ABSTRACT Arterial thrombosis is a leading cause of death and disability worldwide with no effective bioassay for clinical prediction. As a symbolic feature of arterial thrombosis, severe

stenosis in the blood vessel creates a high-shear, high-gradient flow environment that facilitates platelet aggregation towards vessel occlusion. Here, we present a thrombus profiling assay

that monitors the multi-dimensional attributes of thrombi forming in such biomechanical conditions. Using this assay, we demonstrate that different receptor–ligand interactions contribute

distinctively to the composition and activation status of the thrombus. Our investigation into hypertensive and older individuals reveals intensified biomechanical thrombogenesis and

personalized anti-thrombotic drug selection that accommodates each patient’s pathological profile. SIMILAR CONTENT BEING VIEWED BY OTHERS BIORHEOLOGY OF OCCLUSIVE THROMBI FORMATION UNDER

HIGH SHEAR: _IN VITRO_ GROWTH AND SHRINKAGE Article Open access 29 October 2020 PLATELET-PRIMED INTERACTIONS OF COAGULATION AND ANTICOAGULATION PATHWAYS IN FLOW-DEPENDENT THROMBUS FORMATION

Article Open access 17 July 2020 AUTOANTIBODIES IMMUNO-MECHANICALLY MODULATE PLATELET CONTRACTILE FORCE AND BLEEDING RISK Article Open access 25 November 2024 INTRODUCTION Arterial

thrombosis, which describes the formation of pathological blood clots in the artery, is one of the leading causes of mortality and morbidity worldwide1,2. Pathological conditions such as

hypertension, diabetes, metabolic syndrome, and aging not only increase thrombotic risks but also foster resistance to conventional antiplatelets that target soluble agonists (e,g., ADP,

thrombin)-induced platelet activation and aggregation3,4,5,6, contributing to high incidence and recurrence rates of cardiovascular diseases (CVD)7. However, the associated mechanisms are

not fully elucidated. The current clinical paradigm is further challenged by the lack of a standard bioassay for evaluating thrombotic risks: while conventional coagulation assays and

aggregometry assays were indicated to be unreliable in predicting thrombosis or major adverse cardiovascular events, the new generation of hematological function assays (e.g., global

coagulation assays and seer sonorheometry) also have limited evidence supporting their performance, and contain major drawbacks such as high cost, low sensitivity, and lack of

standardization8,9. As an understudied but symbolic mechanism of arterial thrombosis, discoid platelets can be mechanically driven by the elevated shear stress and shear gradient caused by

vessel stenosis to form large aggregates5,10—a phenomenon we termed “biomechanical platelet aggregation”11,12. The biomechanical platelet aggregation process is composed of two steps, mainly

involving three molecular interactions. Firstly, shear-induced von Willebrand factor (VWF) activation13 and glycoprotein (GP) Ibα (GPIbα)–VWF catch bond14 together facilitate GPIbα–VWF

binding under force, which initiates the aggregation of platelets in an activation-independent manner. Then, the GPIbα–VWF binding under force triggers GPIbα mechanosignaling that activates

integrin αIIbβ3 to reach an intermediate affinity and an extended-close (E+Act.-) conformation12, which subsequentially binds to fibrinogen (Fg) and VWF to allow more stable thrombus

development. Notably, E+Act.- integrin αIIbβ3 is only achievable via GPIbα mechanosignaling but not soluble agonist-induced platelet activation12. Biomechanical platelet aggregation cannot

be effectively inhibited by conventional antiplatelets or amplification loop blockers (ALBs), but is strongly impeded by shear rate decrease and reversible upon the release of vessel

stenosis10,12. Unfortunately, platelet mechanobiology was barely investigated in pathological contexts15,16. It remains unclear whether biomechanical platelet aggregation is intensified by

any thrombotic risk factor and contributes to a higher incidence of CVD in certain human populations. Also, existing methods for observing biomechanical platelet aggregation10,12,17 cannot

provide all-around information and quantitative analysis regarding the composition of the thrombus and the activation status of platelets within, which hinders our understanding of arterial

thrombosis and improvement of anti-thrombotic treatment. For example, it remains elusive how the three molecular interactions mediating platelet crosslinking in biomechanical platelet

aggregation, i.e., GPIbα–VWF, integrin αIIbβ3–VWF and integrin αIIbβ3–Fg10,12,18, respectively mediate the VWF and Fg levels and platelet activation in the biomechanical thrombus, and

whether they are dysregulated by thrombotic risk factors to cause abnormal biomechanical thrombogenesis. To address these outstanding clinical and scientific needs, we develop a thrombus

profiling assay that combines a standard stenosis microfluidics setup with multi-color thrombus staining. It enables quick and all-around thrombus characterization under conditions that

mimic the biorheological settings of arterial thrombosis. Using this assay, we delineated the differential roles of complex platelet crosslinking mechanisms in biomechanical thrombogenesis.

We identified exacerbated biomechanical thrombogenesis and multi-dimensional thrombus abnormality associated with hypertension and aging, unraveling a clinical linkage between mechanobiology

and arterial thrombosis. With complementary data from other experimental approaches, we further demonstrated that GPIbα and integrin αIIbβ3 receptors on hypertension patients’ platelets

have endogenous hyperactivity. By using the thrombus profiling assay to study drug–disease interactions and acquire personal thrombus profiles, we identified a gap in standard approaches of

anti-thrombotics evaluation, which urges a re-evaluation of the efficacy and safety of anti-thrombotics using the “thrombus profile” and in the context of different pathology models. All the

above results also showcase the potential of our thrombus profiling assay for anti-thrombotic drug screening, diagnosis of thrombotic risks, and personalized anti-thrombotic regimen

selection. RESULTS THROMBUS PROFILING ASSAY: DEVELOPMENT AND VALIDATION Our microfluidic chip is composed of ten rectangular (width × height: 200 μm × 50 μm) channels with respective inlets

and outlets for tubing connection (Fig. 1a, b). A pump drives a syringe to perfuse heparinized blood (0.5 mL) through the channel pre-coated with VWF. An 80% stenosis site stimulates

biomechanical thrombogenesis (Fig. 1c). A perfusion rate of 18 µL/min was selected, which creates a wall shear stress (WSS) of 857 dyn/cm² at the stenosis site according to fluid dynamics

simulation (Fig. 1d and Supp. Fig. 1a, f). The same stenosis site WSS is achieved by an inlet wall shear rate of 1485 s−1 in a circular vessel with the same cross-sectional area (Supp. Fig. 

1b, d, f), mimicking human arterioles19, human arteries during systole, and mouse arteries20. The calculated Reynolds numbers in channels of different shapes are within the same scale (Supp.

