Multiplexed angiogenic biomarker quantification on single cells

Clinical and biomedical research seeks single-cell quantification to better understand their roles in a complex, multi-cell environment. Recently, quantification of vascular endothelial growth factor receptors (VEGFRs) provided important insights into endothelial cell (EC) characteristics and response in tumor microenvironments. However, data on other angiogenic receptors, such as platelet derived growth factor receptors (PDGFRs), Tie receptors, are also necessary for the development of an accurate angiogenesis model. To gain insights on the involvement of these angiogenic receptors in angiogenesis, I develop a method to quantify receptor concentrations as well as the cell-by-cell heterogeneity. I establish protocols to measure cell membrane VEGFR, NRP1, Tie2, and PDGFR concentration on several cell and tissue models including human dermal fibroblasts (HDFs) in vitro, a 2D endothelial/fibroblast co-culture model in vitro, and a patient-derived xenograft (PDX) model of glioblastoma (GBM). I demonstrate VEGF-A165-mediated downregulation of membrane PDGFRα (~25%) and PDGFRβ (~30%) on HDFs, following a 24-hour treatment. This supports the idea that VEGF-A165 acts independently of VEGFRs to signal through PDGFRα and PDGFRβ. I uncover high intratumoral heterogeneity within the GBM PDX model, with tumor EC-like subpopulations having high concentrations of membrane VEGFR1, VEGFR2, EGFR, IGFR, and PDGFRs. To gain greater insights into cell heterogeneity and examine angiogenic signaling pathways as a whole, I utilize the unique spectral properties of quantum dots (Qdots), and combines Qdots with qFlow cytometry, to dually quantify VEGFR1 and VEGFR2 on human umbilical vein endothelial cells (HUVECs). To enable this quantification, I reduce nonspecific binding between Qdot-conjugated antibodies and cells, identify optimal labeling conditions, and establish that 800 – 20,000 is the dynamic range where accurate Qdot-enabled quantification can be achieved. Through these optimizations we demonstrate measurement of 1,100 VEGFR1 and 6,900 VEGFR2 per HUVEC. 24 h VEGF-A165 treatment induce ~90% upregulation of VEGFR1 and ~30% downregulation of VEGFR2 concentration. We further analyze HUVEC heterogeneity and observe that 24 h VEGF-A165 treatment induces ~15% decrease in VEGFR2 heterogeneity. Overall, we demonstrate experimental and analysis strategies for quantifying two or more RTKs at single-level using Qdots, which will provide new insights into biological systems

Quantitative methods to regulate angiogenesis: applications to cancer and cardiovascular disease

