Colloidal Gels for Guiding Endothelial Cell Organization via

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Biological and Medical Applications of Materials and Interfaces

Colloidal Gels for Guiding Endothelial Cell Organization via Microstructural Morphology Yuan Yuan, Sukanya Basu, Meng Huisan Lin, Shruti Shukla, and Debanjan Sarkar ACS Appl. Mater. Interfaces, Just Accepted Manuscript • DOI: 10.1021/acsami.9b11293 • Publication Date (Web): 12 Aug 2019 Downloaded from pubs.acs.org on August 12, 2019

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ACS Applied Materials & Interfaces

Colloidal Gels for Guiding Endothelial Cell Organization via Microstructural Morphology

Yuan Yuana, Sukanya Basub, Meng Huisan Lina, Shruti Shuklaa Debanjan Sarkara,c,* aDepartment

of Biomedical Engineering, University at Buffalo, The State University of New York, Buffalo, NY 14260, USA

bDepartment

of Pharmacology and Therapeutics, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14263, USA

cDepartment

of Chemical and Biological Engineering, University at Buffalo, The State University of New York, Buffalo, NY 14260, USA

*Correspondence to: D. Sarkar. Biomedical Engineering, University at Buffalo, Ph: 716-645-8497, Fax: 716-645-2207 Email: [email protected]

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Abstract One of the fundamental challenges in vascular morphogenesis is to understand how the microstructural morphology of 3D matrix can provide the spatial cues to organize the endothelial cells (ECs) into specific vascular structures. Colloidal gels can provide well-controlled distinct morphological matrices because these gels are formed by the aggregation of particles. By altering the aggregation mode, the spatial organization of the particles can be controlled to yield different microstructural morphology. To demonstrate this, colloidal aggregates and gels were developed by electrostatic interaction mediated aggregation of cationic polyurethane (PU) colloidal particles by using low molecular weight electrolyte and polyelectrolyte to develop microstructurally different colloidal gels without altering their bulk elasticity. Compact dense colloidal aggregates with constricted voids were developed via electrolyte mediated aggregation whereas stranded branched networks with interconnected voids were formed via polyelectrolyte mediated bridging interactions. Results show that the microstructure of aggregated colloids and gels can regulate EC organizations. Within endothelial matrices, ECs track the microstructure of particulate phase to interconnect with stranded colloidal network but cluster around compact colloidal aggregate. Similarly, in colloidal gels, ECs formed capillary-like structures by interconnecting along the stranded networks with enhanced cell-matrix interactions and increased cell extension but aggregated within the constricted voids of compact dense gel with enhanced cell-cell interaction. Both morphometric analysis and expression of EC markers corroborated the cell organizations in these gels. Using these colloidal gels, we demonstrated the role of 3D microstructural morphology as an important regulator for spatial guidance of ECs, and simultaneously established the significance of colloidal gels as 3D matrix to regulate cellular morphogenesis.

Key words: Colloidal gel, angiogenesis

microstructure,

morphology,

endothelial


cells,

cell-matrix

interaction,

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1. Introduction Spatial organization of endothelial cells (EC) during vascularization remains a critical parameter for the success of regenerative therapies including material based strategies. Organization of ECs coordinated through cell-cell and cell-matrix interactions induce proliferation, elongation and lumenization, in order to form the nascent capillary structures which ultimately mature into vasculatures. Role of growth factors and cytokines and the underlying matrix during these morphogenetic events has been elucidated to a significant extent. In particular, the role of matrix as a structural support, in the form of presentation of ligands for EC receptors and in terms of matrix derived contractile forces, has been elucidated1-3. However, the spatial guidance from the matrix owing to its 3-deminsional (3D) morphology, during EC morphogenesis, remains less understood. Experimental evidence indicates that ECs require vascular guidance channels within 3D collagen matrices through a matrix type 1-metalloproteinase (MT1-MMP)-dependent proteolytic process for the formation of EC network and lumens4. In fact, these features have been engineered in biomaterials where MMP-cleavable linkages in hydrogels enhanced EC network and tube formation5-6. Similarly, EC responses in collagen gels with different microarchitectures but similar bulk density shows spatial morphology is important of cell organization7. In fact, ECs exhibiting reduced networks in high density biomatrices is often due to the smaller mesh size; which imposes 3D spatial constraints to cells for deformation and organization in dense microenvironment8-9. Collectively, these features indicate a role of 3D spatial guidance provided by matrix microstructure in regulating EC organization during network formation. While these studies indicate the importance of the spatial guidance by the microstructured morphology of matrix during EC network formation, they each face limitations. Firstly, 3D microstructures created by cell-mediated processes do not necessarily show how the spatial guidance of matrix microarchitectures controls cell organization; i.e. how a preexisting spatial feature of 3D matrix regulates the organization of randomly distributed cells. Secondly, the 3D microstructures modulated in engineered biomaterials (e.g. hydrogels) and the reconstituted biomatrices (e.g. collagen, matrigel, fibrin) are coupled with the biochemical and mechanical parameters i.e. variations in matrix microstructure inherently change the matrix composition and mechanical stiffness. Thus, it is essential to design a system which can isolate the effect of 3D matrix microstructure independently. In this study, we propose a synthetic



colloidal gel system which can be engineered to provide distinct 3D spatial guidance for EC organization, independent of cell-mediated process or matrix composition and mechanics. Colloidal gels, unlike monolithic hydrogels, are formed by aggregation of submicron colloidal particles into self-similar space-filling networks due to attractive interparticle interactions. Microstructure of the colloidal aggregates are regulated by the mode of aggregation and is dependent on the particle interaction potential (i.e. high surface energy particles interacts strongly to assemble), volume fraction of particles (i.e. number of particles present in a given volume of continuous medium), and range of interaction (depends on external molecules in surrounding medium to modulate the inter-particle interactions)10-14. Modulating these parameters allows either fast diffusion limited cluster aggregation (DCLA) to form tenuous branched aggregates with low fractals or slow reaction limited cluster aggregation (RCLA) to form compact dense aggregates with high fractal, leading to colloidal gel with different microstructures11, 15-16. Altering the range of interaction between colloidal particles, for a given material (i.e. with same interaction potential) at same particle fraction, can achieve different microstructures by inducing highly attractive or less attractive interaction between particles to form branched strands (i.e. DCLA) or compact dense (i.e. RCLA) aggregates respectively, in a kinetically controlled process13, 17-18. Presence of ionic and macromolecular species in continuous fluid phase in surrounding environment of colloidal particles alters the range of interaction to yield different microstructures

14, 19-20.

Altering between DCLA and RCLA through variation in

the interactions is indeed a universal paradigm to achieve different microstructured colloidal gel from same material

17.

Thus, colloidal gels, as cellular matrices, can control the microstructure

independent of other matrix parameters and these microstructured aggregates can provide preformed spatial matrix guidance to cells. Recently we demonstrated that gelatin based colloidal gels can regulate endothelial morphogenesis owing to their 3D microstructural morphology21. These results guided the development of a synthetic polymeric colloidal gel to regulate endothelial organization. In this study, we utilized colloidal gel formed with different microstructured aggregates as an endothelial matrix to regulate the cell organization during network formation. Specifically, cationic polyurethane colloidal particles (C) were aggregated via electrostatic interactions with different types of anions in the surrounding environment to alter the range of interactions between the particles and to aggregate them in different modes. When C particles interacted with


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the anions from the electrolytes present in phosphate buffer saline (PBS), dense CA aggregates are formed whereas when C particles interacted with negatively charged carboxylate groups of a polyelectrolyte, sodium-salt of poly (acrylic) acid (PA), tenuous branched CPA aggregates are formed (Fig. 1A). Ultimately these aggregates are extended in macroscopic dimensions to form colloidal gels. Different spatial arrangements of particles in CA and CPA aggregates allow distinct 3D microarchitectures without altering the physicochemical and mechanical characters of the gel, since the aggregates (and ultimately the gels) are formed from the same constituent particles and at a similar particle fraction. We utilized this system in two ways; where (a) colloidal aggregates were used as discrete localized spatial cue in a reconstituted biomatrix and, (b) the colloidal gels were used as microstructured matrix for 3D scaffold. EC organization and network formation were analyzed in these two systems and data shows that spatial guidance provided by microstructured colloidal aggregates and gels influences EC responses through different mechanisms. These results prove that colloidal gels are uniquely suited for controlled vascular morphogenesis and demonstrate their application as 3D matrix for angiogenesis as well as other cellular morphogenesis. 2. Materials and methods 2.1 Materials Polycaprolactone-diol (PCL, MW: 1250), hexamethylene diisocyanate, N-deimethylethanol amine and Na-salt of polyacrylic acid (MW: 15,000) were purchased from Sigma Aldrich (MO). PCL was used after vacuum drying at 60 °C for 24 hours to remove moisture. Anhydrous dimethyl sulfoxide (DMSO) and acetic acid were purchased from VWR. All other chemicals and solvents were purchased from Sigma Aldrich (MO), unless otherwise mentioned. Human umbilical vein endothelial cells (HUVEC) were purchased from commercial source (Promocell) and grown in endothelial cell growth medium without VEGF (Cell applications #2110-500). Cells were cultured and maintained in 5% CO2 at 37°C with media change every second day and passaged at 80% confluence. Cells were typically used between passage 4-7 for the experiments. Rhodamine-phalloidin and 4,6-diamidino-2-phenylindole (DAPI) were purchased from



Invitrogen CA. Rat tail collagen I with concentration of 3.3 mg/mL in 0.02 N acetic acid and Matrigel® concentration of 10 mg/mL were purchased from Corning Inc 2.2 Polyurethane synthesis and colloidal particle preparation Polycaprolactone based positively charged polyurethane was synthesized by a two-step polycondensation reaction22. Briefly, pre-dried PCL and hexamethylene diisocyanate (HDI) were reacted at 1:2 molar ratio at 90 °C in anhydrous DMSO for 3 h in the presence of tin-2-ethyl hexanoate (0.1 mol%) as catalyst to form the isocyanate terminated prepolymer. Nmethyldiethanol amine (NMDA) was used to chain extend the prepolymer and was added to the reaction mixture with the molar ratio of 1:1 (w.r.t. PCL) and the reaction was continued for 12 hrs at 90 °C. After which reaction mixture was cooled to 40 °C, and acetic acid was slowly added to the reaction mixture (with acetic acid to NMDA molar ratio of 1.5:1) to protonate the tertiary amine groups of NMDA for 3 hrs. To form the cationic PU colloidal particles from this positively charged polymer, reaction mixture was added dropwise into the dd deionized water at 1:4 volume and continuous stirring at 700rpm for 10 mins. The colloidal particle suspension ultra-centrifuged under 13000rpm to collect the p articles (C) and used as wet particles (immediately after preparation) to preserve the size and water content within the particles. Freshly prepared colloidal PU particles were used all characterizations. 2.3 Aggregation of colloidal particles Centrifuged colloidal PU particles were used immediately after preparation (to prevent any loss of structure) to aggregate and form gels. Two colloidal gels were formed by adding electrolyte (1X phosphate buffer saline, PBS - CA), and polyelectrolyte (2.5w/v% Na-salt of polyacrylic acid - CPA). Particle fractions used to form the colloidal aggregates and gels are based on the weight of wet particles (immediately after preparation) to preserve the size and water content of PU particles. 0.1 fraction gels were prepared by adding 1 mL of 1X PBS and Na-salt of polyacrylic acid (2.5% w/v in dd deionized water) to 0.1 gm of PU colloidal particles forming CA- and CPP- colloidal gels respectively. CA and CPA colloidal aggregates and gels from different particle fraction were formed in similar manner. To ensure complete aggregation, the


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aggregation was allowed to equilibrate for 12 hrs after which the coagulated colloidal aggregates were collected by removing the supernatant. To characterize the aggregation, concentration of PBS and Na-salt of polyacrylic acid was varied for CA-gel and CPP-gel respectively. The CA and CPA colloidal aggregates can be used as discrete microstructured aggregate or as an ensemble of collection of aggregates in continuous system as colloidal gels. 2.4 Characterizations of colloidal particles and aggregation Colloidal PU particles were imaged from scanning electron microscopy (SEM) and fluorescence imaging (Fluorescent particles were prepared by adding nile red dye (2mg/mL in DMSO) in reaction mixture as 1:100 by volume after quarternization and imaged in TRITC channel). SEM was performed on air dried sample on glass coverslip with an accelerating voltage of 5.0 kV (Hitachi SU70 FESEM). The samples were coated with 10nm film of carbon in a high vacuum evaporator for 30 minutes. Fluorescence imaging was performed in confocal microscope (Zeiss LSM-510) at 10x to visualize dispersed particles. Dynamic light scattering (DLS, Zetasizer Nano-S, Malvern Instruments Ltd.) was used to measure the particle size and zeta (ξ) potential by dispersing PU particles in deionized water at a solid content of 0.01w/v% (measuring time 60min, one measurement per minute). To monitor the effect of ionic strength on surface charge the particles were dispersed in HEPES buffer with increasing molarity at pH 7(1mM, 20mM, 40mM, 100mM, and 500mM). The shift in the charge with increasing pH (2, 4.5, 7, 8.5 and 10) at a set concentration of 20mM was also measured. DLS measurements were used to characterize the aggregation of gels by measuring size and zeta potential with different concentration of PBS (0.01, 0.1 and 1X) and Na-salt of polyacrylic acid (0.1%, 1% and 2.5% w/v) for CA- and CPArespectively. Turbidimetric analysis of colloidal aggregation were performed by measuring the turbidity of the colloidal dispersion and aggregates from the absorbance (optical density) of the samples in UVvis spectrophotometer (UV1600PC) at room temperature. To calculate the ‘dispersibility factor ( 𝑛)’ optical density (OD) of the samples were measured at wavelength (λ) 650, 600, 550, 500, and 450 nm; and 𝑛 is calculated from the wavelength dependence of OD from the slope of log(OD) vs. log (λ) as, 𝑛 = ―

𝑑log 𝑂𝐷 𝑑log 𝜆

23-24.

