Comparison of networks formed with mixed cells and cells with average properties.

<p><b>A</b>, <b>F</b>, and <b>K</b> morphologies for mixed tip (red) and stalk (gray) cells (<i>F</i>_tip = 0.5). <b>B</b>, <b>G</b>, and <b>L</b> morphologies for averaged cells (<i>F</i>_tip = 0.5). <b>C</b>-<b>E</b>, <b>H</b>-<b>J</b>, and <b>M</b>-<b>O</b> morphometrics for a range of tip cell fractions for both the control and mixed model. The morphometrics were calculated for 50 simulations at 10 000 MCS (error bars represent the standard deviation). p-values were obtained with a Welch’s t-test for the null hypothesis that the mean of mixed model and the control model are identical.</p

Parameter values for the angiogenesis and tip cell selection model.

<p>Underlined parameters are varied in the screen for tip cell behavior.</p

Effects of different tip and stalk cell properties on network morphology.

<p><b>A</b>-<b>F</b> Trends of compactness (black rectangles) and number of lacunae (blue circles) calculated with the morphologies at 10 000 MCS. For each data point 10 morphologies were analyzed and the error bars represent the standard deviation. p-values were obtained with a Welch’s t-test for the null hypothesis that the mean of the sample is identical to that of a reference with the nominal parameters listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0159478#pone.0159478.t001" target="_blank">Table 1</a>. For <b>B</b> this reference is the data for tip cell fraction 1 and for all other graphs this is the data for tip cell fraction 0. <b>G</b>-<b>L</b> Morphologies after 10 000 MCS for each tested parameter value with <i>F</i>_tip = 0.2.</p

Differences in cell properties can enable cells of one type to occupy sprout tips.

<p>The percentage of sprout tips occupied by at least one tip cell was calculated at 10 000 MCS and averaged over 50 simulations (error bars depict the standard deviation). In each simulation 20% of the cells were predefined as tip cells. For each simulation one tip cell parameter was changed, except for the control experiment where the nominal parameters were used for both tip and stalk cells. p-values were obtained with a one sided Welch’s t-test for the null hypothesis that the number of tip cells at the sprout tips is not larger than in the control simulation.</p

Effects of tip cell selection on network formation.

<p><b>A</b>-<b>F</b> Networks formed with varying fractions of predefined tip cells (<i>F</i>_tip) with <i>χ</i>(tip) = 400 at 10 000 MCS. <b>G</b>-<b>L</b> Networks formed with the tip cell selection model for varying NICD thresholds (Θ_NICD) at 10 000 MCS. <b>M</b> Standard deviation of lacuna area in a network after 10 000 MCS. <b>N</b>-<b>Q</b> Close up of the evolution of a network with 20% predefined tip cells (marked area in <b>B</b>). <b>R</b>-<b>T</b> Comparison of the morphometrics for networks formed with predefined and selected tip cells with reduced chemoattractant sensitivity (<i>χ</i>(tip) = 400) and network at 10 000 MCS. For the simulations with tip cell selection, the average tip cell fraction was calculated for each NICD threshold. For all plots (<b>M</b> and <b>R</b>-<b>T</b>) the values were averaged over 50 simulations and error bars depict the standard deviation.</p

Effects of reducing tip cell chemoattractant sensitivity for varying NICD thresholds.

<p>Morphospace of the final morphologies (10 000 MCS) with varying tip cell chemoattractant sensitivities (<i>χ</i>(tip)) and NICD thresholds (Θ_NICD).</p

Effects of Apelin or APJ silencing in spheroid sprouting assays.