Angiogenesis, the growth of new microvasculature from pre-existing blood vessels, is essential for tumor growth and metastasis in several cancers, including breast cancer. The vascular endothelial growth factor (VEGF) is the primary signaling molecule promoting angiogenesis. As such, the VEGF signaling axis is a potential target to inhibit tumor angiogenesis. However, full tumor vascular inhibition has yet to be achieved, attributed to the complexity of the vascular environment. Conversely, the ability to induce angiogenesis to vascularize ischemic tissue would provide treatment options for vascular diseases, including peripheral artery disease and coronary artery disease. Hemodynamic forces drive vascular disease progression, and contribute to the induction and directionality of vessel growth. Thus, full vascular control can be obtained by targeting VEGF signaling to inhibit angiogenesis and by targeting hemodynamic forces to promote angiogenesis. To this end, I developed computational approaches to individually understand the effects of VEGF signaling and hemodynamic forces on angiogenesis. Firstly, VEGF signaling models enable anti-angiogenic treatment efficacy to be correlated to features of cells or the microenvironment, which can help us understand a major challenge in tumor vascular inhibition: tumor heterogeneity. Indeed, cell population heterogeneity has been identified as an important consideration in cellular response to VEGF treatment, and is also a major factor in angiogenic drug resistances. However, there are few techniques available to represent and explore how heterogeneity is linked to population response. Recent high-throughput genomic, proteomic, and cellomic approaches offer opportunities for profiling heterogeneity on several scales. We have recently examined heterogeneity in VEGFR membrane localization in endothelial cells. We and others processed the heterogeneous data through ensemble averaging and integrated the data into computational models of anti-angiogenic drug effects in breast cancer. Here we show that additional modeling insight can be gained when cellular heterogeneity is considered. We present comprehensive statistical and computational methods for analyzing cellomic data sets and integrating them into deterministic models. We present a novel method for optimizing the fit of statistical distributions to heterogeneous data sets to preserve important data and exclude outliers. We compare methods of representing heterogeneous data and show methodology can affect model predictions up to 3.9-fold. We find that VEGF levels, a target for tuning angiogenesis, are more sensitive to VEGFR1 cell surface levels than VEGFR2; updating VEGFR1 levels in the tumor model gave a 64% change in free VEGF levels in the blood compartment, whereas updating VEGFR2 levels gave a 17% change. Furthermore, we find that subpopulations of tumor cells and tumor endothelial cells (tEC) expressing high levels of VEGFR (> 35,000 VEGFR/cell) negate anti-VEGF treatments. We show that lowering the VEGFR membrane insertion rate for these subpopulations recovers the anti-angiogenic effect of anti-VEGF treatment, revealing new treatment targets for specific tumor cell subpopulations. This novel method of characterizing heterogeneous distributions shows for the first time how different representations of the same data set lead to different predictions of drug efficacy. Secondly, to understand how to better promote angiogenesis, accurate quantification of hemodynamic forces is essential. Numerical simulations allow for this quantification. However, due to the complexity of numerical simulations, blood is often assumed to be Newtonian, despite being non-Newtonian in nature. To ensure accurate representation of hemodynamic forces, we compare hemodynamics between Newtonian and non-Newtonian models of blood. We test these models in both healthy and atherosclerotic arteries. For the non-Newtonian model, we employ a shear-rate dependent fluid (SDF) constitutive model, based on the works by Yasuda et al in 1981. We first verify our stabilized finite element numerical method with the benchmark lid-driven cavity flow problem. Numerical simulations show that the Newtonian model gives similar velocity profiles in the 2-dimensional cavity given different height and width dimensions, given the same Reynolds number. Conversely, the SDF model gave dissimilar velocity profiles, differing from the Newtonian velocity profiles by up to 25% in velocity magnitudes. This difference can affect estimation in platelet distribution within blood vessels or magnetic nanoparticle delivery. Wall shear stress (WSS) is an important quantity involved in vascular remodeling through integrin and adhesion molecule mechanotransduction. The SDF model gave a 7.3-fold greater WSS than the Newtonian model at the top of the 3-dimensional cavity. The SDF model gave a 37.7-fold greater WSS than the Newtonian model at artery walls located immediately after bifurcations in the idealized femoral artery tree. The pressure drop across arteries reveals arterial sections highly resistive to flow which correlates with stenosis formation. Numerical simulations give the pressure drop across the idealized femoral artery tree with the SDF model which is approximately 2.3-fold higher than with the Newtonian model. In atherosclerotic lesion models, the SDF model gives over 1 Pa higher WSS than the Newtonian model, a difference correlated with over twice as many adherent monocytes to endothelial cells from the Newtonian model compared to the SDF model. Together, these computational approaches provide a necessary step towards obtaining full vascular control, through inhibiting or promoting angiogenesis, respectively

Beschreibung von Organgrenzen als Äquipotentialverlauf finiter Quellpunkte mit Q/r-Potentialen