Aggregation profile of colloidal particles were obtained by

plotting dispersibility factor (𝑛) with respect to particle fraction for a given sample. Kinetics of



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particle aggregation was investigated by measuring the OD with respect to time for 150 minutes for each sample at 650 nm at room temperature. 𝜏 for all the three aggregates were obtained by fitting the kinetic data into Kohlrausch-William-Watts (KWW) function25 from 5 independent measurements and data is presented as average ± S.D. Confocal fluorescence imaging was performed to image the morphology of the aggregates. CA and CPA colloidal aggregates were formed from 0.1 particle fraction as described above. Quantification of individual aggregates were done calculating equivalent diameter (diameter of

(

the circular projection of an aggregate) and circularity index

4𝜋𝐴𝑟𝑒𝑎

), with index closer to 1

𝑃𝑒𝑟𝑖𝑚𝑒𝑡𝑒𝑟2

indicates circular structures. 2D Fractal dimension was calculated using box-counting method from the binary images in ImageJ (NIH, USA). Microstructure of the aggregates were further analyzed from the binary images following skeletonization of colloidal aggregate using ImageJ. This allows assessing the map of particle arrangement in each aggregate. For each skeleton the length of the centerlines was analyzed as longest unit and the number of branches emanating from the centerline (normalized to the length of the centerline) were measured. For these measurements, individual aggregates were randomly selected fluorescent images for three gels with at least 20 aggregates for each gel type from four experiments. Data is presented as box plot with box representing 80 percent data distribution with maximum and minimum limit. 2.5 Microstructural morphology of colloidal gels Colloidal gels were prepared from CA and CPA aggregates prepared from 0.1 particle fraction. Gel morphology was characterized SEM and confocal fluorescence imaging (imaged at 20X with z-stacks over 4 µm thickness at interval of 0.1 µm), as described earlier. Gel microstructure was quantified using the Image J software to from the binarized fluorescent images to calculate void fraction from the relative distribution black and white regions and aspect ratio of the voids. For each gel, randomly selected 10 images were analyzed from three experiments and data is presented as average ± S.D. For confocal images, 3D interactive surface plots from ImageJ were used to generate the heat-map to show the gel morphology and distribution of voids. 2.6 Mechanical Characterization


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The mechanical properties of the CA and CPA aggregate embedded endothelial matrix and CACPA colloidal gels were assessed from oscillatory rheological measurements. The rheological properties of colloidal gels were characterized using a rheometer (Bohlin CVOD 100NF). All measurements were performed using a flat steel parallel plate geometry at 37°C with a gap distance of 200µm. Solvent trap were used to prevent drying up of the samples. Oscillatory strain sweeps were performed at variable amplitudes in a strain-controlled mode in the range of 0.01 to 1 while keeping frequency at a constant value of 1 Hz. From the linear viscoelastic region of the amplitude sweeps, the storage (elastic) modulus (Gʹ), the loss (viscous) modulus (Gʺ) and tan(δ) (=Gʺ/ Gʹ) were analyzed. Yield strain is determined from the crossover between two moduli. Storage (elastic) moduli (Gʹ)and yield strain of the gels with different particle fractions were assessed from linear viscoelastic region of their respective amplitude sweep. Oscillatory frequency sweep was performed at constant strain as 0.03 (within the linear region from amplitude sweeps) and varied the frequency from 0.1 to 10 Hz. All rheological experiments were performed 3 times each from 3 different samples, and data for elastic moduli (Gʹ)and tan(δ) is presented as average ± S.D. 2.7 Preparation of colloidal aggregate embedded endothelial matrix and composite colloidal gel. To prepare the colloidal aggregate embedded biomatrix, two reconstituted endothelial matrices were used; Matrigel and Collagen I. CA and CPA aggregates were formed from 0.1 particle fraction as described above. The aggregates were embedded by mixing the colloidal aggregates to Matrigel solution (1.5 mg/mL) or type I collagen solution (1mg/mL). The relative fraction of the particulate phase owing to these aggregates were maintained at 0.1 with respect to reconstituted matrix solution. Volume fraction of the colloidal aggregates was estimated from the number of particles (for given weight, measured by Nanosight®) and average size of particles. In a typical experiment, 80 µL of Matrigel® or collagen solution was used to disperse the CA and CPA aggregate. To ensure uniform distribution, the aggregates were gently mixed with solution. The aggregate containing solution was added to 48 well tissue culture plate and allowed to gel at 37°C for 4 hours either in presence or absence of endothelial cells. Colloidal gels from CA and CPA aggregates were formed from 0.1 particle fraction and these gels were used as 3D matrix for endothelial cells. In addition to the native colloidal gels, to



prepare the composite CA and CPA colloidal gels, Matrigel and collagen I solution was added to the respective colloidal aggregates where the volume fraction Matrigel solution (1.5 mg/mL) or type I collagen solution (1mg/mL) were maintained at 0.1 with respect to the volume of colloidal aggregates. These composite gels were allowed to equilibrate at 37°C for 4 hours and were used either in presence or absence of endothelial cells.

2.8 Preparation of EC containing composite matrix. To include HUVEC in the colloidal aggregate containing endothelial matrices (i.e. Matrigel and Collagen based composite matrix), HUVECs were added into the matrigel or collagen solution at a concentration of 500,000/mL prior to mixing with the colloidal aggregate. The volume ratio of cell containing solution to colloidal aggregate was maintained at 9:1 (as described earlier). These structures were allowed to gel at 37°C for 4 hours. These composite structures were characterized to evaluate the endothelial morphology at defined time. To evaluate the EC sprouting in colloidal aggregate embedded composite biomatrix, the outward migration of HUVECs from a cellular aggregate was assessed. HUVEC aggregates were formed on 1w% gelled low melting point agarose (5000 cells for each) overnight in 96 well plate and these aggregates transferred into colloidal aggregate containing composite matrix prior to gelation. After gelation, outward emigration of ECs into composite matrix was characterized from sprouting of cells from the core by measuring distance from the cell core to outer periphery of emigrated cell (defined as sprouting length). 2.9 Preparation of EC containing colloidal gels. To prepare HUVEC embedded colloidal gels, freshly prepared gels from 0.1 particle fraction were used after washing three times with phosphate buffered saline (PBS), and finally, one time with endothelial cell media. EC were seeded onto the colloidal gels at a density of 150,000 cells per mg of gel (typically 4.5x106 cells with 0.03 g gel) by mixing the cells (in 50 µL media) with the colloidal aggregates and cell-gel aggregates were allowed settle in non-adherent U-bottom 96 well plate. After which 100 µL of defined endothelial cell media was added into each of the well. To prepare, the composite colloidal gels with Matrigel and collagen, the cells were suspended in


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either Matrigel solution (1.5mg/mL) or collagen solution (1mg/mL). To maintain the proportion of added endothelial matrix with respect to colloidal gel (as described before) and to keep the total number of added cells constant, the volume of the cell suspension and the concentration of cells were adjusted for the composite colloidal gels. Cell-gel aggregates were maintained in the incubator at 37°C for characterization at defined time points. Viability of the cells were measured by Alamar Blue assay, according to manufacturers’ instruction, with cell only aggregates as control. 2.10. Morphometric analysis of Endothelial Cells HUVEC cell morphology and organization in colloidal aggregate embedded composite matrix were analyzed at 12, 24 and 48 hrs. Cell were visualized after fluorescently staining actin with TRITC-phalloidin and nucleus with DAPI. Briefly, the samples were fixed with 3.7% formaldehyde in PBS at room temperature and were permeabilized with 0.1% Triton X-100 in PBS for 5 mins at room temperature. Permeabilized cells were incubated with TRITC-phalloidin for 1 hr followed by 5 minutes with DAPI at room temperature. Images were captured at 10x with appropriate filtered camera channels of Ti-U Inverted Microscope at 10X. EC morphology was characterized by tube length (total length of continuous interconnected ECs per unit area), number of branches (three or more point junctions per unit area) and network size (perimeter of enclosed network formed by EC tubes) or aggregate size (perimeter of aggregated ECs). Image analysis was performed from at least 10 images of each group from three independent experiments. Morphometric analysis of EC sprouting was performed at 48 hrs from three independent experiments after fixation of cells with 3.7% formaldehyde in PBS and staining of cells with rhodamine-phalloidin (actin) and DAPI (nucleus), as described. Images were acquired at 4X magnification under appropriate channels. Quantification was done using image analysis performed with ImageJ and data is presented as average ± S.D. HUVEC cell morphology and organization in colloidal CA and CPA gels as well as composite CA and CPA gels were analyzed after 48 hrs. All the constructs were fixed after 48 hours with 10% formaldehyde in PBS at room temperature for 1 hour, washed thrice with PBS and were embedded in 3% agarose gels and allowed to solidify at 4°C. Agarose embedded samples were sectioned at 1 µm interval. The sectioned slides were stained with Hematoxylin and Eosin



(H&E) using standard protocol and were fluorescently stained for actin (with TRITC-phalloidin) and nucleus (with DAPI), as described before. The stained slides were imaged at 20X under appropriate channels. EC morphology was characterized (from both H&E stained and fluorescently stained slides) by tube length (total length of continuous interconnected ECs per unit area), network size (perimeter of enclosed network formed by EC tubes), and shape of cell aggregates with circularity index. Image analysis was performed from at least 5 images (sectioned at different depth) of each group from three independent experiments with 5 randomly selected areas from each image. Image analysis was performed with ImageJ and data is presented as average ± S.D. 2.11 Simulation of endothelial patterns The vascular patterns were modeled using an established hybrid model and validated for analyzing endothelial cell organization using with Morpheus, an open source platform for simulation of multicellular model26-27. The details of the model set up is described in the literature26, 28. Briefly, this hybrid model consists of cellular Potts model (CPM) which describes endothelial cells and partial differential equation (PDE) model which represent growth factors and its interaction with extracellular matrix. In general, in CPM cells are modeled in twodimensional lattice with finite volume but deformable shape and cell interfaces with another cell and matrix. A characteristic binding energy is assigned when cell interfaces with another cell (Jcc)

and when cell interfaces with matrix (Jc-m). The lattice type simulations begin with cells

equally spaced on a square lattice and evolve through index-copy attempts of cell membrane fluctuations governed by temperature. The evolution of the index-copy attempts is governed by the Hamiltonian with Boltzmann acceptance probability. Cell-cell (C-C) interaction is set equal to cell-matrix (C-M) interaction when, Jc-c=2Jc-m; when Jc-c < 2Jc-m cell-cell adhesion is higher (i.e. cell-cell binding energy is low) and when Jc-c > 2Jc-m cell-cell adhesion is lower (i.e. cell-cell binding energy is high) compared to cell-matrix adhesion26, 28-29. Additionally, the effect of cell elongation can be studied by cell-length constraint to free energy change with target length (L) and the effect of chemotactic strength (str) of cells on vascular patterns28, 30. We simulated the vascular patterns by altering Jc-c (1, 10, and 20) at constant Jc-m =10 to alter the intercellular adhesion with respect to matrix (increasing Jc-c indicates lower cell-cell adhesion) for different target lengths L (1, 10, 40 and 100) with larger L indicating elongated cell. For all these


