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Published in final edited form as:
Integr Biol (Camb). 2012 March ; 4(3): . doi:10.1039/c2ib00057a.

Statistical Platform to Discern Spatial and Temporal
Coordination of Endothelial Sprouting
William W. Yuen1, Nan R. Du1, Dima Shvartsman1, Praveen R. Arany1, Henry Lam2, andDavid J. Mooney1
1School of Engineering and Applied Sciences, Harvard University, Wyss Institute for Biologically
Inspired Engineering
2Mathematics & Statistics, Boston University

Abstract

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Many biological processes, including angiogenesis, involve intercellular feedback and temporal
coordination, but inference of these relations is often drowned in low sample sizes or noisy
population data. To address this issue, a methodology was developed to statistically study spatial
lateral inhibition and temporal synchronization in one specific biological process, endothelial
sprouting mediated by Notch signaling. Notch plays an essential role in the development oforganized vasculature, but the effects of Notch on the temporal characteristics of angiogenesis are
not well understood. Results from this study showed that Notch lateral inhibition operates at
distances less than 31μm. Furthermore, combining time lapse microscopy with an intraclasscorrelation model typically used to analyze family data showed intrinsic temporal synchronization
among endothelial sprouts originating from the same microcarrier. Such synchronization was
reduced with Notch inhibitors, but was enhanced with the addition of Notch ligands. These results
indicate Notch plays a critical role in the temporal regulation of angiogenesis, as well as spatial
control, and this method of analysis will be of significant utility in studies of a variety of otherbiological processes.

Introduction

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Biological systems often demonstrate high variability, and standard approaches to analyze
these processes may not provide significant insight. Vascular sprouting, a key step in theangiogenesis processes during development and cancer progression, is a prime example. In
recent years, numerous studies have elucidated a qualitative role of Notch signaling inangiogenesis. There are five canonical DSL (Delta, Serrate, LAG-2) Notch ligands: Deltalike 1 (Dll1), Dll3, Dll4, Jagged-1 (Jag1), and Jagged-2 (Jag2). Among them, Dll1, Dll4 andJag1 are the most prevalent in endothelium [1]. Upon binding to DSL ligands, Notch
receptors initiate a series of proteolytic cleavages. First, the A Disintegrin And
Metalloprotease (ADAM) family within the juxtamembrane region is cleaved. Cleavage ofγ-secretase within the transmembrane region subsequently occurs, releasing the Notch
intracellular domain (NICD) that translocates to the cell nucleus.
Recent studies have shown Notch signaling regulates many aspects of vascular biology.
Endothelial cell specification into tip and stalk cells is regulated by Notch, with strongest
Notch activity observed in the stalk cells [1,2]. Pharmacological and genetic inactivation of
Notch causes excessive vessel sprouting and branching [2–9], but the resulting vasculature isoften not lumenized or productive in perfusion. The angiogenic studies on Notch havemainly focused on its spatial lateral inhibition. Limited quantification has been done on thetemporal effects of Notch on angiogenesis [10,11]. Understanding the temporal aspects ofangiogenesis, however, is critical for the development of vascular networks. In particular,

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communication between endothelial cells is required to temporally coordinate sprouting andpave the way for subsequent fusion of nascent blood vessels.

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A number of in vitro assays have been used to model angiogenesis, each with its advantages
and drawbacks. One assay, the aortic ring assay is useful for studies measuring aggregate
proliferation and movements of different cell types, but does not separate simple cell
migration from endothelial activation [12, 13]. Two dimensional morphogenesis assays,
such as the tube formation assay [14], do not represent the 3D environment. In this study, asprouting assay using microcarrier beads embedded in a 3D gel [15, 16] was used to monitor
the temporal effects of Notch on sprouting angiogenesis. In such an assay, the juxtacrine
effects mediated by Notch are isolated, because cells from different microcarriers are
disconnected. Furthermore, the high sample number per experiment and the uniform shapeof the microcarriers potentially allows automated quantitative analysis for large-scaled
experimental data. The spherical structure of the microcarriers also can enable automateddetection of sprout formation.

