 minutes and 1 hour after the procedure was begun.

Patients With Spontaneous Acute Coronary Syndromes and At-Risk Controls

We enrolled 23 patients with spontaneous myocardial infarction (SMI) who underwent cardiac catheterization for acute ST-segment elevation within 8 hours of symptom onset. Femoral venous blood samples were obtained in the coronary catheterization suite on initial presentation. Peripheral blood from 23 patients with negative standard Bruce protocol exercise tests was used as control for SMI.17

Framingham Heart Study Participants

The FHS Offspring cohort was formed in 1971 with the enrollment of 5124 individuals in a community-based longitudinal cohort study.15 The analyses of proteins with cardiometabolic traits were performed on a case-cohort design sampling in which we selected baseline plasma samples from 899 participants (311 participants who developed incident cardiovascular disease and 588 randomly selected controls who did not). Among the 899 participants, we also performed duplicate proteomic analyses on 94 individuals across the lowest and highest tertiles of the Framingham Risk Score (FRS) to assess platform reproducibility. Inclusion criteria as an incident case was based on cardiovascular disease events or diagnoses (including coronary heart disease, myocardial infarction, angina, coronary insufficiency, cerebrovascular accident, atherothrombotic infarction of the brain, transient ischemic attack, cerebrovascular disease, and intermittent claudication) occurring after examination 5. Participants with prevalent cardiovascular disease as defined above at examination 5 were excluded.

Study Samples

Blood samples were collected in K2EDTA-treated tubes in PMI patients and citrate-treated tubes in FHS participants. Samples were centrifuged within 15 minutes at 2000g for 10 minutes to pellet cellular elements. The supernatant plasma was then aliquoted and frozen at −80°C.

Proteomic Assay Overview

The SOMAscan proteomic profiling platform uses single-stranded DNA aptamers that target 1129 proteins. All assays were performed using SOMAscan reagents according to the manufacturer’s detailed protocol.12 A detailed protocol is provided in the Methods in the online-only Data Supplement and in Figure I in the online-only Data Supplement. A complete list of the proteins is included in Table I in the online-only Data Supplement. Many of the proteins are either secreted or known to be shed from the cell surface, and thus the platform is particularly well suited for plasma biomarker discovery.

Statistical Analyses

PMI Studies

All protein values were log transformed because of their nonnormal distributions as determined by the Kolomogorov-Smirnov and Shapiro-Wilk normality tests. For the PMI and cardiac catheterization control studies, 1-way repeated-measures analysis of variance (ANOVA) was used to test differences in protein levels across time points (baseline, 10 minutes, 1 hour, 24 hours). The Greenhouse-Geisser adjustment was used if sphericity was violated. In analyses to identify proteins that might be altered by the cardiac catheterization procedure itself (without overt injury), a P<0.05 was used to determine significant changes. Consequently, 253 proteins that were changed after heparin administration during cardiac catheterization were excluded from the PMI analyses. Bonferroni-corrected P threshold <5.70E-05 (0.05/878 proteins on platform excluding 253 proteins on the SOMAscan that changed in catheterization heparin control patients) and <2.30E-04 (0.05/217 proteins identified as significantly changed in derivation cohort) were used to determine significance in the derivation and validation PMI cohorts, respectively. A Wilcoxon rank-sum test was used in the SMI case-control analysis with a Bonferroni-corrected P threshold <1.25E-03 (0.05/40 validated proteins shown to increase in first hour of myocardial injury) for significance. For the coronary sinus versus peripheral blood PMI analysis, 2-way repeated-measures ANOVA was performed to determine significant changes across time.

FHS Studies

Protein measures were log transformed, then standardized (to mean=0 and SD=1). Age- and sex-adjusted regression analyses were performed for each protein (outcome) versus each clinical trait (FRS, age, female sex, total cholesterol, high-density lipoprotein cholesterol, systolic blood pressure, diabetes mellitus, and smoking). Given the 1129 proteins analyzed, we used a Bonferroni-corrected P threshold of 5.54E-06=[0.05/(1129 proteins × 8 traits)] to account for the number of statistical tests. Inverse probability sampling fractions for weights were applied in the analyses to account for the sampling algorithm. The sampling fractions were f1=311/324 cases and f0=588/2681 noncases, so weights were w1=1/f1 and w0=1/f0 for cases and noncases, respectively. We multiplied these weights by 899/3005 so weights sum to our sample size, 899. In the incidence analyses, we used proportional hazards regression models.

All analyses were performed with SAS Software version 9.4 (SAS Institute, Cary, NC). Figures were generated with Graph Pad Prism 5 (La Jolla, CA) and Tableau 9.1 (Seattle, WA).

Study Approval

All human study protocols were approved by the institutional review boards of Massachusetts General Hospital and Boston University Medical Center. Informed consent was obtained from all participants.