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conditions, the chemotactic strength of cells was varied as low and high to respectively exclude or include the effect of chemotaxis in vascular patterns (with str=3e7 for low and str=15e7 for high). Using no chemotactic strength would not converge the simulation as the ECs in this model are inherently modeled to exhibit chemotaxis. The PDE model based on reaction-diffusion system simulates the presence soluble growth factor, its binding and unbinding rate to matrix, diffusion and decay of growth factor by a system of partial differentiation equations, as described in the literature26. A sensitivity analysis was performed to identify the lower limit for the initial value of soluble growth factor (u=1.5e-6) under which all the simulations were performed since our experiments were under growth factor-free medium and the diffusion coefficient of growth factor was considered high (i.e. well mixed because the colloidal gels exhibit microstructures instead dense mesh). Values of rate constant for production of growth factor, binding and unbinding of growth factors and decay rate of matrix were estimated from the literature26 and were assumed constant for all the conditions. Morphometric analysis of the simulated patterns was performed by ImageJ following the adjusting of the square lattice to resemble the dimension the experimental images and circularity index of cell aggregates and total length of connected cell networks (per unit area) were estimated. For morphometric analysis, multiple simulations were performed (n=5) with different random seeds, of which the average and standard deviation are shown. 2.12 Western blot analysis Immunoblots were adapted from an established protocol31. Proteins were extracted by the addition of trichloracetic acid (TCA) buffer containing 10% TCA; 10mM Tris-HCl pH 8; 25 mM ammonium acetate, and 1mM EDTA. Acid-washed glass beads were added to the scaffold and cell pellet mixture. Cells were lysed by five consecutive one-minute vortex pulses with oneminute rests on ice using fast prep multi-vortex (Labline instrument, Melrose, IL). Proteins were precipitated by centrifugation at 4°C at 16000g for 10 min. Protein pellets were thoroughly resuspended using resuspension buffer containing 0.1M Tris-HCl pH 11 and 3% SDS by boiling the suspension for 5 min at 95°C. Total protein concentration was measured using Biorad BCA protein assay kit (Pierce™ Microplate BCA Protein Assay Kit catalog # 23252). Equal amounts of 4X laemmli sample buffer was added to the re-suspended protein solution and boiled for 5



min at 95°C. Extracted proteins were loaded onto 10% SDS-PAGE separating gel. Approximately 10 μg of protein was loaded per lane. Proteins were transferred to nitrocellulose membranes (protran BA85, VWR International Inc. Bridgeport NJ). Membrane was blocked either with 5% nonfat milk or 5% BSA in 10mM Tris-HCl pH8, 150 mM NaCl and 0.05% Tween 20. Immunoblots were visualized using Western Bright ECL HRP substrate (Menlo Park, CA; LPS #K-12045-D20). To detect Focal adhesion kinase (FAK), primary antibody (FAK antibody # 3285) was used at 1: 1000 dilution, to detect VE-cadherin, primary antibody (VEcadherin antibody # 2158) was used at 1: 1000 dilution, to detect VEGFR2, primary antibody (VEGFR2 antibody # 2479, Cell Signaling Technology) was used at 1: 1000 dilution, and to detect MMP-2, primary antibody (MMP2 Antibody #87809, Cell Signaling Technology) was used at 1:1000 dilution according to manufacturers' protocol with the corresponding secondary antibody. For all western blot analysis, β-actin [β-Actin (13E5) Rabbit mAb #4970] was used as control. All primary and secondary antibodies were purchased from Cell Signaling Technology, MA, US. Quantification of western blots was performed from densitometric analysis of bands using Image Lab 3.0, Biorad software and bands were normalized with respect to β-actin band. Measurements were performed at least 3 times and data is presented as average ± S.D. 2.13 Statistical analysis Data is presented either as an average with ± standard deviation (error bar representing standard deviation) or as a box-plot with 80% data points embedded in the box (error bar representing maximum and minimum value). Statistical significance was determined by ANOVA followed Tukey’s test for multiple comparison. P value ≤ 0.05 is designated with * and value ≤ 0.01 is designated with **. P value ≤ 0.05 is considered significant. 3. Results: 3.1 Colloidal Particles and Aggregation


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The positively charged colloidal particles were developed from segmental cationic polyurethane

Figure 1. Microstructured colloidal aggregation induced by electrostatic interaction. A) Schematic diagram illustrating the different modes of aggregation by electrostatic interactions using electrolyte and polyelectrolyte to induce cationic PU colloidal particles into different microstructured aggregate and gels. B) Scanning electron microscopic and fluorescent images of colloidal PU particles with spherical shape and submicron size. C) Size and zeta potential of colloidal PU particles (C) in dd water and in HEPES buffer. D) Change of size and zeta potential of colloidal PU particles (C) due to aggregation mediated by electrolytes (CA) in phosphate buffer saline of different strength and by polyelectrolyte Na-salt of polyacrylic acid (CPA) of different strength. E) ‘Dispersibility factor, n’ due to aggregation of colloidal PU particles (C) in CA and CPA. Variation of ‘n’ with respect to particle fraction in colloidal PU particles (C) and aggregated CA and CPA system. F) Kinetics of aggregation measured from change in optical density with time for CA and CPA system. Aggregation time estimated from KWW fit of kinetic data. (ANOVA, ** P≤ 0.01, * P≤ 0.05)



(PU), as ionomeric polyurethanes are known to form stable colloidal dispersion with submicron particles32-33. We utilized polycaprolactone based PU where polycaprolactone is used as soft segment and hexamethylene diisocyanate and N-methyl diethanol amine as hard segment followed by quaternization of tertiary amine with acetic acid. The structure of the PU was verified from 1HNMR and FTIR (Fig. S1) and the molecular weight (Mw) of the PU was ~21,500±750 with PDI of 1.70. The resulting cationic PU forms stable colloidal particles in aqueous phase as dispersion through selective fractionation of the polymer chains from polar solvent to the water phase. Cationic PU (C) colloidal particles formed by the solvent extraction method were spherical at sub-micron (~0.5 µm) size, as seen from the scanning electron microscopic image and from fluorescent images (where C particles contains fluorescent Nile red dye) of the particles (Fig 1B). From dynamic light scattering measurement, C particle sizes were around 571±34 nm and were with positively charged surface with zeta potential +44 mV in ddH2O (Fig (and also in presence of 1mM HEPES salt at pH7) (Fig. 1C). To further verify the presence of positive charge, the C particles displayed decreasing zeta potential with increasing ionic strength and pH (Fig. S2). This shows that cationic PUs are spherical with submicron size and are stable in aqueous dispersion due to their positive charges arising from the quaternary amine of PU, and the colloidal particles can be aggregated via electrostatic interaction. To examine, if the positive surface charge of C particles can be reversed and neutralized by altering the ionic strength of the surrounding aqueous phase, and if this can lead to aggregation of C particles, we explored two specific conditions: (a) electrolytes present in phosphate buffer saline (PBS), and (b) polyelectrolyte from sodium-salt of poly(acrylic) acid (PA), both at neutral pH ~7.0. Negative charge of the anions of PBS (i.e. phosphate and chlorides) and PA (i.e. sodium carboxylate of polyacrylic acid) can adsorb on C particle to neutralize the surface positive charges and induce aggregation. Fig 1D shows, addition of anions both from PBS and PA led to increase in particle size with concomitant decrease of surface charges, indicating aggregation due to surface charge reversal and neutralization, leading to formation of CA (from anions of PBS) and CPA (from carboxylate groups of PA) respectively. Furthermore, as the ionic strength surrounding aqueous phase was varied with the concentration of salts in PBS increased from 0.01x to 1x (where 1x PBS approximately corresponds to 10mM phosphate and 140mM chloride) and with the concentration of PA increased from 0.1% to 2.5%(w/v), there was a gradual increase in size and reduction of surface charge in CA and CPA respectively. From the


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size distribution of the colloidal aggregate of these two systems (Fig. S3), CPA aggregates showed broader distribution than CA aggregates, indicating the resultant dimension of the aggregates, following inter-particle association, varied over a wider size range in CPA. While the mechanism of aggregation in both systems are similar through charge reversal and neutralization, but mode of aggregation was different in these two systems. In CPA system, aggregation primarily occurred due to the ‘bridging flocculation’ from the macromolecular PA chains containing multiple anionic sites across the chain, whereas, in CA system the aggregation was due to adsorption flocculation. From these data, we used the 1X PBS and 2.5% PA as the optimized condition to form CA and CPA aggregates and gels. To further validate the aggregation and their modes in these systems, we performed turbidimetric measurements to calculate the ‘dispersibility factor,𝑛’ based on the wavelength dependence of the turbidity of the particles, from the slope of log[turbidity(O.D.)] versus log[wavelength(𝜆)]; where 𝑛 value decreases with particle aggregation and growth of aggregate size24. These measurements were performed at a very dilute particle fraction (~0.005 w/v) to prevent phase separation and coagulation, so that the initial state of aggregation can be assessed. Clearly, a significant decrease in 𝑛 was observed in both CA and CPA compared to particle only system, as aggregation occurred and 𝑛 value was lower for CA compared to that of CPA(Fig. 1E). This shows aggregation of particles into colloidal aggregates occurred through charge reversal and neutralization, leading to reduction of ionic repulsion and destabilization of the dispersion. Increased characteristic dimension (i.e. size) of CA aggregates (from smaller 𝑛 value) compared to CPA ones indicates uniform (and increased) aggregation of particles into compact CA aggregates relative to CPA aggregates of smaller characteristic dimension due to stranded microstructure. To further validate, we analyzed the change of 𝑛 value with different particle fractions for CA and CPA (Fig. 1E). While C particles showed no change in 𝑛 value at all particle fractions, indicating stable colloidal dispersion from cationic PU colloidal particles, both CA and CPA showed rapid decrease in 𝑛 value with increasing particle fractions due to aggregation. At lower particle fractions, the difference of 𝑛 value between CA and CPA was not significant, but increasing particle fractions (>0.005 w/v) 𝑛 value of CA was less than that of CPA. This indicates that during initial state of aggregation where particles formed mainly smaller aggregates e.g. doublet and triplets, there was no difference between the characteristic aggregate size from either adsorption or bridging mode. But, further growth of aggregates (with



increasing particle fraction), lead to smaller dimension (i.e. lower 𝑛 value) of CA compared to CPA at similar particle fraction; indicating branching and stranded growth of aggregates in CPA compared to compact uniform growth of aggregates in CA. Furthermore, it is obvious that to achieve similar size of aggregates (i.e. similar 𝑛 value), CPA aggregation requires more particles to interact compared to CA aggregation, because bridging interactions between particles involve more particles to achieve the same characteristic size compared to adsorption mode. This indicates bridging interaction in CPA leads to branched (and stranded) open microstructure whereas CA aggregates are compact, as observed in gelatin based colloidal aggregates21. To analyze the kinetic pattern of aggregation, we analyzed the change in dispersion turbidity (O.D.) with time (Fig. 1F) which shows decrease of turbidity in CA was faster compared to CPA. Aggregate growths in CA was rapid following charge reversal by small ions due to faster diffusion (i.e. collision) of particles to come in contact whereas in CPA charge reversal by macromolecular PA reduced the diffusion as the PA chains decreased the frequency of collision. This shows that CA aggregate formation was reaction limited and resulted in compact aggregates, whereas CPA aggregates were diffusion limited and resulted in branched aggregates11, 18, 21. This aggregation pattern from turbidity with time was evaluated further by fitting the experimental data the Kohlrausch-Williams-Watts (KWW) function, a function which describes various types of relaxation data including investigation of colloids, polymer solutions, and protein aggregation25, 34. For both systems, the 𝑅2 value for the fitting was greater than 0.95. Two parameters were evaluated from this function: where 𝜏 denotes the aggregation time (i.e. relaxation time) and 𝛽 denotes the relaxation parameter indicating heterogeneity of the process (i.e. 𝛽 close to 1 indicates homogeneous process). 𝛽 value for both the system was similar around 0.4-0.5, indicating both aggregation processes were relatively heterogeneous, as would be expected because multiple organization and reorganization events occur during aggregation i.e. structures evolve with different sizes of flocs aggregating in heterogeneous manner. However aggregation time, calculated from 𝜏 values (Fig. 1F), in CPA increased 2.5 times compared to CA; because macromolecular PA chains require more time to aggregate the particles which results into branched structure. These results indicate that colloidal cationic PU particles aggregate with different mechanisms to form different microstructured aggregates by varying the range of interaction. By varying the nature of anions in the surrounding aqueous phase different level of interactions were induced to aggregate the particles with different microstructures.


