QuantEv: quantifying the spatial distribution of intracellular events

Analysis of the spatial distribution of endomembrane trafficking is fundamental to understand the mechanisms controlling cellular dynamics, cell homeostasy, and cell interaction with its external environment in normal and pathological situations. The development of automated methods to visualize and quantify the spatial distribution of intracellular events is essential to process the ever-increasing amount of data generated with modern light mi-croscopy. We present a generic and non-parametric framework to quantitatively analyze and visualize the spatio-temporal distribution of intracellular events from different conditions in fluorescence microscopy. From the spatial coordinates of intracellular features such as segmented subcellular structures or dynamic processes like vesicle trajectories, QuantEv automatically estimates weighted densities for each dimension of the 3D cylindrical coordinate system and performs a comprehensive statistical analysis from distribution distances. We apply this approach to study the spatio-temporal distribution of moving Rab6 fluorescently labeled membranes with respect to their direction of movement in cells constrained in crossbow-and disk-shaped fibronectin patterns. We also investigate the position of the generating hub of Rab11 positive membranes and the effect of actin disruption on Rab11 trafficking in coordination with cell shape. An Icy plugin and a tutorial are available athttp://icy.bioimageanalysis.org/plugin/QuantEv

A quantitative approach for analyzing the spatio-temporal distribution of 3D intracellular events in fluorescence microscopy

International audienceAnalysis of the spatial distribution of endomembrane trafficking is fundamental to understand the mechanisms controlling cellular dynamics, cell homeostasy, and cell interaction with its external environment in normal and pathological situations. We present a semi-parametric framework to quantitatively analyze and visualize the spatio-temporal distribution of intracellular events from different conditions. From the spatial coordinates of intracellular features such as segmented subcellular structures or vesicle trajectories, QuantEv automatically estimates weighted densities that are easy to interpret and performs a comprehensive statistical analysis from distribution distances. We apply this approach to study the spatio-temporal distribution of moving Rab6 fluorescently labeled membranes with respect to their direction of movement in crossbow-and disk-shaped cells. We also investigate the position of the generating hub of Rab11-positive membranes and the effect of actin disruption on Rab11 trafficking in coordination with cell shape

Automated Microraft Array Platform for Immune Cell Assays and Cell Sorting

Immunology research and cell therapies have provided great advancements in recent years and have highlighted the need to understand the effects of single cell heterogeneity within immune cell populations. Single cell platforms currently used in immune cell analysis often include tedious manual work, are not able to measure immune cell function in a time-dependent manner, or are not selective. The following work describes the development of an automated microraft array platform for analyzing the function of individual immune cells, including helper T cells and chimeric antigen receptor T cells (CAR-T cells), and for the sorting of cells of interest. In this dissertation, an automated microraft array platform was developed and adapted to address the challenges seen in immune cell research. In Chapter 2, an automated microraft array platform was developed to assay thousands of single cells in parallel and sort individual cells of interest. The platform was used to assay single CD4+ T cells, isolate cells displaying proliferation in response to allogeneic cell stimulation, and sequence their T cell receptor genes. In Chapter 3, a next-generation magnetic microbead-based microraft array was developed as an alternative to nanoparticle-based microraft arrays. The microbead-based arrays were shown to substantially reduce fabrication time compared to nanoparticle-based microraft arrays and improve performance in imaging of fluorescently labeled cells. Chapter 4 focused on the development and application of the automated microraft array platform to assay CD19 CAR-T cells for cell-mediated cytotoxicity and isolate T cells of interest for gene expression analysis. CAR-T cells were shown to participate in serial-killing of target cells and T cells demonstrating high cytotoxicity were isolated for future gene expression analysis using single-cell multiplex qPCR. The findings presented in this dissertation demonstrate the capabilities of an automated microraft array platform and its uses in immunology research. The studies described in each chapter provided valuable insight into the behavior and phenotype of immune cells at the single cell level.Doctor of Philosoph

