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Introduction

Oksana Sirenko, PhD | Sr. Research Scientist | Molecular Devices
Angeline Lim, PhD | Applications Scientist | Molecular Devices
Thomas Olivier | Bioinformatics Engineer | MIMETAS

Angiogenesis is the physiological process of formation and remodeling of new blood vessels and capillaries from pre-existing blood vessels. This can be achieved through endothelial sprouting or splitting of the vessels and capillaries. Vascular cells respond to appropriate stimuli by degradation of the extracellular matrix, then proliferation and migration of endothelial cells^1,2.

Cells undergo these processes to create a tube containing a lumen, a dynamic space that facilitates blood flow and exchange of oxygen, carbon dioxide, nitric oxide and nutrients. Angiogenesis is a vital process in growth and development, as well as in wound healing and in the formation of granulation tissue. Angiogenic growth also supports the invasion of tumor cells in healthy tissue and is commonly measured in cancer research. When vascular sprouts extend toward the source of the angiogenic stimulus, endothelial cells migrate in tandem, using adhesion molecules. These sprouts then form loops to become a full vessel lumen as cells migrate to the site of angiogenesis. Sprouting occurs at a rate of several millimeters per day in vivo and enables new vessels to grow across gaps.

Many anti-angiogenic drugs have been developed to use in cancer therapy, while pro-angiogenic molecules may hold potential in regenerative applications. In vitro experiments to date have modeled only some aspects of angiogenic mechanisms including scratch assays, Boyden chambers, and tube formation assays. MIMETAS scientists developed advanced and more physiologically relevant models that included the actual growth and sprouting of vessels from a main perfused vessel into a collagen extracellular matrix as directed by pro- or anti-angiogenic factors. Such cues can be added to either of the perfusion channels but can also be directly secreted by tissues in a co-culture setup.

High-content imaging allows visualization of angiogenic structures, 3D reconstitution, and complex analysis of angiogenesis and sprouting of new blood vessels. Here, we describe the imaging and analysis methods for obtaining multiple quantitative descriptors of angiogenesis that could be used for comparative research into disease phenotypes and compound effects.

Methods

Cell model

The 3D angiogenesis model was established in the MIMETAS OrganoPlate^® 3-lane^3,4. The design of the OrganoPlate 3-lane is based on a standard 384-well plate format with each microfluidic unit represented by 3x3 wells, totaling 40 units (Figure 1). Each microfluidic unit consists of three channels. Collagen-I extracellular matrix (ECM) gel was dispensed into the middle channel. Small pressure barriers called phaseguides pattern the ECM gel and prevent it from flowing into the adjacent perfusion channels. Next, endothelial cells (primary, cell line, or iPSC-derived) are seeded in the top perfusion channel and attach against the ECM gel. Perfusion is started by placing the OrganoPlate on a rocker platform and as cells proliferate, they form an endothelial microvessel. After the vessel has formed, a cocktail of pro-angiogenic factors is added to the bottom perfusion channel, on the opposite side of the parental endothelial vessel. The resulting gradient of angiogenic compounds results in the induction of angiogenic sprouts. Angiogenic sprouts were allowed to form for 0–4 days and were fixed for quantitative comparison. A schematic representation of the 3D angiogenesis model can be found in the addendum.

Figure 1. Schematic presentation of OrganoPlate 3-lane.

Imaging

Vascular cells and sprouts were fixed with 4% formaldehyde and stained with a primary antibody against VE-cadherin, followed by a secondary Alexa488 antibody (green). Actin filaments were stained with ActinRed^™ ReadyProbes^™ reagent (red) and nuclei were stained with Hoechst (blue). Cells were imaged with the ImageXpress® Micro Confocal High-Content Imaging System (Molecular Devices). Images of cells were taken using confocal mode (60 μm pinhole spinning disc) and the 10X, or 20X water immersion objectives. For 20X magnification, z-stacks of 45-58 image planes were acquired at 2–4 μm intervals. For 10X objective, z-stacks of 15–25 images were acquired using 4-6 μm intervals. Nuclei were imaged with the DAPI channel and angiogenic sprouts with the FITC channel, at 100 ms and 400 ms exposures respectively.