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In contrast to previous work that has largely studied sprouting on a population level using
collective statistics, this study utilized an intraclass correlation model to infer the temporalsynchronization of sprouting events within a microcarrier. Despite the low number ofsprouts per microcarrier in the sprouting assay, statistical inference could be drawn by
taking advantage of the large number of microcarriers in the experiment. The intraclasscorrelation model was first developed to analyze family data [17,18], and since then wasused in various other contexts [19]. In familial analysis, this model was used to estimate thedegree of resemblance among family members with respect to certain characteristics, suchas height, test scores, or blood pressure. These effects are often hidden in the collective
statistics such as sample mean and variance. Equivalently, similarity of the times of sprout
initiation is an indirect measure of the synchronization of sprouting. This model, whencombined with automated sprout analysis, is demonstrated here to be powerful in extracting
information that is otherwise subtle due to the indistinguishability of noisy aggregate data
across experimental conditions.

Results
Validation of Notch Ligands and Inhibitors
The effects of the Notch ligands and inhibitors were validated by immunostaining for theactivated Notch intracellular domain (NCID). Treatment with Jag1 and Dll4 significantly
increased the level of Notch activity, whereas addition of DAPT abrogated Notch activity(Fig. 1a).

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In vitro Endothelial Sprouting Assays
The impact of Notch inhibitors and ligands on the overall level of angiogenesis wasanalyzed using a well-studied in vitro sprouting assay [15, 16]. In the sprouting assay,
endothelial cells (ECs) coated on dextran beads formed angiogenic sprouts in fibrin gel. A
sprout is defined as a multicellular extension with > 1 connected ECs that are attached to themicrocarrier (white arrows in Fig. 1b). The sprouting ratio, defined as the number of sprouts
divided by the number of microcarriers, was quantified for various conditions (Fig. 1c). In
accordance with previous studies, treatment with Notch inhibitors, DAPT and anti-Dll4
antibody, significantly increases the sprouting ratios [2–4, 20]. In contrast, theadministration of Dll4 and Jag1 in the gel and media caused a reduction in sprouting ratios.
This reduction was reversed when Notch inhibitors were added simultaneously with theNotch ligands.

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Analysis of Sprout Spatial Distribution

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As a validation of Notch-mediated lateral inhibition in this assay, the numbers of sprouts oneach bead were counted to estimate the distribution of the number of sprouts on the
microcarriers (Fig. 2a). The data showed that DAPT caused a slight reduction in the relative
frequency of microcarriers with zero sprouts. Interestingly, the frequency of microcarriers
with one sprout was also reduced with DAPT treatment. The frequencies at 2 and 3 sprouts,on the other hand, were increased by DAPT. This result was consistent with the notion that
Notch acts as a lateral inhibitor between endothelial cells. When this lateral inhibition was
relieved by the addition of DAPT, the relative frequencies of microcarriers with multiple
sprouts increased.

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In order to spatially characterize and to more directly measure Notch-mediated lateral
inhibition, the distances between each pair of sprouts were measured. The two-dimensional
composite projections for the 3D stacks were taken, and the distance, dij, between each pairof observed sprouts (i, j) along the arc of circular microcarrier was measured. Formally, thedistance was measured as rδθ on the coordinate system defined in Fig. 2b. Fig. 2c illustrated
the measurements of the distances between one sprout (red arrow) and all the other sprouts.
The distances between all possible pairs of sprouts were recorded. It is important to note that
unlike the number of sprouts ns, the distances dij were measured on the 2D projection due tolimited resolution in 3D distance measurements. Nevertheless, these 2D distances could be
used as a proxy for the 3D distances. Fig. S1 shows a simulated scatter plot of the 2D and
3D distances of pairs of sprouts (that were visible on the 2D projection) uniformly sampled
on the surface of a microcarrier. It demonstrates a monotonic relation between 2D and 3D
distances.
In order to illustrate potential positive and negative feedbacks of the sprouting process,experimental histograms of all dij were compared with a theoretical case with no feedback(Fig. 2d). In this theoretical case, all sprouts were independent and equally likely to form
anywhere on the spherical microcarrier. It can be seen that the relative frequencies of thelowest distances (< 31μm) were significantly reduced in the control compared to simulation,
but were increased with the administration of DAPT. Compared to the theoretical simulation
with no feedback, DAPT appears to restore the sprouting frequency fully in the 15μm and
31μm, but its effect is moderate at the < 15μm distance. The inability of DAPT to restore thefrequency at < 15μm was likely due to Notch-independent effects of cell and nutrient
depletion at this length scale of < 15μm. The experimental frequencies at mid range from75μm to 150μm were higher than the theoretical, consistent with length scales of paracrine
signaling that enhances sprouting.