In dieser Arbeit wurde ein neues Verfahren zur komprimierten Beschreibung bereits bekannter Konturlinien kompakter menschlicher Organe und zu deren verbesserter Anpassung an die in individuellen medizinischen Bild-Datensätzen erkennbaren Kantenlinien entwickelt. Dazu wurden physikalisch motivierte mathematische Beschreibungen, hier die durch Äquipotentiallinien und -flächen elektrischer Felder, mit der Bildverarbeitung verknüpft. Die Potentialtheorie liefert die flexible Basis, um kompakte, geschlossene Organe zu modellieren. Zu diesem Zweck wird eine Verteilung von virtuellen Quellpunkten generiert, die über ein Q r -Potential verfügen. Die betrachtete Organschicht schneidet die gemeinsame Äquipotentialfläche und liefert eine Äquipotentiallinie, die den Organrand nachbildet. Die Überführung in eine Äquipotentialdarstellung geschieht mit bereits manuell segmentierten Organen. Die segmentierten Daten stammen von dem Voxelmodell ‘‘Laura’’, das als Grundlage für das ICRP-Referenzphantom RCP-AF verwendet wurde, und hat eine Auflösung von 1,875 x 1,875 x 5mm. Alle Programmierarbeiten wurden in der Interactive Data Language (IDL) 8.2 durchgeführt. Es werden Schichten von Herz, Magen, Blase und Niere mittels Quellenverteilungen modelliert. Eine Darstellung der Organe mittels dieser Methode liefert folgende Vorteile. Die Konstellation an virtuellen Quellpunkten ist intuitiv erfassbar. Die Modellierung mit virtuellen Quellpunkten stellt eine sehr komprimierte Art der Datenspeicherung dar und ist auflösungsunabhängig. Im Bereich hochaufgelöster Datensätze ist dies ein wesentlicher Vorteil. Es genügen die Koordinaten der Quellpunkte, ihre Stärken und eine Potentialangabe. Die Form lässt sich über die Lage und Anzahl der Quellpunkte verändern. Eine Ähnlichkeitstransformation und eine Innerhalb/Außerhalb-Entscheidung sind möglich. Der Einsatz von Standard-Computersystemen sowie die Übertragbarkeit der Daten über gängige Systeme, z.B. derzeitige Internetprotokolle, wäre damit gegeben. Eine Überführung in ein Voxelmodell ist problemlos möglich. Die Potentialflächen sind kontinuierlich und müssen mit Voxeln gefüllt werden. Eine Generierung verschiedener Modelle mit unterschiedlicher Auflösung ist möglich. Gegenüber der Voxeldarstellung von Organgrenzen verspricht das Konzept der Äquipotentialdarstellung eine Zeitersparnis a) bei der interaktiven Anpassung, b) beim Datentransfer und c) bei der Innerhalb/Außerhalb-Entscheidung für Interaktionspunkte im Laufe von Monte-Carlo-Simulationen der Berechnung von Organdosen. Die ermittelten Quellenverteilungen der modellierten Organe werden anschließend in den individuellen CT-Datensatz eingebracht, um die bereits vorhanden Segmentierung der Organgrenzen ausgewählter Schichten nochmals zu verbessern. Dazu werden die Quellpunkte anhand der vorhandenen, detektierten Kanten neu justiert, um eine optimale Platzierung zu generieren. Wo es keine erkennbaren Kanten im Schichtbild gibt, verbleiben die Quellen an ihrem Platz. Das Modell wird nicht verzerrt und kann bei Bedarf manuell ausgerichtet werden. Die gemeinsame Äquipotentiallinie bildet den segmentierten Rand und überbrückt die Gebiete, in denen keine Kanten im medizinischen Bild zu sehen sind, aber ein Organ an ein anderes grenzt. Die Güte der Anpassung der so ermittelten Äquipotentiallinien an die wirklichen Organgrenzen übertrifft nicht selten diejenige, die man durch die Anpassung der Äquipotentiallinien an die bereits voxelierte Organgrenzlinien erhält. Die Anpassung der durch das Potentialmodell bereits beschriebenen Organkonturlinien an die in individuellen medizinischen Bildaten erkennbaren Kanten kann man als zweite Näherung im Rahmen der Segmentierung bezeichnen; ihr Anwendungsgebiet ist die Individualisierung der Darstellung von Organkonturen. Obwohl dieser Algorithmus ein komplexes Wissen in die Bearbeitung einbringt, besteht weiterhin die Möglichkeit, manuell zu interagieren. Ein direktes Zugreifen auf die Quellpunkte ist möglich und sinnvoll, da es auch bei trainierten Algorithmen zu Fehlerkennungen in der Analyse der Organe kommt. Da nur der Quellpunkt