A neuronal network of mitochondrial dynamics regulates metastasis.

The role of mitochondria in cancer is controversial. Using a genome-wide shRNA screen, we now show that tumours reprogram a network of mitochondrial dynamics operative in neurons, including syntaphilin (SNPH), kinesin KIF5B and GTPase Miro1/2 to localize mitochondria to the cortical cytoskeleton and power the membrane machinery of cell movements. When expressed in tumours, SNPH inhibits the speed and distance travelled by individual mitochondria, suppresses organelle dynamics, and blocks chemotaxis and metastasis, in vivo. Tumour progression in humans is associated with downregulation or loss of SNPH, which correlates with shortened patient survival, increased mitochondrial trafficking to the cortical cytoskeleton, greater membrane dynamics and heightened cell invasion. Therefore, a SNPH network regulates metastatic competence and may provide a therapeutic target in cancer

Quantitative automated analysis of host-pathogen interactions

This work aims to broaden knowledge about neutrophil biology in their interaction with fungi species that most frequently cause invasive fungal diseases (IFD). The questions addressed include the alteration of neutrophil morphology after interaction with Candida albicans or C. glabrata, revealing factors that modulate the production and composition of neutrophil-derived extracellular vesicles (EVs) obtained in confrontation assay with conidia of Aspergillus fumigatus and analysing EVs activity against this fungus. Alongside fundamental interests, those questions have important applied aspects in the medicine of IFD. In particular, for diagnostic purposes and infection process monitoring. The results of this work include: 1 a novel segmentation and tracking algorithm which is capable of working with low-contrast cell images, producing accurate cell contours and providing data about positions of clusters, which would improve further analysis; 2 a novel workflow algorithm for analysis of neutrophil continuous morphological spectrum without consensus-based manual annotation; 3 quantitative evidence that morphodynamics of isolated neutrophils depends on the infectious agent (C. albicans or C. glabrata) used in whole blood infection assay; 4 quantitative evidence that neutrophil-derived extracellular vesicles, obtained in confrontation assays with conidia of A. fumigatus could inhibit hyphae development and damage hyphae cell wall; 5 quantitative evidence that EVs inhibition activity is strain-specific

Enabling high-throughput image analysis with deep learning-based tools

Microscopes are a valuable tool in biological research, facilitating information gathering with different magnification scales, samples and markers in single-cell and whole-population studies. However, image acquisition and analysis are very time-consuming, so efficient solutions are needed for the required speed-up to allow high-throughput microscopy. Throughout the work presented in this thesis, I developed new computational methods and software packages to facilitate high-throughput microscopy. My work comprised not only the development of these methods themselves but also their integration into the workflow of the lab, starting from automating the microscopy acquisition to deploying scalable analysis services and providing user-friendly local user interfaces. The main focus of my thesis was YeastMate, a tool for automatic detection and segmentation of yeast cells and sub-type classification of their life-cycle transitions. Development of YeastMate was mainly driven by research on quality control mechanisms of the mitochondrial genome in S. cerevisiae, where yeast cells are imaged during their sexual and asexual reproduction life-cycle stages. YeastMate can automatically detect both single cells and life-cycle transitions, perform segmentation and enable pedigree analysis by determining origin and offspring cells. I developed a novel adaptation of the Mask R-CNN object detection model to integrate the classification of inter-cell connections into the usual detection and segmentation analysis pipelines. Another part of my work focused on the automation of microscopes themselves using deep learning models to detect wings of D. melanogaster. A microscope was programmed to acquire large overview images and then to acquire detailed images at higher magnification on the detected coordinates of each wing. The implementation of this workflow replaced the process of manually imaging slides, usually taking hours to do so, with a fully automated, end-to-end solution

Quantitative Optical Studies of Oxidative Stress in Rodent Models of Eye and Lung Injuries