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Analysis of Tip Cell Speed
To characterize the effects of Notch signaling on the sprout growth rate, the average speedof tip cells were measured over 48 hours through time lapse imaging in 3D (Fig. 3). The
speed of a tip cell was measured once a sprout was formed (Fig. 4a). The average tip cell
speed in the control condition was 6.4μm/hr. Treatment with DAPT decreased the tip cell
speed slightly to 4.9μm/hr (p < 0.05 compared to control). Treatment with anti-Dll4 yielded
no statistically significant difference from the control. The additions of Dll4 and Jag1
increased the average tip cell speed to 9.9μm/hr and 8.0μm/hr respectively (p < 0.05
compared to control using Tukey multiple comparison).
Temporal Analysis of Sprout Formation
To determine if Notch signaling influenced the synchronization of endothelial sprout
formation, time lapsed microscopy movies were analyzed, and sprouts were automaticallydetected using an algorithm (Fig. 3). To test the hypothesis that there was temporal
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synchronization among endothelial sprouts within the same microcarrier, the initiation timesof sprout formation were recorded. tij, defined as the time of initiation for the ith sprout on
the jth microcarrier, was recorded. Temporal synchronization can be characterized as thesimilarity of tij within the same j. This similarity can be quantified as the intraclasscorrelation coefficient ρ of tij.
The observation of these initiation times could be modeled as:

where μ is the population mean of all observations, cj is the microcarrier effect identically
distributed with mean 0 and variance , and eij is the residual error identically distributedwith mean 0 and variance . The variance of the tij is then given by. ρ is definedas, or in other words, the proportion of variation explained by belonging to the sameclass (microcarrier). Hence, it takes value between 0 and 1, with 0 being no intraclasscorrelation (every sample is basically identically distributed and uncorrelated), and 1 being
perfect correlation within class (every sample in the same class has the same value).

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The estimates of ρ, given by ra, were computed with analysis of variance (Table 1). It could
be seen that ra = 32% for the control condition, indicating a moderate level of correlation
among sprouts. ra was reduced to 11.6% and 14.3% when Notch was inhibited by DAPT
and anti-Dll4, respectively (Fig. 4b). In contrast, enhancing Notch signaling via cell
treatment with Dll4 and Jag1 ligands led to an increase of ra to 37% and 53% respectively.
One source of the differences in intraclass correlation could be due to shifts in the initiation
times of the entire population. For example, drug treatments could simply prevent sprouting
for some time, causing the intraclass correlation to be artificially increased or decreased
because all sprouting events would occur over a shorter or longer time period. To eliminate
this possibility, the population statistics of all the sprouts in each condition were plotted(Fig. 4c). It was observed that the average initiation times were the same for all conditions;thus, pharmacological treatments did not skew the sprout initiation times as a population.