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3.2 Microstructure of colloidal aggregates and gel To evaluate the variation in microstructural organization of CA and CPA system, we used original and fluorescent C particles containing Nile red as fluorescent dye for the purpose of visualization. Physical entrapment of dye in the particles did not alter size, shape and surface

Figure 2. Colloidal aggregates show different microstructures. A) Brightfield image of CA and CPA colloidal aggregates. Inset showing traced outline of aggregate boundary. B) Fluorescent microscopic images of CA and CPA colloidal aggregates and a representative binarized image showing different microstructures. C) Characterization of microstructure from circularity index and fractal dimension, and structural organization of colloidal particles network from length of longest unit and number of branches per unit length of an aggregate. D) Oscillatory amplitude sweep test of CA and CPA aggregate embedded Matrigel and Collagen I with 0.1 particulate (by volume). (ANOVA, ** P≤ 0.01, * P≤ 0.05)

charges. These particles were allowed to aggregate by the same manner to develop the CA and CPA system as described above with particle fraction 0.1 w/v. Following aggregation, to examine the structural organization of particles within aggregates, both brightfield (Fig. 2A) and fluorescent microscopic images (Fig. 2B) were analyzed, where brightfield images were acquired at low magnification to capture the overall organization of the aggregates and the fluorescent images were acquired at low magnification to capture the organization of the isolated aggregates. Compared to C particles, both CA and CPA showed aggregation. The CA aggregates showed relatively more uniform and compact growth whereas CPA aggregates were more extended and spread-out. Morphological organization (Fig. 2C) of colloidal aggregates were analyzed from shape using circularity which shows CA aggregates with higher circularity index compared to



CPA aggregates (as circularity index closer to 1 indicates more circular shape). To assess the dimensional characteristics of the aggregates, 2D fractal dimension were analyzed from the fluorescent images. As expected, more compact CA aggregates exhibited higher fractal dimension compared to branched CPA aggregates, supporting the microstructure and the mode of aggregation. Higher fractal values indicate that uniform growth of aggregates in multidimension upon flocculation which is observed in CA; whereas lower fractal values of CPA show growth of aggregates in fewer dimensions leading to branched open structures. To further analyze the morphological organization owing to the particle aggregation, binary images of CA and CPA aggregates (from fluorescent images) were skeletonized, and the longest unit within individual aggregates and number of branches emanating from the longest unit were measured (Fig. 2C). Length of longest unit in CA aggregates was shorter than that of CPA aggregates with lesser number of branches extended in CA compared to that of CPA. This indicates CA aggregates were more compact and dense (hence less extended) whereas CPA aggregates were extended strands with branched networks. Although the aggregation of colloidal particles initiates from singlet level, but the self-similarity of aggregation is extended across the multiple scale lengths comparable to the scale-length of multicellular dimensions. This enables utilizing the colloidal aggregates as discrete preformed localized domains in the matrix for cell organization. Collective organizations of ECs are envisioned from the spatial guidance of these aggregates with distinct microstructural organization. To design this system, both CA and CPA aggregates were embedded in the reconstituted biomatrix, matrigel and collagen I, with overall volume fraction constituted by the aggregate phase was maintained at ~ 0.1(±0.05) compared to biomatrix phase. We restricted the volume fraction within this limit because any further increase in volume fraction of colloidal aggregates leads to relatively dense with almost complete occupancy by the aggregates. The dispersed phases of aggregates from CA and CPA in the both the biomatrices were well distributed and their microstructural features were maintained. The dimensional characteristics of the discrete CA and CPA aggregates correlated to the scale of multicellular dimension, as the equivalent diameter of both type of aggregates ranged between ~85-100µm. Presence of the CA aggregates in matrigel and collagen I gel (i.e. CA-M and CPAM respectively) and CPA aggregates in matrigel and collagen I gel (i.e. CA-C and CPA-C respectively), did not alter the mechanical properties of the composite systems; and also the mechanical properties of the composites with respect to their native biomatrix (Fig. 2D). This


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composite system allows the cells to navigate through the natural matrix, which is the major phase, but with the localized spatial guidance received by tracking the microstructured aggregate. This suggests that composite systems essentially resemble the natural matrix for the ECs to receive biomolecular and mechanical cues but with the morphological spatial guidance are provided by the microstructured aggregates. We further utilized these colloidal aggregates as ensemble to form a continuous 3D matrix in order to develop the colloidal gels. To analyze the 3D microstructural morphology of

Figure 3. Microstructural morphology of colloidal gels. A) Confocal scanning fluorescent images and 3D interactive surface plot (color code indicates z-depth) of CA and CPA gels showing morphology and spatial distribution of voids in the gels. B) Morphological features of the microstructure analyzed by measuring void areas and void aspect ratio of the gels from these images using in ImageJ. C) Microstructured morphology of CA and CPA gels from scanning electron microscopic images. (ANOVA, ** P ≤ 0.01, * P ≤ 0.05)

the colloidal gels formed by CA and CPA system, we examined the microscopic images from the 3D z-stack acquired over a thickness of 4µm. This analysis shows that the organization of colloidal particles following aggregation gives rise to markedly different microstructural



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morphology of CA and CPA colloidal gel. Since colloidal gels are essentially self-spanning 3D network formed from the aggregation of particles, microstructural organization of CA and CPA aggregates are extended and reflected in the gel (Fig. 3A). CA gels are more occupied by the particulate phases with the compact aggregates filling up the 3D space and contain lesser and constricted void spaces as pores and tube-like features. In contrast, in CPA gels the particulate phases are formed by interconnected strands of branched aggregates and contain greater interconnected void spaces enclosed within the connected strands. Microstructural morphology difference is further visualized from the 3D rendering of interactive surface plot of the colloidal gels. Quantified analysis of the gel microstructure, analyzed from binarized fluorescent images, supported the microstructural morphology of the gels (Fig. 3B). CPA colloidal gels showed void space of 63±7% compared to 48±3% void space of CA gels. Further, two-dimensional shape analysis of voids showed that aspect ratio of voids in CPA gels are lesser compared to that of CA gels, because relatively interconnected voids in CPA gels appear more circular but localized voids in dense CA gels appear less circular, in two-dimensional image.

As a result, CA

colloidal gels are dense and increasingly space-filling with the compact particulate phase and the void spaces are localized and restrictive, whereas, CPA colloidal gels loose less space-filling due to the stranded networks of particulate phase and the void spaces are larger and interconnected. The different modes of aggregation of colloidal particles in CA and CPA system lead to clustering of particles into network structure with different spatial and morphological features in the gel. Furthermore, the SEM images (Fig. 3C) show that the organization of particles in CA and CPA gels corroborates the microstructural organization of the particles. To analyze the effect of microstructural morphology on the mechanical properties of the gels, we analyzed the rheological characteristics from the elasticity and viscoelastic responses. The CA and CPA gel showed similar elastic (storage) moduli (~25kPa) but phase angle of CPA was higher that of CA (Fig. 4A), indicating that the bulk elasticity is independent of gel microstructure but CA gels comparatively more solid-like due to its compact microstructural morphology. Furthermore, the dispersion of colloidal particles (i.e. C) at similar weight fraction exhibited elastic modulus of ~1.5Pa and phase angle of 63°, indicating transformation of fluidic dispersion into solid gel following aggregation. Similar elastic moduli of these gels indicate that the colloidal gels formed from the CA and CPA aggregates are essentially limited by intercluster links35-36. This characteristic feature of colloidal gels enables varying the microstructure


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of the aggregates but without altering bulk elasticity of the gels. To verify this further, we measured the elastic moduli and yield strain of CA and CPA gels formed from different particle fractions (Fig. 4B). The elasticity and yield strain of both gels scaled with particle fraction but there was no significant difference between the elastic moduli of CA and CPA gels. Whereas the

Figure 4. Mechanical characterizations of colloidal gel from rheology. A) Elastic moduli (Gʹ) and tan(δ) of CA and CPA gel from 0.1 particle fraction obtained from linear viscoelastic region of strain amplitude sweep measured at constant frequency. B) Variation of elastic moduli (Gʹ) and yield strain of CA and CPA gels prepared from different particle fractions; measured from linear viscoelastic region and from the crossover point of elastic (Gʹ) and viscous (Gʺ) moduli. C) Oscillatory frequency sweep of CA and CPA gel from 0.1 particle fraction.

yield strain of CA gels where became greater than that of CPA gels at higher particle fraction. Similar elasticity indicates that inter-cluster defines bulk elasticity of these colloidal gels but varying yield strain is attributed to the interconnected strands of CPA aggregates and the



resulting microstructural morphology which enables the flow of the stranded CPA aggregates compared to dense CA aggregates upon yielding. Yielding of colloidal gels is reduced (i.e. yield stress is increased) with increasing particle fraction and interparticle interactions (including bridging interaction) which has been theoretically and experimentally established owing to structural organization of colloidal particles36. Typical amplitude strain sweep of the CA and CPA gels (at 0.1 particle fraction) is shown in Fig S4. Furthermore, these gels display frequency independent stable gel-like responses (Fig. 4C). Thus, the two colloidal gels were developed from CA and CPA aggregates can be used as 3D endothelial matrix where the microstructural morphology is varied but without altering the bulk elasticity. In addition, these colloidal gels were combined with matrigel and collagen as biomatrix. In these composite gels with biomatrices, volume fraction of the particulate phase from the colloidal aggregates constituted within the range of 0.90-0.95 compared to biomatrix, i.e. the colloidal gels were essentially derived from CA and CPA aggregates and biomatrix provides a nominal covering of biomacromolecules (i.e. collagen and laminin) on colloidal network, which essentially allowed cell adhesion with appropriate cell-receptors. The mechanical properties of the colloidal gels with matrigel, i.e. CA-M and CPA-M and that with collagen i.e. CA-C and CPA-C displayed similar characteristics where linear elastic modulus (G′) of the gels are comparable and shows frequency independent response (indicating stable solid-gel like structure) (Fig. S5). This suggests that the distinct microstructure of CA and CPA colloidal gels (along with the biomatrix) is regulated independent of matrix mechanics and biomolecular character. Preformed microstructural features of the CA and CPA systems as colloidal aggregates and gels are spatially distinct, and, therefore can provide local spatial cues for guiding cell organization within 3D matrix. 3.3 Organization of EC in colloidal aggregate embedded biomatrix Given that the presence of colloidal aggregates, with distinct microstructure, within the biomatrix creates localized domains with their dimensionality, we expect that ECs within these composite systems will recognize the discrete particulate phase to organize and network like structures. Both matrigel and collagen represent endothelial matrix for cells to organize, interconnect and assemble into capillary like networks. In essence, these two matrices were used to represent the


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two types of endothelial matrix, i.e. the basement membrane (imitated by matrigel) and stromal matrix (imitated by collagen I)1, 37.

Figure 5. Endothelial cell (EC) organization in colloidal aggregate embedded endothelial matrix. Representative fluorescent images of EC organization (red=actin and blue=nucleus) in CA and CPA at 0.1 particulate fraction embedded in A) Matrigel (CA-M and CPA-M), and C) Collagen I (CA-C and CPA-C) at 48 hrs. Morphometric analysis of EC organization measured by analyzing total length of interconnected cell chord per unit area, size (perimeter) of enclosed network form EC chords, and shape of cellular aggregates in B) Matrigel (CA-M and CPA-M), and D) Collagen I (CA-C and CPA-C). (ANOVA, ** P ≤ 0.01, * P ≤ 0.05)

In matrigel matrix with CA and CPA aggregates, i.e. in CA-M and CPA-M, organization of ECs were analyzed from fluorescently stained images which shows after 48 hrs, EC organization and their network like structures are significantly different (Fig. 5A). While the overall vascular morphogenesis of ECs was observed in both composites, but the nature and extent of vascular network patterns differed in CA-M and CPA-M composite. In CA-M, cells tend to form more



capillary network which became frequently anastomosed, compared to that of in CPA-M composite. Thus, the overall length of EC tube-like networks and the number of junctions (branching points) in CA-M were greater than that of CPA-M (Fig. 5B). As a result, branch length of EC tube in between the junctions was smaller in CA-M, and therefore, the enclosed network formed by the EC tubes were larger in CPA-M (Fig. 5B). Moreover, these morphological features of EC organization showed a time-dependent increase over 48 hours. To further analyze if EC sprouting in these composite matrices are influenced due to the presence of colloidal aggregates, EC spheroids were placed in these matrices and sprouting of the cells were analyzed. Results (Fig. S6) show, ECs emigrate out the spheroids and move a larger distance in CPA-M compared to CA-M matrix. When EC responses were analyzed in collagen based composite matrix with CA and CPA aggregates, i.e. in CA-C and CPA-C, the cell organization was different from matrigel based composite matrix. In CA-C composite, cells clustered to isolated multicellular aggregates which were occasionally interconnected, whereas in CPA-C composite cells formed elongated strands with interconnections (Fig. 5C), thus, showing characteristically different cellular organizations in the two composites with collagen as biomatrix. ECs in CPA-C composite formed characteristic tube like networks with greater tube-length and junctions (branching points) compared to ECs in CA-C composite (Fig. 5D). In CA-C composite, cells clustered as aggregates as multicellular spheroids, with larger size compared to that in CPA-C composite. Moreover, EC tube like structures in CPA-C composite and cellular aggregates in CA-C showed time-dependent increase over 48 hours. Furthermore, when EC sprouting were analyzed in these two composite matrices (Fig. S6), cells tends emigrate longer distance in CPA-C composite matrix compared to CA-C composite. These results, overall, show that presence of colloidal aggregates and their microstructure provide localized domains for cells to respond and organize. Preformed spatial cues, due to these aggregates, and nature of the biomatrix guide the cells to organize and interconnect in a different manner to form endothelial structures. 3.4 Organization of EC in colloidal gels