Optical imaging techniques have emerged as essential tools for reliable assessment of organ structure, biochemistry, and metabolic function. The recognition of metabolic markers for disease diagnosis has rekindled significant interest in the development of optical methods to measure the metabolism of the organ. The objective of my research was to employ optical imaging tools and to implement signal and image processing techniques capable of quantifying cellular metabolism for the diagnosis of diseases in human organs such as eyes and lungs. To accomplish this goal, three different tools, cryoimager, fluorescent microscope, and optical coherence tomography system were utilized to study the physiological metabolic markers and early structural changes due to injury in vitro, ex vivo, and at cryogenic temperatures. Cryogenic studies of eye injuries in animal models were performed using a fluorescence cryoimager to monitor two endogenous mitochondrial fluorophores, NADH (nicotinamide adenine dinucleotide) and FAD (flavin adenine dinucleotide). The mitochondrial redox ratio (NADH/ FAD), which is correlated with oxidative stress level, is an optical biomarker. The spatial distribution of mitochondrial redox ratio in injured eyes with different durations of the disease was delineated. This spatiotemporal information was helpful to investigate the heterogeneity of the ocular oxidative stress in the eyes during diseases and its association with retinopathy. To study the metabolism of the eye tissue, the retinal layer was targeted, which required high resolution imaging of the eye as well as developing a segmentation algorithm to quantitatively monitor and measure the metabolic redox state of the retina. To achieve a high signal to noise ratio in fluorescence image acquisition, the imaging was performed at cryogenic temperatures, which increased the quantum yield of the intrinsic fluorophores. Microscopy studies of cells were accomplished by using an inverted fluorescence microscope. Fixed slides of the retina tissue as well as exogenous fluorophores in live lung cells were imaged using fluorescent and time-lapse microscopy. Image processing techniques were developed to quantify subtle changes in the morphological parameters of the retinal vasculature network for the early detection of the injury. This implemented image cytometry tool was capable of segmenting vascular cells, and calculating vasculature features including: area, caliber, branch points, fractal dimension, and acellular capillaries, and classifying the healthy and injured retinas. Using time-lapse microscopy, the dynamics of cellular ROS (Reactive Oxygen Species) concentration was quantified and modeled in ROS-mediated lung injuries. A new methodology and an experimental protocol were designed to quantify changes of oxidative stress in different stress conditions and to localize the site of ROS in an uncoupled state of pulmonary artery endothelial cells (PAECs). Ex vivo studies of lung were conducted using a spectral-domain optical coherence tomography (SD-OCT) system and 3D scanned images of the lung were acquired. An image segmentation algorithm was developed to study the dynamics of structural changes in the lung alveoli in real time. Quantifying the structural dynamics provided information to diagnose pulmonary diseases and to evaluate the severity of the lung injury. The implemented software was able to quantify and present the changes in alveoli compliance in lung injury models, including edema. In conclusion, optical instrumentation, combined with signal and image processing techniques, provides quantitative physiological and structural information reflecting disease progression due to oxidative stress. This tool provides a unique capability to identify early points of intervention, which play a vital role in the early detection of eye and lung injuries. The future goal of this research is to translate optical imaging to clinical settings, and to transfer the instruments developed for animal models to the bedside for patient diagnosis

Evaluating Outer Segment Length as A Surrogate Measure of Peak Foveal Cone Density

Adaptive optics (AO) imaging tools enable direct visualization of the cone photoreceptor mosaic, which facilitates quantitative measurements such as cone density. However, in many individuals, low image quality or excessive eye movements precludes making such measures. As foveal cone specialization is associated with both increased density and outer segment (OS) elongation, we sought to examine whether OS length could be used as a surrogate measure of foveal cone density. The retinas of 43 subjects (23 normal and 20 albinism; aged 6–67 years) were examined. Peak foveal cone density was measured using confocal adaptive optics scanning light ophthalmoscopy (AOSLO), and OS length was measured using optical coherence tomography (OCT) and longitudinal reflectivity profile-based approach. Peak cone density ranged from 29,200 to 214,000 cones/mm2(111,700 ± 46,300 cones/mm2); OS length ranged from 26.3 to 54.5 μm (40.5 ± 7.7 μm). Density was significantly correlated with OS length in albinism (p \u3c 0.0001), but not normals (p = 0.99). A cubic model of density as a function of OS length was created based on histology and optimized to fit the albinism data. The model includes triangular cone packing, a cylindrical OS with a fixed volume of 136.6 μm3, and a ratio of OS to inner segment width that increased linearly with increasing OS length (R2 = 0.72). Normal subjects showed no apparent relationship between cone density and OS length. In the absence of adequate AOSLO imagery, OS length may be used to estimate cone density in patients with albinism. Whether this relationship exists in other patient populations with foveal hypoplasia (e.g., premature birth, aniridia, isolated foveal hypoplasia) remains to be seen

Automated detection and analysis of fluorescence changes evoked by molecular signalling