Discussion

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A fundamental understanding of the angiogenesis process requires elucidation of both thetemporal and spatial characteristics of sprouting events. Temporal profiles of expression
levels for angiogenic molecules, for example, have been shown to be key in the regulation ofvascular development [21–23]. Temporal coordination in the initiation of sprouts may be
important in determining subsequent angiogenic processes, such as tube formation, vessel
fusion, and maturation. By growing endothelial cells on spherical microcarriers andanalyzing sprouting with time lapse microscopy and a statistical model, key temporalcharacteristics were inferred from data sets that were seemingly random and highly variant.
This methodology allowed the measurement and analysis of the dynamics of sprout
initiation.
As expected from the previously discribed lateral inhibition behaviour of Notch signaling[2–4, 20], inhibition of Notch using DAPT and anti-Dll4 Ab increased sprouting. The Notch
ligands Dll4 and Jag1 have been shown to functionally activate Notch signaling in a 3D
environment [20, 24–27]. In this study, these ligands inhibited endothelial sprouting, asconsistent with past studies [20, 26, 28]. It is interesting that endogenous Jag1 and Dll4
expressions have opposing effects in retinal angiogenesis [29] in that Jag1 expression ispositively correlated with angiogenesis. The current study, however, found Jag1 to have
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similar anti-angiogenic effects as Dll4 in the in vitro sprouting assay. Other groups
administering exogenous Jag1 also had similar findings [2, 10, 20]. It is possible that
endogenous expression of Jag1 enables an endothelial cell to adopt the tip cell phenotype[30] via autocrine signaling or cis-inhibition [31,32]. On the other hand, extrinsic Notch
activation with Jag1 activates Notch for the entire cell population.
The experimental distribution of the number of sprouts for each microcarrier was also
consistent with Notch lateral inhibition. Inactivation of Notch relieves lateral inhibition, andcauses some of those microcarriers that would otherwise have one sprout to instead have
two or three sprouts. This lateral inhibition occurs at the length scale of < 31μm. Apparent
promotion of sprouting in some distances at the mid length scales (75 – 150μm) is consistent
with paracrine signaling in angiogenesis [33–35]. It is probable that endothelial cells secrete
cytokines such as VEGF to promote sprouting in neighboring cells at an appropriate distanceto allow the two sprouts to eventually merge and form a functional new capillary [36–38].

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The tracking of average tip cells in this study showed an interesting phenomenon regarding
cell migration speed. After endothelial cells are activated to form tip cells, the tip cell speed
increased with Notch activation and decreased with Notch inhibition. These results mayreconcile the findings of several groups who concluded Notch inhibition decreased
angiogenesis [39–41]. In those studies, the in vitro assay used were either the 2D
morphogenesis assay or the aortic ring assay. It is possible that the activity observed in thoseassays was predominantly cell migration, which was enhanced with Notch signaling, rather
than endothelial activation.
By utilizing the intraclass correlation model, endothelial sprouting was shown to be
intrinsically synchronized. This temporal synchronization is inhibited with the inactivationof Notch, and enhanced with the activation of Notch. Previous studies have shown that
Notch signaling is critical in creating organized and efficient vasculature, despite reduced
vessel density [2–4]. Our study suggests yet another mechanism by which Notch may
mediate efficient vascular growth. Presumably, functional vasculature requires fusion ofdifferent endothelial sprouts. The temporal synchronization of endothelial sprouting may be
critical in coordinating angiogenesis in a neighbourhood of endothelial cells. Past studies
have provided great insight about the synchronization of calcium spikes in endothelial cells,suggesting oscillatory release of endothelial-derived substances [42–45]. However, theseworks had focused on membrane potential oscillations that are on the higher frequencies(e.g. 5–10 min). Furthermore, their effects on sprouting angiogenesis had not been directly
investigated. This work is the first to study synchronization in the context of sprouting
behavior.

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Across conditions, both the estimated population means and variances of the sprout
initiation times did not show any difference. Such pattern was unlikely to alter with anincrease in population sample size. It is likely that the temporal dynamics in this in vitro
process were more subtle than could be discerned by simple mean-variance analyses on the
population level. This is an equivalent problem to analyzing family data. The numbers ofsiblings in each family are usually low - the average American family size was 3.19 from2005–2009 (US Census Bureau). On the other hand, variances of attributes in the population
were high - the Gini index of US income is 46.9 in 2006 (US Census Bureau). However,statistical inferences could still be made by sampling a large number of households.
Similarly, by taking advantage of the high number of microcarriers in each experiment inthis study, the intraclass correlation model compensated for the low sprouting ratio in eachmicrocarrier and the seemingly indistinguishable and noisy population data. This study
illustrated that this methodology could be applied in the context of angiogenesis tocharacterize the otherwise elusive temporal coordination involved in endothelial sprouting.