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To examine the EC organization in 3D colloidal gels, we embedded the cells in the CA and CPA gels and in the composite colloidal gels by CA and CPA system with two biomatrices collagen and matrigel. Cells remained viable in all the colloidal gels and showed no difference in viability compared to cell-only aggregates (Fig. S7). EC organizations in the colloidal gels and the composite systems were examined after 48 hours from histologically stained samples (as well as fluorescently stained samples) (Fig. 6A). The spatial organization of ECs tend to follow the microstructural morphology of the colloidal gels. In dense CA gels, ECs aggregated in the localized voids with some local lumen like structures. These cellular structures were sparingly interconnected depending on the connectivity of the voids in the gel. In contrast, ECs in CPA gels were elongated and interconnected longitudinally to form network-like structures. Quantitatively from morphometric analysis (Fig. 6B), ECs in CPA gels showed longer interconnected tube-like structures and correspondingly the size of the enclosed networks formed by the interconnected structures were larger, compared to those in CA gels. The EC organizations in these gels resulted in either more circular cellular aggregates in CA gels or largely extended networks in CPA gels. The differential EC organizations in the colloidal gels indicate that the spatial cues provided microstructural morphology influence cellular arrangement in 3D matrices. The localized voids in compact CA gels essentially confined the cells to aggregate in the vicinity whereas stranded networks of CPA gels spatially guided the cells to for interconnected structures. These characteristics responses were also reflected in the composite gels where CA and CPA gels were combined reconstituted endothelial matrix i.e. collagen and matrigel. In collagen based composite colloidal gels i.e. CA-C and CPA-C, ECs formed multicellular aggregates with internal free spaces. These aggregates were localized in CA-C gels with some level of interconnections whereas in CPA-C gels these cells formed




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Figure 6. Endothelial cell (EC) organization and morphogenesis in colloidal gels. A) Representative fluorescent images (red=actin and blue=nucleus) and H&E stained images of ECs in CA and CPA colloidal gel prepared from 0.1 particle fraction at 48 hrs. B) Morphometric analysis of EC organization measured by analyzing total length of interconnected cell chord per unit area, size (perimeter) of enclosed network form EC chords, and shape of cellular aggregates in CA and CPA colloidal gel. C) Representative fluorescent images (red=actin and blue=nucleus) and H&E stained images of ECs in composite CA and CPA colloidal gel prepared from 0.1 particle fraction in presence of collagen I (CA-C and CPA-C) and in presence matrigel (CA-M and CPA-M) at 48 hrs. D) Morphometric analysis of EC organization in composite CA and CPA colloidal gel. E) Simulated vascular patterns obtained as a combinatorial function of cell-cell (C-C) adhesion and cell elongation (L) with change in Jc-c (higher value indicates low cell-cell adhesion) and L (larger value indicates increased cell elongation) respectively, under low chemotactic strength. Morphometric analysis of the corresponding simulated vascular patterns. (ANOVA, ** P ≤ 0.01, * P ≤ 0.05)

interconnected networks (Fig. 6C). Thus, interconnected tube-like structures and the enclosed network formed by these tubes were significantly higher in CPA-C gel compared to CA-C gel; whereas, localized cellular aggregates into lumens were prominent in CA-C gel compared to CPA-C gels (Fig. 6D). In matrigel based composite colloidal gels, i.e. i.e. CA-M and CPA-M, ECs aligned to interconnect to form multicellular lumen like morphology in CA-M gels but formed mostly interconnected networks in CPA-M gels (Fig. 6C). Quantified results show (Fig. 6D) that in CPA-M gels ECs formed longer interconnected tube-like structures and, thus the enclosed networks formed by these interconnected tubes were larger compared to that of CA-M gels. Interestingly, there was no clustering of cells in either CA-M or CPA-M gels; instead cells were aligned through the surface of both the gels to form aggregate like structure which were similar in size. In collagen based composite, ECs aggregate within the voids of compact CA-C gel and these aggregates depending on their proximal or distal location tends to either interconnect or remain isolated respectively; whereas in CPA-C gels the cells extend to interconnect in a tube-like network through the gel strands along the surface of branched microstructure of gel. In contrast, for the matrigel based colloidal gels, EC tends to align along the surface of the colloidal gels by following contour of the gel microstructure; in CA-M gels cells organize in lumen like structure by aligning the compact gel surface whereas in CPA-M gels cells organize by aligning along the stranded gel strands. In general, in compact and dense CA colloidal gels cells associate by interacting with the cells within the voids to form luminal morphology but in branched and loose CPA colloidal gels cells align along the strands of the colloidal gel to form tube-like structures. EC organization in these colloidal gels in presence of biomatrix lining evidently shows that spatial guidance received from the microstructure of the colloidal gel influences the cell response.



The differential EC organization observed in response to matrix microstructure prompted to identify how cell organizations in the 3D matrix is regulated by the cell-cell and cell-matrix interactions as well as the ability of the cells to extend and elongate. To do so, we used a mathematical model of vascular patterning developed for analyzing endothelial organization. This mathematical model is a hybrid one which accounts for the effect of growth factor (and its effect on the endothelial cell due chemotactic response) and for the effect cell adhesion through cell-cell and cell-matrix interactions as well as the cell shape. However, to exclusively identify the role of matrix, we used lower limit of soluble growth factor and used the established rates of growth factor binding and unbinding with extracellular matrix26. Additionally, we altered the chemotactic strength of the cells to exclusively identify the microstructural effect from chemoattraction during endothelial organization. We assumed that the microstructural morphology of colloidal gels synchronizes the cell-cell and cell-matrix interactions and the cells’ ability to extend owing to the 3D spatial guidance. The compact gels with constricted voids can localize the cells to induce cell-cell interactions compared to loose stranded gels which can promote cell-matrix interactions from the stranded network of particles with higher interconnected space. Likewise, the cell elongation gives endothelial cells persistence and mobility and is also dependent on the spatial cue of the matrix. Based on this, we simulated the EC patterning where cell-cell (C-C) adhesion was increased with respect to cell-matrix by decreasing the energy of cohesion between the cells (i.e. Jc-c) and under these conditions cells were allowed to elongate by increasing the target length (i.e. L). We systematically altered these parameters over a range to examine the vascular patterns and quantified the morphometric parameters of the resultant patterns (Fig. 6E). When ECs were forced to mutually adhere through increased cell-cell adhesion (C-CH), cells essentially clustered into aggregates and the aggregates tend to interconnect if the cells were elongated (LH). In contrast, when cell-cell adhesion was lowered (C-CL) (i.e. cell-matrix adhesion increased), different responses were observed depending on the cells’ ability to elongate. When cells were able to elongate (LH), cellular networks of interconnected cords were prominent whereas when cells were restricted to elongate, cells remained clustered in spite of enhanced interaction with matrix. These vascular patterns were quantified by measuring the circularity and interconnected cell length. The cells were essentially aggregated with high circularity index under high cell-cell adhesion (C-CH) compared to low cell-cell adhesion (C-CL) for any given L. And circularity


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index was lower when cells were allowed to extend (LH) compared to being restricted (LL) for any given cell-cell adhesion. Accordingly, cells were able to form extended network of interconnected cell when cell-cell adhesion was low (C-CL) and cells were able to elongate (LH). This analysis shows that vascular patterns of EC are strongly correlated to cells’ interaction with the matrix and the neighboring cells as well as their ability to extend. Extended capillary like vascular patterns are favored when two conditions are synchronized: cells are able to extend and cell-cell adhesion is low. The complete evolution of vascular pattern into interconnected capillary network with reduced cell-cell adhesion and increased cell length is shown in Fig. S8. Furthermore, when the chemotactic strength of the cells was increased, cells tended to extend and form better interconnected networks, particularly under reduced cell-cell adhesion (Fig. S9). Under this condition, increased chemotaxis enabled cells to elongate as it can counteract cells’ reduced elongation ability. But increased chemotactic strength could not induce interconnected capillary like vascular patterns when cell-cell adhesion was strong, indicating that chemotaxis of cells cannot supersede the cell-cell aggregation during vascular patterning. This analysis underlines that interconnected cellular networks are favored with reduced cell-cell interaction (relative to cell-matrix interactions) and with increased cell extension. Finally, comparing the vascular patterns from the experimental data and the model provide insight on the role of microstructural morphology of colloidal gels in guiding EC organization. Morphometrically, interconnected cellular network with reduced aggregation observed in CPA gels is similar to what is observed when cell-cell interaction is low and cell can extend. In contrast, enhanced cell aggregations observed in CA gels is similar to what is observed with high cell-cell interaction and reduced cell extension. This indicates that the branched loose microstructure of CPA gel enhances cell-matrix interaction and cells’ ability to extend, leading to interconnected capillary like vascular networks. Whereas, compact and restricted voids of CA gel enhanced cell-cell interaction and prevented cell extension to form cell aggregates. 3.5 Endothelial signaling in colloidal gel Since the differential EC responses in the colloidal gels are influenced by the gel microstructure as well as in combination with the distinct biomolecular cues from the biomatrices, we hypothesized that this effect can regulate the downstream cellular signaling events in ECs during



the spatial organization. Both cell-matrix and cell-cell interactions regulate the morphogenesis of endothelial cells during the formation capillary-like networks. Focal adhesion kinase (FAK) is recruited to focal adhesions following the contact of cell-surface integrins to extracellular ligand38-39 and can be considered as central mediator for cell-matrix interactions. In parallel, we examined the intracellular (i.e. cell-cell) interactions by analyzing the VE-cadherin which is responsible for organization of endothelial cell adheren junctions40. In CPA colloidal gel, FAK expression was significantly higher than that of CA colloidal gel, whereas VE cadherin expression was higher in CA colloidal gels compared to CPA colloidal gels (Fig. 7A&B). Higher FAK expression indicates that branched microstructure of CPA gel enhance cell-matrix interactions through the stranded network and porous interconnected voids. The compact microstructure of CA colloidal gel with constricted voids induced cellular aggregation with high adheren junctions between ECs. ECs in CPA colloidal gel showed three times higher expression of FAK with respect to VE cadherin compared to that of ECs in CA colloidal gel (Fig. 7B). This indicates spatial guidance from the branched network and interconnected voids enhance matrix interactions with ECs with better connectivity and the cell-matrix interactions precede over cellcell interactions in forming interconnected capillary like vascular networks. Whereas, compact dense microstructure of matrix localizes the ECs in constricted voids with occasional luminal structures with enhanced cell-cell interactions. The 3D microstructure of matrix, provided by organization of colloidal gels, resulted in different signal activation in the morphogenesis of ECs with an antagonistic correlation between cell-matrix and cell-cell interactions. Organizational characteristics of ECs, as seen in Fig. 6, can be correlated to the matrix spatial guidance originating from the colloidal gel microstructure which in turn induces different level of signal activation. To further understand if the signaling mechanisms involved in the EC organization can be correlated to angiogenic responses, we analyzed the expression of vascular endothelial growth factor receptor-2 (VEGFR-2) and matrix metalloprotease-2 (MMP-2), as characteristic markers involved in angiogenic responses (Fig. 7A&B). While VEGFR-2 expression is typically associated with VEGF, but VEGF independent expression of VEGFR-2 can provide mechanistic insight for endothelial cell organization41; and MMP-2 expression can be related to cellular motility during endothelial organization due to matrix mediated degradation42. Reduced VEGFR2 was observed in ECs within CPA-colloidal gel. It indicates that the extended cells utilize this


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receptor in an VEGF-independent manner to form interconnected capillary structures. Whereas, higher level of VEGFR-2 in ECs within CA-gel corroborates to high cell-cell aggregation mediated by VE-cadherin. The microstructure mediated EC organization in colloidal gels utilize VEGFR-2 mediated signaling, although the actual mechanism of VEGFR-2 activation can