Early Protein Changes in Peripheral Plasma of PMI Patients

To assess the reproducibility of the platform, we embedded pooled plasma control samples across a total of 43 individual plate assays and documented median intra- and interexperimental coefficients of variance of ≤8.2% for the 1129 proteins (see Methods in the online-only Data Supplement; a list of all proteins assayed are included in Table I in the online-only Data Supplement). Figure 1 demonstrates the intra-assay coefficients of variance of representative proteins across a broad dynamic range of abundance. Using split-sample analyses from 94 individuals, we also documented a median intraclass correlation of >0.95 (Figure II in the online-only Data Supplement).

Figure 1.

Figure 1. Intra-assay CVs of selected proteins.Data represent intra-assay CVs of selected proteins across a broad range of plasma concentrations. Proteins are sorted by increasing plasma concentration reference range ( The color of the circle represents intra-assay CV as denoted in the key above. See Table I in the online-only Data Supplement for full protein names, Entrez Gene symbol/ID, and UniProt ID. CV indicates coefficient of variance.

To assess the linear dynamic response of the aptamer assay, we performed calibration curve experiments with standards for a subset of the novel proteins that were associated with PMI (see below). The calibration curves shown in Figure III in the online-only Data Supplement demonstrate a linear relationship to increasing concentrations of protein standards and provide estimated endogenous protein plasma concentrations.

Clinical characteristics of the PMI study patients are detailed in Table II in the online-only Data Supplement. We profiled peripheral blood samples across serial time points (baseline, 10 minutes, 1 hour, and 24 hours after injury) to characterize protein changes associated with myocardial injury. We determined that 253 proteins were nominally changed (P<0.05) within 1 hour following the exposure of patients to intravenous heparin during cardiac catheterization (Table III in the online-only Data Supplement), and therefore excluded these proteins from the analyses. We identified 217 proteins that were significantly changed within 24 hours postinjury in a derivation cohort of 15 patients (Bonferroni-adjusted P<5.70E-05, 1-way ANOVA repeated measures). In a validation cohort of 15 patients, 87% of the 217 proteins had directionally consistent changes that reached nominal statistical significance (P<0.05); changes in 79 proteins exceeded a repeat Bonferroni threshold (P<2.30E-04, 1-way ANOVA repeated measures). Table 1 details a subset of the 79 validated proteins that had changes of ≥33% at any time point after PMI. All validated proteins are listed in Table IV in the online-only Data Supplement.

