Mahalanobis Distance for Class Averaging of Cryo-EM Images

Single particle reconstruction (SPR) from cryo-electron microscopy (EM) is a technique in which the 3D structure of a molecule needs to be determined from its contrast transfer function (CTF) affected, noisy 2D projection images taken at unknown viewing directions. One of the main challenges in cryo-EM is the typically low signal to noise ratio (SNR) of the acquired images. 2D classification of images, followed by class averaging, improves the SNR of the resulting averages, and is used for selecting particles from micrographs and for inspecting the particle images. We introduce a new affinity measure, akin to the Mahalanobis distance, to compare cryo-EM images belonging to different defocus groups. The new similarity measure is employed to detect similar images, thereby leading to an improved algorithm for class averaging. We evaluate the performance of the proposed class averaging procedure on synthetic datasets, obtaining state of the art classification.Comment: Final version accepted to the 14th IEEE International Symposium on Biomedical Imaging (ISBI 2017

MemBrain: a deep learning-aided pipeline for detection of membrane proteins in cryo-electron tomograms

Cryo-electron tomography (cryo-ET) is an imaging technique that enables 3D visualization of the native cellular environment at sub-nanometer resolution, providing unpreceded insights into the molecular organization of cells. However, cryo-electron tomograms suffer from low signal-to-noise ratios and anisotropic resolution, which makes subsequent image analysis challenging. In particular, the efficient detection of membrane-embedded proteins is a problem still lacking satisfactory solutions. We present MemBrain - a new deep learning-aided pipeline that automatically detects membrane-bound protein complexes in cryo-electron tomograms. After subvolumes are sampled along a segmented membrane, each subvolume is assigned a score using a convolutional neural network (CNN), and protein positions are extracted by a clustering algorithm. Incorporating rotational subvolume normalization and using a tiny receptive field simplify the task of protein detection and thus facilitate the network training. MemBrain requires only a small quantity of training labels and achieves excellent performance with only a single annotated membrane (F1 score: 0.88). A detailed evaluation shows that our fully trained pipeline outperforms existing classical computer vision-based and CNN-based approaches by a large margin (F1 score: 0.92 vs. max. 0.63). Furthermore, in addition to protein center positions, MemBrain can determine protein orientations, which has not been implemented by any existing CNN-based method to date. We also show that a pre-trained MemBrain program generalizes to tomograms acquired using different cryo-ET methods and depicting different types of cells. MemBrain is a powerful and annotation-efficient tool for the detection of membrane protein complexes in cryo-ET data, with the potential to be used in a wide range of biological studies. It is generalizable to various kinds of tomograms, making it possible to use pretrained models for different tasks. Its efficiency in terms of required annotations also allows rapid training and fine-tuning of models. The corresponding code, pretrained models, and instructions for operating the MemBrain program can be found at: https://github.com/CellArchLab/MemBrain

Advances in image processing for single-particle analysis by electron cryomicroscopy and challenges ahead

Electron cryomicroscopy (cryo-EM) is essential for the study and functional understanding of non-crystalline macromolecules such as proteins. These molecules cannot be imaged using X-ray crystallography or other popular methods. CryoEM has been successfully used to visualize molecules such as ribosomes, viruses, and ion channels, for example. Obtaining structural models of these at various conformational states leads to insight on how these molecules function. Recent advances in imaging technology have given cryo-EM a scientific rebirth. Because of imaging improvements, image processing and analysis of the resultant images have increased the resolution such that molecular structures can be resolved at the atomic level. Cryo-EM is ripe with stimulating image processing challenges. In this article, we will touch on the most essential in order to build an accurate structural three-dimensional model from noisy projection images. Traditional approaches, such as k-means clustering for class averaging, will be provided as background. With this review, however, we will highlight fresh approaches from new and varied angles for each image processing sub-problem, including a 3D reconstruction method for asymmetric molecules using just two projection images and deep learning algorithms for automated particle picking. Keywords: Cryo-electron microscopy, Single Particle Analysis, Image processing algorithms

Moment Estimation for Nonparametric Mixture Models Through Implicit Tensor Decomposition

We present an alternating least squares type numerical optimization scheme to estimate conditionally-independent mixture models in $\mathbb{R}^n$, without parameterizing the distributions. Following the method of moments, we tackle an incomplete tensor decomposition problem to learn the mixing weights and componentwise means. Then we compute the cumulative distribution functions, higher moments and other statistics of the component distributions through linear solves. Crucially for computations in high dimensions, the steep costs associated with high-order tensors are evaded, via the development of efficient tensor-free operations. Numerical experiments demonstrate the competitive performance of the algorithm, and its applicability to many models and applications. Furthermore we provide theoretical analyses, establishing identifiability from low-order moments of the mixture and guaranteeing local linear convergence of the ALS algorithm

Image processing tools for the validation of CryoEM maps

The number of maps deposited in public databases (Electron Microscopy Data Bank, EMDB) determined by cryo-electron microscopy has quickly grown in recent years. With this rapid growth, it is critical to guarantee their quality. So far, map validation has primarily focused on the agreement between maps and models. From the image processing perspective, the validation has been mostly restricted to using two half-maps and the measurement of their internal consistency. In this article, we suggest that map validation can be taken much further from the point of view of image processing if 2D classes, particles, angles, coordinates, defoci, and micrographs are also provided. We present a progressive validation scheme that qualifies a result validation status from 0 to 5 and offers three optional qualifiers (A, W, and O) that can be added. The simplest validation state is 0, while the most complete would be 5AWO. This scheme has been implemented in a website https://biocomp.cnb.csic.es/EMValidationService/ to which reconstructed maps and their ESI can be uploaded