Fig. 1f). With the above settings, platelet thrombi can be consistently observed within the channel, which is primarily driven by shear force because no external agonist is added to the

blood and the high-speed perfusion prevents the localized accumulation of agonists released from attached platelets and red blood cells. Due to the high-shear force, most thrombi have a

tendency to grow toward the downstream side of the stenosis. Nonetheless, most thrombi (>85%) cover the whole stenosis apex, and most (>85%) thrombi have the point in their contour

most close to the opposing channel wall positioned above the stenosis apex (Supp. Fig. 2), making the stenosis apex still the most likely position for occlusion. Replacing VWF with collagen

for channel coating did not significantly affect thrombus formation. However, with collagen coating, thrombus formation was basically eliminated by antibody RU5 which blocks plasma VWF

binding to collagen, reflecting an indispensable role of VWF on the hump for platelet attachment (Supp. Fig. 3a). Replacing heparin with citrate or ethylenediaminetetraacetic acid (EDTA) for

anticoagulation attenuated thrombus formation (Supp. Fig. 3a), because the latter two chelate calcium from the blood and inhibit platelet activation, while EDTA also eliminates integrin

αIIbβ3 activity. These results validate the use of VWF and heparin for channel coating and blood anticoagulation, respectively. To comprehensively characterize the thrombus, 7 biomarkers

with their respective molecular sensors were selected (Fig. 1e). Platelets are reported by SZ22, a monoclonal antibody (mAb) against integrin αIIbβ312. Fg is reported by purified Fg that

spikes the blood at 2% of plasma concentration. VWF is reported by non-inhibitory mAb 2.2.921. P-selectin is reported by AK4 to indicate platelet α-granule release22. Phosphatidylserine (PS)

exposure in the membrane is reported by Annexin V to signify platelet procoagulant function23. Conformationally extended (E+) and fully activated (Act.) integrin αIIbβ3 are detected by mAbs

MBC 370.2 and PAC-1, respectively, which together report integrin αIIbβ3 activation status12. The above sensors were grouped into two sets for fluorophore conjugation (Fig. 1e), where SZ22

appears in both sets for reference. All sensors have negligible influence on thrombogenesis (Supp. Fig. 3b–d). Fluorescent signals were observed from all seven biomarkers (Fig. 1f). Agreeing

with previous observations, real-time tracking showed rapid thrombogenesis in the first 300–400 s followed by a quasi-steady phase, in which the thrombus reaches a relative equilibrium

between platelet aggregation and disaggregation10,24 (Fig. 1g). Thus, we selected 450 s after the onset as the time point for quantitating fluorescent signals so as to assess the thrombus in

the fully developed status while avoiding unnecessary waiting (Fig. 1h). Signal intensities of Fg, VWF, P-selectin, PS, and E+ and Act. αIIbβ3 were normalized by platelet signal to assess

their enrichment (Fig. 1i), where the high E+ αIIbβ3 signal and low Act. αIIbβ3 signal agrees with our previous discovery that biomechanical platelet aggregation is mainly mediated by an

intermediate activation state of αIIbβ3 integrins12. P-selectin expression and low-level PS exposure observed here (Fig. 1f, i; further confirmed using different microscope setup, staining

agents, and microfluidic channel design (Supp. Fig. 4)) should be induced by GPIbα and/or integrin αIIbβ3 mechanosignaling25,26,27,28,29. The total signal intensity of platelets (first

dimension, indicating thrombus size) and the normalized signal intensities of Fg, VWF, P-selectin, PS, E+, and Act. αIIbβ3 (2nd–7th dimensions) are summarized into a seven-dimension thrombus

profile (Fig. 1f, i). Blood stored for >6 h or refrigerated overnight failed to generate visible thrombi (Fig. 1h), likely due to a loss of platelet activity during room temperature

storage and GPIbα shedding during cold storage30, respectively. Activated platelets can release and/or help produce soluble agonists such as thromboxane A2 and ADP to further activate

themselves and recruit surrounding platelets to the growing thrombus, wherein the activation signaling processes are called amplification loops. However, conventional antiplatelet aspirin

(targeting thromboxane A2 (TXA2)) and clopidogrel (targeting P2Y12–ADP interaction) rendered negligible inhibition to the biomechanical thrombogenesis both separately and combined at twice

or 20 times of human plasmatic concentrations31 (Fig. 1j). In contrast, both drugs can significantly inhibit ADP or collagen-induced platelet aggregation at much lower concentrations31.

Also, a platelet ALB cocktail (including apyrase, MRS2179 and 2-MeSAMP to block ADP, indomethacin to block TXA2, and hirudin to block thrombin, all at saturating concentrations) only reduced

the thrombus size by ~20% (Fig. 1j). These results corroborate the previous observations that inhibiting platelet amplification loops is ineffective in suppressing biomechanical platelet

aggregation10,12,32, endorsing a secondary role of soluble agonists in biomechanical thrombogenesis. DELINEATING THE CONTRIBUTION OF DIFFERENT RECEPTOR–LIGAND INTERACTIONS Biomechanical

platelet aggregation is mainly mediated by a mechanosensing axis on the platelet surface composed of two mechanoreceptors: GPIbα and integrin αIIbβ3. GPIbα first binds to VWF to initiate

platelet crosslinking, during which GPIbα mechanosignaling induces integrin αIIbβ3 intermediate activation (E+Act.-). The activated integrin αIIbβ3 binds to its ligands VWF and Fg to

reinforce the platelet crosslinking process and also trigger its further activation towards the fully activated state (E+Act.+)12. To investigate how the above platelet-crosslinking

mechanisms, namely, GPIbα–VWF, integrin αIIbβ3–VWF, and integrin αIIbβ3–Fg interactions, respectively mediate the growth, composition, and activation status of biomechanical thrombi, blood

was treated with a panel of highly specific inhibitory mAbs to inhibit the interactions one at a time (Fig. 2a). AK2 and NMC4 both inhibit GPIbα–VWF interaction, with AK2 targeting GPIbα,

and NMC4, previously shown to have anti-thrombotic effects33, targeting VWFA1 domain (VWFA1) which binds to GPIbα33,34. LJ-P5 and 152B6 both inhibit integrin αIIbβ3–VWF interaction, with

LJ-P5 blocking integrin αIIbβ3 binding to VWF but not Fg35, and 152B6 blocking VWF binding to integrin αIIbβ3 but not GPIbα36. 7E9, LJ-155B39, and LJ-134B29 inhibit integrin αIIbβ3–Fg

interaction by respectively blocking one of the three integrin-binding sites in Fg: γ408-411 (AGDV), Aα95-98 (RGDF), and Aα572-575 (RGDS)37,38. Single fluorescence imaging was first used to

measure the dose-dependency of the above mAbs in inhibiting thrombogenesis (Fig. 2b–e). Only AK2 and NMC4, but not the other mAbs, eliminated thrombogenesis (Fig. 2c–e), which agrees with

previous findings that GPIbα–VWF interaction serves as the initiator of biomechanical platelet aggregation10,12. At high concentrations, both LJ-P5 and 152B6 reduced the thrombus size to

<20% (Fig. 2d), and the cocktail of 7E9, LJ-155B39, and LJ-134B29 also reduced the thrombus size to ~5% (Fig. 2e), indicating comparable importance of integrin αIIbβ3–VWF and αIIbβ3–Fg

interactions. 7E9 alone achieved a strong inhibitory effect comparable to the cocktail, which corroborates the primary role of AGDV in Fg for integrin αIIbβ3 binding39,40. However, LJ-155B39

and LJ-134B29 also manifested considerable inhibition (Fig. 2e). Half-maximal inhibitory concentrations (IC50) were acquired for these mAbs via model fitting (Supp. Table 1), which were

then used in thrombus profiling. Both AK2 and NMC4 significantly decreased VWF, P-selectin, and E+ and Act. αIIbβ3 levels in the thrombus (Fig. 2f, g). In comparison, LJ-P5 and 152B6 only

reduced VWF enrichment, while 7E9, LJ-155B39, and LJ-134B29 only reduced Fg enrichment; neither set of mAbs inhibited PS exposure, P-selectin expression, or integrin αIIbβ3 activation (Fig. 

2h–l). None of the above mAbs affected the average signal intensity of SZ22-FITC, ruling out the possibility that the reduced VWF and Fg signals were due to increased platelet density (Supp.

