Automatic approaches for microscopy imaging based on machine learning and spatial statistics

One of the most frequent ways to interact with the surrounding environment occurs as a visual way. Hence imaging is a very common way in order to gain information and learn from the environment. Particularly in the field of cellular biology, imaging is applied in order to get an insight into the minute world of cellular complexes. As a result, in recent years many researches have focused on developing new suitable image processing approaches which have facilitates the extraction of meaningful quantitative information from image data sets. In spite of recent progress, but due to the huge data set of acquired images and the demand for increasing precision, digital image processing and statistical analysis are gaining more and more importance in this field. There are still limitations in bioimaging techniques that are preventing sophisticated optical methods from reaching their full potential. For instance, in the 3D Electron Microscopy(3DEM) process nearly all acquired images require manual postprocessing to enhance the performance, which should be substitute by an automatic and reliable approach (dealt in Part I). Furthermore, the algorithms to localize individual fluorophores in 3D super-resolution microscopy data are still in their initial phase (discussed in Part II). In general, biologists currently lack automated and high throughput methods for quantitative global analysis of 3D gene structures. This thesis focuses mainly on microscopy imaging approaches based on Machine Learning, statistical analysis and image processing in order to cope and improve the task of quantitative analysis of huge image data. The main task consists of building a novel paradigm for microscopy imaging processes which is able to work in an automatic, accurate and reliable way. The specific contributions of this thesis can be summarized as follows: • Substitution of the time-consuming, subjective and laborious task of manual post-picking in Cryo-EM process by a fully automatic particle post-picking routine based on Machine Learning methods (Part I). • Quality enhancement of the 3D reconstruction image due to the high performance of automatically post-picking steps (Part I). • Developing a full automatic tool for detecting subcellular objects in multichannel 3D Fluorescence images (Part II). • Extension of known colocalization analysis by using spatial statistics in order to investigate the surrounding point distribution and enabling to analyze the colocalization in combination with statistical significance (Part II). All introduced approaches are implemented and provided as toolboxes which are free available for research purposes

Automatic post-picking improves particle image detection from Cryo-EM micrographs

Cryo-electron microscopy (cryo-EM) studies using single particle reconstruction is extensively used to reveal structural information of macromolecular complexes. Aiming at the highest achievable resolution, state of the art electron microscopes acquire thousands of high-quality images. Having collected these data, each single particle must be detected and windowed out. Several fully- or semi-automated approaches have been developed for the selection of particle images from digitized micrographs. However they still require laborious manual post processing, which will become the major bottleneck for next generation of electron microscopes. Instead of focusing on improvements in automated particle selection from micrographs, we propose a post-picking step for classifying small windowed images, which are output by common picking software. A supervised strategy for the classification of windowed micrograph images into particles and non-particles reduces the manual workload by orders of magnitude. The method builds on new powerful image features, and the proper training of an ensemble classifier. A few hundred training samples are enough to achieve a human-like classification performance.Comment: 14 pages, 5 figure

A structural model of the active ribosome-bound membrane protein insertase YidC

The integration of most membrane proteins into the cytoplasmic membrane of bacteria occurs co-translationally. The universally conserved YidC protein mediates this process either individually as a membrane protein insertase, or in concert with the SecY complex. Here, we present a structural model of YidC based on evolutionary co-variation analysis, lipid-versus-protein-exposure and molecular dynamics simulations. The model suggests a distinctive arrangement of the conserved five transmembrane domains and a helical hairpin between transmembrane segment 2 (TM2) and TM3 on the cytoplasmic membrane surface. The model was used for docking into a cryo-electron microscopy reconstruction of a translating YidC-ribosome complex carrying the YidC substrate F(O)c. This structure reveals how a single copy of YidC interacts with the ribosome at the ribosomal tunnel exit and identifies a site for membrane protein insertion at the YidC protein-lipid interface. Together, these data suggest a mechanism for the co-translational mode of YidC-mediated membrane protein insertion