Generalizations of the Multicut Problem for Computer Vision

Graph decomposition has always been a very important concept in machine learning and computer vision. Many tasks like image and mesh segmentation, community detection in social networks, as well as object tracking and human pose estimation can be formulated as a graph decomposition problem. The multicut problem in particular is a popular model to optimize for a decomposition of a given graph. Its main advantage is that no prior knowledge about the number of components or their sizes is required. However, it has several limitations, which we address in this thesis: Firstly, the multicut problem allows to specify only cost or reward for putting two direct neighbours into distinct components. This limits the expressibility of the cost function. We introduce special edges into the graph that allow to define cost or reward for putting any two vertices into distinct components, while preserving the original set of feasible solutions. We show that this considerably improves the quality of image and mesh segmentations. Second, multicut is notorious to be NP-hard for general graphs, that limits its applications to small super-pixel graphs. We define and implement two primal feasible heuristics to solve the problem. They do not provide any guarantees on the runtime or quality of solutions, but in practice show good convergence behaviour. We perform an extensive comparison on multiple graphs of different sizes and properties. Third, we extend the multicut framework by introducing node labels, so that we can jointly optimize for graph decomposition and nodes classification by means of exactly the same optimization algorithm, thus eliminating the need to hand-tune optimizers for a particular task. To prove its universality we applied it to diverse computer vision tasks, including human pose estimation, multiple object tracking, and instance-aware semantic segmentation. We show that we can improve the results over the prior art using exactly the same data as in the original works. Finally, we use employ multicuts in two applications: 1) a client-server tool for interactive video segmentation: After the pre-processing of the video a user draws strokes on several frames and a time-coherent segmentation of the entire video is performed on-the-fly. 2) we formulate a method for simultaneous segmentation and tracking of living cells in microscopy data. This task is challenging as cells split and our algorithm accounts for this, creating parental hierarchies. We also present results on multiple model fitting. We find models in data heavily corrupted by noise by finding components defining these models using higher order multicuts. We introduce an interesting extension that allows our optimization to pick better hyperparameters for each discovered model. In summary, this thesis extends the multicut problem in different directions, proposes algorithms for optimization, and applies it to novel data and settings.Die Zerlegung von Graphen ist ein sehr wichtiges Konzept im maschinellen Lernen und maschinellen Sehen. Viele Aufgaben wie Bild- und Gittersegmentierung, Kommunitätserkennung in sozialen Netzwerken, sowie Objektverfolgung und Schätzung von menschlichen Posen können als Graphzerlegungsproblem formuliert werden. Der Mehrfachschnitt-Ansatz ist ein populäres Mittel um über die Zerlegungen eines gegebenen Graphen zu optimieren. Sein größter Vorteil ist, dass kein Vorwissen über die Anzahl an Komponenten und deren Größen benötigt wird. Dennoch hat er mehrere ernsthafte Limitierungen, welche wir in dieser Arbeit behandeln: Erstens erlaubt der klassische Mehrfachschnitt nur die Spezifikation von Kosten oder Belohnungen für die Trennung von zwei Nachbarn in verschiedene Komponenten. Dies schränkt die Ausdrucksfähigkeit der Kostenfunktion ein und führt zu suboptimalen Ergebnissen. Wir fügen dem Graphen spezielle Kanten hinzu, welche es erlauben, Kosten oder Belohnungen für die Trennung von beliebigen Paaren von Knoten in verschiedene Komponenten zu definieren, ohne die Menge an zulässigen Lösungen zu verändern. Wir zeigen, dass dies die Qualität von Bild- und Gittersegmentierungen deutlich verbessert. Zweitens ist das Mehrfachschnittproblem berüchtigt dafür NP-schwer für allgemeine Graphen zu sein, was die Anwendungen auf kleine superpixel-basierte Graphen einschränkt. Wir definieren und implementieren zwei primal-zulässige Heuristiken um das Problem zu lösen. Diese geben keine Garantien bezüglich der Laufzeit oder der Qualität der Lösungen, zeigen in der Praxis jedoch gutes Konvergenzverhalten. Wir führen einen ausführlichen Vergleich auf vielen Graphen verschiedener Größen und Eigenschaften durch. Drittens erweitern wir den Mehrfachschnitt-Ansatz um Knoten-Kennzeichnungen, sodass wir gemeinsam über Zerlegungen und Knoten-Klassifikationen mit dem gleichen Optimierungs-Algorithmus optimieren können. Dadurch wird der Bedarf der Feinabstimmung einzelner aufgabenspezifischer Löser aus dem Weg geräumt. Um die Allgemeingültigkeit dieses Ansatzes zu überprüfen, haben wir ihn auf verschiedenen Aufgaben des maschinellen Sehens, einschließlich menschliche Posenschätzung, Mehrobjektverfolgung und instanz-bewusste semantische Segmentierung, angewandt. Wir zeigen, dass wir Resultate von vorherigen Arbeiten mit exakt den gleichen Daten verbessern können. Abschließend benutzen wir Mehrfachschnitte in zwei Anwendungen: 1) Ein Nutzer-Server-Werkzeug für interaktive Video Segmentierung: Nach der Vorbearbeitung eines Videos zeichnet der Nutzer Striche auf mehrere Einzelbilder und eine zeit-kohärente Segmentierung des gesamten Videos wird in Echtzeit berechnet. 2) Wir formulieren eine Methode für simultane Segmentierung und Verfolgung von lebenden Zellen in Mikroskopie-Aufnahmen. Diese Aufgabe ist anspruchsvoll, da Zellen sich aufteilen und unser Algorithmus dies in der Erstellung von Eltern-Hierarchien mitberücksichtigen muss. Wir präsentieren außerdem Resultate zur Mehrmodellanpassung. Wir berechnen Modelle in stark verrauschten Daten indem wir mithilfe von Mehrfachschnitten höherer Ordnung Komponenten finden, die diesen Modellen entsprechen. Wir führen eine interessante Erweiterung ein, die es unserer Optimierung erlaubt, bessere Hyperparameter für jedes entdeckte Modell auszuwählen. Zusammenfassend erweitert diese Arbeit den Mehrfachschnitt-Ansatz in unterschiedlichen Richtungen, schlägt Algorithmen zur Inferenz in den resultierenden Modellen vor und wendet ihn auf neuartigen Daten und Umgebungen an