Fig. 5a). Altogether, our results indicate that different platelet-crosslinking mechanisms cooperatively mediate biomechanical thrombogenesis, with each having a distinct focus in their

contribution to the thrombus composition and activation status. To succinctly express the effects of different factors on biomechanical platelet aggregation, we created an “effect barcode”

system with seven columns, each corresponding to one dimension of the thrombus profile. A positive, neutral, or negative effect of a factor on a dimension is respectively represented by a

bar at the top, middle, or bottom of the column, also numerically expressed as “+”, “0”, or “−”. Using this system, the effects of AK2 and NMC4 on the thrombus profile are both summarized as

[- 0 - - 0 - -], those of LJ-P5 and 152B6 as [- 0 - 0 0 0 0], and those of 7E9, LJ-155B39, and LJ-134B29 as [- - 0 0 0 0 0] (Fig. 2m). IDENTIFYING AN “ADDITION RULE” IN THE EFFECT BARCODE

SYSTEM Intrigued by how different receptor–ligand interactions synergize in mediating biomechanical thrombogenesis, we tested inhibitors with combinational effects. 7E3 (prototype of the

antiplatelet abciximab) and 10E5 are mAbs that block integrin αIIbβ3 binding to both Fg and VWF12,41,42. Unlike specific inhibitors of integrin αIIbβ3–VWF or αIIbβ3–Fg, both 7E3 and 10E5

eliminated thrombogenesis at high concentrations (Fig. 3a). At IC50, both mAbs reduced Fg and VWF levels in the thrombus without affecting platelet activation markers, rendering an effect

barcode of [- - - 0 0 0 0] (Fig. 3c, d). Interestingly, this barcode equals the add-up of those of integrin αIIbβ3–VWF ([- 0 - 0 0 0 0]) and αIIbβ3–Fg ([- - 0 0 0 0 0]) inhibitors (Fig. 3f).

Negatively charged nanoparticles inhibit platelet aggregation at high-shear rates due to their inhibition of VWF extension and, therefore, VWF–platelet interactions43. We tested two sizes

of polystyrene negatively charged nanoparticles (PS-CNP) (50 and 510 nm), both showing biphasic dose-dependency in thrombus inhibition (Fig. 3b), consistent with the original report43. A

concentration that decreases the thrombus size by ~50% was estimated for the 510-nm PS-CNP to perform thrombus profiling (Fig. 3e), which derived an effect barcode of [- 0 - - 0 - -]. Again,

this barcode equals the add-up of those of GPIbα–VWF ([- 0 - - 0 - -]) and integrin αIIbβ3–VWF ([- 0 - 0 0 0 0]) inhibitors (Fig. 3f). The above results demonstrate that the mathematical

addition rule applies to the effect barcode system. This addition rule will be further validated below in drug–disease interactions. MULTI-DIMENSIONAL THROMBUS PROFILE ABNORMALITY IN

HYPERTENSION AND AGING Aging and hypertension are strong risk factors for thrombosis44,45. To test the performance of our assay in identifying risks of arterial thrombosis, we first compared

the thrombus size of healthy adults at different ages and identified that older ages (≥50) significantly increase the thrombus size (Fig. 4a). Furthermore, we tested blood samples from

primary hypertension patients, which formed much larger biomechanical thrombi than healthy young subjects (Fig. 4b). By fitting the “thrombus size _versus_ time” curves with the sigmoidal

model, it was observed that unlike the growth of healthy young subjects’ thrombi which approached a plateau at ~400 s, hypertension patients’ thrombi remained in the rapid development phase

until ~500 s, again indicating a prothrombotic tendency (Fig. 4b). Characterizing the thrombus profile revealed that aging and hypertension, either alone or together, significantly increased

the thrombus size, Fg level as well as integrin αIIbβ3 activation in the thrombi, rendering the same effect barcode of [+ + 0 0 0 + +] (Fig. 4c). Two-way ANOVA with variance heterogeneity

identified a bi-directional cooperation between hypertension and aging in increasing the thrombus size and E+ αIIbβ3 level, indicating synergy between these two risk factors (Fig. 4d). None

of the above abnormalities was contributed by platelet density changes in the thrombus or hematocrit changes or platelet count increase in the blood (Supp. Fig. 5b–e). We then evaluated the

inter-correlation of the different biomarkers and their performance in distinguishing different cohorts. To address the scattering patterns of the signal intensities (Fig. 4c), which is

likely due to inter-individual variability, multiple statistical analyses were performed for cross-checking. Firstly, by using the linear regression model, Spearman rank correlation

coefficient46 and Kendall’s tau correlation coefficient47, a positive correlation was consistently identified between thrombus size and Fg, E+ αIIbβ3, and Act. αIIbβ3 levels but not the

other factors (Fig. 4e and Supp. Fig. 6), with E+ αIIbβ3 being the strongest correlating factor (Supp. Table 2). Secondly, among all markers, E+ αIIbβ3 has the best performance in separating

healthy young from hypertensive and/or older age groups (Fig. 4e and Supp. Fig. 6a–e), with specificity and sensitivity respectively reaching 86 and 85%, comparable to the performance of

thrombus size (Supp. Table 2). The consistency of E+ αIIbβ3 level with thrombus size in group separation also reached 81%. Altogether, these results unraveled intensified biomechanical

thrombogenesis and multi-dimensional thrombus profile abnormality associated with hypertension and aging, and suggest E+ αIIbβ3 as a potential biomarker for intensified biomechanical

thrombogenesis. Most hypertension patients enrolled in this study had their blood pressure well controlled by medication (systolic/diastolic <140/90 mmHg, respectively) and had hemoglobin

A1C (HbA1C), body mass index (BMI) and cholesterol levels within the healthy range (Fig. 4f and Supp. Table 3). Furthermore, neither the size nor the E+ αIIbβ3 level of these patients’

thrombi has a significant correlation with the disease duration, systolic or diastolic blood pressure, or the sum of the two, or the patients’ HbA1C level, BMI, total cholesterol,

low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), or triglyceride levels (Fig. 4f and Supp. Fig. 7a, b). Also, the thrombi of hypertensive subjects

who have systolic and diastolic blood pressures and HbA1C, BMI, and cholesterol levels all in the normal ranges still have larger sizes and higher E+ αIIbβ3 levels than healthy young

subjects, regardless of aging (Supp. Fig. 7c). These results indicate that hypertension can independently cause intensified biomechanical thrombogenesis and thrombus profile abnormality even

with relatively short disease duration and effective antihypertensive medication. Nonetheless, we cannot exclude the likelihood that poorly controlled blood pressure, diabetes (high HbA1C

level), obesity (high BMI) or dyslipidemia (abnormal cholesterol levels) can have extra contributions to the thrombus profile abnormality, especially considering that the latter three

diseases are known risk factors of CVD. Next, we inspected whether demographics other than age affect the thrombus profile. Within healthy young as well as hypertensive and/or older

subjects, no significant difference in the thrombus size or E+ αIIbβ3 level was found between males and females or among different races/ethnicities (Fig. 4g–l). Seemingly in discrepancy

with previous reports of a higher prevalence of CVD in males than in females and slight prevalence differences in different ancestries, these results corroborate more careful cohort studies

demonstrating that the correlation of gender and ancestry with thrombotic risks is mainly due to the differential prevalence of social determinants of health and cardiovascular risk

factors45,48,49. Due to size variations, different human arteries and arterioles have distinct Reynolds numbers (affecting flow patterns such as laminar versus turbulent) and shear rates in

the blood flow50,51, together resulting in a certain extent of diversification in the shear stress. However, changing the perfusion rate in our assay from 18 to 13.5, 27, and 36 μl/min

(respectively changing the shear stresses to 0.75, 1.5, and 2 times of the original) did not significantly affect the thrombus profiling outcome (Supp. Fig. 8), wherein significantly larger

thrombus size (Supp. Fig. 8a) and higher E+ and Act. αIIbβ3 levels (Supp. Fig. 8f, g), marginally higher Fg level (Supp. Fig. 8b) but comparable VWF, P-selectin, and PS levels (Supp. Fig. 