Table 1. Protein Changes in Peripheral Blood After Myocardial Injury

Protein% Change (IQR)10 min Versus Baseline% Change (IQR)1 h Versus Baseline% Change (IQR)24 h Versus BaselineP
Angiogenin34.1 (29.3 to 61.8)24.5 (19.9 to 38.1)–1.6 (–6.5 to 15.3)2.63E-06
Annexin VI–46.0 (–75.6 to –32.3)–34.9 (–76.9 to –30.7)33.9 (21.5 to 193.1)3.58E-06
b-ECGF72.7 (32.3 to 102.4)103 (82.0 to 158.9)–3.4 (–7.2 to 6.5)1.34E-10
Cadherin-12101 (34.9 to 168.7)213 (84.2 to 239.0)15.4 (–17.6 to 42.5)1.02E-04
CDC3745.0 (2.1 to 69.2)64.3 (22.6 to 81.9)7.4 (–6.5 to 18.3)2.67E-07
Chymase41.3 (22.3 to 52.6)43.3 (30.5 to 67.5)19.1 (–1.7 to 28.9)1.91E-04
CK-MB143 (46.6 to 305.1)506 (374.5 to 690.6)346 (138.7 to 1053.3)9.60E-08
CK-MM42.0 (13.7 to 81.4)233 (146.1 to 287.7)234 (108.1 to 484.9)7.28E-07
Cripto45.5 (14.0 to 66.1)63.6 (24.4 to 80.7)4.9 (–7.3 to 16.4)1.74E-04
DKK-4124 (101.9 to 157.4)56.7 (39.6 to 100.1)–11.2 (–24.1 to 12.5)1.29E-15
FABP178 (105.3 to 399.1)742 (551.8 to 1354.9)69.3 (5.3 to 235.0)1.28E-15
FGF-18134 (83.6 to 173.0)272 (189.9 to 359.2)–8.4 (–27.3 to 98.2)6.31E-08
FGF-8B29.4 (15.7 to 38.9)39.1 (26.1 to 56.9)–0.1 (–10.1 to 8.8)1.54E-04
GAS138.3 (18.8 to 51.7)65.3 (35.7 to 79.5)10.2 (1.0 to 15.1)4.73E-05
GREM1131 (62.2 to 154.3)176 (101.4 to 230.9)2.2 (–5.6 to 20.6)2.78E-07
Histone H2A.z–61.0 (–73.0 to –48.7)–38.3 (–52.6 to –23.5)84.1 (31.4 to 257.0)5.41E-10
IL-1132.6 (15.8 to 48.0)44.4 (19.2 to 63.4)5.3 (–1.3 to 14.4)3.65E-08
IL-531.2 (17.7 to 59.0)48.9 (30.5 to 71.8)23.8 (–5.3 to 50.8)3.98E-05
IL-6–6.0 (–20.2 to 3.9)7.0 (–11.6 to 13.2)113 (35.9 to 173.7)1.20E-04
KREM211.9 (4.6 to 27.8)36.1 (24.9 to 39.7)–1.9 (–10.1 to 6.4)1.08E-04
LBP–28.9 (–40.5 to –26.6)–30.5 (–36.9 to –23.1)78.5 (59.6 to 92.3)1.03E-07
LDH-H16.7 (0.8 to 18.8)8.7 (1.7 to 20.3)73.4 (52.4 to 144.2)8.76E-14
LIFsR196 (126.8 to 247.0)175 (114.2 to 217.0)19.2 (–3.4 to 27.7)2.68E-07
MDHC50.3 (14.6 to 67.0)221 (149.3 to 279.4)143 (45.4 to 232.4)1.48E-08
MMP-13141 (102.7 to 237.8)64.8 (36.1 to 170.4)3.6 (–3.8 to 17.1)1.65E-14
MMP-1650.7 (20.9 to 68.5)62.2 (27.0 to 75.0)7.1 (4.2 to 36.9)7.33E-10
Myoglobin88.3 (59.5 to 161.1)152 (105.7 to 231.0)16.6 (–2.2 to 32.3)8.53E-11
NACA49.7 (16.6 to 70.4)43.3 (26.6 to 65.8)25.8 (11.2 to 46.6)4.72E-05
NET4115 (61.0 to 152.3)63.3 (20.1 to 82.5)1.7 (–9.9 to 23.4)1.11E-04
PPIB42.7 (17.8 to 78.4)61.5 (38.3 to 95.5)22.9 (10.6 to 59.0)3.87E-05
SCGF-alpha31.4 (19.0 to 55.7)48.0 (32.4 to 70.2)13.7 (2.3 to 34.2)5.89E-07
SDF-1120 (78.8 to 193.9)82.8 (45.3 to 119.4)–6.7 (–17.3 to 3.5)4.41E-06
ST4S643.6 (33.1 to 58.8)20.7 (12.9 to 34.5)7.2 (–5.4 to 33.3)9.94E-07
TECK530 (264.8 to 634.9)213 (80.5 to 251.4)–20.1 (–33.3 to 9.7)5.53E-18
TPI45.1 (8.1 to 58.2)89.1 (59.3 to 145.7)2.4 (–9.1 to 37.5)4.21E-08
Troponin I836 (452 to 1241)2692 (1756 to 5241)11 400 (8435 to 20 459)1.34E-26
URB39.1 (23.7 to 51.9)18.9 (12.1 to 25.8)–17.6 (–26.4 to –4.5)1.76E-04

Shown are proteins with ≥33% increase from baseline at any time point within validation cohort and P<2.30E-04 (1-way ANOVA on log transformed RFU values), listed in alphabetic order. All proteins listed were significant in derivation cohort with P<5.70E-05 (1-way ANOVA on log transformed RFU values). Change values denote median percent change (first and third quartiles). See Table I in the online-only Data Supplement for full protein names, Entrez Gene symbol/ID, and UniProt ID. ANOVA indicates analysis of variance; IQR, interquartile range; and RFU, relative fluorescent unit.

The dynamic changes in representative proteins are illustrated in Figure 2. We confirmed increases in well-established clinical markers of myocardial injury including troponin I and creatine kinase MB, and other biomarkers, as well, previously identified by our group and others such as fatty acid–binding protein18,19 and stromal derived factor-1.2022 Among the 79 proteins that changed in both derivation and validation cohorts, 31 increased by ≥33% within 1 hour of injury onset, and 25 proteins were increased within 10 minutes. Many of these protein changes were novel in the context of early myocardial injury (see Table 1), including fibroblast growth factor 18, Dickkopf-related protein 4, teratocarcinoma-derived growth factor 1 (Cripto-1) and phosphodiesterase 7A. We used immunoassays that were available for a subset of the novel proteins to confirm PMI findings (Figure IV in the online-only Data Supplement). Furthermore, 12 of the protein changes identified on the aptamer platform were directionally consistent and significant in previous label-free and isobaric tagging mass spectrometry (MS)–based work on PMI samples.1,2 Nonoverlapping findings between the aptamer and previous MS-based studies are, in part, attributable to differences in experimental design and the targeted nature of the aptamer platform, although specific proteins were identified exclusively by each of the distinct proteomic approaches.

