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ACS Nano
ACS Nano
nn
ancac3
ACS Nano
1936-0851
1936-086X
American Chemical Society

39208278
10.1021/acsnano.4c05537
Article
Integrating Vascular Phenotypic and Proteomic Analysis in an Open Microfluidic Platform
Jung Sangmin †
https://orcid.org/0009-0007-8143-8432
Cheong Sunghun ‡
https://orcid.org/0009-0005-6512-7939
Lee Yoonho ‡
Lee Jungseub †
Lee Jihye §
Kwon Min-Seok §∥
Oh Young Sun †§
Kim Taewan ⊥
Ha Sungjae #
https://orcid.org/0000-0002-8622-1765
Kim Sung Jae ⊥∇○
https://orcid.org/0000-0002-6320-6829
Jo Dong Hyun ◆
Ko Jihoon *¶
https://orcid.org/0000-0003-0490-3592
Jeon Noo Li *†‡&●
† Department of Mechanical Engineering, Seoul National University, Seoul 08826, Republic of Korea
‡ Interdisciplinary Program in Bioengineering, Seoul National University, Seoul 08826, Republic of Korea
§ Target Link Therapeutics, Inc., Seoul 04545, Republic of Korea
∥ Department of Public Health Science, Graduate School of Public Health, Seoul National University, Seoul 08826, Republic of Korea
⊥ Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Republic of Korea
# ProvaLabs, Inc., Seoul 08826, Republic of Korea
∇ SOFT Foundry, Seoul National University, Seoul 08826, Republic of Korea
○ Inter-university Semiconductor Research Center, Seoul National University, Seoul 08826, Republic of Korea
◆ Department of Anatomy and Cell Biology, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
¶ Department of BioNano Technology, Gachon University, Seongnam-si, Gyeonggi-do 13120, Republic of Korea
& Institute of Advanced Machines and Design, Seoul National University, Seoul 08826, Republic of Korea
● Qureator, Inc., San Diego, California 92121, United States
* Email: koch@gachon.ac.kr.
* Email: njeon@snu.ac.kr.
29 08 2024
10 09 2024
18 36 2490924928
26 04 2024
20 08 2024
19 08 2024
© 2024 The Authors. Published by American Chemical Society
2024
The Authors
https://creativecommons.org/licenses/by-nc-nd/4.0/ Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/).

This research introduces a vascular phenotypic and proteomic analysis (VPT) platform designed to perform high-throughput experiments on vascular development. The VPT platform utilizes an open-channel configuration that facilitates angiogenesis by precise alignment of endothelial cells, allowing for a 3D morphological examination and protein analysis. We study the effects of antiangiogenic agents—bevacizumab, ramucirumab, cabozantinib, regorafenib, wortmannin, chloroquine, and paclitaxel—on cytoskeletal integrity and angiogenic sprouting, observing an approximately 50% reduction in sprouting at higher drug concentrations. Precise LC-MS/MS analyses reveal global protein expression changes in response to four of these drugs, providing insights into the signaling pathways related to the cell cycle, cytoskeleton, cellular senescence, and angiogenesis. Our findings emphasize the intricate relationship between cytoskeletal alterations and angiogenic responses, underlining the significance of integrating morphological and proteomic data for a comprehensive understanding of angiogenesis. The VPT platform not only advances our understanding of drug impacts on vascular biology but also offers a versatile tool for analyzing proteome and morphological features across various models beyond blood vessels.

microphysiological system
open microfluidics
angiogenesis
proteomics
image analysis
drug screening
Gachon University 10.13039/501100002631 GCU-202110350001 National Research Foundation of Korea 10.13039/501100003725 NRF-2022M3A9B6082 National Research Foundation of Korea 10.13039/501100003725 NRF-2021R1A3B1077481 document-id-old-9nn4c05537
document-id-new-14nn4c05537
ccc-price
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pmcAngiogenesis, the intricate process of forming new blood vessels from existing endothelial cells,1,2 is pivotal for various pathological conditions.3,4 It serves as an essential in vitro model for understanding vascular development and exploring a wide range of medical conditions. This focus on angiogenesis facilitates critical advances in developing targeted therapies aimed at controlling abnormal vascular growth.5−7 Through precise in vitro modeling, researchers can dissect the cellular and molecular dynamics that govern angiogenesis, offering a strategic pathway for identifying and evaluating potential modulatory compounds.

To investigate angiogenesis in depth, researchers utilize several in vitro techniques designed to reveal the cellular and molecular events that drive this complex process. Techniques such as the tube formation assay8 and spheroid sprouting assay,9−12 in conjunction with supportive matrices like Matrigel or collagen,13 facilitate detailed studies of endothelial cell behavior by simulating the extracellular environment. These methods allow for the observation of capillary-like structure formation,14,15 endothelial cell migration,16 and the initiation of vascular networks,8 closely mirroring physiological angiogenesis. Such studies aim to understand how endothelial cells navigate and shape their extracellular matrix, offering insights that are instrumental in the development of therapies targeting angiogenesis. However, these methods may not fully replicate the complex in vivo environment, potentially affecting the accuracy of the results related to cellular behavior and angiogenesis.

Over recent decades, the evolution of microphysiological systems (MPS) from basic 3D cell culture platforms to sophisticated, scalable, and potentially automated methodologies has been significant, driven by enhancements in productivity and mechanical design.17−19 Notably, the integration of 3D printing and injection molding has markedly increased the efficiency of device production and experimental reproducibility, promoting widespread adoption by end-users.20−24 These platforms excel in delivering image-driven data analysis, enabling the visual confirmation of model development from cellular constructs to complex tissue formations and beyond.25,26 Presently, the scientific community is expressing increasing interest in obtaining high-resolution data, particularly concerning protein expression within these model systems. This interest is part of a broader movement toward integrating phenotypic and proteomic data within the MPS framework. The objective extends beyond merely visualizing angiogenesis in real time; researchers aim to delineate and understand the specific protein expression associated with angiogenesis within newly formed vascular structures. This convergence of phenotypic and proteomic analysis within MPS highlights a pivotal shift toward a more integrated and comprehensive understanding of biological processes at a molecular level.

We introduce the vascular phenotypic and proteomic analysis-integrated (VPT) platform, an innovative device for angiogenesis research that cultures endothelial layers within an open microfluidic environment. The VPT platform excels in analyzing angiogenesis inhibitors, reproducing 3D vascular structures, and facilitating reversible protein expression assays. It has been instrumental in identifying how various drugs affect the cytoskeletal integrity and angiogenic sprouting. By supporting proteomics and phenotypic screening, the VPT platform provides insights into the cellular and molecular aspects of angiogenesis, offering additional metrics for understanding angiogenesis phenomena and their implications for health and disease.

Results and Discussion

Open Microfluidic Cells and Hydrogel Pattern Design

The VPT platform represents an advancement in creating 3D cellular models by forming stable cell layers on hydrogel interfaces, addressing the limitations of traditional 2D monolayers.27,28 It enables the assembly of 3D cell structures for detailed cellular assays, focusing on uniform cell layers at the hydrogel boundary. The platform is designed for reproducibility, efficiency, and high-throughput compatibility with existing assay standards.

Investigations into 3D patterning with cells and hydrogels have shown the potential and challenges of various methods. For instance, PDMS microfluidic chips, utilizing micropillar arrays,29−32 demonstrated 3D cell placement based on surface tension.33 However, their application in assays was hindered by low throughput in fabrication and experimentation, as well as user-dependent reproducibility.34 Alternatively, the injection molding enables mass production with consistent quality, and open microfluidics using spontaneous capillary flow (SCF)20 for cell patterning demonstrates greater reliability.23,35,36 The VPT platform uses this SCF to organize hydrogels and cells within its channels, providing a reliable method for forming cellular barriers.