selber verschoben werden muss, nicht z.B. die Interpolationspunkte von Splines auf dem Organrand, stellt dies einen akzeptablen manuellen Aufwand dar, der geringer ist als die Kombination von grauwertbasierten Techniken mit Splines. Es ist auch möglich, die Quellstärke anzupassen und auf diese Weise die Äquipotentiallinie zu verschieben. Dafür muss keine Umwandlung in eine andere Modellform vorgenommen werden oder die Organabgrenzung neu approximiert werden. Für die Strahlentherapie, in der die persönliche Verantwortung über die Segmentierungsarbeit bei den Ärzten liegt, ist eine manuelle Überprüfung und Bearbeitungsmöglichkeit unabdingbar. Die vorgestellte Methode liefert somit gute Resultate für die automatische Modellierung und die verbesserte Segmentierung kompakter konvexer Organe. Damit ist eine flexible Basis für weitere Anpassungen an verschiedene Aufgabenstellungen geschaffen. Die Möglichkeit zu einer einfachen Ähnlichkeitstransformation der im Äquipotentialmodell dargestellten Organkonturen lässt sich auch bei der Anpassung von Organkonturen, z.B. an unterschiedliche Lebensalter oder an Unterschiede im Körpergewicht, als Mittel zur Zeitersparnis verwenden. Somit dient die in dieser Arbeit vorgestellte Äquipotentialdarstellung der Organkonturen gleichermaßen dem herkömmlichen Zweck der Organdosisberechnungen im Strahlenschutz als auch der immer aktueller werdenden Aufgabe der komprimierten digitalen Übermittlung von Organkonturen zu medizinischen Zwecken. Die vorliegende Betrachtung beschränkt sich auf die zweidimensionale Beschreibung der Organgrenzen und schlägt die dreidimensionale Darstellung mittels einer Normierungsmatrix für die Potentiale auf dem Organrand vor. Der Algorithmus beinhaltet ein komplexes Modellwissen und kann als “High Level”-Algorithmus angesehen werden.The scope of the dissertation is to introduce a new method for organ contour modelling and segmentation in radiology and radiation protection. The method makes use of functions customary in physics, in this case the equipotential lines caused by a distribution of point sources. The mathematical description of electrical fields is transferred to virtual anatomy modelling and image segmentation. Each source point is assumed to have a Q/r potential, and the distribution of point sources is so optimized that one of their resulting equipotential lines traces the given organ contour. Therefore, the source points are placed in accordance to an organ border in a human voxel phantom that had previously been generated from 2-dimensional CT images of a real patient. The results for several closed and compact organs shall be presented, appropriate models for the contours for e.g. heart, stomach and bladder were generated. After the creation of the organ contour by source points these new organ models shall be adapted to the segmentation of organs from medical images. The distribution of source points is transferred to CT data, and the edges of the images are overlain with the equipotential line. The source points are able to move within a given area, thereby the equipotential line is editable. The principle of electrical fields offers an aspect that serves as criteria for optimising the place of a source point. Their field lines are perpendicular to the equipotential lines and lead in radial direction from the source point. The edge detection of the medical images is performed by means of gradient methods which provide vectors of the edge directions. The single source points shall be adjusted to the vectors of the edges and put on an optimised place. The resulting match of the equipotential line with the detectable edges is considered in the optimisation process and aimed to maximise. A better tracing of the equipotential lines with the existing edges is expected and the results of the suggested outlines for several organ will be presented in 2D slices of the CT data. The organ modelling by equipotential lines provides the advantages of compacted data and of the mathematical continuity of the equipotential lines, different from the limited resolution of voxelised organ contours