Figure 7. Endothelial cell signaling in native and composite colloidal gels. A) Levels of focal adhesion kinase (FAK), VE-cadherin, VEGFR2, and MMP2 in ECs measured by immunoblot analysis with β-actin as loading control in CA and CPA gel prepared from 0.1 particle fraction at 48 hrs. B) Quantified level of expressions normalized to β-actin shows differential level of markers in ECs. Comparison FAK to VE-cadherin expression in CA and CPA colloidal gels. Levels of focal adhesion kinase (FAK), VE-cadherin, VEGFR2, and MMP2 and the quantified expression in ECs measured by immunoblot analysis with β-actin as loading control in C) collagen based composite colloidal gels (CA-C and CPA-C) and D) Matrigel based composite colloidal gels (CA-M and CPA-M) at 48 hrs. (ANOVA, ** P ≤ 0.01, * P ≤ 0.05)

involve different pathways. In parallel, activation pattern of MMP-2 was examined as a representative protease because invading ECs secrete proteases during angiogenesis. Significant upregulation of MMP-2 is observed in CPA-colloidal gels where the ECs display typical



angiogenic vascular pattern through interconnected network. In contrast, aggregated ECs in CAcolloidal gels have relatively less expression of MMP-2. In CPA-colloidal gel as EC extends and interconnects to the form the capillary networks, higher level of MMP-2 is expressed to enable EC motility and migration along the branched microstructure of the colloidal gel. However, compact microstructure and constricted voids of CA-colloidal gel presented significant barrier in EC motility. We further analyzed these expressions in the composite gels which are combined with reconstituted endothelial matrix i.e. collagen and matrigel collagen-based composite colloidal gel. FAK expression in CPA-C colloidal gels were higher compared to that in CA-C gels (Fig. 7C) whereas this trend reversed in matrigel based composite colloidal gel. FAK expression was less in CPA-M colloidal gel compared to that in CA-M gels (Fig. 7D). VE-cadherin expression by ECs in these gels showed opposite trend when compared FAK expressions. VE-cadherin expression was higher in CA-C gels compared to that of CPA-C gels (Fig. 7C) whereas it was lower in CA-M gels compared to that of CPA-M gel (Fig. 7D). These results indicate that the microstructure of the composite colloidal gels regulates cellular organization to influence intracellular connections. In branched and porous CPA colloidal gels ECs interact with matrix more that when the cells encounter ligands from stromal collagen I matrix compared to CA colloidal gels where ECs tend self-interact with greater intracellular connections. Whereas, when ECs interact with basement matrix ligands, cell-matrix interactions are more in compact CA colloidal gels compared to loose CPA gels and the intracellular connections between ECs through VEcadherin were less in CA colloidal gels compared to CPA colloidal gels. Cell-matrix and cell interactions of ECs in collagen based composite gels were similar to that of native colloidal gels, whereas in matrigel based composite this trend reversed. The microstructures of colloidal gel with specific matrix ligand, from matrigel or collagen, modulate the EC interactions in a differential manner through the cell-matrix and cell interactions. Similar to native gels, we also analyzed VEGFR-2 and MMP-2 expressions in composite colloidal gels. VEGFR-2 expression was evident in cells within collagen based composite gels with CA colloidal gels showing significantly higher expression compared to that of CPA gels (Fig. 7C), whereas in matrigel based composite gels the expression was lower (compared to collagen based composite) with no significant differences between CA and CPA colloidal gel (Fig. 7D). Enhanced intracellular connections in dense and compact CA-C gels promoted


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VEFGR-2 compared to CPA-C gels, similar to the expression observed in native CA and CPA gel. Whereas, similar response was not observed in the matrigel based composite gels, where both CA-M and CPA-M gels showed comparable expression of VEGFR-2, because in presence of basement matrix ligands, cells tend to extend as networks by following the contour of the composite gels. Data indicates that increased connection between cells guided by dense microstructures of compact gel influenced the VEGFR-2 activation pattern with stromal collagen I matrix but localized cell-matrix interactions with basement matrix ligand of matrigel do not differentiate the VEGFR-2 activation according to gel microstructure. Overall, VEGFR-2 activation in presence of specific biomatrix ligands was dependent on colloidal gel microstructure. In parallel, activation pattern of matrix degrading MMP-2 was examined; as MMP-2 is a representative matrix degrading enzyme for both collagen I and matrigel and is relevant for endothelial cell organization43-44. Qualitatively, the overall expression of MMP-2 was higher in collagen based composite gels compared to matrigel based gels; however, there was no significant differences between MMP-2 levels in CA-C and CPA-C (Fig. 7C). In contrast, MMP-2 level was less in compact and dense CA-M compared to branched and porous CPA-M gel, similar to the expression in native CA and CPA-gel. CPA-M gel induced higher MMP-2 expression as HUVECs aligned along the strands of CPA-M gel compared to CA-M gels where HUVECs formed multicellular lumen-like structures. Thus, MMP-2 activation was induced when cells interconnected longitudinally through porous structures of colloidal gels, particularly in presence of basement matrix ligands but similar effect was not observed in presence of collagen based composite gels. This difference indicates mainly because the extent of interconnections was lesser in CPA-C gels compared to CPA-M gels. Clearly, the microstructure of colloidal gel influenced expression of MMP-2 which indicates the ability of the cells to remodel the matrix depending on the matrix microstructure and corresponds to cell organization. 4. Discussions 4.1 Aggregation of colloidal particles forms differential microstructured aggregates and gels.



The unique features of colloidal aggregates and gels can tune the microstructural morphology independent of bulk elasticity. The mechanism of aggregation can modulate the 3D arrangement of the particles with distinct microstructure and the elasticity of the aggregated colloids scale with the particle fractions from which the aggregates and gels are formed. Specifically, electrostatic interaction mediated aggregation of ionic colloids provides a feasible path to alter the aggregation mode and yield different microstructural morphology. Previously, we have shown that ionic gelatin based colloids can be aggregated into gels with distinct mechanomorphology by altering the mode of aggregation21. In this study, we utilized ionic polyurethane (PU) hydrocolloid and designed colloidal gels from this PU based colloids. Ionic PU colloids are versatile because the properties of the colloidal particles can be tuned in terms of hardness-softness, ionic charge and degradability by changing the segmental composition of the polyurethanes45. As such, synthetic PU colloids can offer more opportunities to design colloidal gels with wide range of mechanomorphology. Specifically, in this work we developed polycaprolactone based cationic PU colloids which are spherical in shape with submicron size and displayed positive zeta potential due to quarternarized amine groups of the chain extender in polyurethane. Presence of positive charge renders colloidal stability of these PU particles in aqueous media, as this has been observed in different cationic polyurethanes and subsequently their use as biomaterials32, 46-48. To form colloidal gels with distinct microstructural morphology, cationic PU particles were aggregated by electrostatic interactions via two different modes. Essentially neutralization and screening of charge in an ionic colloid by the presence of electrolyte in the aqueous dispersion media decreases the repulsive interaction between particles and aggregates the particles. Smaller size electrolytes (e.g. inorganic salts) neutralize and screen the charge at a slow rate due to its low molecular weight and form compact small aggregates by reaction limited mechanism. Whereas polyelectrolytes can neutralize and simultaneously attach to multiple particles through the multiple charged sites via bridging interaction. Thus, the aggregation from bridging interaction is rapid and forms open structured aggregates by diffusion limited mechanism. This approach has been used create colloidal aggregates with different microstructures and fractal dimensions21, 49. We applied this approach to aggregate the PU particles into compact aggregates (CA) using electrolytes present in phosphate buffer saline and open loose aggregates (CPA) using Na-salt of polyacrylic acid. Both size measurements and kinetics (using turbidimetric


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analysis) show that CPA aggregates are extended and open in structure and requires significantly longer time for the relaxation of macromolecular polyacrylic acid, as often seen in bridging interactions25,

49.

Whereas CA aggregates were smaller and compact. Further analysis from

microscopic images, supports the microstructural features of CA and CPA aggregates. It shows that variation of particle aggregation mode and kinetics alter the 3D microstructure of the aggregates of PU based colloidal system. These discrete colloidal aggregates developed from 0.1 particle fraction, essentially have similar size comparable to the length scale of multiple cells, can therefore act as spatial guide for cell organization in biomatrices. While the effective strengths of phosphate buffer and Na-salt of polyacrylic acid were determined from complete neutralization of surface charges leading to effective aggregation, a partial residual positive charge or excess of negative charge can lead to difference in the aggregation mode, their microstructure, and ultimately the cell responses. To negate these effects from the microstructural features, we used these conditions to develop the CA and CPA aggregates and the gels. Previous studies have shown that presence of particulate phase in an otherwise dense fibrin matrix enhance cell spreading and migration50, however, the particles in these matrices were not defined in terms of spatial microstructure. We envisioned that preformed CA and CPA aggregates embedded in the reconstituted endothelial matrix can provide spatial guidance for endothelial cell organization due to the distinct microstructure of the aggregates. In addition, these colloidal aggregates can be engineered as a continuous matrix by using these systems as gel. Generally, as the aggregates grow in size with time and from increasing fraction of particles, the aggregated colloidal particles form volume spanning 3D gel structures. Since the gels develops from the aggregates via same mechanism, the colloidal gel reflects the morphological features of the respective aggregates. CA colloidal gels were compact with confined voids and CPA colloidal gels were stranded network with porous microstructure and interconnected voids. Distinctly different microstructural morphology of the gels provides a 3D matrix to spatially guide the organization of cells. Most importantly, the CA and CPA colloidal gels showed similar bulk stiffness, i.e. elastic modulus, at all the particle fractions. Even though aggregate microstructures are different but it is the links between the ‘primary’ clusters which defines the elasticity of these gels, as the clusters are connected to form the macroscopic aggregate and gels. This essentially enables decoupling of 3D microstructural morphology from mechanics in a colloidal gel; a feature which is typically absent in cross-linked hydrogels



because the elasticity and morphology are directly correlated. Whereas, yielding of the colloidal gels depends on the number of effective bonds formed between the particles. Since CPA has lower number bonds between particles due to its stranded branched microstructure, CPA yields at lower strain (or stress) compared to CA and this difference increases as the particle fraction increases. Clearly, by altering the aggregation mode, the microstructural morphology of colloidal gels is varied in CA and CPA gels. These results provide a perspective on the characteristic structural features of colloidal gels which can be engineered by modulating the kinetics of aggregation as well as the extent of interparticle interactions. While electrostatic interaction mediated colloidal gels are developed as biomaterials from dextran, PLGA particles, but these gels were not designed to control the microstructural morphology51-52. Given the complex nature of 3D matrices influencing cell responses, these gels can regulate the spatial organization of cells during morphogenesis. However, it is important to note that mechanomorphology of colloidal dispersion and gels is complex and is associated with multiple levels of interactions and timedependent effects but, within the defined design space explored in this study, these PU based colloidal gels provides an opportunity to explore differences in 3D microstructure and their role in EC morphogenesis. It would be important to explore the effect of aggregated colloidal systems, in terms of their mechanomorphology, by altering the strength of added electrolyte and by using different particle fractions. 4.2 Spatial organization of endothelial cells is guided by microstructured colloidal aggregates and gels EC organization is important during vasculogenesis and angiogenesis to establish appropriate vascular patterns. Presence of specific matrix ligands and stiffness of the matrix influence the interaction between the ECs and the surrounding matrix for controlling proliferation, migration, differentiation, and apoptosis of cells. But how the spatial microstructure of matrix guides ECs is not clearly understood, although it is evident that ECs extend and interconnect through the voids and the tracks created by cell-mediated matrix degradation. Along these lines, it is also known that cells encounter significant barrier for extension and migration in dense mesh-like matrices. Therefore, preformed tracks in 3D matrices can facilitate cell extension and migration by providing spatial guidance. Recent studies have shown that network of colloidal particles in


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dense fibrin matrix provides domains for cells migration, but cellular responses were essentially guided by the fraction of particulate phase and not by the structural feature of the colloidal network50. In this study, preformed colloidal aggregates with different microstructure were embedded within two different reconstituted endothelial matrices, i.e. Matrigel (representative of endothelial basement membrane) and collagen I (representative of endothelial stromal matrix). Two endothelial matrices were analyzed because ECs form vascular structures in these matrices by mechanistically different approaches: in matrigel ECs essentially reorganizes to stabilize interconnected cellular chords into capillary structures whereas in collagen ECs typically use invasive sprouting to form lumens and vacuoles1,

53.