Efficient design of the VPT platform incorporates 16 samples into a slide glass format, ensuring compatibility with standard assay formats and facilitating high-throughput studies (Figure 1a). Each sample includes a center channel (channel C), a side channel (channel S), and an open channel (channel O), complemented by two separate media reservoirs. This upper body is completed by attaching a single-sided pressure-sensitive adhesive (PSA) film at the bottom side (Figure 1b). The development of robust, multilayered cellular structures on the hydrogel interface within the VPT platform involves a three-stage process. Initially, the hydrogel is applied to channel C to delineate channels S and O as separate entities. Subsequently, channels S and O are patterned in sequence, with channel O receiving targeted patterning through a designated hole (Figure 1c). The dimensions of channels C–O are adjusted, ensuring a reliable and repeatable experimental framework. By leveraging SCF through the guide structure in channels C and S (Figure 1d), the system achieves accurate fluid patterning along the channel structures (Figure 1e), optimizing the setup for detailed studies.

Figure 1 Overview of the VPT platform design and fluid patterning. (a) Photograph of the VPT platform. Scale bar = 10 mm. (b) Details of the chip preparation, which is completed by attaching the transparent substrate to the bottom surface of the injection-molded body. The VPT platform has three channels for fluid patterning: an open channel (O), a center channel (C), and a side channel (S). (c) 3D view of the detailed patterning sequence; channel C is patterned first followed by channel S and O. Two media reservoirs are filled with a cell culture medium as the last step. (d) Fluid patterning is achieved by spontaneous capillary force (SCF) along a guide structure within the VPT platform. A representative diagram illustrates the process of fluid filling each channel according to the patterning steps. (e) Photograph depicting fluid patterning of three channels within a single well. Green, red, and blue dyes visualize channels O, C, and S, respectively.

Achieving a Highly Reproducible and Uniform Cell Monolayer with the VPT Platform

The VPT platform leverages SCF to pattern hydrogels, resulting in a concave meniscus with a minimal contact angle due to the hydrogel’s hydrophilic nature.20 This geometry aids in the establishment of a cell monolayer at the hydrogel interface. For effective coverage, it is crucial for cells to be adequately proximate to the hydrogel interface to proliferate and cover it swiftly. Traditional methods, which involve tilting the device to use gravity for cell attachment, often fail to uniformly populate the surface, requiring over 30 min and potentially reducing experimental efficiency.37

To address these challenges, we engineered features within the media reservoirs to create an adjacent open space by the hydrogel interface in channel C, aligning the widths of channels C, S, and O. This design eliminates the need for tilting, allowing cells to naturally settle and proliferate into a monolayer with fewer cells, thus enhancing the method’s reliability and eliminating the 30 min tilting step (Figure 2a). Moreover, injecting a cell suspension into channel O without introducing air bubbles, which could hinder cell layer formation, was achieved by designing a bubble-free circulation structure.

Figure 2 Endothelial cell monolayer formation in the VPT platform. (a) Advanced method for cell layer formation attributed to the presence of an open channel. Cells can form a monolayer through proliferation as the open channel allows direct cell deposition near the hydrogel interface. (b,c) Isometric and top views of a confocal image showing the endothelial cell monolayer within the VPT platform. Blue staining indicates cell nuclei, while green staining delineates the tight junctions of the endothelial cells. Scale bar = 200 μm. (d) Quantitative assessment of the barrier function of the enhanced layer over time, utilizing 10 and 70 kDa FITC-dextran. (e) Daily increasing transendothelial electrical resistance (TEER) value with the EC monolayer formation. *p < 0.05, ****p < 0.0001, and n.s., not significant, Student t-tests. Data are means ± SEM.

Barrier function assessment involved culturing ECs within the VPT platform to form an EC monolayer. Acellular fibrinogen hydrogel was patterned in channel C, with ECs introduced into channel O, leaving channel S empty. Fluorescence imaging verified the monolayer’s presence on the hydrogel and the bottom surface (Figure 2b and Figure S1a,b), showing well-developed tight junctions (Figure 2c). In contrast, the traditional method resulted in a clumped EC layer (Figure S1c). Permeability tests with FITC-dextran of different molecular weights (10 and 70 kDa) over 2 days indicated selective substance blocking by the EC monolayer, aligning with previous findings (Figure 2d).38,39 Transendothelial electrical resistance (TEER) measurements further validated the barrier’s integrity and uniform coverage across the hydrogel interface, with day 2 showing significantly higher TEER in the monolayer setup, indicating a more effective and uniform cell layer formation compared to previous approaches (Figure S2 and Figure 2e). These settings ensure a baseline for precise quantitative analysis and promote uniform cell distribution and barrier function (Figure S1d,f), a significant improvement over traditional methods, which yield approximately 30% with the regular baseline (Figure S1e,f).

High-Throughput Angiogenesis Assay with High Interexperimental Reproducibility

Utilizing the VPT platform, we have expanded the potential for scalable 3D angiogenesis studies, facilitating endothelium formation that captures the dynamic interaction between ECs and fibroblasts essential for mimicking the complex signaling pathways of vascular development. The platform supports the exchange of a broad spectrum of proteins and growth factors, which are crucial for initiating angiogenic processes beyond the simple VEGF pathway.

The spatial configuration of the VPT platform allows ECs and fibroblasts to be cultured within adjacent but separate microchannels (Figure 3a), mirroring in vivo paracrine signaling conditions closely. Fibroblasts act as sources for angiogenic factors like VEGF, fibroblast growth factor (FGF), platelet-derived growth factor (PDGF), and angiopoietins, crucial for EC proliferation, migration, and tubular structure development.40 This setup enables detailed studies of how different concentrations and combinations of angiogenic factors influence vascular network formation, reflecting the gradient-driven nature of angiogenic signaling found in biological tissues. By adjusting media reservoir levels, growth factors from fibroblasts are delivered from channel S to channel O in a gradient, with the opposing flow direction further enhancing mechanical stimulation to promote angiogenic sprout growth, as evidenced by previous studies.41,42 Following the establishment of an EC monolayer, a culture period of 4–5 days with live imaging allows for the monitoring of progressive growth (Figure S3), with confocal fluorescence microscopy showing the formation of tight junctions and the dynamics of tip and stalk cells (Figure 3b). Vascular lumenization is validated by microbead flow through perfusable sprouts (Figure 3c).

Figure 3 Angiogenesis assay using fibroblasts and HUVECs in the VPT platform. (a) Cell placement and flow direction within the platform for fibroblast-induced angiogenesis modeling. An acellular gel is placed in channel C, and fibroblasts and endothelial cells are cultured in channels S and O, respectively. A flow is directed from channel S to channel O to facilitate the transfer of growth factors from fibroblasts to endothelial cells, promoting angiogenesis. (b) Confocal images of angiogenic sprouts. Magnified images highlight the nuclei (blue), endothelial cells (green), tight junctions (ZO-1, red), and DLL-4 (magenta). Scale bar = 300 μm. (c) Microspheres with red fluorescence went into the perfusable sprouts. (d) Algorithm-based strategies for quantitative analysis of the angiogenic sprout length, number, and thickness within the VPT platform. (e) 3D scatter plot of relative variation among the sprout length, number, and thickness across 6 individual chips. Sprouts showed low morphological variation among 96 samples, with 16 samples within a chip.

Quantitative Morphological Analysis of Vascular Sprouts

Integrating the strengths of the VPT platform with advanced quantitative analysis significantly enhances our ability to rapidly produce and analyze a large number of barrier samples. To enhance our analysis system’s efficiency, we merged it with a high-throughput, quantitative analysis-optimized platform capable of handling large sample sets. Traditional image analysis methods from 3D cell culture models often depend on manual preprocessing and measurements using tools like ImageJ,43−45 a process that is time-consuming and subject to variability. To address these issues, we implemented a Python algorithm leveraging OpenCV to systematically quantify the morphological features of vascular sprouts developed by using the VPT platform. Our enhanced image analysis process includes converting confocal images of fluorescently stained ECs to grayscale, applying noise reduction, and filling voids using Gaussian blur and thresholding methods. Skeletonization then simplifies regions to lines, identifying endpoints and intersections (Figure S4), facilitating the subsequent steps of baseline setting and the calculation of critical parameters such as the sprout length, number, and thickness. This setting enables high-throughput variable interpretation and real-time quantitative analysis, a precise, easy-to-use approach to analysis that is no longer limited to software like ImageJ.

