Automatic Mitochondria Segmentation for EM Data Using a 3D Supervised Convolutional Network

Recent studies have supported the relation between mitochondrial functions and degenerative disorders related to ageing, such as Alzheimer's and Parkinson's diseases. Since these studies have exposed the need for detailed and high-resolution analysis of physical alterations in mitochondria, it is necessary to be able to perform segmentation and 3D reconstruction of mitochondria. However, due to the variety of mitochondrial structures, automated mitochondria segmentation and reconstruction in electron microscopy (EM) images have proven to be a difficult and challenging task. This paper puts forward an effective and automated pipeline based on deep learning to realize mitochondria segmentation in different EM images. The proposed pipeline consists of three parts: (1) utilizing image registration and histogram equalization as image pre-processing steps to maintain the consistency of the dataset; (2) proposing an effective approach for 3D mitochondria segmentation based on a volumetric, residual convolutional and deeply supervised network; and (3) employing a 3D connection method to obtain the relationship of mitochondria and displaying the 3D reconstruction results. To our knowledge, we are the first researchers to utilize a 3D fully residual convolutional network with a deeply supervised strategy to improve the accuracy of mitochondria segmentation. The experimental results on anisotropic and isotropic EM volumes demonstrate the effectiveness of our method, and the Jaccard index of our segmentation (91.8% in anisotropy, 90.0% in isotropy) and F1 score of detection (92.2% in anisotropy, 90.9% in isotropy) suggest that our approach achieved state-of-the-art results. Our fully automated pipeline contributes to the development of neuroscience by providing neurologists with a rapid approach for obtaining rich mitochondria statistics and helping them elucidate the mechanism and function of mitochondria

Taramalı electron mikroskobu görüntülerinde mitokondrilerin otomatik olarak bölütlenmesi

Many studies have shown that shape of mitochondria indicates the occurrence of diseases. Scanning Electron Microscopy (SEM) enables to obtain image of internal structures of the cell and mitochondria. Automatic segmentation of mitochondria contributes to the decision of diseases by specialists. There is limited study about automatic segmentation of mitochondria in Serial Block-Face Scanning Electron Microscopy (SFBSEM) images. SBFSEM imaging technique provides full automation, well registered images, less time and less effort for data acquisition. Therefore, SBFSEM imaging technique is selected for this study. Recently, deep learning methods have been implemented for image processing of SEM datasets. However, due to requirement of huge datasets, much effort and powerful computers for preparing testing and training data, energy based model is implemented for this study. The algorithms used in this thesis are primarily the algorithms developed by Tasel et al for mitochondria segmentation in TEM images. The method includes preprocessing, ridge detection, energy mapping, curve fitting, snake-based shape extraction, validation and post-processing steps. In this thesis, these algorithms are adapted and refined for SBFSEM images to obtain optimum performance. Evaluations are made by using Dice Similarity Coefficient (DSC), precision, recall and F-Score metrics.Birçok çalışma mitokondri ve kristaların şeklinin hastalıkların oluşumunu belirttiğini göstermektedir. Taramalı Elektron Mikroskobu (SEM), hücrenin iç yapılarının ve mitokondrilerin görüntülerinin elde edilmesini sağlar. Mitokondrilerin otomatik bölütlenmesi uzmanlar tarafından hastalıkların karar verilmesine katkı sağlar. Seri Blok-Yüz Taramalı Elektron Mikroskobu (SFBSEM) görüntülerinde mitokondrinin otomatik segmentasyonu hakkında sınırlı çalışma vardır. SBFSEM görüntüleme tekniği, tam otomasyon, iyi kaydedilmiş görüntüler, veri elde etmek için daha az zaman ve daha az çaba sağlar. Bu nedenle, bu çalışma için SBFSEM görüntüleme tekniği seçilmiştir. Son zamanlarda, derin ögrenme yöntemleri SEM veri setlerinin görüntü işlemesi için uygulanmaktadır. Ancak, büyük veri setlerinin, fazla çabanın ve test ve eğitim verilerinin hazırlanması için güçlü bilgisayarların gerekliliğinden bu çalışma için enerji tabanlı model uygulanmaktadır. Bu tezde kullanılan algoritmalar öncelikle TEM görüntülerinde mitokondri bölütlenmesi için Taşel ve arkadaşları tarafından geliştirilen algoritmalardır. Yöntem, ön işleme, sırt algılama, enerji haritalama, eğri uyumlandırma, yılan temelli şekil çıkarma, doğrulama ve son işlem adımlarını içerir. Bu tezde, bu algoritmalar optimum performans elde etmek için SBFSEM görüntüleri için uyarlanmış ve yeniden düzenlenmiştir. Değerlendirmeler Dice Benzerlik Katsayısı(DSC), kesinlik, hatırlama ve F-Skoru metrikleri kullanılarak yapılır.M.S. - Master of Scienc

