Protein nanobarcodes enable single-step multiplexed fluorescence imaging

Multiplexed cellular imaging typically relies on the sequential application of detection probes, as antibodies or DNA barcodes, which is complex and time-consuming. To address this, we developed here protein nanobarcodes, composed of combinations of epitopes recognized by specific sets of nanobodies. The nanobarcodes are read in a single imaging step, relying on nanobodies conjugated to distinct fluorophores, which enables a precise analysis of large numbers of protein combinations. Fluorescence images from nanobarcodes were used as input images for a deep neural network, which was able to identify proteins with high precision. We thus present an efficient and straightforward protein identification method, which is applicable to relatively complex biological assays. We demonstrate this by a multicell competition assay, in which we successfully used our nanobarcoded proteins together with neurexin and neuroligin isoforms, thereby testing the preferred binding combinations of multiple isoforms, in parallel

Simulation der internen Struktur von Zellen mittels Delaunay-Triangulation

The goal of this project is to develop a framework for a cell that takes in consideration its internal structure, using an agent-based approach. In this framework, a cell was simulated as many sub-particles interacting to each other. This sub-particles can, in principle, represent any internal structure from the cell (organelles, etc). In the model discussed here, two types of sub-particles were used: membrane sub-particles and cytosolic elements. A kinetic and dynamic Delaunay triangulation was used in order to define the neighborhood relations between the sub-particles. However, it was soon noted that the relations defined by the Delaunay triangulation were not suitable to define the interactions between membrane sub-particles. The cell membrane is a lipid bilayer, and does not present any long range interactions between their sub-particles. This means that the membrane particles should not be able to interact in a long range. Instead, their interactions should be confined to the two-dimensional surface supposedly formed by the membrane. A method to select, from the original three-dimensional triangulations, connections restricted to the two-dimensional surface formed by the cell membrane was then developed. The algorithm uses as starting point the three-dimensional Delaunay triangulation involving both internal and membrane sub-particles. From this triangulation, only the subset of connections between membrane sub-particles was considered. Since the cell is full of internal particles, the collection of the membrane particles' connections will resemble the surface to be obtained, even though it will still have many connections that do not belong to the restricted triangulation on the surface. This "thick surface" was called a quasi-surface. The following step was to refine the quasi-surface, cutting out some of the connections so that the ones left made a proper surface triangulation with the membrane points. For that, the quasi-surface was separated in clusters. Clusters are defined as areas on the quasi-surface that are not yet properly triangulated on a two-dimensional surface. Each of the clusters was then re-triangulated independently, using re-triangulation methods also developed during this work. The interactions between cytosolic elements was given by a Lennard-Jones potential, as well as the interactions between cytosolic elements and membrane particles. Between only membrane particles, the interactions were given by an elastic interaction. For each particle, the equation of motion was written. The algorithm chosen to solve the equations of motion was the Verlet algorithm. Since the cytosol can be approximated as a gel, it is reasonable to suppose that the sub-cellular particles are moving in an overdamped environment. Therefore, an overdamped approximation was used for all interactions. Additionally, an adaptive algorithm was used in order to define the size of the time step used in each interaction. After the method to re-triangulate the membrane points was implemented, the time needed to re-triangulate a single cluster was studied, followed by an analysis on how the time needed to re-triangulate each point in a cluster varied with the cluster size. The frequency of appearance for each cluster size was also compared, as this information is necessary to guarantee that the total time needed by to re-triangulate a cell is convergent. At last, the total time spent re-triangulating a surface was plotted, as well as a scaling for the total re-triangulation time with the variation. Even though there is still a lot to be done, the work presented here is an important step on the way to the main goal of this project: to create an agent-based framework that not only allows the simulation of any sub-cellular structure of interest but also provides meaningful interaction relations to particles belonging to the cell membrane.Das Ziel dieser Arbeit ist die Entwicklung eines Modells für eine Zelle, welches die Eigenschaften der internen Struktur berücksichtigen kann. Hierzu wird ein Agenten-basierter Ansatz verwendet, in dem die Zelle durch eine Vielzahl miteinan- der wechselwirkender Teilchen simuliert wird. Prinzipiell können diese Teilchen jede interne Struktur der Zelle repräsentieren. In dem hier besprochenen Modell werden zwei Arten verwendet: Membranteilchen und zytosolische Elemente. Das entwickelte Modell eignet sich damit sowohl zur Simulation von einzelnen Zellen als auch zur Beschreibung multi-zellulären Systemen. Eine kinetische und dynamische Delaunay-Triangulation wurde zur Definition der Nachbarschaftsbeziehungen zwischen den Teilchen verwendet. Allerdings eignen sich die Eigenschaften der Delaunay-Triangulation nicht, die Wechselwirkungen zwischen Membranteilchen zu beschreiben. Zur Lösung dieses Problems wurde eine Methode entwickelt, die aus der ur- sprünglich dreidimensionalen Triangulation nur diejenigen Verbindungen auswählt, die die Oberfläche der Zelle, also die Membran, bilden. Das Problem der Rekonstruktion einer auf eine zwei-dimensionale gekrümmte Fläche eingeschränkten Punktmenge ist sehr komplex und nicht vollständig gelöst. Der hier vorgestellte Algorithmus ist stark an die spezifische Anwendung innerhalb dieser Arbeit angepasst, aber nicht vollständig fehlerfrei. Die entwickelte Methode hat zwei Schwachpunkte, an denen es zu Fehlern kommen kann: die Bestimmung und Auswahl der Ränder der Cluster und zu strikte Einschränkungen bei der Retriangulation für Cluster mit vielen internen Punkten. Obwohl noch einige Erweiterungen möglich sind, ist die Autorin überzeugt, dass die hier vorgestellte Arbeit einen wichtigen Schritt auf dem Weg zu einem Agenten- basierten Modell ist, welches nicht nur die Simulation von sub-zellulären Struktu- ren erlaubt sondern auch sinnvolle Wechselwirkungen zwischen Membranteilchen berücksichtigt