While quantitative image analysis is instrumental in delineating morphological characteristics through algorithms, it traditionally depends on z-stacked projection fluorescence images, which do not capture height information, thus hindering precise sprout classification. Even after adjustments to preserve original contours, modifications from Gaussian blur can obscure exact outlines, affecting the accuracy of measurements related to sprout dimensions. To mitigate these limitations and enhance measurement precision, we considered the inclusion of 3D or height-dimensional images.46 Our methodology involves placing acellular hydrogel in the channel C to accentuate angiogenic sprouts more clearly and defining morphological parameters with greater specificity (Figure 3d): The number of sprouts is calculated by counting the tips, or endpoints, on the skeletonized image. The length of each sprout is measured from these endpoints to the established baseline along the skeleton line. Thickness is assessed by measuring the distance across lines perpendicular to the sprout’s skeleton, marked at regular intervals on the fluorescence image.

Employing this enhanced assay system, we rigorously tested the capacity of the VPT platform for a robust and reproducible 3D angiogenesis assay. By analyzing defined vascular metrics across six chips (comprising 96 samples) in a three-dimensional manner, we were able to measure the consistency of results between experiments effectively (Figure 3e). The data, normalized against chip 1, confirmed that sprout formation was consistent and reproducible across all chips, underscoring the platform’s reliability for 3D angiogenesis studies (Figure S5). This methodology not only overcomes previous limitations but also sets a standard for precision and reproducibility in the study of angiogenic processes.

Distinctive Angiogenic Patterns Revealed by the VPT Platform across Various Angiogenic Inhibitors

Leveraging the VPT platform features, we embarked on a detailed evaluation of drug impacts on angiogenesis, with a focus on agents affecting the VEGF/VEGFR signaling pathway,47−50 autophagy,51−53 and cell growth.54,55 We administered these drugs at three concentrations, chosen based on prior research56−59 and their relevance in vivo,60−66 to discern pronounced morphological differences within our models (Figure 4a and Table 1). These concentrations were optimized for ensuring cell viability during the critical phase of tube formation.67 To clearly observe the primary mechanisms of drugs that inhibit new vessel formation, we applied the treatments at the initiation of sprouting and closely monitored their efficacy over a 48 h period.

Figure 4 Morphological response of angiogenic inhibitors in the VPT platform. (a) Experimental timeline and mechanisms of drugs used for evaluating drug responses in the angiogenesis model within the platform. After the EC monolayer formation on day 2, drugs were treated on day 3 when the sprout length reached approximately 20% of the channel C, and the drug responses were observed over a 48 h period. Seven drugs, targeting the inhibition of VEGF, VEGFR, autophagy, and proliferation, were used. (b) Representative confocal images showing dose-dependent phenotypic differences under antiangiogenic conditions. Scale bar = 500 μm. The dashed line represents the average sprout length for the control group. (c) Graphs of algorithm-based quantitative analyses for the sprout length and number across different drug concentrations. The analyses revealed a trend of reduction in both the sprout length and number as the drug concentration increased from low to high. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, and n.s., not significant, Student t-tests. Data are means ± SEM.

Table 1 Drug Concentrations Used In Vivo and In Vitroa

 	bevacizumab	ramucirumab	cabozantinib	regorafenib	wortmannin	chloroquine	paclitaxel	
2D culture	0.5 mg/mL148	6.6 μg/mL149	50 μM150	0.5 μM151	 	32 μM152	22 μM153	
3D chip culture	0.01–10 mg/mL	0.1–100 μM	0.01–1 μM	0.1–10 μM	0.03–10 μM	3–25 μM	0.5–50 μM	
dose (clinical trials, mg/kg)	2.5–1560,61	8–1062	2.625–7.87563,64	1.71–2.2965	 	 	3.66–4.7566	
working concentration at plasma	0.036–0.214 mg/mL	0.2–20 μM	0.747–2.243 μM	5.18 μM	 	 	0.613–0.795 μM	
in vitro concentration	0.01–10 mg/mL	0.2–20 μM	0.01–1 μM	0.1–10 μM	0.5–50 μM	1–100 μM	0.5–50 μM	
a Average blood volume per weight = 70 mL/kg.154 Average body surface area: 1.81 m2.155

We examined angiogenic sprouts under various conditions. Our findings revealed a dose-responsive reduction in sprout length for the drug-treated groups compared to that of the controls (Figure 4b). Using the analysis algorithm, we quantitatively measured changes in vascular metrics for a direct comparison of drug effects, normalizing the data against controls to highlight the drugs’ significant morphological impacts. For example, even at the lowest concentrations, the sprout length was generally reduced by about 10%, with certain drugs like ramucirumab, regorafenib, and wortmannin demonstrating more substantial effects. Further analysis unveiled dose-dependent outcomes for sprout numbers, with notable variations across the different drugs. Inhibitors targeting VEGF and VEGFR, such as bevacizumab, ramucirumab, cabozantinib, and regorafenib, showed a reduction in sprout numbers by 20–50% at various concentrations. Conversely, autophagy inhibitors and paclitaxel, a proliferation inhibitor, exhibited distinct patterns of sprout number reduction, with paclitaxel showing the most marked decrease across all tested concentrations (Figure 4c). Sprout thickness generally increased at higher concentrations, likely due to impaired cell migration and resultant thicker baselines,68 except for wortmannin, which resulted in thinner sprouting (Figure S6). We also examined the morphological changes in sprout tip cells across different drug concentrations, observing a stark contrast in the presence of filopodia at the control and low concentrations compared to the middle and high concentrations. (Figure S7).

Using real-time imaging, we observed that high concentrations of bevacizumab, ramucirumab, and paclitaxel each led to an approximately 50% reduction in sprout length within 48 h, alongside distinct morphological changes. Moreover, when comparing the effects of monoclonal antibodies combined with chemotherapeutics versus monotherapy, the combination treatments showed significantly enhanced efficacy over the same period (Figure S3). This observation aligns with existing studies, underscoring the superior effectiveness of combination therapies compared to single-drug treatments.

Investigating Drug Mechanisms via Protein Expression Analysis in the VPT Platform

Our study advanced traditional in vitro analyses by adopting a comprehensive approach to examining drug effects at the molecular level. Prior research primarily relied on assessing the morphological changes through fluorescence imaging, a method proficient in visualizing cellular responses but limited in its ability to decipher complex intracellular responses.69−71 Such morphological insights are invaluable in understanding drug impacts on a molecular scale and necessitate examination of shifts in protein expression. Moreover, we aimed to understand the interactions among cellular proteins by observing the overall changes in protein within the cells using LC-MS/MS, moving beyond observing specific protein expression through Western blotting or ELISA. This global pattern recognition enables not only the identification of specific cellular responses expected from known mechanisms but also the unveiling of changes in mechanisms not previously identified, facilitating a comprehensive biological approach.

We analyzed the effects of seven drugs, categorized into four distinct mechanisms of action (MOA) on protein expression changes using our platform (Figure 4a). Our focus was on representative drugs from each category: bevacizumab(a VEGF-neutralizing monoclonal antibody), cabozantinib (a VEGFR-targeting small-molecule inhibitor), wortmannin (a PI3K inhibitor), and paclitaxel (a microtubule-stabilizing chemotherapeutic agent). To quantify proteins within the VPT platform, we employed nattokinase to dissolve the hydrogel and isolate cells followed by cell lysis for protein extraction (Figure 5a).72 Subsequent LC-MS/MS analysis in technical triplicates indicated consistent protein abundance fold changes across the samples (Figure 5b). Hierarchical clustering revealed the highest correlation between bevacizumab and the cabozantinib group, both affecting the VEGF/VEGFR pathway, followed by wortmannin and paclitaxel (Figure 5c and Figure S8).