Image analysis

Images were analyzed using the Custom Module Editor in MetaXpress® High-Content Image Acquisition and Analysis Software. Details are described in the Results section. Briefly, a Neurite Outgrowth module was used to identify sprout extensions, and the Count Nuclei module for nuclei characterization. Then the objects were connected between z-planes in 3D space using “connect by best match” function. Secondary analysis was completed using Microsoft Excel software.

Images were analyzed using a 3D custom module within the MetaXpress environment. Custom Module Editor (CME) and 3D image analysis capabilities are needed for described analysis method. The custom module contained several steps. First, angiogenic sprouts were defined and segmented in each image using Neurite Outgrowth module, then objects in different z-planes were connected in 3D space using the “connect by best match” option. Then the number of angiogenic sprouts, as well as their volumes and intensities were defined during analysis. Cell nuclei were defined as an optional step and either total number of nuclei per image, or number of nuclei per sprout was calculated. The region of interest mask was used during the analysis to include only objects that are located in the gel channel, but not in the endothelial tube channel. This way only angiogenic sprouts, not cells in the upper channel, were counted during analysis. The developed custom module was able to be used with both 20X and 10X images. Alternatively, image analysis can be performed in 2D by using a maximum projection image.

For evaluation of length of sprouts, a slightly different Custom Module Editor was also developed using a “Fibers” application module (not shown).

Results

Time dependence of the angiogenesis process was modeled in OrganoPlate 3-lane. Endothelial cells seeded in the upper channel formed a tube in three days. The model includes a tube of endothelial cells formed in the top channel line, or in both top and bottom channels, with collagen in the middle gel channel (Figure 1). Addition of growth factors to the bottom channel promoted formation of angiogenic sprouts through the collagen that could be imaged and analyzed (Figure 2).

Figure 2. Images of angiogenic sprouts in OrganoPlate. Maximum projection images presented for angiogenic sprouts formed after 1 day and 4 days in culture. Note the tube of vascular cells in the upper part of the images. Angiogenic sprouts coming from the upper tube into the lane with collagen, toward the lower line that contained growth factors. Nuclear stain (Hoechst) shown in blue, VE-cadherin in green, and Actin in red.

Samples were imaged at 10X or 20X as depicted in Figure 2. A 20X objective with water immersion enabled sharp and precise resolution of cells inside a solid matrix. Using 10X objective resolved less details of the objects but acquisition was faster since only one site per well was imaged with fewer planes. Importantly, the region of interest was used to separate the area of sprouting of new vessels from the preexisting endothelial tube. Representative images of angiogenesis sprouts are shown in Figure 2.

Figure 3 depicts the process of image analysis including segmentation per plane and connecting features in 3D using the “connect by best match” function.

Figure 3.A.* Images and imaging masks using Custom Module Editor. Individual sprouts and nuclei identified in each z-image by using Neurite Outgrowth module, then objects in each image are connected in 3D using “connect by best match” function. Analysis allows for identifying total number of sprouts, total volume, average intensity, or nuclear counts. B. 3D visualization of angiogenic sprouts done by MetaXpress software. C. Objects in each image are connected in 3D using “connect by best match” function. Separate sprouts and nuclei shown as pseudo-colored. D. Nuclei were identified in each image using “Find Nuclei” module, then the objects were connected by using “connect by touching” option.*

A time-dependent increase in the number and volume of sprouts was observed, as well as an increased number of cells or nuclei (Figures 4–5).

Figure 4.* Angiogenic sprouting in OrganoPlate 3-lane over time (RFP- HUVECs). From left to right, cultures were stimulated with an angiogenic cocktail in the bottom channel for 0, 1, 2, 3, and 4 days, respectively, resulting in the formation of angiogenic sprouts. Cultures were stained with for Actin (red) and VE-cadherin (green). Nuclei were stained with Hoechst.*

Figure 5.* Quantitative assessment of angiogenesis. Examples represent growth of angiogenic sprouts in 3D collagen during four consecutive days. Bar graphs demonstrate quantitative measurements of angiogenic sprouts. Assay was performed in triplicates, error bars represent STDEV*

Analysis of the images of an entire plate performed automatically without user intervention. Additional adjustment of the image intensity thresholds might be needed between experiments if staining intensities vary significantly. Using Power Core is essential for analysis.