Spatial statistics from hyperplexed immunofluorescence images: to elucidate tumor microenvironment, to characterize intratumor heterogeneity, and to predict metastatic potential

The composition of the tumor microenvironment (TME)–the malignant, immune, and stromal cells implicated in tumor biology as well as the extracellular matrix and noncellular elements–and the spatial relationships between its constituents are important diagnostic biomarkers for cancer progression, proliferation, and therapeutic response. In this thesis, we develop methods to quantify spatial intratumor heterogeneity (ITH). We apply a novel pattern recognition framework to phenotype cells, encode spatial information, and calculate pairwise association statistics between cell phenotypes in the tumor using pointwise mutual information. These association statistics are summarized in a heterogeneity map, used to compare and contrast cancer subtypes and identify interaction motifs that may underlie signaling pathways and functional heterogeneity. Additionally, we test the prognostic power of spatial protein expression and association profiles for predicting clinical cancer staging and recurrence, using multivariate modeling techniques. By demonstrating the relationship between spatial ITH and outcome, we advocate this method as a novel source of information for cancer diagnostics. To this end, we have released an open-source analysis and visualization platform, THRIVE (Tumor Heterogeneity Research Image Visualization Environment), to segment and quantify multiplexed imaging samples, and assess underlying heterogeneity of those samples. The quantification of spatial ITH will uncover key spatial interactions, which contribute to disease proliferation and progression, and may confer metastatic potential in the primary neoplasm

Novel bioinformatics approach for encoding and interrogating the progression and modulation of the mammalian cell cycle

The cell cycle, with its highly conserved features, is a fundamental driver for the temporal control of cell growth and proliferation in tissues - while abnormal control and modulation of the cell cycle are characteristic of cancer cells, particularly in response to therapy. A central theme in cancer biology is to resolve and understand the origin and nature of innate and induced heterogeneity at the cell population level. Cellular heterogeneity - comprising structural, temporal and functional dimensions - is a confounding factor in the analysis of cell population dynamics and has implications at physiological, pathological and therapeutic levels. There is an exceptional advancement in the applications of imaging and cell tracking technologies dedicated to the area of cytometric research, that demand an integrated bioinformatics environment for high-content data extraction and interrogation. Image-derived cell-based analyses, where time is the quality parameter also demand unique solutions with the aim of enabling image encoding of spatiotemporal cellular events within complex cell populations. The perspective for this thesis is the complex yet poorly understood nature of cancer and the opportunities offered by rapidly evolving cytometric technologies. The research addresses the intellectual aspects of a bioinformatics framework for cellular informatics that encompass integrated data encoding, archiving, mining and analysis tools and methods capable of producing in silico cellular fingerprints for the responses of cell populations to perturbing influences. The overall goal is to understand the effects of anti-cancer drugs in complex and potentially heterogeneous neoplastic cellular systems by providing hypothesis testing opportunities. Cell lineage maps encoded from timelapse microscopy image sequences sit at the core of the proposed bioinformatics infrastructure developed in the current work. Through a number of data mining, analysis and visualisation tools the interactions and relationships within and between lineages have provided dynamic patterns for the modulation of the cell cycle in disease and under stress. The lineage data, accessible through databases implemented during the current study, has provided a rich repository for pharmacodynamic (PD) modelling and validation and has thus laid the foundation for fabricating a comprehensive knowledge base for linking both cellular and molecular behaviour patterns. These provide the foundation for meeting the aspirations of systems biology and drug discovery.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Novel bioinformatics approach for encoding and interrogating the progression and modulation of the mammalian cell cycle

The cell cycle, with its highly conserved features, is a fundamental driver for the temporal control of cell growth and proliferation in tissues - while abnormal control and modulation of the cell cycle are characteristic of cancer cells, particularly in response to therapy. A central theme in cancer biology is to resolve and understand the origin and nature of innate and induced heterogeneity at the cell population level. Cellular heterogeneity - comprising structural, temporal and functional dimensions - is a confounding factor in the analysis of cell population dynamics and has implications at physiological, pathological and therapeutic levels. There is an exceptional advancement in the applications of imaging and cell tracking technologies dedicated to the area of cytometric research, that demand an integrated bioinformatics environment for high-content data extraction and interrogation. Image-derived cell-based analyses, where time is the quality parameter also demand unique solutions with the aim of enabling image encoding of spatiotemporal cellular events within complex cell populations. The perspective for this thesis is the complex yet poorly understood nature of cancer and the opportunities offered by rapidly evolving cytometric technologies. The research addresses the intellectual aspects of a bioinformatics framework for cellular informatics that encompass integrated data encoding, archiving, mining and analysis tools and methods capable of producing in silico cellular fingerprints for the responses of cell populations to perturbing influences. The overall goal is to understand the effects of anti-cancer drugs in complex and potentially heterogeneous neoplastic cellular systems by providing hypothesis testing opportunities. Cell lineage maps encoded from timelapse microscopy image sequences sit at the core of the proposed bioinformatics infrastructure developed in the current work. Through a number of data mining, analysis and visualisation tools the interactions and relationships within and between lineages have provided dynamic patterns for the modulation of the cell cycle in disease and under stress. The lineage data, accessible through databases implemented during the current study, has provided a rich repository for pharmacodynamic (PD) modelling and validation and has thus laid the foundation for fabricating a comprehensive knowledge base for linking both cellular and molecular behaviour patterns. These provide the foundation for meeting the aspirations of systems biology and drug discovery