Figure 5 Drug-dependent protein expression changes in the VPT platform. (a) Schematic diagram for sample preparation in the VPT for LC-MS/MS analysis. (b) Graph showing the protein abundance fold change among samples. All samples showed fold change values close to 0, indicating well-prepared samples. (c) Hierarchical clustering showing similarities between drug treatment conditions. Bevacizumab and cabozantinib, both targeting the VEGF/VEGFR pathway, are closest in distance, with paclitaxel being the farthest. (d) Heat map of the overall protein expression level changes. (e) Volcano plot representing protein expression changes in each drug treatment group compared with the control. The blue line represents the p-value = 0.2, the black line represents the q-value, and the red line represents the Bonferroni p-value threshold. (f) Changes in key proteins related to the mechanism of action for each drug. Bevacizumab and cabozantinib showed changes in proteins related to VEGF/VEGFR, wortmannin in proteins related to autophagy, and paclitaxel in proteins related to the cell cycle and cytoskeleton.

Significant protein expression changes were identified compared to control samples, employing q-value and Bonferroni p-value thresholds to ensure accuracy (Figure 5d). The drug-treated groups exhibited upregulation and downregulation in various cellular functions compared to the control group, including the cell structure, cell cycle regulation, signaling, genomic repair, transcription, and protein synthesis, as well as immune and inflammatory responses, and stress regulation. Despite the variability within the same mechanism, a trend is discernible among proteins with significantly large fold changes. Bevacizumab treatment upregulated proteins associated with cell differentiation and stress adaptation while downregulating signaling and cytoskeletal proteins. Cabozantinib elevated proteins involved in energy metabolism and stress response, reducing cell membrane and transport protein levels. Wortmannin’s effects included the upregulation of proteins involved in energy metabolism, protein degradation, and transport, alongside a decrease in extracellular matrix composition and cell interaction proteins. Paclitaxel induced extensive changes, enhancing energy metabolism and cell division elements while inhibiting membrane transport and certain metabolic pathways (Figure 5e).

Further analysis of proteins linked to each drugs’ MOA confirmed their functional impacts within the VPT platform (Figure 5f). Bevacizumab, reducing protein expression in the PI3K/Akt and Erk pathways, was observed, indicating effective suppression of the VEGF/VEGFR signaling pathway. Additionally, proteins such as SRC, YES1, PTPN11, and MMP2 were upregulated, suggesting that ECs may have activated alternative pathways for survival.73−77 Cabozantinib, also a suppressive agent of the VEGF/VEGFR pathway, exhibited a more pronounced downregulation in the PI3K/Akt pathway compared with bevacizumab, with minimal changes in the Erk pathway and a decrease in the SRC pathway. This difference can be attributed to bevacizumab’s specific action of neutralizing the VEGF ligand, in contrast to cabozantinib’s broader role as a tyrosine kinase inhibitor affecting multiple pathways, including VEGFR and MET. Wortmannin, targeting the PI3K, affects the autophagy process. The downregulation of ATG3, ATG5, GABARAPL1, and GABARAPL2 suggests inhibited formation and expansion of initial autophagosomes, while an increase in ATG7 may indicate a compensatory upregulation.52,78 The rise in levels of selective autophagy receptor SQSTM1,79−81 lysosomal proteins LAMP1 and LAMP2,82 lysosomal enzymes CTSD and CTSL,83,84 and PI3P-binding protein WIPI185 implies disruptions in various stages of autophagy, from autophagosome formation to lysosomal fusion. Lastly, with paclitaxel, changes in cell cycle proteins CDK2, CDKN1A, SKP1, CUL1, and BUB3 indicate cell cycle arrest,86−89 and an increase in microtubule-associated proteins suggests the induction of mitotic apoptosis.90

Linking Protein Expression Changes to Cellular Functions, Including Cell Senescence and Angiogenesis

Beyond the proteins directly linked to the mechanisms of action (MOA) of the drugs, a broad spectrum of other proteins also exhibited changes. We investigated functional shifts across several critical areas, including cell cycle regulation, metabolism, and cytoskeletal dynamics (Figure S9 and Table 2). Notably, paclitaxel treatment uniquely upregulated YWHA family proteins, indicating potential cell cycle arrest or stress response mechanisms.91−93 This was further evidenced by decreased expression of CDK2, crucial for the G1-S phase checkpoint,86 and SKP1 and CUL1, indicating a blockade in the G1-S phase progression,94 implying at a broader G1-S phase arrest. Furthermore, BUB3 upregulation pointed to a possible M-G1 phase arrest.95

Table 2 Significant Proteins Identified through LC-MS/MS Analysis

pathway	functional classification	protein	function of each protein	
VEGF/VEGFR	survival	SRC	nonreceptor tyrosine kinases that regulate cell growth, differentiation, and survival	
YES1	SRC family (regulating cell growth, differentiation, and survival)	
PTPN11	protein tyrosine phosphatase	
MMP2	breakdown of the extracellular matrix	
autophagy	initial autophagosome formation	ATG3	lipidation of ATG8 family proteins, essential for autophagosome formation	
ATG5	participating in the expansion of autophagosomes	
ATG7	E1-like activating enzyme, pivotal in autophagosome formation	
GABARAPL1	autophagosome formation and intracellular trafficking, functioning similarly to LC3 in the autophagy	
GABARAPL2	autophagosome maturation and endoplasmic reticulum to Golgi transport	
autophagy receptor	SQSTM1(p62)	link between LC3 and ubiquitinated substrates, playing a crucial role in the degradation of aggregated proteins and organelles	
lysosomal proteins	LAMP1	mediating the fusion of autophagosomes and lysosomes	
LAMP2	key role in lysosomal stability, autophagy, and chaperone-mediated autophagy	
lysosomal enzymes	CTSD	lysosomal aspartic protease	
CTSL	lysosomal cysteine protease	
autophagy stage disruptions	WIPI11	formation of autophagosomes by recognizing and binding phosphatidylinositol 3-phosphate (PI3P) on the membrane of forming autophagosomes	
cell cycle	DNA replicatoin and repair	CDK2	transition from the G1 to S phase and the control of DNA replication	
cell cycle control	CDKN1A (p21)	inhibiting the activity of CDK2, leading to cell cycle arrest in response to DNA damage	
SKP1	core component of the SCF (SKP1-CUL1-F-box protein) E3 ubiquitin ligase complex, targeting proteins for ubiquitination and subsequent proteasomal degradation, regulating cell cycle progression and signal transduction	
CUL1	critical part of the SCF (SKP1-CUL1-F-box protein) E3 ubiquitin ligase complex	
cell cycle checkpoints and spindle assembly	BUB3	mitotic checkpoint protein	
protein interaction modulators	YWHAE	modulates cellular processes including cell cycle progression and apoptosis	
YWHAG	mediating signal pathways and cellular processes, cancer progression, and stress responses	
YWHAH	mediating signal pathways and cellular processes	
YWHAQ	stress response and metabolic control	
YWHAB	involvement in protein kinase pathways	
YWHAZ	key regulator in signal transduction, apoptosis, and cell cycle checkpoints	
glycolysis	fatty acid synthesis	FASN	biosynthesis of long-chain fatty acids from acetyl-CoA and malonyl-CoA	
gluconeogenesis	PCK2	conversion of oxaloacetate to phosphoenolpyruvate in gluconeogenesis	
oxidative phosphorylation	NDUFS1	core subunit of complex I in the mitochondrial electron transport chain and transfer of electrons from NADH to ubiquinone	
cytoskeleton	cell mobility and integrin activation	WASL	promotes actin filament assembly	
microtubule dynamics	TUBB	key structural component of microtubules	
DIAPH	involved in actin polymerization	
cellular senescence	cell proliferation	PAK1IP1	involved in cell proliferation regulation via the p53–MDM2 loop	
SKP1	core component of the SCF (SKP1-CUL1-F-box protein) E3 ubiquitin ligase complex	
DNA damage and cell stress response	IFI16	involved in DNA damage response, innate immune response, and the regulation of cell proliferation and apoptosis	
TP53BP1	plays a central role in the cellular response to DNA damage, particularly double-strand breaks	
ATM	
NPM1	involved in ribosome biogenesis and cellular stress responses	
angiogenesis	cell growth	FLT1	receptor for the vascular endothelial growth factor (VEGF)	
HGF	involved in cell growth, cell motility, morphogenesis, and angiogenesis	
COL18A1	inhibits endothelial cell proliferation	
LGALS3	involved in cell adhesion, cell activation and proliferation, apoptosis, and pre-mRNA splicing	
protein degradation	CTSD	plays a role in the degradation of proteins within the lysosome	
cholesterol synthesis	SQLE	key enzyme in the cholesterol biosynthesis pathway	
extracellular matrix	COL6A2	contributes to the integrity and stability of the extracellular matrix	
THBS1	plays roles in cell-to-cell and cell-to-matrix interactions	
SERPINE1	inhibits the plasminogen activator system	
TIMP1	inhibits the activity of matrix metalloproteinases	