ROLE OF STAT3 IN HUMAN NK CELL FUNCTIONS

Natural Killer (NK) cells are cytotoxic lymphocytes, which play a critical role in the immune response against malignant cells and microbial infections. NK cells are equipped with activating receptors, which upon detecting ligands expressed on stressed cells induce cytolytic activity of NK cells. Stimulation of NK cell proliferation and priming of NK cytolytic capability are accomplished by cytokines, which mediate their signals mainly through JAK-STAT signaling pathway. Previously, we found that K562 cells genetically modified to express membrane bound IL-21 (mbIL-21), which predominantly activates STAT3, induce robust expansion and activation of human NK cells. Further investigations revealed role of STAT3 in the transcriptional regulation of NKG2D, a primary activating NK receptor. Based on these findings, I hypothesized that STAT3 signaling plays a critical role in human cytolytic function and proliferation. I analyzed NK cells from Job syndrome patients to test my hypothesis. Job syndrome caused by dominant negative STAT3 mutations is a naturally occurring STAT3 deficient genetic model. Assessment of cytolytic activity revealed impaired cytolytic function in Job Syndrome patients’ NK cells. Investigations into the probable underlying causes of impaired cytotoxicity showed deficient NKG2D receptor expression and impaired polarization of cytolytic granules to the immune synapse formed between Job syndrome patients’ NK cell and target cell. I validated these findings in STAT3 knock-down primary human NK cells, which also displayed impaired cytolytic function and cytolytic granule polarization. Expansion of Job syndrome patients’ NK cells with mbIL21 stimulation restored NKG2D expression and cytolytic granule polarization to normal levels and enhanced cytolytic activity. As constitutively active STAT3 is oncogenic, STAT3 is major drug target in cancer therapeutics. To assess a probable side effect of pharmacological inhibition of STAT3, I assessed its effect on human NK cell cytolytic function. Treatment of primary human NK cells with small molecule STAT3 inhibitor S3I-201 suppressed NK cytolytic function. I employed pharmacological and genetic models of STAT3 deficiency to study the role of STAT3 in human NK cell proliferation. Treatment with STAT3 inhibitor S3I-201 reduced expansion of human NK cells stimulated with mbIL21 and membrane bound IL-15 (mbIL15). mbIL21 and mbIL15 induced expansion was also deficient in Job syndrome patients’ NK cells. Thus, both pharmacological and genetic models complemented each other in underlying role of STAT3 signaling in human NK cell proliferation. Employing pharmacological and genetic approaches, I showed that STAT3 deficiency in primary human NK cells causes impairment of cytolytic function and cytokine induced expansion. This is the first report to demonstrate the role of STAT3 in the transport of cytolytic granules in NK cell and NK cell functional deficiency in Job syndrome patients, which may provide an immunological basis for their proclivity to cancer. Restoration of NK cell function upon mbIL21 stimulation, suggests adoptive NK cell therapy as a treatment option for Job syndrome patients. By assessing the effect of pharmacological STAT3 inhibition on NK cytotoxicity and proliferation, this study provides potential biomarkers for monitoring side effects of STAT3 inhibition, which is fast emerging as a therapeutic approach in cancer treatment