Automated Correlative Light and Electron Microscopy using FIB-SEM as a tool to screen for ultrastructural phenotypes

In Correlative Light and Electron Microscopy (CLEM), two imaging modalities are combined to take advantage of the localization capabilities of light microscopy (LM) to guide the capture of high-resolution details in the electron microscope (EM). However, traditional approaches have proven to be very laborious, thus yielding a too low throughput for quantitative or exploratory studies of populations. Recently, in the electron microscopy field, FIB-SEM (Focused Ion Beam -Scanning Electron Microscope) tomography has emerged as a flexible method that enables semi-automated 3D volume acquisitions. During my thesis, I developed CLEMSite, a tool that takes advantage of the semi-automation and scanning capabilities of the FIB-SEM to automatically acquire volumes of adherent cultured cells. CLEMSite is a combination of computer vision and machine learning applications with a library for controlling the microscope ( product from a collaboration with Carl Zeiss GmbH and Fibics Inc.). Thanks to this, the microscope was able to automatically track, find and acquire cell regions previously identified in the light microscope. More specifically, two main modules were implemented. First, a correlation module was designed to detect and record reference points from a grid pattern present on the culture substrate in both modalities (LM and EM). Second, I designed a module that retrieves the regions of interest in the FIB-SEM and that drives the acquisition of image stacks between different targets in an unattended fashion. The automated CLEM approach is demonstrated on a project where 3D EM volumes are examined upon multiple siRNA treatments for knocking down genes involved in the morphogenesis of the Golgi apparatus. Additionally, the power of CLEM approaches using FIB-SEM is demonstrated with the detailed structural analysis of two events: the breakage of the nuclear envelope within constricted cells and an intriguing catastrophic DNA Damage Response in binucleated cells. Our results demonstrate that executing high throughput volume acquisition in electron microscopy is possible and that EM can provide incredible insights to guide new biological discoveries

Partial connectomes of labeled dopaminergic circuits reveal non-synaptic communication and axonal remodeling after exposure to cocaine

Dopaminergic (DA) neurons exert profound influences on behavior including addiction. However, how DA axons communicate with target neurons and how those communications change with drug exposure remains poorly understood. We leverage cell type-specific labeling with large volume serial electron microscopy to detail DA connections in the nucleus accumbens (NAc) of the mouse (Mus musculus) before and after exposure to cocaine. We find that individual DA axons contain different varicosity types based on their vesicle contents. Spatially ordering along individual axons further suggests that varicosity types are non-randomly organized. DA axon varicosities rarely make specific synapses (<2%, 6/410), but instead are more likely to form spinule-like structures (15%, 61/410) with neighboring neurons. Days after a brief exposure to cocaine, DA axons were extensively branched relative to controls, formed blind-ended ‘bulbs’ filled with mitochondria, and were surrounded by elaborated glia. Finally, mitochondrial lengths increased by ~2.2 times relative to control only in DA axons and NAc spiny dendrites after cocaine exposure. We conclude that DA axonal transmission is unlikely to be mediated via classical synapses in the NAc and that the major locus of anatomical plasticity of DA circuits after exposure to cocaine are large-scale axonal re-arrangements with correlated changes in mitochondria

