The genetic encoded toolbox for electron microscopy and connectomics

Developments in bioengineering and molecular biology have introduced a palette of genetically encoded probes for identification of specific cell populations in electron microscopy. These probes can be targeted to distinct cellular compartments, rendering them electron dense through a subsequent chemical reaction. These electron densities strongly increase the local contrast in samples prepared for electron microscopy, allowing three major advances in ultrastructural mapping of circuits: genetic identification of circuit components, targeted imaging of regions of interest and automated analysis of the tagged circuits. Together, the gains from these advances can decrease the time required for the analysis of targeted circuit motifs by over two orders of magnitude. These genetic encoded tags for electron microscopy promise to simplify the analysis of circuit motifs and become a central tool for structure‐function studies of synaptic connections in the brain. We review the current state‐of‐the‐art with an emphasis on connectomics, the quantitative analysis of neuronal structures and motifs

Niwaki Instead of Random Forests: Targeted Serial Sectioning Scanning Electron Microscopy With Reimaging Capabilities for Exploring Central Nervous System Cell Biology and Pathology

Ultrastructural analysis of discrete neurobiological structures by volume scanning electron microscopy (SEM) often constitutes a “needle-in-the-haystack” problem and therefore relies on sophisticated search strategies. The appropriate SEM approach for a given relocation task not only depends on the desired final image quality but also on the complexity and required accuracy of the screening process. Block-face SEM techniques like Focused Ion Beam or serial block-face SEM are “one-shot” imaging runs by nature and, thus, require precise relocation prior to acquisition. In contrast, “multi-shot” approaches conserve the sectioned tissue through the collection of serial sections onto solid support and allow reimaging. These tissue libraries generated by Array Tomography or Automated Tape Collecting Ultramicrotomy can be screened at low resolution to target high resolution SEM. This is particularly useful if a structure of interest is rare or has been predetermined by correlated light microscopy, which can assign molecular, dynamic and functional information to an ultrastructure. As such approaches require bridging mm to nm scales, they rely on tissue trimming at different stages of sample processing. Relocation is facilitated by endogenous or exogenous landmarks that are visible by several imaging modalities, combined with appropriate registration strategies that allow overlaying images of various sources. Here, we discuss the opportunities of using multi-shot serial sectioning SEM approaches, as well as suitable trimming and registration techniques, to slim down the high-resolution imaging volume to the actual structure of interest and hence facilitate ambitious targeted volume SEM projects

Learning to recommend descriptive tags for questions in social forums

10.1145/2559157ACM Transactions on Information Systems321-ATIS

Semantic multimedia modelling & interpretation for annotation

The emergence of multimedia enabled devices, particularly the incorporation of cameras in mobile phones, and the accelerated revolutions in the low cost storage devices, boosts the multimedia data production rate drastically. Witnessing such an iniquitousness of digital images and videos, the research community has been projecting the issue of its significant utilization and management. Stored in monumental multimedia corpora, digital data need to be retrieved and organized in an intelligent way, leaning on the rich semantics involved. The utilization of these image and video collections demands proficient image and video annotation and retrieval techniques. Recently, the multimedia research community is progressively veering its emphasis to the personalization of these media. The main impediment in the image and video analysis is the semantic gap, which is the discrepancy among a user’s high-level interpretation of an image and the video and the low level computational interpretation of it. Content-based image and video annotation systems are remarkably susceptible to the semantic gap due to their reliance on low-level visual features for delineating semantically rich image and video contents. However, the fact is that the visual similarity is not semantic similarity, so there is a demand to break through this dilemma through an alternative way. The semantic gap can be narrowed by counting high-level and user-generated information in the annotation. High-level descriptions of images and or videos are more proficient of capturing the semantic meaning of multimedia content, but it is not always applicable to collect this information. It is commonly agreed that the problem of high level semantic annotation of multimedia is still far from being answered. This dissertation puts forward approaches for intelligent multimedia semantic extraction for high level annotation. This dissertation intends to bridge the gap between the visual features and semantics. It proposes a framework for annotation enhancement and refinement for the object/concept annotated images and videos datasets. The entire theme is to first purify the datasets from noisy keyword and then expand the concepts lexically and commonsensical to fill the vocabulary and lexical gap to achieve high level semantics for the corpus. This dissertation also explored a novel approach for high level semantic (HLS) propagation through the images corpora. The HLS propagation takes the advantages of the semantic intensity (SI), which is the concept dominancy factor in the image and annotation based semantic similarity of the images. As we are aware of the fact that the image is the combination of various concepts and among the list of concepts some of them are more dominant then the other, while semantic similarity of the images are based on the SI and concept semantic similarity among the pair of images. Moreover, the HLS exploits the clustering techniques to group similar images, where a single effort of the human experts to assign high level semantic to a randomly selected image and propagate to other images through clustering. The investigation has been made on the LabelMe image and LabelMe video dataset. Experiments exhibit that the proposed approaches perform a noticeable improvement towards bridging the semantic gap and reveal that our proposed system outperforms the traditional systems