In metabolic pathways, bevacizumab and cabozantinib treatments reduced glycolysis enzyme expressions from fructose-6-phosphate to pyruvate, aligning with VEGF/VEGFR signaling inhibition effects.96 Wortmannin treatment slightly lowered FASN levels, associated with fatty acid synthesis under cellular stress, suggesting that inhibited autophagy might reduce lipid breakdown, consequently leading to low lipid accumulation.97−100 Conversely, PCK2, essential for energy production during stress, increased with wortmannin, signifying an energy production shift toward gluconeogenesis.101,102 Oxidative phosphorylation exhibited a general decrease in expression with paclitaxel treatment, suggesting mitochondrial damage and reduced energy metabolism capabilities. Notably, NDUFS1, which is involved in NADH dehydrogenation,103 was significantly reduced. WASL, associated with cell mobility and integrin activation,104 exhibited a similar reduction in expression to integrins. Specifically, paclitaxel treatment led to notable disruptions in microtubule dynamics, as evidenced by changes in TUBB and DIAPH105−107 (Figure 6a).

Figure 6 Protein expression changes by biological function in the VPT platform. (a) Relative protein expression fold changes of representative proteins in each biological function in response to drugs. (b) p53-dependent cellular senescence pathway and the related upstream proteins. Protein expression changes affected by the four drugs (boxes from left to right; beva, cabo, wort, and pacl) are indicated by color. (c) Plot for changes in angiogenesis-related protein expression. Upregulation is indicated in red, and downregulation in blue, and the size of each circle represents the magnitude of the p-value.

Drug-induced cellular stress responses include oxidative stress, inflammatory responses, cell death, and DNA damage.108 Among these, cell death or apoptosis can originate from cellular senescence.109 In relation to cellular senescence, we examined proteins acting as activators of p53 and p21, IFI16, NPM1, SKP1, PAK1IP1, TP53BP1, and ATM. IFI16 was upregulated in all drug-treated groups, leading to an increase in p53 expression.110 Upregulation of NPM1 and SKP1 led to a decrease in p53 expression,111−113 with a slight decrease observed in response to bevacizumab, an increase in response to wortmannin, and decreases in other drug groups. PAK1IP1, a protein that activates p53114 interacting with p21-activated kinase1, was notably upregulated in response to bevacizumab, also upregulated by cabozantinib and paclitaxel, but was undetected in wortmannin. TP53BP1 activates p53 by activating ATM in its phosphorylated form.115 In response to cabozantinib and paclitaxel, both TP53BP1 and ATM showed increased levels of expression. However, in response to bevacizumab, TP53BP1 expression increased, while ATM expression significantly decreased, and in the wortmannin group, both TP53BP1 and ATM expressions slightly decreased. It is inferred that in the bevacizumab group, activation by PAK1IP1 was more significant than the TP53BP1-ATM-mediated p53 activation. These findings indicate a universal trend toward cellular senescence across all drug-treated groups, mediated by efforts to activate p53 (Figure 6b).

Additionally, we closely examined proteins related to angiogenesis: FLT1, HGF, COL6A2, CTSD, LGALS3, COL18A1, SQLE, THBS1, SERPINE1, and TIMP1.The FLT1 expression level decreased under all drug conditions except wortmannin, correlating with the filopodia presence in tip cells (Figure S7). HGF, crucial for cell migration and angiogenesis induction,116 increased with bevacizumab treatment but decreased with cabozantinib and paclitaxel, indicating the complexity of angiogenesis pathways and cell viability maintenance under drug influence. In the wortmannin treatment group, the absence of FLT1 and HGF likely resulted from disrupted sprouts and reduced cell numbers. Col6A2’s decrease across treatments signifies ECM component impacts crucial for cell adhesion and migration,117 which also decreased across all treatments, reflecting the influence on ECM components critical in cell adhesion, migration, and sprout stability.118 It aligns with the known role of VEGF in promoting ECM elements118 and the effect of pathways like VEGFR, MET, and PI3K/AKT on ECM production.119 Cathepsin D activates the Rho/ROCK pathway, increasing the density and thickness of actin stress fibers.120 This leads to endothelial cells (ECs) separating from each other, reducing vessel stability. All drug groups showed higher levels compared with the control. Galectin-3 expression also increased in all drug groups, particularly in cabozantinib and paclitaxel, known to promote EC migration and proliferation.121,122 However, in the presence of high oxidized LDL (oxLDL), it reportedly increases EC dysfunction and suppresses proliferation.123,124 This could be associated with the elevated expression of FASN, observed notably in cabozantinib and paclitaxel, which boosts lipid synthesis and accumulation, leading to the activation of inflammatory responses, escalation of oxidative stress, and an increase in oxLDL levels. COL18A1 encodes for endostatin, a known angiogenesis inhibitor that inhibits the activity of VEGFA, especially observed in bevacizumab and cabozantinib. The interaction with α5β1-integrin activates autophagy through the Src pathway, with a lower expression seen in Wort, indicating well-suppressed autophagy. The reduction in paclitaxel could be due to protein degradation from cellular stress responses or apoptosis.125 SQLE, involved in intracellular cholesterol synthesis, is negatively regulated by MARCH6 protein. An increase in SQLE or dysfunctional MARCH6 disrupts VE-cadherin-based cell–cell junctions, affecting the stability of angiogenic sprouts.126 Except for wortmannin, all groups showed an increase, indicating the destabilization of angiogenic sprouts. The actions of thrombospondin-1 and SERPINE1 (PAI-1) are interconnected;127 thrombospondin-1 induces apoptosis, inhibiting angiogenesis,128 while PAI-1 inhibits the conversion of plasminogens to plasmins, suppressing angiogenesis.129−132 The decrease in wortmannin and paclitaxel could be interpreted in various ways, including promoting abnormal cell death under cellular stress or inhibiting autophagy or attempting cell recovery and regeneration, necessitating further research. TIMP1 promotes cell proliferation and inhibits apoptosis through various pathways such as PI3K/Akt, JAK, NFκB, and MAPK.133−135 A decrease indicates the drug-induced suppression of cell proliferation and increased apoptosis. Wortmannin, an inhibitor of PI3K classes I and III, suggests induced apoptosis and increased TIMP1 expression for survival along with inflammation. TIMP1, an inhibitor of MMPs,134 suppresses angiogenesis, but its apoptosis mechanism and MMP inhibition are independent.135 Additionally, TGFBRAP1 and IGFBP4 were detected in the wortmannin group; the increase in TGFBRAP1 induces pathological angiogenic sprouts.136 IGFBP4 binds to IGF, facilitating its delivery to the receptors. IGFBP-4 protects against H2O2-induced cell damage/death in HUVEC and, together with VEGF, promotes angiogenesis (Figure 6c).137

The cellular response to drugs in terms of angiogenesis exhibits a complex interplay between promoting and inhibiting effects, with the balance between the two effects regulating the overall outcome. Furthermore, many mechanisms remain unclear, leading to expectations that research based on the results from our chip can consider various factors directly related to drug-specific angiogenesis aspects. Additionally, it is essential to recognize that protein changes related to angiogenesis and other processes may not consistently correspond with the expected drug effects or mechanisms, highlighting the importance of considering the timing of protein analysis.138 Protein expression within cells changes in real time following drug exposure. Our study observed protein expression after 48 h, failing to capture all immediate changes. Nevertheless, our analysis took place within a three-dimensional in vitro culture system that closely simulates physiological conditions, allowing for a well-informed interpretation of each drug’s mechanisms. Moreover, securing a sufficient number of reproducible samples and combining this with real-time imaging facilitate the unveiling of hidden mechanisms.