Figure 2.

Figure 2. Protein markers that are increased early after the onset of myocardial injury in peripheral blood.Data from selected proteins that increased in both derivation and validation cohorts (P<5.70E-05 in derivation cohort [n=15] and P<2.30E-04 in validation cohort [n=15]). P calculated by 1-way ANOVA performed on log-transformed RFU values. Edges of boxes denote 25th and 75th percentiles; lines denote median; and whiskers denote minimum and maximum values. See Table I in the online-only Data Supplement for full protein names, Entrez Gene symbol/ID, and UniProt ID. ANOVA indicates analysis of variance; CK-MB, creatine kinase MB; DKK-4, Dickkopf-related protein 4; FABP, fatty acid–binding protein; IL-34, interleukin 34; RFU, relative fluorescent units; and SDF-1, stromal derived factor-1.

Changes in the Coronary Sinus During Planned Myocardial Injury

In a small cohort of 6 PMI patients, we compared protein levels in samples obtained concurrently from peripheral blood and the coronary sinus, the venous outflow from the heart. Higher protein levels in the coronary sinus samples in comparison with the periphery suggest cardiac origin. Troponin-I and creatine kinase MB, well-established markers of myocardial injury, were modestly enriched (≈1.2-fold) in the coronary sinus at 1 hour after injury in this subset of individuals. Among the 79 validated proteins, 24 proteins were increased in the coronary sinus with a ≥1.2-fold increase in comparison with peripheral blood within 1 hour after myocardial injury. Table V in the online-only Data Supplementhighlights the novel proteins found to be enriched in the coronary sinus within 1 hour of injury (nominal P<0.05; 2-way ANOVA). Overall, there was concordance in the directionality of changes of the 79 proteins in the peripheral and coronary sinus samples (r > 0.88; P<2.2E-16), again confirming the consistency of our findings across multiple sample time points and sampling sites.

Validation of Candidate Protein Markers in SMI

To begin to assess the generalizability of the findings from the PMI cohort, we profiled a cohort of SMI patients presenting for acute coronary angiography (n=23 cases), and at-risk individuals without ischemia as determined by exercise stress testing (n=23 nonischemic controls), as well. Because the timing of sample collection relative to SMI onset was variable (7.0±2.5 hours), we focused on the PMI-derived candidate protein markers that were elevated at 1 hour, the closest available time point after the procedure. Among the 40 proteins that were increased after 1 hour of injury, 23 were elevated in SMI at nominal significance (P<0.05). Fourteen of these proteins exceeded repeat Bonferroni threshold for statistical significance (Table 2; P<1.25E-03; Wilcoxon rank-sum). Several of these proteins are novel in the setting of acute myocardial infarction including malate dehydrogenase, cytoplasmic, triose phosphate isomerase, leukocyte inhibitory factor soluble receptor, and fibroblast growth factor 18. Representative data for 3 known biomarkers and 3 novel proteins are demonstrated in Figure 3. Of note, 12 of the proteins that reached Bonferroni significance across all data sets were elevated as early as 10 minutes after myocardial injury.

Table 2. Proteins Increased in Spontaneous and Planned MI

ProteinFold Change(SMI Versus Control)P
Troponin I1772.43E-13
LDH-H 11.701.73E-05

Shown are proteins increased in SMI cases vs controls with P<1.25E-03, listed in ascending P (Wilcoxon rank-sum on log transformed RFU values). All proteins listed were also increased in the PMI cohorts (derivation P<5.70E-05 and validation P<2.30E-04; 1-way ANOVA on log transformed RFU values). See Table I in the online-only Data Supplement for full protein names, Entrez Gene symbol/ID, and UniProt ID. ANOVA indicates analysis of variance; IQR, interquartile range; MI, myocardial infarction; PMI, planned myocardial infarction; RFU, relative fluorescent unit; and SMI, spontaneous myocardial infarction.

Figure 3.

Figure 3. Proteins increased during planned and spontaneous myocardial injury.Representative proteins that increased in PMI derivation and validation cohorts and an independent SMI cohort. PMI data shown from validation cohort (n=15; P<2.30E-04; 1-way ANOVA on log RFU values). SMI cohort (n=23 cases and 23 controls, P<1.25E-03; Wilcoxon rank-sum on log RFU values). Edges of boxes denote 25th and 75th percentiles, lines denote median, whiskers minimum and maximum values. See Table I in the online-only Data Supplement for full protein names, Entrez Gene symbol/ID and UniProt ID. ANOVA indicates analysis of variance; CK-MB, creatine kinase MB; FABP, fatty acid–binding protein; FGF-18, fibroblast growth factor 18; LIFsr, leukocyte inhibitory factor soluble receptor; MDHC, malate dehydrogenase, cytoplasmic; PMI, planned myocardial infarction; RFU, relative fluorescent units; and SMI, spontaneous myocardial infarction.