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In sum, this work was the first to address and quantify the temporal synchronization ofendothelial sprouts to date. These techniques allow inference of subtle information thatwould otherwise be drowned in low sample sizes or noisy population data. It is important topoint out that this study started with a clear objective of elucidating temporalsynchronization, and a specific statistical model is calibrated against the underlying
assumptions. Thus, in this setting, a large number of sample size increases the power of thestatistical tests. The algorithm and statistical approach developed here could be extended toanalyze other cellular events that involve synchronization. This approach can also be
automated for large-scale and target-based screens for therapeutic discoveries.

Materials and Methods
Immunohistochemistry of Notch signaling

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HUVECS (Human Umbilical Vein Endothelial Cells) were seeded onto coverslips at 37°C.
For the immobilized ligand condition, previously, cells were starved, and cover-slips were
submerged in poly-l-lysine for an hour, with ligand solutions (Jagged and anti-Dll4) wereadded for atleast 4 hours in RT. The conditions of Control, Starved (for 6 hours) and DAPT
in full growth medium were conducted in 24 hours with no previous cell starvation. Samples
were fixed with 4% paraformaldehyde for 1 hour at room temperature. Samples were
washed two times with PBS, non-specific sites were blocked with Pharmingen Stain Buffer(BSA) in PBS and permeabilized with 0.1% Triton-X+ Normal Goat IgG (1:50) in PBS for
30 min. Samples were immunostained for 45 minutes at room temperature with anti-NICD
primary antibody (10ug/ml, 1:100, all antibodies diluted using Pharmingen Stain Buffer).
After incubation, samples were washed two times with PBS, once with stain buffer for 5
minutes, and then incubated for atleast 45 minutes at room temperature with secondary
antibody, AlexaFluor 647 Goat Anti-Rabbit (Molecular Probes 4mg/ml, 1:500). Finally,
samples were washed three times with PBS, and mounted onto slides by using Prolong Goldantifade reagent with DAPI (Molecular Probes).
3D Cell Culture and in vitro Sprouting Assay

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Human umbilical cord endothelial cells (HUVECs) were cultured to confluence at 37°C and
5% CO2 in EGM-2MV medium (Lonza) containing all supplements. Angiogenic activity ofendothelial cells was assessed using a modification of a widely used in vitro sprouting assay[15]. Briefly, dextran beads microcarriers (Cytodex 3) with a dry weight of 50mg wereswollen in PBS and autoclaved. The microcarriers were washed in EGM-2MV medium andseeded with 3 × 106 HUVECs in a spinner flask. The microcarriers were stirred for 2
minutes out of 30 minutes for 3 hours at 37°C incubation. After 3 hours, the beads and cells
mixture were continuously stirred and incubated for an additional 21 hours. The cell-coatedbeads were then seeded in fibrin gel in a 24-well-plate. The composition of the fibrin gel ineach well was 0.682mg fibrinogen (Sigma, T3879), 11.4μg aprotinin (Sigma, A4529),
0.455U thrombin (Sigma, T6884) in 393μL of PBS and 57μL of EGM2-MV. Gels wereincubated at 37°C for 30 minutes and media of experimental conditions were placed on topof the gel.
Media contained the addition of 50ng/mL VEGF. Recombinant human Jag1-Fc (R&D
Systems, 1277-JG-050) and Dll4-Fc (Enzo Life Sciences, ALX-201-386-C010), when used,were 1μg/mL. Adopted from methods in [24], Dll4-Fc was clustered with anti-Fc antibody(1:1 ratio) at room temperature for 60 minutes prior to addition to the media. When Notch
inhibitors were used, the concentrations were 2.5mM DAPT (Sigma-Aldrich, D5942) and5μg/mL anti-Dll4 (gift from Genentech) respectively. DAPT was dissolved in a minuteamount of DMSO. All conditions contained the same amount of DMSO (< 0.01%). Media
were changed every 24 hours. In the static analysis of the sprouting ratios, cells were