Figure 6 demonstrates the workflow of the Custom Module Editor used for analysis.

Figure 6.* Custom Module Editor. Step cards shown for the Custom Module Editor*

Conclusion

It is essential to derive quantitative data from phenotypic changes of complex biological processes like angiogenesis. While 3D biological models offer a better representation of the complexity of human biology, image analysis of convoluted 3D structures can be challenging.

We developed and optimized imaging and analysis protocols that allow capturing, visualization, and quantitative analysis of angiogenic sprouts in MIMETAS assay. The imaging protocols were developed for ImageXpress Micro Confocal system and MetaXpress software to offer an integrated workflow for imaging and analysis. Combining the system with the strength of a scalable organ-on-a-chip platform unlock quantitative characterization of phenotypic effects for disease modeling and compound screening.

Addendum

Description of the Custom Module Editor

Images were analyzed in 3D using MetaXpress 6.6 software. To quantify the number of sprouts in a 3D volume, a customized analysis was set using the MetaXpress Custom Module Editor (CME). Briefly, for each plane, the FITC channel representing the sprouts were processed with a Gaussian filter. Sprouts were then segmented using Neurite Outgrowth objects analysis module. The Region mask* created during acquisition was used to select sprouts growing into the middle gel channel using Keep Marked Objects. Because Keep Marked Objects is an object based selection, sprout objects with areas outside the Region mask will also be selected. Alternatively, instead of using Keep Marked Objects, sprouts can be selected using the Logical Operation AND to keep sprout regions that are inside the Region mask (Region Mask AND Neurite Outgrowth Objects). Lastly, sprouts from each plane were connected in 3D using the Connect by Best Match algorithm. Output measurements included volume, diameter and intensity. To increase the analysis speed, MetaXpress® PowerCore^™ High-Content Distributed Image Analysis Software was used to run the analysis for the entire plate.

* A custom Create Region journal was used during acquisition to create a user-defined region that includes only the middle gel channel of the microfluidic unit. The journal converts the defined region into an image mask for downstream analysis.

Description of 3D Angiogenesis Model

Formation of membrane-free angiogenic sprouts in the OrganoPlate 3-lane. A. Bottom of the OrganoPlate, a microfluidic device comprising 40 chips. B. Each chip consists of three channels: one ‘gel’ channel for gel patterning, and two adjacent channels. Phaseguides prevent the patterned gel from flowing into the adjacent channels. C. Schematic representation of the formation of angiogenic sprouts.

References

1. Birbrair A, Zhang T, Wang ZM, Messi ML, Mintz A, Delbono O (January 2015). “Pericytes at the intersection between tissue regeneration and pathology”. Clinical Science. 128 (2): 81–93. doi:10.1042/CS20140278. PMC 4200531. PMID 25236972.
2. Birbrair A, Zhang T, Wang ZM, Messi ML, Olson JD, Mintz A, Delbono O (July 2014). “Type-2 pericytes participate in normal and tumoral angiogenesis”. American Journal of Physiology. Cell Physiology. 307 (1): C25-38. doi:10.1152/ajpcell.00084.2014. PMC 4080181. PMID 24788248
3. van Duinen, V., Zhu, D., Ramakers, C. et al. Perfused 3D angiogenic sprouting in a high-throughput in vitro platform. Angiogenesis 22, 157–165 (2019).
4. Trietsch SJ et al (2013) Microfluidic titer plate for stratified 3D cell culture. Lab Chip 13(18):3548–3554.