Correlative light and electron microscopy: new strategies for improved throughput and targeting precision

The need for quantitative analysis is crucial when studying fundamental mechanisms in cell biology. Common assays consist of interfering with a system via protein knockdowns or drug treatments. These very often lead to important response variability that is generally addressed by analyzing large populations. Whilst the imaging throughput in light microscopy (LM) is high enough for such large screens, electron microscopy (EM) still lags behind and is not adapted to collect large amounts of data from highly heterogeneous cell populations. Nevertheless, EM is the only technique that offers high-resolution imaging of the entire subcellular context. Correlative light and electron microscopy (CLEM) has made it possible to look at rare events or addressing heterogeneous populations. Our goal is to develop new strategies in CLEM. More specifically, we aim at automatizing the processes of screening large cell populations (living cells or pre-fixed), identifying the sub-populations of interest by LM, targeting these by EM and measuring the key components of the subcellular organization. New 3D-EM techniques like focused ion beam - scanning electron microscopy (FIB-SEM) enable a high degree of automation for the acquisition of high-resolution, full cell datasets. So far, this has only been applied to individual target volumes, often isotropic and has not been designed to acquire multiple regions of interest. The ability to acquire full cells with up to 5 nm x 5 nm x 5 nm voxel size (x, y referring to pixel size, z referring to slice thickness), leads to the accumulation of large datasets. Their analysis involves tedious manual segmentation or so far not well established automated segmentation algorithms. To enable the analysis and quantification of an extensive amount of data, we decided to explore the potential of stereology protocols in combination with automated acquisition in the FIB-SEM. Instead of isotropic datasets, a few evenly spaced sections are used to quantify subcellular structures. Our strategy therefore combines CLEM, 3D-EM and stereology to collect and analyze large amounts of cells selected based on their phenotype as visible by fluorescence microscopy. We demonstrate the power of the approach in a systematic screen of the Golgi apparatus morphology upon alteration of the expression of 10 proteins, plus negative and positive control. In parallel to this core project, we demonstrate the power of combining correlative approaches with 3D-EM for the detailed structural analysis of fundamental cell biology events during cell division and also for the understanding on complex physiological transitions in a multicellular model organism

Towards efficient siRNA delivery and gene silencing kinetics on the single cell level