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allowed to sprout from beads into surrounding gel over 4 days. After 4 days, the gels wererinsed with PBS and fixed with 4% paraformaldehyde prior to imaging. Subsequent tofixing, samples were stained with DAPI and visualized at 10X objective magnification with
an Olympus IX2 microscope. Sprouts were identified as continuous multi-cellular structures
extended from the microcarrier beads with a minimum of two cells in the structure.
Simulation of Probability Distribution of Sprouts
A sprout was assumed to form anywhere on the spherical surface with equal probability. The
distance between each pair of visible sprouts was measured as the induced arc length in the
2D projection on the XY plane. The resulting probability distribution in this 2D projection,
as illustrated in Fig. 2b and 2c, was generated as follows:

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Sampling the position of a sprout involves two steps: First, the horizontal angle generated by
the sprout in Fig. 2b, namely θ, follows a uniform distribution over [0, 2π) by symmetry.
The infinitesimal sliced surface dS = 2r2dθ shows the area from which a sprout with a given
angle dθ can possibly reside. Next, the vertical angle φ is sampled according to the density
over [0, π]. These two steps together determine the position of the sprout on the
microcarrier’s surface. Furthermore, we resample the empirical distributions obtained fromour experimental data to generate the number of sprouts on each microcarrier ns (Fig. 2a), as
well as the radius of each microcarrier r (see Supporting Information for more detailed
explanation). The distances between each i-j pair of visible samples were calculated as di,j =r (min(|θi − θj|, 2π − |θi − θj|)).
Time Lapse Imaging and Tip Cell Tracking
Prior to seeding into fibrin gels, cells were stained with 250ng/mL octadecyl rhodamine B
chloride (R18) (Invitrogen O-246) overnight. During the staining process, cells were
simultaneously synchronized by serum-starvation. Immediately after seeding into fibrin gels,
cells were placed on the microscope stage, time lapse microscopy images were taken for 48
hours. Images were taken with an Olympus IX2 microscope with the CARV II spinning disk
confocal imager. The image stacks were taken 60 minutes apart and at 20μm Z-resolution.
The acquired 4D datasets were analyzed using the spot detection and tracking modules in
Imaris detection to track tip cell movements.
Intraclass Correlation Model

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To analyze the effects of clustering within sprouts in microcarriers, the intraclass correlation
coefficient (ICC) ρ is defined as. As demonstrated by Donner [46], there are
multiple ways in estimating the parameter ρ. Since the number of sprouts on eachmicrocarrier was not constant for all sprouts in this case, the modified analysis of varianceintraclass correlation, ra was appropriate for estimating ρ (and also because it wasdemonstrated to perform better than other estimators, such as MLE, when the value of ρ issmall [47]). This estimation utilized the standard analysis of variance (ANOVA) table anddefined MSW as the mean square within microcarriers, and MSA as the mean square among
microcarriers. A further parameter n0 = n̄ − Σi(ni − n̄)2(k − 1)N was needed to adjust for the
non-uniformity of the number of sprouts in different microcarriers, with ni being the numberof sprouts in the ith microcarrier, i = 1, 2, …, k, and n̄ = Σi ni/k = N/k the average family size.
The mean squares within microcarriers and among microcarriers were thenrespectively. The modified ANOVA ICC was then:Integr Biol (Camb). Author manuscript; available in PMC 2013 May 15.

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where microcarriers with less than 2 sprouts were not included. The reason was that suchinclusions tend to increase the standard error for ra when both ra and the number of sprouts
per microcarrier was small [17]. The significance of ra could be evaluated with an F-test.
These statistical inferences were derived in the supporting information.
The estimation of ρ using analysis of variance made several assumptions: 1) cj and eij arenormally distributed, 2) samples across classes are independent, and 3) homoscedasticity
across classes. As derived and discussed in [48–51], the estimate of ICC is robust against thenormality assumption. Independent sampling was assumed from the experimental settings
since microcarriers were far apart from each other (> 500μm). Homoscedasticity acrossclasses was confirmed using Bartlett’s test and Levene’s test, respectively with p-values of
0.596 and 0.879, indicating that the null hypothesis of equal variance cannot be rejected.