8c–e) were consistently observed in the thrombi of hypertensive young subjects than healthy young subjects. These results validated that our assay could assess the general shear-driven

platelet “aggregatability” of blood samples. HYPERTENSION CAUSES HYPERACTIVITY OF THE GPIBΑ-INTEGRIN ΑIIBΒ3 MECHANOSENSING AXIS We previously identified that the intermediate activation

state of αIIbβ3 integrin with an extended-close conformation (E+Act.−) plays a crucial role in biomechanical platelet aggregation12. Thus, the over-expressed E+ αIIbβ3, predominantly E+Act.−

αIIbβ3 in the thrombi of hypertensive patients (Fig. 4c and Supp. Fig. 6f) should directly contribute to their intensified biomechanical thrombogenesis. We hypothesize that the E+ αIIbβ3

over-expression is possibly due to (1) hyperactivity in GPIbα, with triggers stronger mechanosignaling for integrin activation12 and/or (2) integrin αIIbβ3 pre-activation in the patients. To

test these two hypotheses, we used four complementary approaches to investigate the activities of GPIbα and integrin αIIbβ3 in hypertension patients. Firstly, a conventional laminar flow

chamber assay was used to assess the overall ligand binding activity of the two receptors. Unlike the stenosis assay, here, the channels adopt a plain surface pre-coated with VWFA1 or Fg to

engage GPIbα and integrin αIIbβ3, respectively. Plasma in the blood was depleted and replaced with buffer to remove endogenous VWF and Fg and prevent platelet aggregation. By perfusing blood

through the channels under varied shear rates, it was found that platelets from hypertensive young, hypertensive older, and healthy older groups all achieved much higher surface coverage

and slower rolling on VWFA1 than the healthy young group (Fig. 5a–c and Supp. Fig. 9a, b). On the other hand, only hypertensive young and hypertensive older groups achieved high surface

coverage on Fg (Fig. 5a, d). These results indicate that both hypertension and aging cause GPIbα hyperactivity, but only hypertension induces hyperactivity in integrin αIIbβ3 at the same

time. Considering that the activities of GPIbα and integrin αIIbβ3 in hypertensive young and hypertensive older subjects were comparable (Fig. 5b–d), mechanistic studies below combined young

and older hypertensive subjects into a single cohort to compare with healthy young subjects. However, this does not exclude the possibility that aging can influence hypertensive patients’

GPIbα and integrin αIIbβ3 as a secondary factor, which shall be inspected in future studies. Secondly, a single-molecule force spectroscopy technique, biomembrane force probe (BFP)52, was

used to measure the ligand binding of single platelets. A micropipette-aspirated biotinylated human red blood cell (RBC) was used as an ultrasensitive force transducer, and a probe bead

co-functionalized with streptavidin and VWFA1 or Fg was glued to the RBC apex. A platelet was aspirated by an opposing micropipette and driven to repeatedly contact the bead, which induced

adhesion events to measure the receptor–ligand binding kinetics (Fig. 5e). Adhesion frequency assay was first deployed to enumerate the absence or presence of adhesion events after long

contacts to calculate the steady-state adhesion frequency, _P_a53. The _P_a of hypertensive subjects’ platelets adhering to the same batch of VWFA1 and Fg beads were significantly higher

than healthy young (Fig. 5f, i), reflecting a significantly higher effective avidity (ligand-binding capability of each unit of platelet surface area) of both GPIbα and integrin αIIbβ3 (Fig.

 5g, j, left), consistent with the platelets’ enhanced capability of engaging VWFA1 and Fg in the flow chamber (Fig. 5a–d). Dividing effective avidities by the receptors’ surface densities

showed that the average effective affinities of GPIbα and integrin αIIbβ3 on the hypertensive subjects’ platelets were also significantly enhanced (Fig. 5g, j, right). Then, the BFP

force-clamp assay was used to measure the stability of single GPIbα–VWFA1 and single integrin αIIbβ3–Fg bonds under force. This was achieved by adjusting the contact time between the bead

and the platelet to achieve _P_a ≈ 20%, thereby realizing a ~90% probability of single bonds53. The GPIbα–VWFA1 bond lifetime of hypertensive subjects’ platelets manifested a ‘slip bond’

instead of a triphasic ‘slip-catch-slip’ trend seen on healthy young subjects’ platelets54, resulting in a substantial prolongation of bond lifetimes under forces <20 pN (Fig. 5h). On the

other hand, hypertension caused a substantial rightward and upward shift of the integrin αIIbβ3–Fg catch bond12, so that the peak force increased from ~15 to ~35 pN, the peak lifetime

increased from ~5 to ~10 s, and the force range where lifetime events were observable was widened from 0-40 to 0-65 pN (Fig. 5k). Notably, this lifetime curve from hypertensive subjects’

platelets also resembles healthy young subjects’ E+Act.- integrin αIIbβ3–Fg lifetime curve characterized before12. Altogether, our BFP results indicate that hypertension increases not only

the avidity, but also the affinity and force-regulated ligand binding strength of platelet GPIbα and integrin αIIbβ3. Thirdly, we combined fluorescence imaging with BFP (fBFP) to study

whether the increased affinity and ligand binding strength of GPIbα in hypertension patients can result in stronger mechanosignaling to better induce integrin αIIbβ3 activation. Platelets

pre-loaded with a Ca2+ dye (Fura-2) were repeatedly stimulated by a VWFA1-coated bead in force-clamp cycles at a fixed 2-s contact time for 5 min (Fig. 5l), while the normalized

intraplatelet Ca2+ level was monitored (Fig. 5m). Agreeing with our hypothesis, hypertension patients’ platelets fluxed stronger Ca2+ signals—reflected by higher Ca2+ peak increase—than

healthy young subjects’ platelets under a wide force range (Fig. 5n). Unlike healthy young subjects’ platelets where the Ca2+ signal intensity first increases and then decreases as clamping

force increases, mirroring their lifetime’s ‘catch-slip’ trend, the Ca2+ signal intensity of hypertension patients’ platelets manifested a gradual decline, also consistent with the shape of

their GPIbα–VWFA1 lifetime slip bond (Fig. 5n). This corroborates our previous finding that the GPIbα mechanosignaling intensity, manifested by both Ca2+ flux and integrin αIIbβ3 activation,

heavily relies on the duration of force pulling on GPIbα55. Fourthly, flow cytometry was used to investigate whether the αIIbβ3 integrins on hypertension patients’ platelets are

pre-activated. While similar high expression of integrin αIIbβ3 and baseline expression of Act. αIIbβ3 and P-selectin were detected on the platelets of healthy young and hypertensive

subjects, the expression of E+ αIIbβ3 in the hypertensive group was much higher than in the healthy young group (Fig. 5o–s). Although hypertension patients’ platelets are slightly larger

than healthy young subjects’, a positive correlation between E+ αIIbβ3 signal and platelet volume was found only in the hypertensive group but not the healthy young group (Supp. Fig. 9c, d).