Studying dimerization of Roco proteins in living cells:a tool to better understand Parkinson's Disease

This thesis describes the development of a method to measure protein pairing. This is important, among other things, for the protein LRRK2 (pronounced: lark-two) that is involved in Parkinson's Disease (PD). The insights resulting from this research contribute to the development of drugs that can inhibit or prevent Parkinson's disease. In a significant proportion of PD patients, the LRRK2 protein is hyperactive: it carries out a certain chemical reaction too often. Researchers would like to know how this protein activity is regulated and what it depends on so that it can be blocked. Previous experiments with disrupted cells indicated that it is important for protein activity that LRRK2 proteins are attached to each other in pairs ('dimers'). However, this had not yet been studied in living cells. The aim of my research was therefore to measure the pairing of LRRK2 in living cells. Observing protein pairs in their biological context is not easy, however. In my dissertation I adapted a certain microscope technique, in which the light intensity is calculated that each protein particle (provided with a green label) emits. Protein pairs are twice as bright as single proteins, and so we can now see in living cells (common lab cell lines) that LRRK2 'dimers' are indeed present. The next step is to block pairing with substances and observe this in living cells. Ultimately, Parkinson's drugs can be developed that counteract the LRRK2 hyperactivity and thus potentially halt the death of neurons and disease progression

Imaging studies of peripheral nerve regeneration induced by porous collagen biomaterials

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2013.Cataloged from PDF version of thesis.Includes bibliographical references.There is urgent need to develop treatments for inducing regeneration in injured organs. Porous collagen-based scaffolds have been utilized clinically to induce regeneration in skin and peripheral nerves, however still there is no complete explanation about the underlying mechanism. This thesis utilizes advanced microscopy to study the expression of contractile cell phenotypes during wound healing, a phenotype believed to affect significantly the final outcome. The first part develops an efficient pipeline for processing challenging spectral fluorescence microscopy images. Images are segmented into regions of objects by refining the outcome of a pixel-wide model selection classifier by an efficient Markov Random Field model. The methods of this part are utilized by the following parts. The second part extends the image informatics methodology in studying signal transduction networks in cells interacting with 3D matrices. The methodology is applied in a pilot study of TGFP signal transduction by the SMAD pathway in fibroblasts seeded in porous collagen scaffolds. Preliminary analysis suggests that the differential effect of TGFP1 and TGFP3 to cells could be attributed to the "non-canonical" SMADI and SMAD5. The third part is an ex vivo imaging study of peripheral nerve regeneration, which focuses on the formation of a capsule of contractile cells around transected rat sciatic nerves grafted with collagen scaffolds, 1 or 2 weeks post-injury. It follows a recent study that highlights an inverse relationship between the quality of the newly formed nerve tissue and the size of the contractile cell capsule 9 weeks post-injury. Results suggest that "active" biomaterials result in significantly thinner capsule already 1 week post-injury. The fourth part describes a novel method for quantifying the surface chemistry of 3D matrices. The method is an in situ binding assay that utilizes fluorescently labeled recombinant proteins that emulate the receptor of , and is applied to quantify the density of ligands for integrins a113, a2p1 on the surface of porous collagen scaffolds. Results provide estimates for the density of ligands on "active" and "inactive" scaffolds and demonstrate that chemical crosslinking can affect the surface chemistry of biomaterials, therefore can affect the way cells sense and respond to the material.by Dimitrios S. Tzeranis.Ph. D

Methodology and applications of elemental mapping by laser induced breakdown spectroscopy

In the last few years, LIBS has become an established technique for the assessment of elemental concentrations in various sample types. However, for many applications knowledge about the overall elemental composition is not sufficient. In addition, detailed information about the elemental distribution within a heterogeneous sample is needed. LIBS has become of great interest in elemental imaging studies, since this technique allows to associate the obtained elemental composition information with the spatial coordinates of the investigated sample. The possibility of simultaneous multi-elemental analysis of major, minor, and trace constituents in almost all types of solid materials with no or negligible sample preparation combined with a high speed of analysis are benefits which make LIBS especially attractive when compared to other elemental imaging techniques. The first part of this review is aimed at providing information about the instrumental requirements necessary for successful LIBS imaging measurements and points out and discusses state-of-the-art LIBS instrumentation and upcoming developments. The second part is dedicated to data processing and evaluation of LIBS imaging data. This chapter is focused on different approaches of multivariate data evaluation and chemometrics which can be used e.g. for classification but also for the quantification of obtained LIBS imaging data. In the final part, current literature of different LIBS imaging applications ranging from bioimaging, geoscientific and cultural heritage studies to the field of materials science is summarized and reviewed. 2020 The Authors. Published by Elsevier B.V

90th Annual Meeting of the Virginia Academy of Science: Proceedings

Full proceedings of the 90th Annual Meeting of the Virginia Academy of Science, Norfolk State University, Norfolk Virginia, May 23-25, 201