Fig 2: Schematic overview of experimental approachTo compare possible different influences of Dll4 vs Jag1 patterns on endothelial sprouting, the method previously described in Tiemeijer et al. (2018) was adapted.(A–C and E) In short, microcontact printing (μCP) stamps (A) were inked with fluorescent beads functionalized with either Dll4 or Jagged1, and lines of 100 μm with 100 μm spacing were printed on glass slides (B). (D) In addition, endothelial cells were seeded on top, via microfluidic channels (C and C2) perpendicular to the lines. After channel removal, Matrigel was added, and the endothelial cells were left to sprout freely for 24 h. With respect to the ligand-functionalized lines, we envision the possibility of three different spatial sprouting patterns (E), random, positive, and negative patterning. Scale bar represents 1mm.

Fig 3: The computational model quantitatively replicates the experiments(A) Due to periodicity, cell-cell signaling was modeled only for a small portion of the cells on the patterned substrate. The first enlargement shows the cells that were analyzed in the simulations. The second enlargement shows the main features of the simulated signaling network.(B) Representative computational results, with each row corresponding to one simulation, with different ligand concentrations on the micropatterns (decreasing from top to bottom). Without ligands on the patterns (bottom row) most cells have a tip/stalk (T/S) hybrid phenotype (red). With increasing concentration, Dll4 induces the stalk (S, blue) phenotype for cells on top of the lines, whereas Jag1 does not have evident effects. Only few tip (T, green) cells were observed.(C and D) The effects of homogeneous coating of Fc, Jag1, or Dll4 on the gene expression of Jag1 were compared between experimental (C) and computational (D) data for the time period of 6 h. qPCR revealed a significant difference of Jag1 expression for Dll4 versus Fc samples (p < 0.0001) and for Dll4 versus Jag1 samples (p < 0.001). For the computational data, averages over 10,000 simulations for each concentration are reported.(E) The efficiency of controlled sprouting was computed for simulations of cells on patterned lines with varying ligand densities. The graph reports the averages of the computed values over 10,000 simulations.(F) Boxplots obtained for the simulations with Dline = Jline = 3000 molecules. The simulations captured the higher spatial control of Dll4 lines, compared with Jag1 lines, on the location of the sprouts (E and F). In (C) and (F), data are represented as boxplots, where the boxes span from the 25th until the 75th percentile, the whiskers extend down to the 10th percentile and up to the 90th percentile, and the horizontal lines represent the median values.

Fig 4: Patterned Dll4 lines result in unidirectional endothelial sprouting. Direction of 5 × 5 pixel matrices was calculated for all ROI of either side of the vessel mimicking monolayer in one microscopy image, using the Image J plugin ‘Directionality’. (a) The dispersion of the fitted graph through the histograms in degrees. (b) The correctness of this fit through the direction histograms. *p < 0.05, **p < 0.01 ***p < 0.001, ****p < 0.0001. NP n = 15, BO n = 21, BD-A n = 23, BD-B n = 13, BD-C = 22. (c) Histogram of the directions found in the no print condition (d) for the bead-only control condition and (e) for the patterned Dll4 condition, with similar chips BeadDll4-B and C (f and g).

Fig 5: The efficiency of controlled sprouting (Efcs) induced by ligand patterns(A and B) Efcs is defined by the formula in (A) and quantifies the level and type of control of the functionalized lines over the spatial location of the endothelial sprouts. Below the formula, a schematic is presented of the patterned lines (red lines), the cell nuclei (blue ellipses), and the corresponding outline of the sprouting front (discontinuous green line). The formula of Efcs then accounts for the amount of cells in the subregion of the ROI in between the ligand lines (Coff, white area) as a percentage of the total amount of cells (Coff + Con) in that ROI. In the definition, Con represents the number of cells in the subregion of the ROI on top of the ligand lines (red area). This definition of Efcs enables the identification of different sprouting patterns and control: Efcs ≈ 50% for random patterning and no control; Efcs ≈ 0% for high control toward positive patterning; and Efcs ≈ 100% for high control toward negative patterning. Comparison of the Efcs between Fc, Jag1, and Dll4 line samples is shown in (B), where boxplots represent N = 31/112/58 ROIs pooled from a total of three Fc/four Jag1/4 Dll4 microchips, respectively. There is a significant difference between the Efcs of Jag1 and Dll4 samples (p < 0.0001) and between the Fc and Dll4 (p = 0.0004).(C) No significant difference in the number of cells per ROI. In (B) and (C), data are represented as boxplots, where the boxes span from the 25th until the 75th percentile, the whiskers extend down to the 10th percentile and up to the 90th percentile, and the horizontal lines represent the median values.