Supplementary Material
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Refer to Web version on PubMed Central for supplementary material.

Acknowledgments
Financial support was provided by Wyss Institute and by NIH R01 HL069957. The authors are thankful for the
VEGF provided by the Biological Resources of the National Cancer Institute. The authors also thank Michael P.
Brenner for helpful discussion on statistical methods.

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Figure 1.

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In vitro endothelial sprouting in response to Notch ligands and inhibitors. (a) Representative
images of cell-seeded dextran beads embedded in fibrin gel under different conditions.
Images are compositions of Z-stacks of 50 Z-planes at 3 μm apart. A sprout was defined as amulticellular extension with >1 connected ECs that were attached to the microcarrier (seearrows). (b) Immunostained images where cells were stained for activated Notchintracellular domain (NICD) and with DAPI. “Full” refers to the fully supplemented media
and “starvation” refers to serum starvation for 6 hours. The scale bar represents 100μm. (c)Quantification of the number of sprouts per bead at different conditions. Sprouting ratio
refers to the number of sprouts per microcarrier bead. The condition “Dll4 + anti-” refers to
the treatment with both Dll4 and anti-Dll4 antibody. Values represent means and error bars
represent standard errors (n ≥ 7). One and two stars represent, respectively, p < 0.05 and p <0.01 statistical significance from control with Bonferroni correction.

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Figure 2.

Analysis of sprout spatial distribution. (a) Histogram of the number of sprouts on eachmicrocarrier bead (number of microcarriers >1200). (b) Coordinate system used to define
distances measured, and to generate the theoretical histogram if all sprouts are uniformly
distributed. (c) Composite images of three dimensional stacks at 48h. The reconstruction
showed the representative image of a microcarrier with 7 sprouts (indicated by green
arrows). The distance between each closest pair of sprouts was measured and indicated byyellow arcs with double ended arrows. (d) Histogram of the distances between each pair ofsprouts for control (red) and DAPT treatment (blue) (n>1000 each condition). The

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theoretical distances if all sprouts were equally likely to appear at each spot on the
microcarrier was illustrated in green.

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Figure 3.

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Automated sprout tracking system with sample fluorescent images at 9 different Z-slices.
Cyan circle represents the location of the microcarrier detected by maximization of theaccumulation function in the Hough transform. Yellow circle dash-dotted circle represents
the threshold a cell protrusion had to cross to be counted as a sprout. Blue dashed circle
represents the outer limit, outside of which no sprout is counted. Green star represents asprout present at the current z-slice. In the image of z = 4, sprouts from all planes areindicated, denoted by a cyan number representing the z-slice that the sprout is present in.

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Figure 4.

Temporal aspects of sprouting. (a) Average speed of the tip cell at each condition (n ≥ 135
for each, where each sample is the average speed of one tip cell). Bars represent means anderror bars represent standard errors. Statistical significance was computed using multiple
comparison ANOVA with Tukey’s honestly significant difference criterion (* represents p <0.05). (b) Intraclass correlation coefficients for different conditions. Errors bars represent
standard errors estimated. * and ** represent, respectively, p < 0.05 and p < 0.01
significance levels compared to control as tested using the two-sample Z-tests in [52]. (c)Population statistics of the means and standard deviations of sprout initiation times.

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360
170
17.97
0.00339μ̂Standard error

0.0000p (null of ra = 0)

N (# of sprouts)

0.321

ra

k (# of microcarriers)

3.0786

F-statistic

Control

0.0037

17.50

69

236

0.01370.11571.6081DAPT

5.73E-04

18.38

312

1510

0.00000.14272.7562anti-Dll4

0.0019

18.43

450

945

0.00000.36657.7186Dll4

0.0032

17.26

381

669

0.00000.525716.2438

Jag1

Table of statistics showing intraclass correlations at different conditions.

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Table 1
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