Fluorescent dyes and genetically encoded fluorescence indicators (GEFI) are common tools for visualizing concentration changes of specific ions and messenger molecules during intra- as well as intercellular communication. While fluorescent dyes have to be directly loaded into target cells and function only transiently, the expression of GEFIs can be controlled in a cell and time-specific fashion, even allowing long-term analysis in living organisms. Dye and GEFI based fluorescence fluctuations, recorded using advanced imaging technologies, are the foundation for the analysis of physiological molecular signaling. Analyzing the plethora of complex fluorescence signals is a laborious and time-consuming task. An automated analysis of fluorescent signals circumvents user bias and time constraints. However, it requires to overcome several challenges, including correct estimation of fluorescence fluctuations at basal concentrations of messenger molecules, detection and extraction of events themselves, proper segmentation of neighboring events as well as tracking of propagating events. Moreover, event detection algorithms need to be sensitive enough to accurately capture localized and low amplitude events exhibiting a limited spatial extent. This thesis presents three novel algorithms, PBasE, CoRoDe and KalEve, for the automated analysis of fluorescence events, developed to overcome the aforementioned challenges. The algorithms are integrated into a graphical application called MSparkles, specifically designed for the analysis of fluorescence signals, developed in MATLAB. The capabilities of the algorithms are demonstrated by analyzing astroglial Ca2+ events, recorded in anesthetized and awake mice, visualized using genetically encoded Ca2+ indicators (GECIs) GCaMP3 as well as GCaMP5. The results were compared to those obtained by other software packages. In addition, the analysis of neuronal Na+ events recorded in acute brain slices using SBFI-AM serve to indicate the putatively broad application range of the presented algorithms. Finally, due to increasing evidence of the pivotal role of astrocytes in neurodegenerative diseases such as epilepsy, a metric to assess the synchronous occurrence of fluorescence events is introduced. In a proof-of-principle analysis, this metric is used to correlate astroglial Ca2+ events with EEG measurementsFluoreszenzfarbstoffe und genetisch kodierte Fluoreszenzindikatoren (GEFI) sind gängige Werkzeuge zur Visualisierung von Konzentrationsänderungen bestimmter Ionen und Botenmoleküle der intra- sowie interzellulären Kommunikation. Während Fluoreszenzfarbstoffe direkt in die Zielzellen eingebracht werden müssen und nur über einen begrenzten Zeitraum funktionieren, kann die Expression von GEFIs zell- und zeitspezifisch gesteuert werden, was darüber hinaus Langzeitanalysen in lebenden Organismen ermöglicht. Farbstoff- und GEFI-basierte Fluoreszenzfluktuationen, die mit Hilfe moderner bildgebender Verfahren aufgezeichnet werden, bilden die Grundlage für die Analyse physiologischer molekularer Kommunikation. Die Analyse einer großen Zahl komplexer Fluoreszenzsignale ist jedoch eine schwierige und zeitaufwändige Aufgabe. Eine automatisierte Analyse ist dagegen weniger zeitaufwändig und unabhängig von der Voreingenommenheit des Anwenders. Allerdings müssen hierzu mehrere Herausforderungen bewältigt werden. Unter anderem die korrekte Schätzung von Fluoreszenzschwankungen bei Basalkonzentrationen von Botenmolekülen, die Detektion und Extraktion von Signalen selbst, die korrekte Segmentierung benachbarter Signale sowie die Verfolgung sich ausbreitender Signale. Darüber hinaus müssen die Algorithmen zur Signalerkennung empfindlich genug sein, um lokalisierte Signale mit geringer Amplitude sowie begrenzter räumlicher Ausdehnung genau zu erfassen. In dieser Arbeit werden drei neue Algorithmen, PBasE, CoRoDe und KalEve, für die automatische Extraktion und Analyse von Fluoreszenzsignalen vorgestellt, die entwickelt wurden, um die oben genannten Herausforderungen zu bewältigen. Die Algorithmen sind in eine grafische Anwendung namens MSparkles integriert, die speziell für die Analyse von Fluoreszenzsignalen entwickelt und in MATLAB implementiert wurde. Die Fähigkeiten der Algorithmen werden anhand der Analyse astroglialer Ca2+-Signale demonstriert, die in narkotisierten sowie wachen Mäusen aufgezeichnet und mit den genetisch kodierten Ca2+-Indikatoren (GECIs) GCaMP3 und GCaMP5 visualisiert wurden. Erlangte Ergebnisse werden anschließend mit denen anderer Softwarepakete verglichen. Darüber hinaus dient die Analyse neuronaler Na+-Signale, die in akuten Hirnschnitten mit SBFI-AM aufgezeichnet wurden, dazu, den breiten Anwendungsbereich der Algorithmen aufzuzeigen. Zu guter Letzt wird aufgrund der zunehmenden Indizien auf die zentrale Rolle von Astrozyten bei neurodegenerativen Erkrankungen wie Epilepsie eine Metrik zur Bewertung des synchronen Auftretens fluoreszenter Signale eingeführt. In einer Proof-of-Principle-Analyse wird diese Metrik verwendet, um astrogliale Ca2+-Signale mit EEG-Messungen zu korrelieren