RNA interference (RNAi) is a natural sequence-specific mechanism of post-transcriptional gene regulation leaded by short, double stranded RNA fragments e.g. small interfering RNAs (siRNA). Despite its high therapeutic potential, the safe and efficient systemic delivery of siRNAs into a large number of diseased cells to trigger therapeutic gene knockdown remains challenging. Moreover, novel quantitative methods for assessing activity of siRNA-based therapeutic agents in a fast and precise manner are needed. In this work, we firstly developed the folate-targeted monomolecular nucleic acid/lipid particles (FolA-mNALPs) formed using microfluidic-based method and studied their functionality regarding prospective use as a siRNA delivery agent. Secondly, we quantify the single-cell kinetics of siRNA-mediated gene silencing using micro-patterned cell cultivation substrates combined with time-lapse fluorescence microscopy (life-cell imaging on single-cell arrays, LISCA). In particular, we demonstrate that microfluidic self-assembly combined with rational design of lipid formulation results in nanoparticles of small size and narrow size distribution that in average contain single siRNA molecule covered with a single lipid bilayer (mNALP). We investigate the stability of folate-functionalized mNALPs in biological fluids, and their biological performance in terms of cellular internalisation and silencing efficiency. Small sizes, efficient targeting and presented silencing capability following facilitated endosomal release make mNALP a promising system for the future development of an in vivo siRNA delivery agent. Furthermore, using LISCA we investigate the magnitude of siRNA-induced mRNA degradation. By mathematical modelling of gene expression and fitting of expression time-courses we obtain the population distributions of rate constants related with the model, including single-cell mRNA degradation rate constants. The expression time-courses are gained by monitoring the dynamic changes in single-cell fluorescence intensities of reporter proteins (eGFP target and CayRFP reference). Obtained kinetic parameters allow us to quantify the silencing efficiency as the relative fold-change in mRNA degradation rate constants, to identify the subpopulations of cells affected by siRNA activity and, by analysis of correlations between kinetic parameters of CayRFP and eGFP expression, to infer on the properties of mRNA delivery and expression kinetics. Presented approach allows for the precise quantification of the activity of siRNA-based therapeutics in an accurate and fast (<30h) manner based on the analysis of time-independent kinetic parameters describing the silencing process.RNA-Interferenz (RNAi) ist ein natürlicher Mechanismus der posttranskriptionalen Genregulation in eukaryotischen Zellen. RNAi kann spezifische Gene ansteuern und bietet hohe Flexilitiät in der Wahl der angesteuerten mRNA Sequenzregionen.Diese beiden Charakteristika machen RNAi zu einem vielseitigen Werkzeug bei der Untersuchung von Genfunktionen und zu einem möglichen Therapeutikum für eine große Vielfalt an Erkrankungen. Im Rahmen dieser Arbeit wurden a) eine mikrofluidik-basierte Methode zur verbesserten Selbst-Assemblierung von monomolekularen Nukleinsäure/ -lipidteilchen (mNALPs) für ihren möglichen zukünftigen Nutzen als siRNA-Lieferant entwickelt und b) die Einzelzellantworten auf siRNA-induzierte Genstilllegung untersucht. Wir bestimmen insbesondere die optimalen Parameter für die Selbst-Assemblierung von mNALPs, untersuchen deren Stabilität in biologischen Flüssigkeiten und ihre Wirkungsweise bezüglich zelltypspezifischer Internalisierung und Stilllegungseffizienz in in-vitro Zellexperimenten. Des Weiteren verwenden wir Lebendzell-Videomikroskopie auf mikrostrukturierten Substraten („live-cell imaging on single cell arrys“, LISCA) um die, durch siRNA-Aktivität induzierte, relative Veränderung der mRNA-Degradierungsratenkonstanten zu untersuchen.Eine Aussage über die Stärke der siRNA-induzierten mRNA Degradierung kann durch das mathematische Modell der Genexpression und das Fitten der Fluoreszenz-Zeitkurven getroffen werden, die aus den dynamischen Veränderungen in der Einzelzellfluoreszenzintensitäten der Reporterproteine gewonnen wird. Diese Prozedur liefert die Populationsverteilung von Ratenkonstanten, welche mit dem Modell verbunden sind. Dadurch können wir die Effizienz der Gen-Stilllegung als relative Veränderung der mRNA-Degradationsratenkonstanten quantifizieren und zusätzlich Subpopulationen von Zellen identifizieren, welche von der siRNA-Aktivität nicht betroffen sind. Zudem kann die Analyse der Korrelationen zwischen den kinetischen Parametern der CayRFP- und eGFP-Expressionen einen Rückschluss auf die Eigenschaften der mRNA-Lieferung der Expressionskinetik erlauben. Die nanoskalige Größe, Stabilität, spezifisches Targeting und die demonstrierte spezifische Stillegung eines Gens, machen mNALP zu einer vielversprechenden Grundlage für ein zukünftiges in-vivo siRNA-Transfersystems. Zudem stellen wir die mikroskopiebasierte Methode LISCA vor, welche eine präzise Quantifizierung der Aktivität von siRNA-basierten Therapeutika erlaubt. Auf akkurate und schnelle Weise (< 30h) können damit zeitabhängige kinetische Parameter, welche den Stillegungsprozess von Genen beschreiben, gewonnen werden