Additionally, biochemically these

endothelial matrices represent different characteristics37. In presence of the microstructured colloidal aggregates, ECs in matrigel formed interconnected capillary like tubes but the overall length of tubes and junctions of the tubes were higher in presence of compact CA aggregates in comparison to those of extended CPA strands. This indicates contact guidance of compact CA allows and enforces EC interconnect more frequently to form longer tubes, where by similar mechanism the extended CPA aggregates tend ECs to connect distally. In collagen matrices, the EC responses were characteristically different, as ECs tend to protrude and invade throughout the matrix by using the contact guidance from the aggregates. Compact CA aggregates enforced ECs to essentially aggregate in clusters whereas CPA aggregates tended ECs to interconnect into tubes. For CPA systems, in both biomatrices, EC formed interconnected strands indicating that the cells can sense the extended strands in a comparable manner. As a result, ECs exhibit tubelike structures of similar length in the CPA embedded matrices however the branching within these tubes were less in collagen I, as ECs tend to more invasive. This eventually leads to fewer enclosed EC networks in CPA-C compared to CPA-M. Whereas in CA systems, Matrigel stabilized the ECs and enabled their interconnection but collagen I essentially aggregated the cells. Collectively these data indicate that ECs can ‘sense’ these domains to essentially reorient and adjust their spatial organization from the contact guidance. And the microstructural differences of the colloidal aggregates induce ECs to conform into the respective patterns in different endothelial matrices. The exact underlying mechanism by which the cells receive these cues can be both physiochemical due to change in interfacial properties and mechanical stiffness due to local geometric structure, and it is likely that the exact mechanism can involve multiple transduction

pathways.

Nevertheless,

the

results

provide


significant

evidence

that


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microstructured colloidal aggregates spatially guide endothelial vascular patterns, and potentially can be used to engineer vascular networks. We further examined how the differential microstructure of the CA and CPA aggregates as continuous 3D colloidal gels guide EC organization. Results shows that EC organized differentially in these gel matrices by following the spatial features; lumen like structures were localized in the constricted voids of compact CA colloidal gels whereas interconnected tubular networks were formed along the stranded structures and the porous interconnected voids of CPA gels. While both the gels exhibited similar elastic moduli, it is the microstructural morphology which guides the EC organizations in 3D. When the morphometric features of the vascular patterns were examined by computational model, it was evident that the stranded microstructure and the interconnected voids of CPA gel reduced cell-cell aggregation and enabled cell elongation. In contrast, ECs in compact CA gel with constricted voids induced cell-cell aggregation and restricted cell extension. The morphology of the gels guided to organize and orient according to geometric constraints of the microstructure. Several hydrogels have explored the effect of gel morphology on EC morphogenesis by altering gel fraction, crosslinking and fiber thickness7-8,

54-55.

But these changes in morphology are often associated with change in

elastic modulus as increased gel fraction leads to denser matrix

8, 55-56.

Additionally, these gels

essentially offer mesh like microstructure at nanoscale length which are limited in providing spatial microenvironment for cells to organize and orient. In contrast, colloidal gels provide structurally defined microstructural morphology independent of bulk elasticity and can spatially regulate the organization of ECs. While in colloidal gels we were able to decouple the effect of matrix morphology from mechanics, but these data represent the effect for a given elastic modulus. It would be critical to analyze how the change of elasticity for a given microstructural morphology alters EC organization, as examined in gelatin based colloidal gels21. Furthermore, as we were able to identify to role of spatial guidance with two distinctly different microstructural morphology of EC responses, but it only represents a subset of microstructural design. By using additional designs, well-defined microstructured colloidal gels can be used to interrogate EC organization and vascular pattern formation, as well as apply these microstructurally designed gel for controlled vascular morphogenesis. Differential EC organization in the colloidal gels indicates that different signaling mechanisms were involved during the morphogenesis of ECs. The organization of ECs in terms


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of cell-matrix and cell-cell interactions were corroborated from the FAK and VE-cadherin expression, respectively. ECs in CPA gel promoted enhanced adhesion to matrix with upregulated FAK as cells increasingly interacted with matrix during the network formation. Whereas, enhanced cell-cell aggregation resulted in upregulated VE-cadherin in ECs in CA gel. Based on morphometric analysis, ECs in CPA gels were intuitively more directional and invasive during capillary like network formation. To qualitatively assess this, we analyzed VEGFR2 expression which is often associated with tip cells during EC sprouting, as well as expression of MMP2 as a candidate protease involved during EC invasion. VEGFR2 expression in tip cells are associated with VEGF signaling41, 57, but in this study growth factor free condition was used. Therefore, VEGFR2 expression in ECs in the colloidal gel is via VEGF independent pathways, because VEGFR2 expression can vary with matrix stiffness, cell organization and external shear stress58-60. Enhanced VEGFR2 expression in compact CA gel is likely mediated via VE-cadherin from enhanced cell-cell adhesion, as several studies have shown a direct crosstalk between VEcadherin and VEGFR259,

61-62.

Clearly, VEGFR2 was not correlated to extended and

interconnected cellular structures in CPA gels, as typically seen in directionally oriented tip cells. There can be additional pathways mediating this expression, but in general, the spatial organization of ECs induced differential VEGFR2 expression. However, MMP2 expression was higher for ECs in CPA gel compared to that in CA gel is indicative that cells in CPA gels are more invasive as the ECs navigate along the stranded network of colloids through the interconnected voids. Expression on MMP2, along with several other MMPs has been implicated in angiogenesis by recognizing their role in matrix degradation and remodeling as ECs invade the matrix to form tubular capillary like structures1, 44, 63-66. In particular, ECs attain migratory character to interconnect by secretion of MMPs, has been recognized as partial mesenchymal transition during angiogenesis67. It is also important to recognize that involvement of MMPs, as well as their activator and inhibitors are complexly interrelated and highly context dependent1, 66. Thus, it is likely that EC responses examined in these gels will involve other factors and mechanisms. But nonetheless, within the limited scope, we have shown that as ECs are extended and interconnected in CPA gel, the cells are more invasive due to the spatial microstructure of the gel compared to CA gel. Clearly, microstructural guidance of matrix differentially regulates the signaling in EC during their organization.



To further understand the role of spatial guidance in the EC organization, we explored two endothelial matrices (i.e. collagen I and Matrigel) within both CA and CPA gels because EC responses change in presence of different endothelial matrices due to different receptor-ligand interactions e.g. integrins. EC organization in the CA and CPA gels in presence of these matrices were similar to their respective native gels, however, some subtle differences were observed. In collagen system, while ECs in compact CA-C gels were localized in the constricted voids with occasional luminal structures which attempted to connect distally, but in CPA-C gels the cells were mainly interconnected. It appears that collagen I as stromal matrix promotes invasive character in ECs in the colloidal gels. In contrast, matrigel as basement membrane rather organizes and stabilizes the ECs, and ECs tend to follow the contour of the voids to arrange. As a result, ECs in compact CA-M gel stabilized on the 2D surface of the constricted voids but in CPA-M gel aligned along the stranded network of the gel. As such, 2 dimensional shape of EC networks in CA-M and CPA-M gels appear similar although their size was larger in CPA-M. In general, from the analysis of EC morphometric parameters, ECs in stromal matrix tended to form larger network but length of the interconnected EC chords (per unit area) were shorter and ECs in basement membrane matrix formed smaller networks but length of the interconnected EC chords were larger. The morphometric differences in EC patterns attributed by the different endothelial matrices was reflected in the expression of the proteins. Similar to native CA and CPA colloidal gel, FAK was upregulated and VE-cadherin was reduced in CPA-C compared to CA-C, but the trend reversed in their respective counterparts in matrigel based composite gels. As expected, in presence of collagen EC followed similar trend where matrix adhesion is promoted in stranded microstructure of CPA-C but cell-cell aggregation was induced in compact CA-C. But matrigel changed this trend because ECs were stabilized on the accessible surface of the constricted voids of compact CA-M gel with higher cell-matrix interactions compared to lower surface area of stranded microstructure. Consequently, VE-cadherin expression was lower in CA-M but relatively higher in CPA-M gel. Furthermore, VEGFR2 expression in the collagen based composite gels showed similar expression level compared to the native colloidal gels but in matrigel based composite gels no significant difference was observed between CA-M and CPA-M gels. This apparently indicates that in matrigel based composite gels the VEGFR2 signals were influenced by the spatial organization of ECs, but it is also important to recognize that VEGFR2 signaling is dependent on multiple factors. In contrast, MMP2 expression in


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matrigel based composite gels differed with CPA-M showing higher expression compared to that of CA-M gels but there was no significant difference in collagen based gels. Although matrigel essentially stabilizes ECs, MMP2 degrades basement membrane43, 63, 68 and in CPA-M gels, the stranded network and interconnected voids induced invasive character in ECs to upregulate MMP2 compared to CA-M. Collagen I, as interstitial stromal matrix is expected to regulate MMPs in the colloidal but gel showed no difference (although some basal level on MMP2 was detected). This is mainly because MMP2 are less responsive to collagen I and are expressed later during the degradation in stromal collagen I1, 63. While difference in MMP2 was not detected, it is likely that other members of the MMP family are involved in the collagen based composite gels. Finally, the presence of endothelial matrix can also facilitate the presentation of growth factors, e.g. autocrine VEGF secretion by ECs and differential binding of VEGF to matrigel and collagen, to regulate EC organization and its signaling pattern69-70. Therefore, understanding the effect VEGF (and other growth factors), both intrinsic and extrinsic, in the colloidal gel will provide additional ideas regarding the endothelial morphogenesis. In fact, the simulated model used in this study includes the possibility of growth factor binding-unbinding to matrix as well as its decay (although these parameters were not validated through simulation in this study). Overall, the spatial guidance provided by microstructured colloidal aggregates and gels have significant role in terms of EC organization and the resultant vascular patterns. The microstructural morphology of the colloidal gels regulated cell-matrix/cell-cell interactions and alongside enabled cell extension to organize and orient ECs in a particular pattern. This showed that EC morphogenesis is dependent on the available 3D space and on the geometric constraints imposed by microstructure (in comparison to dense mesh-like 3D matrix). Furthermore, these colloidal gels are relevant as functional biomaterials for regulated vasculogenesis and angiogenesis, and their application in regenerative matrix based tissue engineering. While the different EC organization in the colloidal gels, both in native state and in presence of endothelial matrices, showed differential signaling based on the selected expression of EC markers, but additional receptor-ligand interactions and cell transduction pathways should be examined. In particular, involvement of integrins, cell-cell adhesions and deposition and turnover of cell deposited matrix should be analyzed to comprehensively understand the mechanistic pathways. It is also important to recognize that EC organization is dependent on time (beyond the early time event analyzed in this study), presence of additional cells (e.g. perivascular cells71) and growth



factors (e.g. VEGF72). Synergistic or antagonistic effect of these factors on EC organization should be analyzed. Furthermore, time dependent remodeling of vascular structures and change of colloidal gel morphology owing to remodeling and degradation will provide better understanding of the spatial guidance in EC morphogenesis. 5. Conclusions The current study focuses on the development of colloidal aggregates and gels to provide spatial guidance for ECs during their organization into specific vascular patterns. Colloidal gels can provide distinct microstructural morphology because these gels are formed from the interconnected network of colloidal particles, and therefore, the morphology is regulated independent of bulk elasticity. By altering the aggregation mode of particles, colloidal aggregates and gels were developed from cationic polyurethane colloid by electrostatic interaction mediated aggregation. When the aggregation was induced by electrolytes, the colloidal aggregates and gels formed compact dense microstructure with constricted voids, whereas polyelectrolyte mediated aggregation resulted into branched stranded network with interconnected pores. When the microstructured aggregates were used as spatial guides within the reconstituted endothelial matrix, i.e. matrigel and collagen I, ECs organized into different patterns by sensing the spatial cues. Stranded colloidal aggregates promoted interconnected capillary like structures whereas compact dense promoted more compact cell aggregation. When the microstructured aggregates were used as 3D gels, ECs in compact dense colloidal gels aggregated with enhanced cell-cell adhesion but formed interconnected capillary like tubular structure in gels with stranded microstructures and interconnected voids via enhanced cell-matrix interaction. Both simulated vascular patterns and expression of specific EC markers indicated that ECs induce cell-cell interaction in compact gels as the constricted voids impose intercellular physical contacts but enables cell elongation via cell-matrix interactions in stranded microstructured gels. Accordingly, ECs in stranded microstructured gels expressed higher FAK compared to VEcadherin, but FAK expression was reduced in compact gel. These colloidal gels were further examined in presence of matrigel and collagen to demonstrate that EC organization and subsequent signaling were further influenced by the biochemical character. Overall, this study established the colloidal gels as unique 3D matrix which can display distinct microstructural


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morphology. These colloidal gels controlled the EC organization into different patterns owing to the spatial guidance of the matrix, independent of matrix elasticity. Future studies should focus on the long term effect of these materials to understand remodeling of vascular structures and to validate these materials for other endothelial cells. Engineering of colloidal gels provides an opportunity to develop these system as biomaterials where not only the fundamentals of cellular morphogenesis can be interrogated, but these materials can be applied as regenerative biomaterials for angiogenic and other cell based tissue engineering applications. Supporting Information: The Supporting Information is available free of charge on the ACS Publications website at DOI: NMR and FTIR characterizations of polyurethane, DLS data of zeta potential and size distribution, rheological analysis of gels, EC sprouting analysis, computational modeling of EC patterns, additional images for analysis (PDF). Conflict of Interest: The authors declare no conflict of interest. Acknowledgements: Authors thank Dr. Chong Cheng of Chemical and Biological Engineering for assistance with DLS measurements and Peter Bush of School of Medicine for assistance with SEM imaging. This research was supported by the National Institute of Biomedical Imaging and Bioengineering of the NIH under the award R03EB022201 (D.S.).