Predicting angiogenic receptor trafficking and signaling via computational systems biology

Angiogenesis is defined as the growth of new blood vessels from preexisting vessels. Systematic regulation of angiogenesis could lead to new treatments of vascular diseases and cancer. As such, vascular endothelial growth factor (VEGF), a potent angiogenic growth factor, offers a promising therapeutic target. Despite this promise, VEGF targeted therapies are not clinically effective for many pathologies, such as breast cancer. Thus, a better understanding of the VEGF network for regulating angiogenesis, along with identifying key nodes controlling angiogenesis within this network, are necessary to provide effective VEGF therapeutics. Systems biology, defined as applying experiment and computational modeling to understand a biological system, can readily define this VEGF-angiogenesis network. In this dissertation, I provide an overview of how computational systems biology has been used to provide basic biological insights into angiogenesis, explore anti-angiogenic therapeutic options for cancer, and pro-angiogenic therapeutic options for vascular disease. Using systems biology, I have previously predicted that VEGFR1 acts as a predictive biomarker of anti-VEGF efficacy in breast cancer. Particularly, tumor endothelial cell subpopulations exhibiting high VEGFR1 levels result in ineffective anti-VEGF treatment. These high VEGFR1 subpopulations are characterized by a high amount of VEGF-VEGFR1 complex formation, and subsequently high VEGF-VEGFR1 internalization. The high VEGF-VEGFR1 complex formation implies a possible VEGFR1 signaling role beyond its classically defined decoy status. In this dissertation, I introduce a computational approach that accurately predicts the cell response elicited via VEGFR1 signaling. I show that VEGFR1 promotes cell migration through PLCγ and PI3K pathways, and promotes cell proliferation through a PLCγ pathway. These results provide new biological insight into VEGFR1 signaling and angiogenesis while offering a system for directing angiogenesis. Cell subpopulations expressing high VEGFR1 levels are characterized by a large amount of VEGF-VEGFR1 internalization. Thus, endocytosis may regulate VEGFR1 signaling; indeed, intracellular-based receptor signaling has recently emerged as a key component in mediating cell responses for receptor tyrosine kinases (RTKs). However, how endocytosis fundamentally mediates signaling for any RTK remains poorly defined. Understanding how endocytosis fundamentally directs intracellular receptor signaling requires receptor-specific endocytosis mechanisms to be delineated. This delineation requires identifying the signaling mechanisms common to all receptor types. To this end, I conduct a computational meta-analysis predicting endocytic compartment signaling across eight RTKs, and identify their common signaling mechanisms. I find that endocytic vesicles are the primary cell signaling compartment; over 43% total receptor phosphorylation occurs within the endocytic vesicle compartment for all eight RTKs. Conversely, all RTKs exhibit low membrane-based receptor signaling, exhibiting < 1% total receptor phosphorylation. Mechanistically, this high RTK phosphorylation within endocytic vesicles may be attributed to their low volume, which facilitates an enriched ligand concentration. The late endosome and nucleus are also important contributors to receptor signaling, where 26% and 18% average receptor phosphorylation occurs, respectively. Furthermore, nuclear translocation requires late endosomal transport; blocking receptor trafficking from late endosomes to the nucleus reduces nuclear signaling 96%. These findings can be applied to understand specific RTK signaling functions in terms of cell response, and optimize RTK therapeutics targeting endocytic pathways. Overall, I reveal the role of VEGFR1 and its signaling mechanisms, which is essential information to the field of angiogenesis. This information advances angiogenesis therapeutics by identifying the VEGF-VEGFR1 signaling axis as an essential target. I identify the primary adapters that can be targeted to critically regulate VEGF-VEGFR1 signaling, and endocytic compartmentalization that can be targeted for tuning receptor signaling. Furthermore, the computational techniques I develop advance the field of systems biology by delineating the signal-to-response of receptor signaling, improving receptor investigation by allowing adapter phosphorylation and cell responses to be quantified simultaneously, in addition to compartmentalized receptor signaling. These computational techniques improve disease treatment by allowing optimal receptor signaling targets to be identified quickly. Additionally, unknown receptor signaling can be mapped from adapter phosphorylation to cell response. These computational techniques can be integrated into multiscale computational models to provide clinically relevant, patient-specific platforms for directing disease treatment

Grand challenges in interfacing engineering with life sciences and medicine

This paper summarizes the discussions held during the First IEEE Life Sciences Grand Challenges Conference, held on October 4-5, 2012, at the National Academy of Sciences,Washington, DC, and the grand challenges identified by the conference participants. Despite tremendous efforts to develop the knowledge and ability that are essential in addressing biomedical and health problems using engineering methodologies, the optimization of this approach toward engineering the life sciences and healthcare remains a grand challenge. The conference was aimed at high-level discussions by participants representing various sectors, including academia, government, and industry. Grand challenges were identified by the conference participants in five areas including engineering the brain and nervous system; engineering the cardiovascular system; engineering of cancer diagnostics, therapeutics, and prevention; translation of discoveries to clinical applications; and education and training. A number of these challenges are identified and summarized in this paper. © 2013 IEEE

Grand Challenges in Interfacing Engineering With Life Sciences and Medicine

the First IEEE Life Sciences Grand Challenges Conference, hel