Proteins Associated With Cardiovascular Risk Traits in the FHS

We extended our initial perturbational studies to test whether the platform could identify novel protein associations with cardiac risk factors in a community-based sample. We studied 899 individuals from the Framingham Offspring Study (Table VI in the online-only Data Supplement) across a spectrum of the FRS,23 which includes age, sex, total cholesterol, high-density lipoprotein cholesterol, systolic blood pressure, smoking, and diabetes status. The mean age for this cohort was 56 (±12) years with a slight female predominance (52%) and mean FRS 3.43 (±4.88). In the FHS sample, age- and sex-adjusted median absolute pairwise correlations between the proteins on the platform was 0.05, with >93% of pairwise correlations <0.2 and 99% <0.4.

Top protein correlations with established cardiovascular risk factors including sex, age, total cholesterol, high-density lipoprotein cholesterol, systolic blood pressure, diabetes mellitus, and smoking are shown in Tables VII through XIII in the online-only Data Supplement. As expected, female sex was positively associated with luteinizing and follicle-stimulating hormones (P=3.06E-149 and P=2.72E-145, respectively) and negatively associated with prostate-specific antigen (P=6.70E-52). In addition, we observed many novel relationships including smoking with circulating levels of the bone-associated osteomodulin (P=4.25E-25) and age with antiangiogenic factor endostatin (P=2.48E-10).

Age- and sex-adjusted regression analyses identified 156 proteins significantly associated with the FRS (P<5.54E-06; Bonferroni correction for [1129 proteins × 8 traits]). Known biomarkers such as C-reactive protein (P=5.18E-27) and apolipoprotein E subclasses (P<3.66E-12) were positively associated with the FRS. There were numerous novel relationships identified as well, including aminoacylase 1 (P=2.54E-22), laminin (P=1.07E-22), trigger factor 2 (P=5.71E-17), and matrix metalloproteinase-12 (P=3.93E-16). Figure 4 includes the top protein associations with FHS risk score along with the corresponding protein data with the clinical risk factors that comprise the FRS. All significant protein associations with FRS are listed in Table XIV in the online-only Data Supplement.

Figure 4.

Figure 4. Top protein associations with the Framingham Risk Score and component clinical traits.Shown are the top 30 proteins with positive (A) and negative (B) associations. Within groups of positive/negative associations, proteins are sorted by ascending P. Estimated β-coefficients and P were generated from age- and sex- adjusted regression analyses of each protein (log transformed then standardized) with FRS and clinical traits (standardized). The size of the circles corresponds to P (larger circles represent smaller P). Protein-trait associations with P>5.54E-06 were not represented by circles. The color of the circles represents estimated β-coefficients as denoted in the key above. Diabetes mellitus and current smoking status were categorical variables. See Table I in the online-only Data Supplement for full protein names, Entrez Gene symbol/ID, and UniProt ID. FRS indicates Framingham Risk Score; HDL, high-density lipoprotein; SBP, systolic blood pressure; and TC, total cholesterol.

In exploratory analyses, proportional hazards models were used to assess the association between baseline protein levels and future cardiovascular disease, adjusting for age and sex (Table 3). One protein (tissue plasminogen activator) reached Bonferroni-adjusted significance. Proteins associated with incident disease with nominal P<0.05 are presented in Table XV in the online-only Data Supplement. Following multivariable adjustment for all the FHS components, cathepsin H was most strongly associated with incident disease, although this finding did not reach Bonferroni correction for multiple hypothesis testing (P=0.002).

Table 3. Top FHS Protein Associations With Incident Cardiovascular Disease (Age- and Sex-Adjusted)

ProteinHR per SD (CI)P
tPA1.65 (1.31–2.07)2.1E-05
Cathepsin H1.47 (1.21–1.79)1.3E-04
NCAM 1200.68 (0.55–0.83)2.5E-04
IDS0.69 (0.56–0.85)3.9E-04
C1s1.42 (1.17–1.73)5.1E-04
IGFBP41.48 (1.18–1.86)7.8E-04

Shown are proteins with P<0.001, listed by ascending P. Values are hazard ratios per standard deviation (95% confidence intervals) for incident CVD from proportional hazards regression models. See Table I in the online-only for full protein names, Entrez Gene symbol/ID, and UniProt ID. CI indicates confidence interval; HR, hazard ratio; and SD, standard deviation.