These results indicate that hypertension patients’ platelets are pre-activated, with integrin αIIbβ3 up-regulated to the intermediate activation state (E+Act.-) and minimal P-selectin

expression. Altogether, our results indicate that two mechanisms work in parallel to induce E+ αIIbβ3 over-expression in the biomechanical thrombi of hypertensive patients (Fig. 5t): (1)

some αIIbβ3 integrins already adopt a native E+ status rather than remaining inactive as on healthy platelets; and (2) hyperactive GPIbα triggers stronger mechanosignaling upon VWF binding,

inducing more αIIbβ3 integrins to undergo E+ activation than on healthy platelets. EXPANDING THE ADDITION RULE TO DRUG–DISEASE INTERACTIONS Using the thrombus profiling assay, we tested how

anti-thrombotic inhibitors affect the thrombus profile of hypertension patients. Consistent with our results on healthy subjects, the combination of aspirin and clopidogrel at twice their

human plasmatic concentrations in clinical practice31 showed no effect on hypertension patients’ thrombi (Fig. 6a). In contrast, at IC50, NMC4 reduced the thrombus size and E+ αIIbβ3 and

Act. αIIbβ3 expressions to healthy levels, but also lowered VWF and P-selectin levels that were unaffected by hypertension (Fig. 6a). The resulting effect barcode, [0 + - - 0 0 0], equals

the add-up of those of NMC4 and hypertension (Fig. 6b). Similarly, adding 7E3 to hypertension patients’ blood resulted in an effect barcode of [0 0 - 0 0 + +], equaling the add-up of the

effect barcodes of 7E3 and hypertension (Fig. 6a, b). These results indicate that the addition rule of the barcode system can also be applied to predict drug–disease interactions. Neither

NMC4 nor 7E3 completely corrected the effect barcode of hypertension, with 7E3 even incapable of suppressing the integrin αIIbβ3 over-activation, implying a treatment mismatch between the

inhibitors and the patients. INTER-INDIVIDUAL VARIABILITY IN PERSONAL THROMBUS BARCODES Lastly, to evaluate the normality and abnormalities of individuals’ thrombus profiles, we created the

concept of “personal thrombus barcodes”. From the thrombus profiles of healthy young subjects, values of each dimension were fitted to a Gaussian distribution, of which the mean ± 2 s.d.

(~95% confidence interval) was defined as the reference range (“0”) (Supp. Fig. 10), and values lower or higher were defined as abnormally low (“−”) and high (“+”), respectively (Fig. 6c).

Applying this system to healthy young subjects rendered all dimensions of thrombus profiles being dominated by normal values, with only very small fractions being abnormally low or high,

which is consistent with the definition of the reference ranges (Fig. 6d). In contrast, much larger fractions of healthy older, hypertensive young, and hypertensive older subjects had

abnormally large thrombi and high E+ and Act. αIIbβ3 levels, with moderately higher fractions also having abnormally high Fg levels (Fig. 6d). Most of these subjects (26/36) have abnormally

high values in thrombus size and E+ αIIbβ3 level, yet 3 subjects with abnormally large thrombi have a normal E+ αIIbβ3 level. An abnormally high Act. αIIbβ3 level was observed in half of the

subjects with large thrombi (14/29), but also in two subjects with normal-sized thrombi (Supp. Fig. 11). Most subjects in these three groups (23/36) have abnormal VWF, P-selectin, and PS

levels, which may or may not co-exist with abnormally high values of thrombus size and E+ αIIbβ3 level. Of all the 69 subjects, a total of 30 different personal thrombus barcodes were

identified (Supp. Fig. 11). Overall, the above results indicate strong inter-individual variability in the personal thrombus barcode that cannot be ascribed to only disease and aging, and

demonstrate obvious decoupling of the different dimensions in the thrombus profile. Notably, we repeated our test on 14 randomly picked subjects after different time intervals (from 2 weeks

to 9 months). Among a total of 21 re-tests, only two showed changes in the personal thrombus barcodes, which were associated with the longest time intervals (7 and 9 months, respectively)

(Supp. Fig. 11). This reflects the high reliability of our assay and indicates that the personal thrombus profiles of individuals are relatively stable but can still vary over time.

Inter-individual variability was also observed in the subjects’ responses to anti-thrombotic inhibitors. While NMC4 effectively corrected the size and E+ αIIbβ3 level of most hypertension

patients’ thrombi (Fig. 6d), it did not uniformly modify their personal thrombus barcodes, but instead produced three different barcodes in five patients’ blood samples (Fig. 6e). Similar

diversification was found in 7E3, despite its consistent negative effect on the thrombus size and neutral effect on E+ αIIbβ3 level (Fig. 6d, e). These diversifications cannot be completely

ascribed to differences in the patients’ original personal thrombus barcodes (Fig. 6e). DISCUSSION The methodology framework developed in this study includes not only the experimental setup

itself, but also the ‘thrombus profile’, the barcode systems, and the “addition rule” as conceptual elements. Unlike conventional laboratory and point-of-care assays, our thrombus profiling

assay mainly assesses biomechanical platelet aggregation. Because the high-shear, high-gradient blood flow associated with arterial thrombosis reinforces biomechanical platelet

aggregation10,32 and, at the same time, impedes soluble agonist-induced platelet aggregation and coagulation by limiting the local accumulation of soluble substances56, biomechanical

platelet aggregation should be one, and possibly the most, essential mechanism of arterial thrombosis. This rationalizes the outstanding performance of our thrombus profiling assay in

testing clinical subjects associated with higher risks of arterial thrombosis, demonstrating its potential to clinically assess thrombotic risks in general populations. In this context, the

detection of integrin αIIbβ3 over-activation and the identification of “treatment mismatch” further showcase the assay’s ability in identifying the mechanisms of prothrombotic tendency and

in evaluating prevention/treatment strategies. Meanwhile, cost-effectiveness and low sample volume represent additional advantages. Hardware upgrades, e.g., using a multi-channel syringe

pump and a motorized stage or a multi-camera array to reach relatively high throughput, and/or system automation, will enable the current setup to become more suitable for clinical practice.

To provide more accurate diagnosis and treatment suggestions, the assay can benefit from more detailed segmentation (e.g., borderline, stage-I, and stage-II abnormal) in judging normal

_versus_ abnormal thrombus barcodes, and can be combined with other existing diagnostic approaches, e.g., risk score assessment57,58. Notably, the assay also has the potential of evaluating

bleeding tendency in humans and the bleeding side effect of anti-thrombotic agents24, which warrants future investigation. As a limitation, our assay cannot recapitulate the biomechanical

scenarios of thrombosis in all different arteries and arterioles, especially in large stenotic arteries where the Reynolds number can reach sufficiently high to trigger turbulence50.

Nonetheless, by replicating critical aspects of thrombosis, the assay allows the evaluation of the general prothrombotic tendency of blood samples. Using a panel of mAbs with highly specific

targets, we showed that GPIbα–VWF, integrin αIIbβ3–VWF, and integrin αIIbβ3–Fg interactions all contribute to the size growth of biomechanical thrombi. However, suggesting a central role of

GPIbα–VWF interaction in biomechanical thrombogenesis, only its blockage but not the blockage of integrin αIIbβ3–VWF or integrin αIIbβ3–Fg interaction can eliminate thrombus formation.