导言

Oksana Sirenko, PhD | Sr. Research Scientist | Molecular Devices
Angeline Lim, PhD | Applications Scientist | Molecular Devices
Thomas Olivier | Bioinformatics Engineer | MIMETAS

血管生成是由预先存在的血管所形成和重塑的新血管及 毛细血管的生理过程。这可以通过血管和毛细血管的内 皮细胞出芽或分裂来实现。血管细胞通过降解细胞外基 质对适当的刺激做出反应，随后诱导内皮细胞增殖和迁 移 ^1,2。

细胞经历过这些过程后，形成一个包含腔的管，一个动态的空间，促进血液流动和氧、二氧化碳、一氧化氮和营养物质的交换。血管生成是生长发育、伤口愈合和肉芽组织形成的重要过程。血管生成生长也会支持肿瘤细胞在健康组织中的侵袭，在癌症研究中通常被量化监测。当血管芽向血管生成刺激源延伸时，内皮细胞利用黏附分子进行纵向迁移。这些芽随后形成环状，利用迁移至此的细胞形成一个完整的血管腔。出芽过程在体内以每天几毫米的速度进行着，并使新的血管能够跨越间隙生长。

c许多抗血管生成药物已被开发于癌症治疗，而促血管生成分子则可能在再生应用中具有潜力。

迄今为止的体外实验仅模拟了血管生成机制的某些方面，包括划痕实验、博伊登室和管形成实验。

MIMETAS 的科学家开发了先进的、更具生理相关性的模 型，其中包括在促 / 抑制 - 血管生成因子条件下，包埋进 细胞外胶质基中主血管的实际生长和出芽。这些诱因既 可以通过灌注泳道加入，也可以通过一个共培养装置由 组织直接分泌出来。

高内涵成像可以进行可视化血管生长结构、三维重构，并能进行血管生成和新血管萌发相关的复杂分析。在这里，我们描述了一种可获得血管生成相关的多维量化结果的成像分析方法，以用于疾病表型和复合效应的比较研究。

方法

细胞模型

3D 血管生成模型是在 MIMETAS OrganoPlate 3-lane3,4 细胞芯片板上构建而成。OrganoPlate 3-lane 板是基 于 384 孔板上设计而成的，每个微流控单元由 3x3 个孔 组合而成，整板共 40 个单元 ( 图 1 )。每一个微流控单 元包含了三个泳道。胶原 -I 细胞外基质 (ECM) 胶被填埋 入中间的泳道内。被称为“ 相导 ”(phaseguides) 的小 型压力屏障将 ECM 凝胶定型，防止其流入邻近的灌注 泳道。接着，将内皮细胞 ( 原代细胞系，iPSC 诱导 ) 种 到灌注泳道上方，并附着于 ECM 胶上。灌注开始时将 OrganoPlate 放置在摇床上，当细胞增殖时，它们形成 内皮微血管。在血管形成后，在母体内皮血管的另一侧的 底部灌注泳道中加入一系列促血管生成因子。由此产生的 血管生成的化合物梯度会诱导血管生成芽。血管新生芽 培养 0-4 天，然后固定，并进行定量分析比对。可以在附 录中找到三维血管生成模型的示意图。

图 1 OrganoPlate 3-lane 芯片板示意图

成像

血管细 胞 和芽用 4% 甲醛固定，用抗 VE-cadherin 的 一抗结合样本，再用二抗 Alexa488 ( 绿色 ) 进行染色 标记。肌动蛋白丝用 ActinRed ™ ReadyProbes ^™试剂 ( 红色 ) 染色，细胞核用 Hoechst ( 蓝色 ) 染色。细胞用 ImageXpress® Micro 共聚焦高内涵成像系统 (Molecular Devices) 进行成像。细胞图像使用共聚焦模式 ( 60 µm pinhole 转盘 )，在 10x 或 20x 水镜下进行成像。20x 成 像中，采 集了 45-58 层的 z-stack 图层图像，每层 2-4 µm 间距。对于 10x 物镜，采集了 15-25 层图层，每层 4-6 µm 间距。细胞核用 DAPI 通道，血管生成的芽用 FITC 通道，分别在 100 ms 和 400 ms 曝光时间下采集 图像。