Technical Validation of Aptamer Findings

To begin to systematically assess platform specificity, we developed protocols using aptamers for immunoaffinity pulldown of intact proteins followed by digestion to peptides and analysis by targeted mass spectrometry. We acquired 29 aptamers and their cognate proteins, across a range of sizes and functions, including both intracellular and secreted proteins. We first added a mixture of the protein standards into plasma with the corresponding mixture of aptamer-coated beads. After overnight incubation, the beads were boiled and analyzed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis. All the proteins were characterized by 1 predominant band at the anticipated molecular weight (data not shown). Conditions for eluting bound proteins from aptamers were then optimized and eluates were digested with trypsin and analyzed by LC-MS/MS (see Methods in the online-only Data Supplement). Figure 5Ademonstrates confirmatory ion chromatograms for 8 of the 29 proteins that were also found to be statistically significant in our FHS analyses (FRS, age or sex). All 29 aptamers demonstrated enrichment of their corresponding spiked-in protein relative to the plasma proteome background. A total of 532 peptides from the 29 proteins were identified by LC-MS/MS following aptamer affinity enrichment (Tables XVI and XVII in the online-only Data Supplement). In addition to the proteins targeted for enrichment and analysis, we also detected many high-abundance plasma proteins by LC-MS/MS (Table XVIII in the online-only Data Supplement). This is a common finding in all bead pulldown experiments. Response curves for aptamer-enriched proteins were linear over a wide dynamic range of spiked protein concentrations (Figure 5B and Tables XIX and XX in the online-only Data Supplement). The median LOD (limit of detection) was 0.41 fmol/μL for 19 peptides (corresponding to 9 proteins) using heavy isotope peptides quantified by amino acid analysis (median R2=0.96). A similar median LOD for 106 peptides (corresponding to 29 proteins) was derived using light peak areas only (median R2=0.93). These LODs are comparable to antibody-based enrichment methods analyzed by LC-multiple reaction monitoring-MS using heavy isotope peptide standards. These results indicate that the presence of nonspecific proteins of even high abundance does not substantially affect quantification in a targeted MS mode of analysis and that the reproducibility of the aptamer-based enrichment process offers sufficient interference-free precision to allow for comparable peptide (and therefore protein) quantification from matrices as complex as plasma.

Figure 5.

Figure 5. Mass spectrometry verification of aptamer-protein binding. A, Aptamers bind their cognate proteins spiked into plasma. Proteins were spiked into plasma in the absence (Top) or presence (Bottom) of biotinylated aptamers bound to streptavidin bead. Following elution and digestion of the affinity-enriched sample, LC-MRM-MS analysis was performed. Shown are MS signal intensities for peptides unique to 8 proteins in the study that were extracted from the total ion chromatogram. B, Response curves for ERBB1, prostate-specific antigen (PSA), and Annexin A1 spiked into plasma. Measured protein concentrations are compared with the expected concentrations based on the observed ratio of analyte peptide from spiked protein standard to heavy isotope–labeled standard. Limits of detection (LODs) were determined for each of the 3 selected transitions for the proteins. Calibration curves were prepared by method of standard addition in plasma, enriched using a multiplex of 29 aptamers, digested with trypsin and quantified by LC-MRM-MS using heavy isotope–labeled standards for selected peptides as described in the Methods in the online-only Data Supplement. Regression analysis was performed and the slope and the x intercept were used to estimate the relative recovery and lower limit of detection (LLOD), respectively (see Methods and Tables XIX and XX in the online-only Data Supplement). Response was linear over 3 orders of concentration for all peptides, and LODs were in the range of 10s to 100s of attomole/μL except in cases where the endogenous protein was present at sufficiently high levels and caused an earlier plateau in response as was observed for ERBB1. C, Demonstration of aptamers binding endogenous protein (ERBB1). MS response of fragment-ion transitions monitored for 3 peptides unique to ERBB1 are plotted for plasma samples containing protein standards enriched using SA beads without aptamers (left column), and with aptamers in the absence (center column) or presence (right column) of the added protein standard. The peak areas of coeluting fragment-ion transitions in the same retention time window are designated by color (Table XVII in the online-only Data Supplement). The relative intensities of the fragment ions for each peptide observed in the positive control (protein standards and aptamer added), and the patient plasma samples without exogenous protein added, confirms the identification and detection of these peptides and of endogenous ERBB1 in patient plasma using aptamer affinity enrichment. See Methods in the online-only Data Supplement for details of sample handling, affinity enrichment, elution, digestion, and MS analysis. LC-MRM-MS indicates liquid chromatography-multiple reaction monitoring-mass spectrometry; and MS, mass spectrometry.

We then tested the limits of detection of this system for endogenous proteins. Aptamer-enriched samples were digested and then analyzed by LC-multiple reaction monitoring-MS for proteotypic peptides for the 8 proteins shown in Figure 5A(and Table XVII in the online-only Data Supplement). Figure 5C details findings for the ERBB1 protein in control versus aptamer-containing samples. As expected, we confirmed the detection of peptides unique for ERBB1 protein in samples containing both the aptamer and the added protein standard. However, even in samples that did not have protein standards added, the same peptides from endogenous ERBB1 protein were detected by LC-multiple reaction monitoring-MS. None was observed in the absence of aptamer. Approximately one-quarter of the proteins could be detected at endogenous levels under these conditions (Tables XIX and XX in the online-only Data Supplement).