Also, only blocking GPIbα–VWF interaction inhibits integrin αIIbβ3 activation while blocking integrin αIIbβ3–ligand interactions failed so, reflecting a primary role of GPIbα for integrin

αIIbβ3 activation in the GPIbα-integrin αIIbβ3 mechanosensing axis12. We showed that GPIbα–VWF and integrin αIIbβ3–VWF interactions both modulate the deposition of VWF into the thrombus,

while integrin αIIbβ3–Fg only modulates that of Fg, which seems intuitive because both VWF and Fg need to be bound to their respective platelet receptors to maintain their presence in the

thrombus. However, the fact that inhibiting either VWF or Fg binding to integrin αIIbβ3 does not enrich the other ligand, but both reduce the thrombus size, suggests that VWF and Fg

cooperate, rather than mutually compensate, in integrin αIIbβ3 crosslinking for biomechanical platelet aggregation. Our observation that inhibiting the two RGD sequences in Fg effectively

reduces the thrombus size contrasts with the previous report that mutating either of these two sequences did not impair Fg function in mediating ADP-induced platelet aggregation59. This is

likely because ADP activates integrin αIIbβ3 to the fully active state, while biomechanical platelet aggregation is mainly driven by intermediate state integrin αIIbβ312, so that the RGD

sequences in Fg are redundant in the former scenario for platelet crosslinking but become a useful supplement to the Fg AGDV sequences in the latter. This suggests mechanistic distinctions

when Fg mediates biomechanical _versus_ biochemical platelet aggregation and unravels an underestimated contribution of the Fg RGD sequences to arterial thrombosis. On the other hand,

previous works showed that when the shear rate increases, the dependency of shear-induced platelet aggregation on GPIbα and integrin αIIbβ3 becomes progressively stronger and weaker,

respectively60. It will be interesting to test whether changing the shear rate in our assay affects how the three receptor–ligand interactions contribute to the thrombus profile. Lastly, our

results appear to indicate that the effect barcode of each anti-thrombotic agent is dictated by its target rather than its pharmacological design. Moreover, the observed “addition rule”

suggests a lack of synergy or discord when multiple targets are concurrently inhibited, indicating that different molecular interactions and signaling pathways function in relatively

independent and parallel ways. These principles are potentially useful for drug screening, enabling us to quickly narrow down the possible target(s) of uncharacterized anti-thrombotic agents

using their effect barcode. To serve the above purpose, inhibitors of all other contributing factors of biomechanical platelet aggregation, e.g., mechanosignaling of GPIbα, integrin αIIbβ3,

and Piezo116,61,62, need to be tested to acquire their effect barcodes. Hypertension is the leading cause of CVD and is also closely associated with antiplatelet (e.g., aspirin and

clopidogrel) resistance3,4. Among multiple postulated mechanisms, abnormal platelet activation has been identified as a central contributor to the prothrombotic status of hypertension

patients, where changes in platelet morphology and biochemical activities (e.g., elevated sensitivity to soluble agonists, reduced sensitivity to exogenous nitric oxide) were reported63. In

comparison, we discover that GPIbα in hypertension patients are hyperactive and can induce stronger mechanosignaling, while a substantial amount of integrin αIIbβ3 molecules are already in

the E+ status, which together results in an over-expression of E+ integrin αIIbβ3 in the patients’ biomechanical thrombi. Considering the central roles of GPIbα and E+ integrin αIIbβ3 in

biomechanical platelet aggregation12, these results explain the intensified biomechanical thrombogenesis observed in hypertension patients’ blood, and suggest that GPIbα-integrin αIIbβ3

mechanosensing axis hyperactivity directly contributes to the high incidence rate of CVD in hypertension patients. On the other hand, antiplatelet resistance is conventionally believed to be

due to patients’ lack of sensitivity to antiplatelets in inhibiting platelet amplification loops64. However, we found that biomechanical thrombogenesis is essentially “immune” to aspirin

and clopidogrel in both healthy young subjects and hypertension patients (Supp. Fig. 12). These, together with similar observations by other works10,12,32, indicate a new mechanism of

antiplatelet resistance: biomechanical platelet aggregation can mediate arterial thrombosis independent of platelet amplification mechanisms, and therefore the sole inhibition of platelet

amplification loops allows thrombotic risks to persist by leaving biomechanical platelet aggregation active. Altogether, our results strongly advocate the development of GPIbα and/or

integrin αIIbβ3 targeting anti-thrombotic “mechanomedicines” that can work complementarily with conventional antiplatelets for enhanced treatment efficacy. The results also underscore the

pathophysiological relevance of E+-closed integrin αIIbβ3, which should inspire future investigations on the importance of the E+-closed conformation in other integrins and in the context of

other diseases. The causes of GPIbα and integrin αIIbβ3 hyperactivity in hypertension patients as well as the similar trend of platelet hyperreactivity in older people warrant further

investigation, which are possibly relevant to hypertension/aging-associated oxidative stress and inflammation that cause platelet pre-activation65,66,67, dysregulated glycosylation of GPIbα

and integrin αIIbβ3 by metabolic disorders68,69, and/or the activation of mechanosensitive ion channel Piezo1 that causes platelet hyper-sensitivity to shear force16. On the other hand, the

slightly higher Fg level in hypertensive and older subjects’ thrombi is likely due to the elevated Fg plasma concentration in these populations70,71 as well as integrin αIIbβ3 hyperactivity

that more efficiently recruits Fg. P-selectin and Act. αIIbβ3 are widely used markers of platelet activation, but their performance in diagnosing thrombosis is unsatisfactory due to low

sensitivity72. We show that E+ αIIbβ3 has a much better performance than P-selectin and Act. αIIbβ3 in correlating with the biomechanical thrombus size and in separating healthy young

subjects and subjects carrying thrombotic risk factors. Furthermore, only E+ αIIbβ3, but not P-selectin or Act. αIIbβ3, was detected on platelets freshly isolated from hypertension patients.

These results underscore the accuracy and sensitivity of E+ αIIbβ3 in detecting platelet hyperreactivity, suggesting its use as an independent biomarker for predicting arterial thrombosis

in certain populations. To validate this application requires an investigation on the correlation between native E+ αIIbβ3 expression (assessed by flow cytometry) and thrombus size (assessed

by thrombus profiling assay) in different patient cohorts. Over the past decades, a routine has formed to evaluate the efficacy of new anti-thrombotic strategies solely based on thrombus

size reduction and without considering inter-individual variability11,73. Our work demonstrates that thrombi possess multi-dimensional characteristics that can be orthogonal, which should be

summarized as a “profile” or a “barcode”. Because different individuals have differential personal thrombus barcodes, and different anti-thrombotics have differential effect barcodes, a

treatment mismatch can easily occur. Conceptually distinguished from antiplatelet resistance, a drug with treatment mismatch is still effective in reducing the thrombus size, but has limited

or undesired effects on changing the thrombus composition and/or activation status (Supp. Fig. 12). The life-threatening danger of treatment mismatch has been documented in multiple phase

III trials where conventional integrin αIIbβ3 antagonists (e.g., orbofiban), despite high potency in inhibiting soluble agonist-induced platelet aggregation (and also biomechanical platelet

aggregation as demonstrated in this work), paradoxically increased patient mortality by enhancing the risk of myocardial infarction74,75. It was later realized that the failure of these

drugs was associated with their effect of stimulating integrin αIIbβ3 activation76. Addressing this issue, a chemical principle was recently discovered to develop anti-thrombotic candidates

that lock integrin αIIbβ3 in the inactive state77. Developing diversified anti-thrombotics and determining their effect barcodes and their interactions with thrombosis-exacerbating factors

can help avoid treatment mismatch, in which the “addition rule” could be helpful for prediction. The inter-individual variability in drug efficacy further urges the personalized selection of

anti-thrombotics for treatment optimization. METHODS REAGENTS SZ22-FITC and P2-Alexa Fluor 488 (Beckman Coulter), Type I collagen, AK4-Alexa Fluor 647 and PAC-1-Alexa Fluor 647 (BioLegend),

AK2, HIP-8-Alexa Fluor 488, Annexin V-Pacific Blue, Annexin V-Alexa Fluor 488, heparin, DiOC6(3), and Alexa Fluor 405, 555, and 647 conjugation kits (Thermo Fisher Scientific), MBC 370.2

(Kerafast), fibrinogen (Innovative Research), NMC4, 2.2.9, LJ-P5, 152B6, LJ-155B39, LJ-134B29 and VWFA178 (MERU VasImmune), VWF monomer (Sino Biological), RU5 (Creative Biolabs), and PS-CNP

beads (Bangs Laboratories) were purchased. 7E9, 7E3, and 10E5 were gifts from Barry S. Coller (Rockefeller University). HUMAN SUBJECTS All procedures involving human subjects were approved

by the Institutional Review Board of the University of Texas Medical Branch (protocol number: 22-0015) and the University of Sydney (ethics reference number: 2023/582). Informed consent was

obtained from all subjects to allow the publishing of data acquired from their blood samples and their demographic information that is relevant to research while protecting their privacy.