图像分析

图像分析使用了 MetaXpress 高内涵成像分析软件中 的 Custom Module Editor 客 制 化 分 析功 能。 细 节 在 结果部分 做了详细描述。简单来说，使 用了 Neurite Outgrowth 模块对延伸的血管芽进行识别，使用 Count Nuclei 模块进行细胞核表征。然后用“ 最佳匹配连接 ” 功能将物体在三维空间的 z 平面之间进行连接。二次分 析则是在 Excel 软件中完成的。

使用 MetaXpress 软件中的 3D 自定义模块分析图像。分 析方法的展示需要用到 Custom Module Editor (CME) 和 3D 图像分析能力。自定义模块包含了几个步骤，首先用 Neurite Outgrowth 模块识别出每张图中的血管芽，处 于 3D 空间中不同 Z 层面的物体利用“ 最佳匹配连接 ” 选项进行三维拼接。随后血管芽的数量，以及它们的体 积和荧光强度值都能在分析中获得。细胞核的计数是可 选项，无论是每张图的总细胞核数量，还是每个芽的细 胞核数量都可以被定量。在分析过程中使用的感兴趣区 域图层只包括位于凝胶泳道内的物体，而不包括内皮细 胞管通道内的物体。用这种方法可以只计算血管生成芽， 而不计算上泳道的细胞。所开发出的自定义分析模块在 20x 和 10x 图像中都可以应用。或者，图像分析也可以在 2D 最大值投影图像上进行分析。

对于芽的长 度 评价，略 有不同的是，在 CME 中使 用 “Fibers”应用模块来进行分析 ( 未显示 )。

结果

时间依赖的血管生成过程模型在 OrganoPlate 3-lane 中 进行建模。血管内皮细胞被种于上泳道中，三天左右形 成血管。该模型包括内皮细胞在上层泳道或同时在上层 和底层泳道中生成的血管，或者在中间的胶原蛋白胶质 层 ( 图 1 )。底部泳道添加生长因子可以促进在胶原蛋白 中生成血管新生芽，并能进行成像和分析 ( 图 2 )。

图 2 器官芯片板中血管生成芽的图像。培养 1 天和 4 天后形成的新生芽的最大投影图像。注意图像上部的管状血管细胞。血管新生芽 从上管进入含有胶原蛋白的泳道，向含有生长因子的下线延伸。核染色 (Hoechst) 为蓝色，VE-cadherin 为绿色，肌动蛋白为红色。

如图二所示，图像以 10x 或 20x 进行展示。使用一颗 20x 水镜成像时，可以看到细胞在固体基质中非常锐利 和高分辨率的图像。用 10x 物镜成像虽然少了部分细节， 但是拍摄速度会更快，因为每个孔只需要拍摄一个视野 和更少的图层数量。更重要的是，感兴趣区域可以将原 有血管和新生血管有效区分开来。血管新生芽的代表性 图像如图 2 所示。

图 3 描述了使用“ 最佳匹配连接 ”功能进行图像分析的 过程，包括平面分割和三维特征连接。

图 3 A. 成像原图及使用 CME 编辑后的图层。利用 Neurite Outgrowth 模块在每层 z-image 中识别出单个的芽和核，然后利用“ 最佳 匹配连接 ”函数将每个图像中的对象进行三维连接。该分析能够识别芽的总数量，总体积，平均强度，以及核计数。B. 用 MetaXpress 软件实现血管生成芽的三维可视化。C. 每幅图像中的对象通过“ 最佳匹配连接 ”功能进行三维连接。分离的芽和核显示为假色。D. 使 用“Find Nuclei”模块对每幅图像中的细胞核进行识别，然后使用“connect by touching”选项对目标进行连接。

观察到芽的数量和体积随时间而增加，细胞或核的数量 也在增加 ( 图 4-5 )。

图 4 随着时间的推移，器官芯片板内的血管新生萌发 (RFP- HUVECs)。从左至右，在底部泳道用血管生成混合物分别刺激 0、1、2、3、4 天，形成血管生成芽。样本染色为肌动蛋白 ( 红色 ) 和 VE-cadherin ( 绿色 )。用 Hoechst 染核。