Correlating Proteomic Alterations with the Cellular Morphology on the VPT Platform

We examined the correlation between changes in protein expression and alterations in sprout morphology following treatment with various drugs. Initially, we summarized the morphological changes in sprouts compared with the control group (Figure 7a). Bevacizumab treatment resulted in minimal changes in sprout thickness, with a reduction in length. Cabozantinib treatment caused an increase in thickness along with a decrease in length. Wortmannin exhibited the most pronounced morphological changes, with both a decrease in length and thickness, and notably, sprouts showed a tendency to break apart. In contrast, the paclitaxel-treated group exhibited the most significant increase in thickness and a considerable reduction in length, forming aggregates rather than breaking apart, unlike the wortmannin group.

Figure 7 Correlation between morphological variations and protein expression level changes in angiogenic sprouts. (a) Representative confocal images and linear regression of qualification showing the morphological differences in response to various drugs. Scale bar = 100 μm. (b) GO chord plot of proteins associated with cytoskeletal activity, with proteins grouped by GO terms. The boxes on the annulus display protein expression levels in relation to the administered drugs. (c) Heat map illustrating the differential expression of cytoskeleton-related proteins in response to various drugs. (d) Principal component analysis (PCA) plot demonstrating the relationship between morphological differences and changes in protein expression, based on (a) and (c). The size and color of the circles indicate the thickness and length of the sprouts, respectively.

Next, we categorized the proteins associated with these morphological changes according to their involvement in cytoskeletal activity: cell adhesion, microtubule cytoskeleton, cytoskeletal protein, extracellular matrix protein, protein binding activity, cell motility, kinase activity, and phosphate activity. We summarized the changes in protein expression levels in each category following drug treatment (Figure 7b). In general, within each category, the proteins exhibited consistent trends depending on the type of drug with notable differences observed in actin-related proteins and integrin and cell adhesion-related proteins across treatments (Figure 7c). Our treatments with bevacizumab and cabozantinib were found to enhance microtubule dynamics and cell motility, as well as promote structural regulation. The increased expression of fibronectin and integrins led to enhanced cell adhesion to the extracellular matrix and migration.139 Simultaneously, the reduction in PIP4K (phosphatidylinositol 5-phosphate 4-kinase) and vinculin decreased cell focal adhesions, increasing cell motility.140 This contributed to the maintenance of sprout morphology and resulted in the development of longer angiogenic sprouts compared with those observed with wortmannin and paclitaxel. Wortmannin displayed opposite protein expression patterns, weakening connections with the ECM, which likely led to the fragmentation of the sprout morphology.141,142 Additionally, the increased expression levels of LAMP proteins, known lysosomal markers143 associated with increased lysosomal membrane permeabilization leading to apoptosis and cell death,144 might also contribute to these breaking morphological changes. Specifically, paclitaxel treatment resulted in cellular aggregation due to its impact on microtubule dynamics and the concurrent increase in proteins that facilitate cellular assembly. The reduced levels of integrins and fibronectin weakened cell–ECM interactions, which are critical for cell adhesion, migration, and signaling. The decrease in microtubules further complicates this by potentially impairing cell polarity and directional migration, which are crucial for organized vessel formation. However, the increased levels of ARPC and GTPase proteins may compensate to some extent by enhancing actin cytoskeleton dynamics and cell motility.145 The increase in vinculin suggests an effort to stabilize focal adhesions and support mechanotransduction despite reduced integrin levels.

Finally, we used PCA analysis to visually summarize the correlation between changes in sprout morphology and protein expression (Figure 7d). The PCA plot reveals that the changes induced by bevacizumab and cabozantinib treatments exhibit similar trends, whereas wortmannin and paclitaxel treatments show significant differences. Paclitaxel had the most substantial impact, as evidenced by its high values along the highly variable PC1 axis. Wortmannin also showed a significant influence, indicated by high values along PC2, highlighting its substantial effect on the sprout morphology and protein expression changes. These results demonstrate a clear correlation between morphological changes and protein expression differences, emphasizing the complex interplay between cytoskeletal protein alterations and angiogenic morphology.

Conclusions

The VPT platform enhances angiogenesis research by establishing consistent endothelial cell layers, allowing precise measurement of antiangiogenic drug effects. It tracks both cellular morphological changes and molecular-level proteomic alterations, linking these aspects to provide a comprehensive view of angiogenesis. This integrated approach identifies key relationships and fills the knowledge gaps left by single-focus studies. The fusion of morphology and proteomics in an open microfluidic setting advances our understanding of angiogenesis control mechanisms, suggesting additional therapeutic possibilities.

Materials and Methods

Device Design and Fabrication

The VPT platform underwent an initial design phase utilizing the CAD (Computer-Aided Design) program and fabrication through 3D printing (Figure 4 Standalone, 3D Systems) for preliminary design prototyping before transitioning to injection molding. The 3D printed prototypes were rinsed with isopropyl alcohol for 20 min and cured under ultraviolet light (380 nm) for 1 h. To facilitate fluid patterning channels, a single-sided pressure-sensitive adhesive (PSA) film (IS08820, IS solution) was attached to the prototype’s bottom, and the surface underwent air plasma treatment (Femto Science) at 75 W for 3 min to induce hydrophilic conditions. The optimized platform was then fabricated through injection molding (R&D Factory, Korea) employing polystyrene (PS). The mold core, fabricated from an aluminum alloy (Al 7075), underwent machining and polishing. The injection process was executed with a force of 130 tons, reaching a maximum pressure of 68 bar, a cycle time of 30 s, and a nozzle temperature of 230 °C. The resulting injection-molded device retained the attachment of the PSA film akin to the 3D printed prototypes, with air plasma treatment ensuring experimental readiness.

Cell Preparation

Human umbilical vein endothelial cells (HUVECs; Lonza) were maintained in endothelial growth medium 2 (EGM-2; Lonza) and utilized at passages 5–6 in the experimental procedures. Lung fibroblasts (LFs; Lonza) were cultured in fibroblast growth medium 2 (FGM-2; Lonza) and employed at passages 6–7 for experimental purposes. The cells were grown in T75 flasks (Thermo Fisher Scientific) and incubated at 37 °C in 5.0% CO2 for 2–3 days before seeding them into the microfluidic devices. Detachment of cultured cells from the flask was achieved using 0.25% trypsin-EDTA (Hyclone, USA), and they were subsequently resuspended in a bovine fibrinogen solution at the required concentrations for the specific experimental conditions. A universal mycoplasma detection kit (American Type Culture Collection, cat. no. 30-1012K) was used to confirm the absence of mycoplasma contamination.

Hydrogel and Cell Patterning

The microscale fluid patterning within the microfluidic device commenced following air plasma treatment, adhering to the sequence of the center channel (channel C), side channel (channel S), and open channel (channel O). Utilizing spontaneous capillary flow, all fluids were selectively injected into the channels. A 10.0 mg/mL bovine fibrinogen solution, blended with aprotinin solution (0.15 U/mL, Sigma) in a ratio of 25:4 (v/v), was prepared to prevent degradation and instability of the fibrin hydrogel during the 3D angiogenesis assay. In channel C, a 2.0 μL acellular fibrinogen and thrombin mixture was introduced along the inner edge of channel O, following the addition of 0.8 μL of bovine thrombin (0.5 U/ml, Sigma) in 40 μL of bovine fibrinogen solution (final concentration, 5.0 mg/mL; Sigma). The primed hydrogel mixture was left to cross-link for 13 min before the patterning of channel S. The channel S was patterned along the inner edge, with a 7.5 μL cellular fibrinogen and thrombin mixture, composed of 64 μL of cellular bovine fibrinogen solution (final concentration, 2.5 mg/mL) and 1.2 μL of bovine thrombin. For channel O, 30 μL of a cell suspension was injected through the hole connected to the channel to facilitate cell adhesion to the hydrogel interface. Media were then sequentially dispensed using a multichannel pipet, up to 200 and 140 μL per reservoir on the channel S side and channel O side, respectively. Hydrostatic flow conditions were induced by the difference in the media level between two reservoirs.