Finally, we performed orthogonal analyses to assess quantitation of proteins leveraging antipeptide antibodies, as we have done previously.2Figure V in the online-only Data Supplement demonstrates concordance between the aptamer-based scan and antipeptide immunoaffinity enrichment MS for 2 representative proteins, troponin I and spondin-1 in PMI patients (n=11 and 12, respectively). Troponin serves as a model positive control. Spondin-1 increased in myocardial injury, although it is also significantly changed in heparinized catheterization control patients. Taken together, these findings highlight a new workflow leveraging the strength of LC-MS to verify the specificity and quantitation of the higher-throughput aptamer method.


Recent advances in aptamer-based proteomic technologies offer the prospect of proteomic analyses with breadth and throughput not previously possible. However, there are limited data available in studies integrating replication studies or in archived population-based samples. To assess the applicability of this technology in human cardiovascular disease, we used a clinical model of myocardial infarction that produced robust clinical and biochemical changes and allowed precise kinetic analysis in patients who served as their own controls. Using an aptamer-based platform, we confirmed established biomarkers of myocardial injury such as creatine kinase MB and troponin and extended previous work by identifying many new proteins not previously associated with myocardial infarction. Many of the protein changes occurred within 10 minutes of myocardial injury, which might ultimately provide additional information on top of established biomarkers and electrocardiographic changes. We next applied the platform to investigate proteins associated with subtle cardiovascular risk factors in samples from FHS participants. We found many novel protein associations with cardiovascular risk traits in samples archived for over 20 years.

Techniques that stochastically sample proteins or peptides, such as mass spectrometry, face formidable challenges when applied to the human plasma proteome, including the sheer number of proteins and their extensive range of concentrations. Depletion strategies targeting a subset of high-abundance proteins mitigate this problem somewhat, but at the cost of throughput and reproducibility. We and others have integrated protocols for deep (ie, low abundance) biomarker discovery, coupling chemical isobaric mass tag labeling, and label-free approaches, as well, with LC-MS/MS.18,24,25 Recent reports have identified >5000 proteins in plasma, but these analyses take ≈24 hours per sample when accounting for depletion strategies and multiple types of chromatographic separation before MS analysis.1 Others have reported higher-throughput MS-based approaches using alternative multiplexing techniques and minimal fractionation that can identify ≈150 to 250 plasma proteins at a faster rate of 2.4 hours per sample.26 The aptamer-based technique we used here is >20-fold faster than deep proteomic analyses1 and 6-fold more rapid than the higher-throughput approach reported by Cominetti et al, which detects less than one-quarter of the number of analytes.26 Although the number of proteins presently assayed is less than can be obtained by deep mass spectrometry, this trade-off is reasonable if the goal is to interrogate large cohorts. It is anticipated that additional reagents will become available as an increasing number of aptamers are generated against recombinant proteins. The experimental design of our PMI model addressed some of the study design challenges that have limited proteomic biomarker studies. By allowing each individual to serve as his/her own biological control in a perturbation experiment with serial sampling, we constrained interindividual variability and enhanced power to identify statistically meaningful changes. In addition to the known markers of myocardial injury, we were able to identify several candidate biomarkers that may also highlight specific pathways in response to myocardial injury, including myocardial cytoprotection and regeneration. Proteins implicated in angiogenesis, such as angiogenin and fibroblast growth factor 1,2730 are increased in PMI. We identified an increase in WNT pathway inhibitor, Dickkopf-related protein 4 and Dickkopf-related receptor KREM2, highlighting possible WNT pathway involvement in myocardial injury.31 Changes in the leukocyte inhibitory factor pathway, including decreases in cardiotrophin 1 and increases in leukocyte inhibitory factor soluble receptor, may suggest a cell differentiation response to injury.32,33 Other proteins important in cardiac development and regeneration also increased early after myocardial injury including stromal derived factor-1,34 Cripto,35 and fibroblast growth factor 8β.36,37 Unfortunately the small samples size of the coronary sinus studies does not allow for definitive differentiation between cardiac and noncardiac origin of protein changes.