All subjects were compensated for their participation. Number (_n_) and age (mean ± s.d.) of subjects who participated in the thrombus profiling assay: healthy young: _n_ = 33, age = 34.0 ± 

6.3; healthy older: _n_ = 14, age = 62.1 ± 8.9; hypertensive young: _n_ = 9, age = 36.1 ± 8.7; hypertensive older: _n_ = 13, age = 60.2 ± 9.4. All groups contained both male and female

subjects with multiple races and both non-Hispanic/Latino and Hispanic/Latino ethnicities. All hypertension patients were taking prescribed hypertension medications (e.g., prazosin,

amlodipine, and enalapril). Patients taking other medications or under treatment for other diseases within 2 weeks before the blood draw were excluded from this study. BLOOD COLLECTION,

RECONSTITUTION, AND PLATELET ISOLATION For whole blood stenosis assay, blood was slowly drawn from the vein of a volunteer into a syringe pre-loaded with heparin (20 U/mL). In some control

experiments, sodium citrate (4%) or EDTA (1.5 mg/ml) was used as the anticoagulant instead. For laminar flow chamber assay, BFP assays, and flow cytometry, blood was drawn into a syringe

pre-loaded with ACD buffer. Then blood reconstitution78 was performed for laminar flow chamber assay to deplete plasma and reach a hematocrit of 45% and platelet count of 20,000 µL−1. Or,

platelet isolation was performed for BFP assays and flow cytometry12, with platelets finally resuspended in modified Tyrode’s buffer (135 mM NaCl, 11.9 mM NaHCO3, 2.9 mM KCl, 0.42 mM

NaH2PO4, 10 mM Hepes, 5.5 mM dextrose, and pH 7.4). MICROFLUIDIC DEVICE PREPARATION Polydimethylsiloxane (PDMS) was applied on a silicon mold (1-μm resolution), which was heated at 75 °C for

1 h for curing, peeled off, and cut into single pieces. Holes were drilled to create outlets and inlets. The devices then underwent plasma treatment and were bonded to glass coverslips.

MICROFLUIDIC STENOSIS ASSAY Microfluidic channels were coated with VWF monomer (2 μg/mL) for 1 h. In some control experiments, the coating was done with 100 µg/ml collagen instead. Blood was

incubated with DiOC6(3) (5 μM) for 1 min, or with Sensor Set 1 (SZ22-FITC (0.5 μg/mL), Fg-Alexa Fluor 405 (60 μg/mL), 2.2.9-Alexa Fluor 555 (1 μg/mL) and AK4-Alexa Fluor 647 (1 μg/mL)), or

Set 2 (SZ22-FITC (0.5 μg/mL), Annexin V-Pacific Blue (1 μg/mL), MBC 370.2-Alexa Fluor 555 (1 μg/mL), and PAC-1-Alexa Fluor 647 (1 μg/mL)) for 10 min, and perfused through the channel.

Thrombus formation was observed using a Leica DM IL LED microscope (camera: Leica DFC360 FX; objective lens: air, 20×; acquisition software: LAS X). No bleed-through between fluorescence

channels was observed. Platelet autofluorescence was detected in 391-nm channel79, which was subtracted when calculating signals. Data analysis was performed using ImageJ 1.53 (Fiji,

National Institutes of Health). In some experiments, different concentrations of aspirin (with 15 µg/mL defined as 2×) and/or clopidogrel (with 6 µg/mL defined as 2×) or ALB cocktail (1 U/mL

apyrase, 100 mM MRS2179, 10 mM 2-MeSAMP, 10 μM indomethacin, 800 U mL−1 hirudin) were added into blood to inhibit platelet amplification loops. Hill equation was used to derive IC50 of

inhibitors: $${{{\rm{Residue}}}}\; {{{\rm{size}}}}=R+\left(100-R\right)/ \left(1+\left({{{\rm{IC}}}}50/C\right)^{{{\rm{HillSlope}}}}\right)$$ (1) wherein _C_ is the inhibitor concentration,

_R_ is the residue size when the effect of the inhibitor saturates, and HillSlope is a constant. For subjects who were tested multiple times, average values of these test results were used

for data presentation and statistical analyses. MICROFLUIDIC LAMINAR FLOW CHAMBER ASSAY Reconstituted blood added with DiOC6(3) (10 μM) was perfused at different shear rates over straight

channels pre-coated with VWFA1 or fibrinogen. After 5 min, fluorescent signals from platelets were recorded at 40 frame s−1. Data analysis was performed using ImageJ 1.53 (Fiji, National

Institutes of Health). BIOMEMBRANE FORCE PROBE (BFP) AND FLUORESCENCE BFP (FBFP) In a chamber filled with modified Tyrode’s buffer + 0.5% BSA (plus 1 mM Ca2+/Mg2+ when interrogating

platelets with Fg beads), a streptavidin-coated glass probe bead was glued to the apex of a biotinylated RBC, which is aspirated by a micropipette to form an ultrasensitive force probe52.

The probe bead was also coated with VWFA1 or Fg. On the opposing target side, a freshly isolated platelet was aspirated by a second micropipette, which was driven by a piezoelectric

translator (Physical Instrument) to repeatedly bring the platelet in and out of contact with the bead to form adhesion events. The bead was monitored under an inverted microscope (IX83,

Olympus) by a high-speed camera. A custom image analysis LabView (National Instrument) program tracks the bead position with 3 nm precision in real time. The BFP spring constant _k_ was

determined by the suction pressure inside the probe pipette and the geometric parameters of the force transducer assembly80. For adhesion frequency assay, the platelet was repeatedly brought

into contact with the probe bead for 2 seconds and retracted. Adhesion events were signified by the elongation of the RBC upon platelet retraction, which yielded a tensile force signal on

the bead. Adhesion and non-adhesion events in 30 cycles were enumerated to calculate adhesion frequency, _P_a. The effective avidity (_A_c_K_a_m_r) and affinity (_A_c_K_a) were derived by

the following equation53, $${P}_{a}=1-\exp \left\{{-{m}_{r}{m}_{l}A}_{c}{K}_{a}\right\}$$ (2) where _m_r and _m_l are the receptor and ligand surface densities derived from flow cytometry.