Permeability Assay

The permeability coefficient was determined by analyzing the fluorescence images of the FITC-dextran diffusion across the endothelial cell (EC) monolayer. Prior to imaging, 50 μL of the medium was retained in the media reservoir on the channel S side, and the media reservoir on the channel O side was completely aspirated. Subsequently, 100 μL of both 10 and 70 kDa FITC-dextran solutions was introduced into channel O through the designated hole. Fluorescence images were captured at 1 min intervals over a 15 min duration using a confocal microscope (Nikon Ti2-E) under live conditions to preserve the integrity of tight junctions. The permeability coefficient was calculated using an equation established in a prior study.29,146

Transendothelial Electrical Resistance (TEER) Measurement

To measure the transendothelial electrical resistance (TEER), we utilized a customized ScalloP and EVOM2 (epithelial tissue volt-ohm meter, World Precision Instruments, Sarasota, Fl) adapted to fit the VPT platform (Figure S2b,c). The ScalloP, a probe custom-made by ProvaLabs, Inc., is designed to simultaneously insert into the 16 wells of the VPT platform, reading TEER values from each barrier sample. It is equipped with a total of four Ag/AgCl electrodes, positioned at both ends of each well, 100 μm away from the substrate. These electrodes are connected to EVOM2, which numerically displays the measurements. The TEER values were measured 0, 24, and 48 h after patterning endothelial cells on channel O, considering the time of patterning as day 0. Prior to each measurement, the electrodes of ScalloP were washed with 70% ethanol and sterile distilled water. For the measurements, the media reservoir of the VPT platform was filled with 200 μL of PBS and EGM-2 on the channel S side and 140 μL on the channel O side, ensuring no flow conditions. The raw data numbers displayed on EVOM2 were normalized by multiplying the surface area of the barrier.

Microbead Assay

A suspension containing red-fluorescent 2.0 μm Fluoro-max dyed particles was prepared by mixing them with phosphate-buffered saline (PBS). This particle-PBS mixture was introduced into the open channel through the designated hole. The hydrostatic pressure facilitated the flow of particles into the angiogenic sprouts, and the entire process was visualized using a confocal microscope.

Real-Time Imaging

Imaging was performed using a real-time imaging microscope (Celloger Mini Plus, Curiosis) located inside an incubator to monitor angiogenesis in real time. Following drug administration, imaging commenced promptly and continued at 30 min intervals for a period of 48 h.

Drug Treatment

Angiogenic inhibitors were categorized into monoclonal antibodies, small-molecule inhibitors, and chemotherapeutics. Bevacizumab and ramucirumab are monoclonal antibodies, and cabozantinib and regorafenib are small-molecule inhibitors. Chemotherapeutics encompassed wortmannin, chloroquine, and paclitaxel. All inhibitors were prepared in stock concentrations following manufacturers' instructions and subsequently diluted to working concentrations using a cell culture medium (bevacizumab: 0.01 0.1, and 1 mg/mL; ramucirumab: 0.2, 2, and 20 μM; cabozantinib: 0.01, 0.1, and 1 μM; regorafenib: 0.1, 1, and 10 μM; wortmannin: 0.5, 5, and 50 μM; chloroquine: 1, 10, and 100 μM; paclitaxel: 0.5, 5, and 50 μM). The inhibitors were introduced into the media reservoir and subjected to 48 h of treatment under flow induced by hydrostatic pressure, with the flow being maintained every 24 h.

Immunocytochemistry

Angiogenic sprout samples within the device underwent PBS rinsing and fixation with 4% paraformaldehyde (PFA, Biosesang, Korea) in PBS for 15 min at room temperature. Subsequently, permeabilization was achieved using 0.2% Triton X-100 (Sigma-Aldrich) for 20 min followed by treatment with 3% bovine serum albumin (BSA, Sigma-Aldrich) for 20 min to prevent nonspecific binding of immunofluorescent antibodies. ECs were specifically labeled using 488 fluorescein-labeled Ulex Europaeus Agglutinin I (1:500 ratio of dye to BSA; Vector, UK). Nuclear staining was performed using Hoechst 33342 (1:1000 dilution; Molecular Probes). The tight junctions of ECs were targeted with Alexa Fluor 488-tagged anti-VE-cadherin (CD144) at a 1:200 dilution in BSA. A 50 μL dye solution was added to both reservoirs per unit well. Incubation occurred overnight at 4 °C followed by storage in PBS at 4 °C before imaging. Imaging, conducted with a confocal microscope (Nikon Ti2, Japan), generated 3D and z-stackable images of the angiogenic sprouts.

Sample Preparation for Liquid Chromatography with Tandem Mass Spectrometry (LC-MS/MS)

Based on the protocol from Carrion et al.’s work,147 the nattokinase powder was dissolved in the PBS at a concentration of 100 FU and was warmed enough in 37 °C water baths. The solution was centrifuged at 1500 rpm for 5 min and filtered with the 0.2 μm pore-size filter before treatment in the microfluidic chip. After washing the samples in the microfluidic chip with PBS, the nattokinase solution was treated directly with fibrin hydrogel through the medium reservoir for 20 min. Completely detached cells were harvested from the device, centrifuged to remove the residue of nattokinase, and lysed using a radioimmunoprecipitation assay (RIPA) buffer for 30 min with ice incubation, and the supernatant of lysis solution was centrifuged at a 14,600 relative centrifugal force (rcf) for 15 min. The amount of extracted proteins was quantified via a bicinchoninic acid (BCA) assay (Thermo Fisher Scientific). For LC-MS/MS analysis, each 50 μg of protein lysates was reduced and alkylated by using a Mini MS Sample Prep Kit (Thermo Fisher Scientific, no. A40006). Samples were digested with 2 μg of a trypsin/Lys-C protease mix (Thermo Fisher Scientific, no. A40009) at 37 °C for 16 h, and peptides were desalted with a peptide clean-up column.

LC-MS/MS Analysis

Proteome analysis was performed by an Orbitrap Eclipse Tribrid mass spectrometer (Thermo Fisher Scientific) with an EASY-nLC 1200 system (Thermo Fisher Scientific). An autosampler was used to load 10 μL aliquots of the peptide solutions into a trap column (PepMap 100 C18 3 μm 75 μm × 2 cm nanoViper, Thermo Fisher Scientific) and an EASY-Spray column (15 cm × 50 μm PepMap RSLC C18 2 μm, Thermo Fisher Scientific). The peptides were separated with a linear gradient from 5 to 40% mobile phase B (100% water (A) and 80% acetonitrile (ACN) (B); each contained 0.1% formic acid) at 300 nL/min and injected for MS analysis. MS scans were acquired in the Orbitrap with a 120,000 resolution, and the scan range was 400–1600 m/z. Precursor ions with charges of +2 to +8 were isolated for MS/MS sequencing. MS/MS scans were acquired in the ion trap using higher energy collision-induced dissociation (HCD) fragmentation with a normalized collision energy of 33%, and the first mass was set to 120 m/z.

Protein Identification and Label-Free Quantification

The identification of proteins was performed with Proteome Discoverer 2.4 (Thermo Scientific) with a SEQUEST HT search engine using the Uniprot Homo sapiens (TaxID = 9606) database. A precursor mass tolerance of 25 ppm, a fragment mass tolerance of 0.8 Da, and a maximum missed cleavage of 2 were set. Oxidation (methionine, +15.995 Da) was set as a dynamic modification, and carbamidomethyl (cysteine, +57.021 Da) was set as a static modification. Abundances were based on the area and normalized based on the total peptide amount.

