Organ-on-chips made of blood: endothelial progenitor cells from blood reconstitute vascular thromboinflammation in vessel-chips

Abstract

Development of therapeutic approaches to treat vascular dysfunction and thrombosis at disease- and patient-specific levels is an exciting proposed direction in biomedical research. However, this cannot be achieved with animal preclinical models alone, and new in vitro techniques, like human organ-on-chips, currently lack inclusion of easily obtainable and phenotypically-similar human cell sources. Therefore, there is an unmet need to identify sources of patient primary cells and apply them in organ-on-chips to increase personalized mechanistic understanding of diseases and to assess drugs. In this study, we provide a proof-of-feasibility of utilizing blood outgrowth endothelial cells (BOECs) as a disease-specific primary cell source to analyze vascular inflammation and thrombosis in vascular organ-chips or "vessel-chips". These blood-derived BOECs express several factors that confirm their endothelial identity. The vessel-chips are cultured with BOECs from healthy or diabetic patients and form an intact 3D endothelial lumen. Inflammation of the BOEC endothelium with exogenous cytokines reveals vascular dysfunction and thrombosis in vitro similar to in vivo observations. Interestingly, our study with vessel-chips also reveals that unstimulated BOECs of type 1 diabetic pigs show phenotypic behavior of the disease - high vascular dysfunction and thrombogenicity - when compared to control BOECs or normal primary endothelial cells. These results demonstrate the potential of organ-on-chips made from autologous endothelial cells obtained from blood in modeling vascular pathologies and therapeutic outcomes at a disease and patient-specific level.

Figure 1. Blood outgrowth endothelial cell isolation and characterization.

(A) Schematic showing the BOEC isolation process. Whole blood is withdrawn in citrated tubes and diluted with PBS. The diluted blood is layered over density gradient (DG) media and centrifuged at 400g for 35 minutes after which the buffy layer is carefully collected, washed twice with PBS and finally added to culture flasks with growth media. (B) Timelapse images showing the gradual removal of non-adherent leukocytes and platelets from culture flasks seeded with cells isolated from buffy layer (scale bar: 50 µm). (C) Representative timelapse graph showing BOEC time of outgrowth colony appearance, subsequent subculture (passage 1) and confluence in a T25 cell culture flask (black line). Dotted line represents the growth of BOECs from passage 1 till they reach approximately 1 million cells. The red line represents the area coverage (%) of all the non-adherent cells (leukocytes, macrophages, platelets etc.) with time. (D) Brightfield image of cultured BOECs after isolation from whole blood. BOECs exhibit the classic endothelial “cobblestone” morphology (scale bar: 200 µm). Flow cytometry analysis of surface markers, (E) CD14, (F) CD45, (G) CD34, (H) CD31, (I) CD144 and (J) KDR present on BOECs. Red histograms represent staining with the antibody of interest; blue histograms are the relevant isotype controls.

Figure 2. Analysis of vessel-chips lined with BOECs.

(A) Schematic of the arteriole-sized microchannel (vessel-chip) with an inlet, a 200 µm wide and 75 µm high straight duct, and an outlet connected to the syringe pump to draw blood through the channels. (B) Photographic representation of the vessel-chip made of polydimethylsiloxane (PDMS) containing two independent microchannels on a collagen-coated glass slide (scale bar: 10 mm). (C) Quantification of BOEC growth and spreading in microchannels with time (left); snapshots (right) show BOEC coverage at (i) seeding, (ii) initial attachment and (iii) confluence (scale bar: 100µm). (D) Confocal micrograph showing a section of the endothelial lumen formed by BOECs in the microchannels. The orthogonal views of the endothelial lumen with the top (‘xy’), front (‘xz’) and side (‘yz’) views validate the complete coverage of BOECs on all the four walls of the microchannels (green: VE-cadherin; blue: nuclei; scale bar: 100 µm). (E) Graph showing cell orientation inside the vessel-chip. Cells are randomly oriented at the time of seeding into vessel-chips. At 18 hours, BOECs align along the flow direction. (F) Quantification of cell coverage of BOEC vs HUVEC laden microchannels (n=6). Nearly complete coverage was observed upon reaching confluence (G) Endothelial barrier permeability of BOECs correlated to the diffusion of fluorescent FITC-dextran (4 kDa) through the transwell plates after treatment with TNF-α treatment. Results demonstrate the disruption of barrier function with increasing TNF-α dosage. (H) Quantification of mean fluorescence intensity of BOECs immunostained for VWF and ICAM-1 after TNF-α treatment, normalized to untreated endothelium. NS: not significant, *p<0.05, **p<0.005, ***p<0.0005, ****p<0.0001; n=3 for all experiments wherever not specified.