For the force-clamp assay, contact time was shortened until achieving infrequent (~20%) adhesion, which ensures that most (~90%) of the adhesion events are mediated by single receptor–ligand

bonds. Once an adhesion event was observed, the platelet would be held at a desired clamping force to wait for the bond to dissociate52. Lifetime was determined as the time from the instant

when the force reached the desired level to the instant of bond dissociation. The collected lifetimes were categorized into bins that cover successive force ranges. The average lifetime in

each force bin was calculated to plot the “lifetime vs. clamping force” curve. For fBFP, platelets were pre-loaded with Fura-2-AM and interrogated by VWFA1 beads with the force-clamp assay

mode, but the contact time was kept at 2 s. Ratiometric imaging with a light source that alternates between 340 nm (to excite Ca2+-engaged Fura-2) and 380 nm (to excite Ca2+-free Fura-2) was

used to measure the Ca2+ level in the aspirated platelet55. Matlab R2020b was used to analyze fluorescence images from fBFP experiments. Signal intensity from the 340-nm channel was divided

by that from the 380-nm channel and then normalized by the average value of the first 10 frames to derive the normalized Ca2+ level. FLOW CYTOMETRY ASSAY Platelet suspension was incubated

with 2 μg/mL of HIP-8-Alexa Fluor 488, MBC 370.2-Alexa Fluor 555, PAC-1-Alexa Fluor 647, or AK4-Alexa Fluor 647 for 10 min, diluted with Hepes-Tyrode buffer by ten times, and immediately

analyzed by flow cytometry. STATISTICAL ANALYSIS GraphPad Prism 10 was used for data plotting and statistical analysis. The statistical significance of the differences between the two groups

was determined by a two-sided Student's _t_-test or multiple t-test. For the test of drug effects, multiple _t_-test assuming paired experimental design was used. For multi-group

analysis, one-way or two-way ANOVA was used. When significant differences were shown, data was subjected to the Tukey test for multiple comparisons. A regression slope test was used to

assess whether the slope of a linear fitting is significantly non-zero. Spearman rank correlation coefficient46 and Kendall’s tau correlation coefficient47 were also used to test whether a

positive correlation exists between different readouts of the thrombus profile. _P_ values <0.05 were considered significant. REPORTING SUMMARY Further information on research design is

available in the Nature Portfolio Reporting Summary linked to this article. DATA AVAILABILITY All data supporting the findings of this study are available within the article and its

supplementary files. Source data of fluid dynamics simulation generated in this study have been deposited in the Harvard Dataverse repository under accession code

(https://doi.org/10.7910/DVN/D4SJIP). Source data of human blood sample experiments are protected and unavailable for public deposition or upon request in accordance with the signed consent

of study subjects. Any additional requests for information can be directed to, and will be fulfilled by, the corresponding authors. Source data are provided with this paper. REFERENCES *

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Article  ADS  PubMed  PubMed Central  CAS  Google Scholar  Download references ACKNOWLEDGEMENTS We thank B. S. Coller (Rockefeller University) for sharing precious reagents, F. Ola-Daniel,

Y. Wang (The University of Texas Medical Branch), and A. Dupuy and Y. C. Zhao (The University of Sydney) for technical support, and Z. M. Ruggeri (The Scripps Research Institute) for

providing valuable suggestions. This work was supported by the following funding sources: National Heart, Lung, and Blood Institute grant R00HL153678 (Y.C.). National Institute on Aging the

Claude D. Pepper Older Americans Independence Center Award #P30-AG024832 (Y.C.). UT System Rising STARs award (Y.C.). American Heart Association Postdoctoral Fellowship 20POST35080023

(Y.C.). National Institute of General Medical Sciences grant 1R01GM152812 (L.X.). National Science Foundation grants DMS-1953189, CCF-2007823, and DMS-2210775 (L.X.). MRFF Cardiovascular

Health Mission Grants APP2016165 and APP2023977 (L.A.J.). National Heart Foundation Future Leader Fellowship Level 2 (105863) (L.A.J.). Snow Medical Research Foundation Fellowship 2022SF176

(L.A.J.). AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Department of Biochemistry and Molecular Biology, The University of Texas Medical Branch, Galveston, TX, 77555, USA Misbahud Din, 

Souvik Paul, Sana Ullah, Rong-Guang Xu, Bari Chowdhury, Stephenie Rogers, Mariel Miller, Atreyee Biswas & Yunfeng Chen * Department of Pathology, The University of Texas Medical Branch,

Galveston, TX, 77555, USA Misbahud Din, Souvik Paul, Sana Ullah, Rong-Guang Xu, Bari Chowdhury, Mariel Miller, Atreyee Biswas, Christopher Zahner & Yunfeng Chen * Department of

Statistics, The Pennsylvania State University, University Park, Pennsylvania, PA, 16802, USA Haoyi Yang & Lingzhou Xue * Division of Thoracic Surgery, Brigham and Women’s Hospital,

Harvard Medical School, Boston, MA, 02115, USA Rong-Guang Xu & Zi Chen * School of Biomedical Engineering, The University of Sydney, Darlington, NSW, 2008, Australia Nurul Aisha Zainal

Abidin, Allan Sun, Yiyao Catherine Chen, Rui Gao & Lining Arnold Ju * Charles Perkins Centre, The University of Sydney, Camperdown, NSW, 2006, Australia Allan Sun & Lining Arnold Ju

* Heart Research Institute, Newtown, NSW, 2042, Australia Allan Sun & Lining Arnold Ju * The University of Sydney Nano Institute (Sydney Nano), The University of Sydney, Camperdown, NSW,

2006, Australia Allan Sun, Rui Gao & Lining Arnold Ju * Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA Fangyuan Zhou * School of

Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China Liang Hu * Department of Immunology, School of Medicine, UConn Health, Farmington, CT,

06030, USA Zhichao Fan * Department of Mechanical Engineering, The City University of New York - City College, New York, NY, 10031, USA Jing Fan * Department of Internal Medicine, The

University of Texas Medical Branch, Galveston, TX, 77555, USA Megan Berman Authors * Misbahud Din View author publications You can also search for this author inPubMed Google Scholar *

Souvik Paul View author publications You can also search for this author inPubMed Google Scholar * Sana Ullah View author publications You can also search for this author inPubMed Google

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publications You can also search for this author inPubMed Google Scholar * Stephenie Rogers View author publications You can also search for this author inPubMed Google Scholar * Mariel

Miller View author publications You can also search for this author inPubMed Google Scholar * Atreyee Biswas View author publications You can also search for this author inPubMed Google

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author inPubMed Google Scholar * Zi Chen View author publications You can also search for this author inPubMed Google Scholar * Megan Berman View author publications You can also search for

this author inPubMed Google Scholar * Lingzhou Xue View author publications You can also search for this author inPubMed Google Scholar * Lining Arnold Ju View author publications You can

also search for this author inPubMed Google Scholar * Yunfeng Chen View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS Methodology: M.D., S.P.,

R.-G.X., F.Z., Z.C., and Y.C. Experiments: M.D., S.P., S.U., B.C., N.A.Z.A., A.S., Y.C.C., R.G., S.R., A.B., and Y.C. Data analysis: M.D., S.P., S.U., B.C., N.A.Z.A., A.S., Y.C.C., S.R.,

A.B., H.Y., L.X., R.-G.X., F.Z., J.F., and Y.C. Donor recruitment and blood collection: M.D., S.P., S.U., A.B., and M.B. Funding acquisition: L.X., L.A.J., and Y.C. Supervision: L.A.J. and

Y.C. Writing: M.D., H.Y., R.-G.X., F.Z., N.A.Z.A., M.M., L.H., Z.F., C.Z., M.B., L.X., L.A.J., and Y.C. CORRESPONDING AUTHOR Correspondence to Yunfeng Chen. ETHICS DECLARATIONS COMPETING

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S. _et al._ Multi-parametric thrombus profiling microfluidics detects intensified biomechanical thrombogenesis associated with hypertension and aging. _Nat Commun_ 15, 9067 (2024).

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