With several layers of validation and potentially interesting biological findings from our PMI studies, we investigated the more subtle phenotype of cardiovascular risk in archived samples from FHS participants (n=899). We examined the protein associations with the clinical risk factors of cardiovascular disease including age, sex, total cholesterol, high-density lipoprotein, systolic blood pressure, diabetes mellitus, and smoking. We identified circulating proteins not previously associated with these clinical traits, highlighting potentially novel pathways. For example, osteomodulin, a protein involved in biomineralization of bones, demonstrates a strongly negative association with smoking, an established risk factor for osteoporosis.38 Thus, lower levels may reflect (or be participating in) decreased bone density. We then evaluated protein associations with the composite of these risk factors in the FRS. As might be expected, C-reactive protein57 and apolipoprotein E39,40 were confirmed as top associations with FRS. Novel associations with FRS included metalloproteinase-12 and laminin, and numerous other proteins, as well. Interestingly, a common variant in the metalloproteinase-12 locus was recently found to be associated with atherosclerotic stroke, raising the possibility of a causal pathway.41 Laminin chain isotype composition was found to be different in the atherosclerotic plaque tissue than in the intima media from both apolipoprotein E–deficient mice and humans.42

Furthermore, we developed a new method incorporating DNA-aptamers into an immunoaffinity LC-MS–based workflow for orthogonal validation of findings. Our data confirm that the aptamers enriched for their targeted proteins in spike-in studies. In the conditions tested to date, LC-MS/MS analyses also provided unambiguous identification of endogenous peptides from targeted proteins as well, although sensitivity was not sufficient for all proteins studied. Additional studies modifying key analytic parameters such as the amount of starting material or aptamer, and LC-MS conditions, as well, are ongoing.

We recognize limitations of our studies. Although this aptamer-based proteomics platform provides very broad coverage with high throughput, its coverage of the global human proteome remains limited and may therefore be agnostic to changes of analytes not targeted. In our PMI studies, we excluded >200 proteins changed by heparin administration and the cardiac catheterization procedure itself (nominal P<0.05). This very conservative effort to limit potential confounding may result in a bias toward the null where we fail to identify significant protein changes. Although our PMI studies included derivation and validation cohorts, the total number of patients remains relatively small. Although our FHS study was well powered to test the analytic aspects of the platform and cross-sectional associations, we were less powered for incident cardiovascular disease analyses. Future analyses must be performed in larger, ethnically diverse populations to confirm and extend our findings. Finally, because groups of proteins cluster within biological pathways, the assumption of independent statistical tests is overly stringent. Thus, it is important to acknowledge the possibility of false-negative results from using Bonferroni-corrected P values.

In summary, we demonstrated the application of a novel aptamer-based proteomics platform for the discovery of blood markers of myocardial injury and to highlight potential pathways associated with cardiovascular risk traits. Further, we developed a novel DNA aptamer-based immunoaffinity MS-based workflow for the technical validation of these findings. Beyond the potential diagnostic utility we demonstrate here in our PMI studies, findings in the archived FHS samples highlight a broad spectrum of proteins associated with cardiometabolic risk for exploration in functional studies.


Dr Ngo designed the study, analyzed and interpreted data, and wrote the manuscript. Drs Sinha and Shen, and L. Farrell designed and performed the aptamer-based proteomic scan experiments. Dr Keyes, under the direction of Dr Larson, performed statistical analyses. Dr Kuhn, Dr Keshishian under the direction of Dr Carr designed, performed and analyzed the mass spectrometry–based proteomic experiments, and Dr Carr contributed to the writing of the manuscript. Drs Shi, Benson, and O’Sullivan contributed to data analyses and manuscript generation. Drs Fifer and Vasan contributed to experimental design. Dr Sabatine contributed to interpretation of data and manuscript revision. Dr Wang conceived the study, designed the experiments, and analyzed and interpreted the data. Dr Gerszten conceived the study, designed the experiments, analyzed and interpreted the data, and wrote the manuscript.


The online-only Data Supplement is available with this article at

Sources of Funding, see page 283

Circulation is available at

Correspondence to: Robert E. Gerszten, MD, Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, 330 Brookline Ave, Boston, MA 02115. E-mail ; or Steven A. Carr, PhD, Broad Institute of Harvard and MIT, 415 Main St, Cambridge, MA 02142. E-mail


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Clinical Perspective

What Is New?

  • Proteomic technologies hold considerable promise for biomarker and pathway discovery in cardiovascular disease. However, existing techniques have significant drawbacks, including variable reproducibility and low sample throughput, which together have hindered proteomic analyses of large numbers of human samples.

  • Here we applied a novel technology that uses single-stranded DNA aptamers to measure >1100 proteins in a single blood sample.

What Are the Clinical Implications?

  • We found very early markers of myocardial injury in a robust, clinically relevant model of planned myocardial injury patients undergoing septal ablation for hypertrophic cardiomyopathy; we then tested whether the platform could detect novel associations between proteins and cardiovascular risk factors in individuals without overt cardiovascular disease using archived samples from the Framingham Heart Study.

  • Our results highlight an emerging proteomics tool that captures a large number of low-abundance analytes with high sensitivity and high precision, and we provide important proof-of-principle for clinical applications.


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