Protein Relative Quantification

The normalized protein quantification data derived from Proteome Discoverer 2.4 (Thermo Scientific) were grouped into sets comprising three technical replicates each for Be, Ca, Pa, and controls as well as sets for Wo and the control with DMSO. The former set included a total of 3386 proteins, identified by Proteome Discoverer 2.4, with 2649 proteins without missing data. Through Missforest (Stekhoven et al., 2012), 266 proteins were imputed, and then, 2915 proteins with complete quantification values were generated. In the latter set, consisting of Wo and the control with DMSO, 3349 proteins were identified. Among them, 3190 proteins were utilized, which consist of 2908 proteins without any missing and 282 proteins including imputed values. Scale normalization was performed for each set. To facilitate the integrated analysis of the two sets, relative expression values for Be, Ca, Pa, and Wo were calculated by adjusting the abundance of control samples. The relative expression values (Rij) for the ith protein and jth sample were calculated

where C is the expression value of control samples and s is a constant to transform expression values to positive. Using 2538 intersected proteins from the two sets, relative quantification values for Be, Ca, Pa, and Wo were obtained for the following analysis.

To check the overall expression quality, using 2538 intersected proteins, principal components (PC) for the triplet set of beva, cabo, wort, and pacl were determined. PC1 and PC2 were then used to create a PC plot. Hierarchical clustering was performed using the hclust function with the complete agglomeration method.

Pathway-Related Proteins

From the MSigDB (v2022.1) KEGG and REACTOME, proteins related to the cell cycle, glycolysis, oxidative phosphorylation, p53 signaling pathway, cell cycle arrest, apoptosis, signaling by VEGF, Notch signaling pathway, and regulation of the actin cytoskeleton were selected, among the 2538 proteins.

Image Processing and Quantitative Analysis on the Morphology of Angiogenic Sprouts

Confocal images were processed using the open-access software Fiji and NIS-Elements, provided by the Nikon confocal microscope. 3D confocal images were converted to 2D images through z-projection stacking and then cropped to ensure consistent regions of interest. Multiple and consistent preprocessing methods were used to denoise each image, such as averaging filtering, median filtering, and Gaussian filtering. As Gaussian filtering could preserve the value of the original shape of sprouts better than other methods, the Gaussian filter was first adapted to denoise images. Then, we developed a small-blob-remove algorithm process by using Python OpenCV to denoise small blobs made from the Gaussian filter and dead cell debris. After denoising, skeletonization was conducted with an equal threshold value for every image in a single set of analysis. We also developed an analytic algorithm for quantifying morphological features based on skeletonization images. By using this algorithm, the total pixel number of the masked image was quantified and represented as the sprout area, and the total pixel number of the skeletonized image was defined as the total length of the sprouts within a sample. In order to count the total sprout number within a sample, we first recorded the coordinates of each endpoint in the skeletonized sprout. Since there were lots of endpoints around the roots of the sprouts, which affects the average of y-coordinates and interrupts quantification of images reflecting morphological features, we then established the criterion to only record the skeleton endpoint that has its y-coordinates higher than the median value within each sample and applied this criterion to all samples in the analysis. We counted the recorded number of skeleton endpoints and represented it as the total endpoint number of sprouts, and the average of y-coordinate values was defined as the average sprout length coefficient.

Statistical Analysis

All data were plotted as means ± SEM using GraphPad Prism 9 software, and statistical analysis was performed using unpaired t-tests. A p-value of less than 0.05 was considered significant (*p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001; n.s., not significant). In the permeability assay for 10 kDa, the statistical sample size for day 0 and 1 was n = 4, and for the rest, it was n = 3. In the TEER measurement, the sample size for the monolayer condition on days 0 and 1 was n = 15, for the clumped EC layer condition on day 0, it was n = 14, and for the rest, it was n = 16. Additionally, in quantitative analysis using an algorithm, the z-score was used to identify and exclude outliers for data following a normal distribution, while the interquartile range was employed for data not following a normal distribution. The sample size for the control was n = 5, and for drug-treated conditions, it was n = 3 each. For the differential expression analysis in protein expression level quantification, two-sample t-tests were conducted with each treatment group (beva, cabo, wort, and pacl) to identify significant differentially expressed proteins. To address the issue of multiple comparisons, Bonferroni-corrected p-values and q-values were utilized to determine the differential expression significance. All these analyses were conducted using the R package. In the volcano plot of t-test results, the x-axis represents log 2 (fold change), the red dashed line corresponds to the Bonferroni-corrected p-value = 0.05 threshold, and the blue dashed line represents the q-value = 0.05 threshold. The black dashed line indicates the uncorrected p-value = 0.05 threshold.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsnano.4c05537.Confocal images of cell layer formation, TEER setup, real-time images, quantification process scheme, reproducibility, sprout thickness, confocal images of sprout tip cells, PCA plot, and protein expression heat map (PDF)

Supplementary Material

nn4c05537_si_001.pdf

Author Contributions

S.J. performed conceptualization, methodologies, software analysis, validation, formal analysis, investigation, data curation, and writing of the original draft; S.C. performed methodologies, validation, formal analysis, and investigation; Y.L. performed formal analysis and investigation; Jungseub Lee performed software and formal analysis; Jihye Lee performed methodologies and resource acquisition; M.-S.K. performed methodologies and formal analysis; Y.S.O. performed conceptualization, methodologies, resource acquisition, and review and editing of the manuscript; T.K., S.H., and S.J.K. performed resource acquisition; D.H.J. performed review and editing of the manuscript; J.K. performed conceptualization, methodologies, data curation, review and editing of the manuscript, and funding acquisition; N.L.J. performed conceptualization, methodologies, investigation, data curation, review and editing, supervision, project administration, and funding acquisition.

The authors declare the following competing financial interest(s): N.L.J. reports a relationship with Qureator, Inc. that includes board membership and equity or stocks.

Acknowledgments

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2021R1A3B1077481 and NRF-2022M3A9B6082) and by the Gachon University research fund (GCU-202110350001).

Abbreviations

SRC SRC proto-oncogene, nonreceptor tyrosine kinase

YES1 YES proto-oncogene 1, Src family tyrosine kinase

PTPN11 protein tyrosine phosphatase, nonreceptor type 11 (also known as SHP-2)

MMP2 matrix metallopeptidase 2 (also known as gelatinase A)

ATG3 autophagy-related 3

ATG5 autophagy-related 5

ATG7 autophagy-related 7

GABARAPL1 GABA type A receptor-associated protein-like 1

GABARAPL2 GABA type A receptor-associated protein-like 2

SQSTM1 (p62) sequestosome 1

LAMP1 lysosomal associated membrane protein 1

LAMP2 lysosomal associated membrane protein 2

CTSD cathepsin D

CTSL cathepsin L

WIPI1 WD repeat domain, phosphoinositide interacting 1

CDK2 cyclin-dependent kinase 2

CDKN1A (p21) cyclin-dependent kinase inhibitor 1A

SKP1 S phase kinase-associated protein 1

CUL1 cullin 1

BUB3 BUB3 mitotic checkpoint protein

YWHAE tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, epsilon

YWHAG tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, gamma

YWHAH tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, eta

YWHAQ tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, theta

YWHAB tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, beta

YWHAZ tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, zeta

FASN fatty acid synthase

PCK2 phosphoenolpyruvate carboxykinase 2 (mitochondrial)

NDUFS1 NADH:ubiquinone oxidoreductase core subunit S1

WASL Wiskott–Aldrich syndrome-like

TUBB tubulin beta class I

DIAPH1 diaphanous related formin 1

PAK1IP1 P21-activated kinase 1 inhibitor protein 1

IFI16 interferon gamma inducible protein 16

TP53BP1 tumor protein P53 binding protein 1

ATM ATM serine/threonine kinase

NPM1 nucleophosmin 1

FLT1 Fms-related receptor tyrosine kinase 1

HGF hepatocyte growth factor

COL18A1 collagen type XVIII alpha 1 chain

LGALS3 galectin-3

CTSD cathepsin D

SQLE squalene epoxidase

COL6A2 collagen type VI alpha 2 chain

THBS1 thrombospondin-1

SERPINE1 serpin family E member 1 (also known as PAI-1)

TIMP1 TIMP metallopeptidase inhibitor 1
==== Refs
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