图 5 血管生成的定量评估。例如连续四天在 3D 胶原蛋白中的血管新生芽生长状况。柱状图显示了血管生成芽的定量测量。实验进行 了三次，误差条代表 STDEV。

整块板的图像分析自动执行，无需用户干预。如果染色强 度显著不同，可能需要在实验之间额外调整图像强度阈 值。另外使用 Power Core 软件对分析也是必要的。

图 6 演示了自定义模块编辑 (CME) 用于分析的工作流程。

图 6 Customer Module Editor。展示自定义模块编辑器的分析步骤。

结论

从血管生成等复杂生物过程的表型变化中获得定量数据具有非常重要的意义。三维生物模型提供了一个更好的代表复杂人类生物学的方法，而复杂的三维结构的图像分析是具有挑战性的。

我们开发和优化了一套完整的成像和分析方案，能够在 MIMETAS 平台上进行血管生成芽的拍摄、可视化及定量分析。 成像方案是基于 ImageXpress Micro 共聚焦系统和 MetaXpress 软件开发的，以提供成像和分析的集成式工作流程。 将该系统与可扩展的器官芯片平台的功能相结合，为疾病建模和化合物筛选解锁新的表型效应的定量表征。

附录

Customer Module Editor

图像的 3D 分析使用了 MetaXpress 6.6 软件。为了在 3D 维度下定量分析出新生芽的数量，使用了 MetaXpress 软件中的 Customer Module Editor 进行客制化分析。简单来说，每一层图像中的 FITC 通道都使用了的高斯拟合进行展示。虽然血管芽通过 Neurite Outgrowth 识别分析模块进行对象分割。在拍摄过程中使用 Region Mask 工具 * 来创建中间胶质层区域，再利用“Keep Marked Objects”进行血管芽识别。因为“Keep Marked Objects”是一个基于对象选择的方式，选中区域在之外的对象也会被选中。另 外，也可以使用逻辑操作步骤“AND”进行区分，保留 Region Mask (Region Mask AND Neurite Outgrowth objective) 内的血管 芽，而不使用“Keep Marked Objects”。最后，采用“ 最佳匹配连接 ”算法对各平面上的节点进行三维连接。可输出的测量值包括 体积、直径和强度。为了提高分析速度，使用 MetaXpress® PowerCore ™ 高通量分布式图像分析软件对整个平板进行分析加速。

* 在图像获取期间使用自定义创建区域 journal 创建用户定义的区域，该区域仅包含微流控单元的中间凝胶泳道。该 journal 将定义 的区域转换为用于下游分析的图像图层。

3D 血管生成模型的建模

OrganoPlate 3-lane 无膜血管新生芽的形成。A. OrganoPlate 板的底部，由 40 个微流控芯片组成。B. 每一个芯片由三个泳道组成： 位于中间的凝胶层，以及左右两个相邻泳道。相导材料可防止成形的凝胶流入相邻两个泳道内。C. 血管生成芽的形成示意图。

参考文献

1. Birbrair A, Zhang T, Wang ZM, Messi ML, Mintz A, Delbono O (January 2015). “Pericytes at the intersection between tissue regeneration and pathology”. Clinical Science. 128 (2): 81–93. doi:10.1042/CS20140278. PMC 4200531. PMID 25236972.
2. Birbrair A, Zhang T, Wang ZM, Messi ML, Olson JD, Mintz A, Delbono O (July 2014). “Type-2 pericytes participate in normal and tumoral angiogenesis”. American Journal of Physiology. Cell Physiology. 307 (1): C25-38. doi:10.1152/ajpcell.00084.2014. PMC 4080181. PMID 24788248
3. van Duinen, V., Zhu, D., Ramakers, C. et al. Perfused 3D angiogenic sprouting in a high-throughput in vitro platform. Angiogenesis 22, 157–165 (2019).
4. Trietsch SJ et al (2013) Microfluidic titer plate for stratified 3D cell culture. Lab Chip 13(18):3548–3554.

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