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Figure Caption: Figure 1. Microstructured colloidal aggregation induced by electrostatic interaction. A) Schematic diagram illustrating the different modes of aggregation by electrostatic interactions using electrolyte and polyelectrolyte to induce cationic PU colloidal particles into different microstructured aggregate and gels. B) Scanning electron microscopic and fluorescent images of colloidal PU particles with spherical shape and submicron size. C) Size and zeta potential of colloidal PU particles (C) in dd water and in HEPES buffer. D) Change of size and zeta potential of colloidal PU particles (C) due to aggregation mediated by electrolytes (CA) in phosphate buffer saline of different strength and by polyelectrolyte Na-salt of polyacrylic acid (CPA) of different strength. E) ‘Dispersibility factor, n’ due to aggregation of colloidal PU particles (C) in CA and CPA. Variation of ‘n’ with respect to particle fraction in colloidal PU particles (C) and aggregated CA and CPA system. F) Kinetics of aggregation measured from change in optical density with time for CA and CPA system. Aggregation time estimated from KWW fit of kinetic data. (ANOVA, ** P≤ 0.01, * P≤ 0.05) Figure 2. Colloidal aggregates show different microstructures. A) Brightfield image of CA and CPA colloidal aggregates. Inset showing traced outline of aggregate boundary. B) Fluorescent microscopic images of CA and CPA colloidal aggregates and a representative binarized image showing different microstructures. C) Characterization of microstructure from circularity index and fractal dimension, and structural organization of colloidal particles network from length of longest unit and number of branches per unit length of an aggregate. D) Oscillatory amplitude sweep test of CA and CPA aggregate embedded Matrigel and Collagen I with 0.1 particulate (by volume). (ANOVA, ** P≤ 0.01, * P≤ 0.05) Figure 3. Microstructural morphology of colloidal gels. A) Confocal scanning fluorescent images and 3D interactive surface plot (color code indicates z-depth) of CA and CPA gels showing morphology and spatial distribution of voids in the gels. B) Morphological features of the microstructure analyzed by measuring void areas and void aspect ratio of the gels from these images using in ImageJ. C) Microstructured morphology of CA and CPA gels from scanning electron microscopic images. (ANOVA, ** P ≤ 0.01, * P ≤ 0.05) Figure 4. Mechanical characterizations of colloidal gel from rheology. A) Elastic moduli (Gʹ) and tan(δ) of CA and CPA gel from 0.1 particle fraction obtained from linear viscoelastic region of strain amplitude sweep measured at constant frequency. B) Variation of elastic moduli (Gʹ) and yield strain of CA and CPA gels prepared from different particle fractions; measured from linear viscoelastic region and from the crossover point of elastic (Gʹ) and viscous (Gʺ) moduli. C) Oscillatory frequency sweep of CA and CPA gel from 0.1 particle fraction.

Figure 5. Endothelial cell (EC) organization in colloidal aggregate embedded endothelial matrix. Representative fluorescent images of EC organization (red=actin and blue=nucleus) in CA and CPA at 0.1 particulate fraction embedded in A) Matrigel (CA-M and CPA-M), and C)


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Collagen I (CA-C and CPA-C) at 48 hrs. Morphometric analysis of EC organization measured by analyzing total length of interconnected cell chord per unit area, size (perimeter) of enclosed network form EC chords, and shape of cellular aggregates in B) Matrigel (CA-M and CPA-M), and D) Collagen I (CA-C and CPA-C). (ANOVA, ** P ≤ 0.01, * P ≤ 0.05) Figure 6. Endothelial cell (EC) organization and morphogenesis in colloidal gels. A) Representative fluorescent images (red=actin and blue=nucleus) and H&E stained images of ECs in CA and CPA colloidal gel prepared from 0.1 particle fraction at 48 hrs. B) Morphometric analysis of EC organization measured by analyzing total length of interconnected cell chord per unit area, size (perimeter) of enclosed network form EC chords, and shape of cellular aggregates in CA and CPA colloidal gel. C) Representative fluorescent images (red=actin and blue=nucleus) and H&E stained images of ECs in composite CA and CPA colloidal gel prepared from 0.1 particle fraction in presence of collagen I (CA-C and CPA-C) and in presence matrigel (CA-M and CPA-M) at 48 hrs. D) Morphometric analysis of EC organization in composite CA and CPA colloidal gel. E) Simulated vascular patterns obtained as a combinatorial function of cell-cell (CC) adhesion and cell elongation (L) with change in Jc-c (higher value indicates low cell-cell adhesion) and L (larger value indicates increased cell elongation) respectively, under low chemotactic strength. Morphometric analysis of the corresponding simulated vascular patterns. (ANOVA, ** P ≤ 0.01, * P ≤ 0.05) Figure 7. Endothelial cell signaling in native and composite colloidal gels. A) Levels of focal adhesion kinase (FAK), VE-cadherin, VEGFR2, and MMP2 in ECs measured by immunoblot analysis with β-actin as loading control in CA and CPA gel prepared from 0.1 particle fraction at 48 hrs. B) Quantified level of expressions normalized to β-actin shows differential level of markers in ECs. Comparison FAK to VE-cadherin expression in CA and CPA colloidal gels. Levels of focal adhesion kinase (FAK), VE-cadherin, VEGFR2, and MMP2 and the quantified expression in ECs measured by immunoblot analysis with β-actin as loading control in C) collagen based composite colloidal gels (CA-C and CPA-C) and D) Matrigel based composite colloidal gels (CA-M and CPA-M) at 48 hrs. (ANOVA, ** P ≤ 0.01, * P ≤ 0.05)



Table of Content (TOC) Graphic:


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TOC graphic 64x44mm (300 x 300 DPI)



Figure 1. Microstructured colloidal aggregation induced by electrostatic interaction. A) Schematic diagram illustrating the different modes of aggregation by electrostatic interactions using electrolyte and polyelectrolyte to induce cationic PU colloidal particles into different microstructured aggregate and gels. B) Scanning electron microscopic and fluorescent images of colloidal PU particles with spherical shape and submicron size. C) Size and zeta potential of colloidal PU particles (C) in dd water and in HEPES buffer. D) Change of size and zeta potential of colloidal PU particles (C) due to aggregation mediated by electrolytes (CA) in phosphate buffer saline of different strength and by polyelectrolyte Na-salt of polyacrylic acid (CPA) of different strength. E) ‘Dispersibility factor, n’ due to aggregation of colloidal PU particles (C) in CA and CPA. Variation of ‘n’ with respect to particle fraction in colloidal PU particles (C) and aggregated CA and CPA system. F) Kinetics of aggregation measured from change in optical density with time for CA and CPA system. Aggregation time estimated from KWW fit of kinetic data. (ANOVA, ** P≤ 0.01, * P≤ 0.05) 272x343mm (300 x 300 DPI)


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Figure 2. Colloidal aggregates show different microstructures. A) Brightfield image of CA and CPA colloidal aggregates. Inset showing traced outline of aggregate boundary. B) Fluorescent microscopic images of CA and CPA colloidal aggregates and a representative binarized image showing different microstructures. C) Characterization of microstructure from circularity index and fractal dimension, and structural organization of colloidal particles network from length of longest unit and number of branches per unit length of an aggregate. D) Oscillatory amplitude sweep test of CA and CPA aggregate embedded Matrigel and Collagen I with 0.1 particulate (by volume). (ANOVA, ** P≤ 0.01, * P≤ 0.05) 370x163mm (300 x 300 DPI)


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Figure 3. Microstructural morphology of colloidal gels. A) Confocal scanning fluorescent images and 3D interactive surface plot (color code indicates z-depth) of CA and CPA gels showing morphology and spatial distribution of voids in the gels. B) Morphological features of the microstructure analyzed by measuring void areas and void aspect ratio of the gels from these images using in ImageJ. C) Microstructured morphology of CA and CPA gels from scanning electron microscopic images. (ANOVA, ** P ≤0.01, * P ≤0.05) 288x220mm (300 x 300 DPI)



Figure 4. Mechanical characterizations of colloidal gel from rheology. A) Elastic moduli (Gʹ) and tan(δ) of CA and CPA gel from 0.1 particle fraction obtained from linear viscoelastic region of strain amplitude sweep measured at constant frequency. B) Variation of elastic moduli (Gʹ) and yield strain of CA and CPA gels prepared from different particle fractions; measured from linear viscoelastic region and from the crossover point of elastic (Gʹ) and viscous (Gʺ) moduli. C) Oscillatory frequency sweep of CA and CPA gel from 0.1 particle fraction. 177x257mm (300 x 300 DPI)


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Figure 5. Endothelial cell (EC) organization in colloidal aggregate embedded endothelial matrix. Representative fluorescent images of EC organization (red=actin and blue=nucleus) in CA and CPA at 0.1 particulate fraction embedded in A) Matrigel (CA-M and CPA-M), and C) Collagen I (CA-C and CPA-C) at 48 hrs. Morphometric analysis of EC organization measured by analyzing total length of interconnected cell chord per unit area, size (perimeter) of enclosed network form EC chords, and shape of cellular aggregates in B) Matrigel (CA-M and CPA-M), and D) Collagen I (CA-C and CPA-C). (ANOVA, ** P ≤0.01, * P ≤0.05) 309x265mm (300 x 300 DPI)



Figure 6. Endothelial cell (EC) organization and morphogenesis in colloidal gels. A) Representative fluorescent images (red=actin and blue=nucleus) and H&E stained images of ECs in CA and CPA colloidal gel prepared from 0.1 particle fraction at 48 hrs. B) Morphometric analysis of EC organization measured by analyzing total length of interconnected cell chord per unit area, size (perimeter) of enclosed network form EC chords, and shape of cellular aggregates in CA and CPA colloidal gel. C) Representative fluorescent images (red=actin and blue=nucleus) and H&E stained images of ECs in composite CA and CPA colloidal gel prepared from 0.1 particle fraction in presence of collagen I (CA-C and CPA-C) and in presence matrigel (CA-M and CPA-M) at 48 hrs. D) Morphometric analysis of EC organization in composite CA and CPA colloidal gel. E) Simulated vascular patterns obtained as a combinatorial function of cell-cell (C-C) adhesion and cell elongation (L) with change in Jc-c (higher value indicates low cell-cell adhesion) and L (larger value indicates increased cell elongation) respectively, under low chemotactic strength. Morphometric analysis of the corresponding simulated vascular patterns. (ANOVA, ** P ≤0.01, * P ≤0.05) 348x515mm (300 x 300 DPI)


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Figure 7. Endothelial cell signaling in native and composite colloidal gels. A) Levels of focal adhesion kinase (FAK), VE-cadherin, VEGFR2, and MMP2 in ECs measured by immunoblot analysis with β-actin as loading control in CA and CPA gel prepared from 0.1 particle fraction at 48 hrs. B) Quantified level of expressions normalized to β-actin shows differential level of markers in ECs. Comparison FAK to VE-cadherin expression in CA and CPA colloidal gels. Levels of focal adhesion kinase (FAK), VE-cadherin, VEGFR2, and MMP2 and the quantified expression in ECs measured by immunoblot analysis with β-actin as loading control in C) collagen based composite colloidal gels (CA-C and CPA-C) and D) Matrigel based composite colloidal gels (CA-M and CPA-M) at 48 hrs. (ANOVA, ** P ≤0.01, * P ≤0.05) 362x302mm (300 x 300 DPI)


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Colloidal Gels for Guiding Endothelial Cell Organization via

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