Figure 3. Inflammatory and prothrombotic responses of TNF-α stimulated BOEC-vessel-chips.

(A) Platelet adhesion micrographs of unstimulated (top) and stimulated (bottom) BOEC (left) and HUVEC (right) vessel-chips after perfusion of recalcified citrated whole blood. Platelet adhesion to the endothelial lumen increases after TNF-α treatment (scale bar: 100 µm). (B) TNF-α treatment of BOEC-vessel-chips produces “comet” shaped platelet aggregates along with fibrin formation in the surroundings (red: fibrin; green: platelets; scale bar (top): 100 µm; scale bar (bottom): 50 µm). (C) Platelet area coverage after a 15-minute perfusion of blood through the vessel-chips lined with BOECs and HUVECs with and without TNF-α treatment. The platelet coverage was not significantly different (p>0.05) between BOECs and HUVECs for the unstimulated and stimulated endothelium. (D) Fibrin (measured with fluorescence) after blood perfusion through BOECs and HUVECs vessel-chips with and without TNF-α treatment. Fibrin formation was not significantly different (p>0.05) between BOECs and HUVECs at different TNF-α doses. NS: not significant; n = 4 for all experiments.

Figure 4. Functional analysis of diabetic porcine BOEC (PBOECs) in vessel-chips

(A) Fluorescence micrographs of type 1 diabetic and control PBOECs cultured in vessel-chips for 24 hours (yellow: F-actin; blue: nuclei; scale bar: 100 µm). (B) Graph showing growth of control PBOECs (black) and diabetic PBOECs (red) with time. Solid lines represent mean and the shaded regions represent error (SEM). (C) Average time required for diabetic PBOECs to reach confluence compared to control PBOECs. Time to confluence is defined as the time when cell coverage in vessel-chip reaches 90%. (D) Proliferation of control (square) and diabetic (circle) PBOECs in transwell plates normalized to respective day 1 values (p-values calculated versus control PBOEC on corresponding day). Dotted line represents the ratio of proliferation of control to diabetic cells. (E) Proliferation of control (square) and diabetic (circle) PBOECs in the vessel-chip, normalized to respective day 1 values (p-values calculated versus control PBOEC on corresponding day). Dotted line represents the ratio of proliferation of control to diabetic cells. (F) Oxidative stress measured via flow cytometry. The fluorescence reported has been normalized to the total number of cells analyzed. (G) Representative platelet adhesion micrographs of porcine primary vein endothelial cell (PVEC), TNF-α stimulated PVEC, control PBOEC and diabetic PBOEC vessel-chip devices after perfusion of whole blood (scale bar: 100 µm). (H) Platelet area coverage after 15-minute perfusion of blood in the vessel-chips. *p<0.05, **p<0.005, ***p<0.0005, ****p<0.0001; n = 3 for all experiments.

Figure 5. Future potential of blood-derived cells to engineer “personalized” organ-chip models.

(A) Schematic showing the potential of patient derived BOEC and autologous blood to engineer patient-specific vessel models that are more representative of patient distress, allow functional evaluation of disease by incorporating digital automation and overcome the predictive power of existing models. This may bridge gaps in the basic understanding of disease, ultimately leading to improved pharmaceutical research. (B) The patient-derived vessel models could potentially also provide patient-to-patient differences in disease severity, making personalized therapeutics more effective.