<p><b>A</b>-<b>F</b> Microscopy images of the WT and CD34- spheroids in VEGF-enriched collage after 24 hours. <b>G</b>-<b>H</b> Number of sprouts, relative to siNT treatment, after 24 hours for spheroids with mixed cells and CD34- spheroids. These metrics are the mean of the normalized, average number of sprouts of each replicate with the error bars depicting standard deviation. The * denotes <i>p</i> < 0.05, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0159478#sec011" target="_blank">Materials and Methods</a> for details of the normalization and statistical analysis. <b>I</b>-<b>L</b> Example morphologies formed in the computational angiogenesis model (750 MCS); <b>(I-J)</b> model including tip cells (<i>θ</i>_<i>NICD</i> = 0.2, in absence (<b>I</b>) and in presence (<b>J</b>) of chemoattractant inhibition; <b>(K-L)</b> model with reduced tip cell number (<i>θ</i>_<i>NICD</i> = 0) in presence (<b>K</b>) and in absence (<b>M</b>) of chemoattractant inhibition. <b>M</b> Number of sprouts after 750 MCS for <i>n</i> = 20 simulations; error bars show the standard deviation; asterisks denote <i>p</i> < 0.05 for p-values obtained with Welch’s t-test in comparison with controls (no inhibition).</p

Overview of the angiogenesis model and the parameter search.

<p><b>A</b> Time-lapse of angiogenesis model behavior <b>B</b> For each parameter P that is tested in the parameter search a morphospace is created to compare the different parameter values for different tip cell fractions. <b>C</b> Each morphology is studied in detail to see if the sprout tips are occupied by tip cells (red). <b>D</b> Each row of morphologies is studied to find rows in which the morphologies differ, indicating that network formation depends on the tip cell fraction.</p

Identification of proteins associated with clinical and pathological features of proliferative diabetic retinopathy in vitreous and fibrovascular membranes

<div><p>Purpose</p><p>To identify the protein profiles in vitreous associated with retinal fibrosis, angiogenesis, and neurite formation in epiretinal fibrovascular membranes (FVMs) in patients with proliferative diabetic retinopathy (PDR).</p><p>Methods</p><p>Vitreous samples of 5 non-diabetic control patients with vitreous debris and 7 patients with PDR membranes were screened for 507 preselected proteins using the semi-quantitative RayBio® L-series 507 antibody array. From this array, 60 proteins were selected for a custom quantitative antibody array (Raybiotech, Human Quantibody® array), analyzing 7 control patients, 8 PDR patients with FVMs, and 5 PDR patients without FVMs. Additionally, mRNA levels of proteins of interest were measured in 10 PDR membranes and 11 idiopathic membranes and in retinal tissues and cells to identify possible sources of protein production.</p><p>Results</p><p>Of the 507 proteins screened, 21 were found to be significantly elevated in PDR patients, including neurogenic and angiogenic factors such as neuregulin 1 (NRG1), nerve growth factor receptor (NGFR), placental growth factor (PlGF) and platelet derived growth factor (PDGF). Angiopoietin-2 (Ang2) concentrations were strongly correlated to the degree of fibrosis and the presence of FVMs in patients with PDR. Protein correlation analysis showed PDGF to be extensively co-regulated with other proteins, including thrombospondin-1 and Ang2. mRNA levels of glial-derived and brain/derived neurotrophic factor (GDNF and BDNF) were elevated in PDR membranes. These results were validated in a second study of 52 vitreous samples of 32 PDR patients and 20 control patients.</p><p>Conclusions</p><p>This exploratory study reveals protein networks that potentially contribute to neurite outgrowth, angiogenesis and fibrosis in the formation of fibrovascular membranes in PDR. We identified a possible role of Ang2 in fibrosis and the formation of FVMs, and of the neurotrophic factors NRG1, PDGF and GDNF in neurite growth that occurs in all FVMs in PDR.</p></div

Effect of anti-VEGF therapy on protein levels.

<p>Samples were divided in three groups: CON, non-diabetic control patients; PDR—B, PDR patients that did not receive bevacizumab; PDR + B, PDR patients that received bevacizumab. Differences between groups were analyzed with an unpaired t test with Welch's correction. Lines represent mean values.</p