Application of Super-Resolution and Advanced Quantitative Microscopy to the Spatio-Temporal Analysis of Influenza Virus Replication

With an estimated three to five million human cases annually and the potential to infect domestic and wild animal populations, influenza viruses are one of the greatest health and economic burdens to our society, and pose an ongoing threat of large-scale pandemics. Despite our knowledge of many important aspects of influenza virus biology, there is still much to learn about how influenza viruses replicate in infected cells, for instance, how they use entry receptors or exploit host cell trafficking pathways. These gaps in our knowledge are due, in part, to the difficulty of directly observing viruses in living cells. In recent years, advances in light microscopy, including super-resolution microscopy and single-molecule imaging, have enabled many viral replication steps to be visualised dynamically in living cells. In particular, the ability to track single virions and their components, in real time, now allows specific pathways to be interrogated, providing new insights to various aspects of the virus-host cell interaction. In this review, we discuss how state-of-the-art imaging technologies, notably quantitative live-cell and super-resolution microscopy, are providing new nanoscale and molecular insights into influenza virus replication and revealing new opportunities for developing antiviral strategies

Visualizing active membrane protein complexes by electron cryotomography.

This is the final version of the article. Available from Nature Publishing Group via the DOI in this record.Unravelling the structural organization of membrane protein machines in their active state and native lipid environment is a major challenge in modern cell biology research. Here we develop the STAMP (Specifically TArgeted Membrane nanoParticle) technique as a strategy to localize protein complexes in situ by electron cryotomography (cryo-ET). STAMP selects active membrane protein complexes and marks them with quantum dots. Taking advantage of new electron detector technology that is currently revolutionizing cryotomography in terms of achievable resolution, this approach enables us to visualize the three-dimensional distribution and organization of protein import sites in mitochondria. We show that import sites cluster together in the vicinity of crista membranes, and we reveal unique details of the mitochondrial protein import machinery in action. STAMP can be used as a tool for site-specific labelling of a multitude of membrane proteins by cryo-ET in the future.We thank Drs Ulrike Endesfelder and Mike Heilemann (Institute of Physical and Theoretical Chemistry, University of Frankfurt) for help with confocal microscopy, Deryck Mills (MPI of Biophysics, Frankfurt) for maintenance of the EM facility, and Paolo Lastrico (Graphics Department, MPI of Biophysics, Frankfurt) for assistance with Supplementary Movies and Fig. 1a. We thank Drs Bertram Daum and Karen Davies for helpful discussions on tomography. The plasmids pMAL-c2x-MT2 and pMAL-c2x-MT3 were a gift from Dr Christina Risco (CNB-CSIC, Madrid). This work was supported by the Max Planck Society, Deutsche Forschungsgemeinschaft (Sonderforschungsbereich 746), Excellence Initiative of the German Federal & State Governments (EXC 294 BIOSS) and by an EMBO Long-Term Fellowship to V.A.M.G. (ALTF 1035-2010)

Structural learning for large scale image classification

To leverage large-scale collaboratively-tagged (loosely-tagged) images for training a large number of classifiers to support large-scale image classification, we need to develop new frameworks to deal with the following issues: (1) spam tags, i.e., tags are not relevant to the semantic of the images; (2) loose object tags, i.e., multiple object tags are loosely given at the image level without their locations in the images; (3) missing object tags, i.e. some object tags are missed due to incomplete tagging; (4) inter-related object classes, i.e., some object classes are visually correlated and their classifiers need to be trained jointly instead of independently; (5) large scale object classes, which requires to limit the computational time complexity for classifier training algorithms as well as the storage spaces for intermediate results. To deal with these issues, we propose a structural learning framework which consists of the following key components: (1) cluster-based junk image filtering to address the issue of spam tags; (2) automatic tag-instance alignment to address the issue of loose object tags; (3) automatic missing object tag prediction; (4) object correlation network for inter-class visual correlation characterization to address the issue of missing tags; (5) large-scale structural learning with object correlation network for enhancing the discrimination power of object classifiers. To obtain enough numbers of labeled training images, our proposed framework leverages the abundant web images and their social tags. To make those web images usable, tag cleansing has to be done to neutralize the noise from user tagging preferences, in particularly junk tags, loose tags and missing tags. Then a discriminative learning algorithm is developed to train a large number of inter-related classifiers for achieving large-scale image classification, e.g., learning a large number of classifiers for categorizing large-scale images into a large number of inter-related object classes and image concepts. A visual concept network is first constructed for organizing enumorus object classes and image concepts according to their inter-concept visual correlations. The visual concept network is further used to: (a) identify inter-related learning tasks for classifier training; (b) determine groups of visually-similar object classes and image concepts; and (c) estimate the learning complexity for classifier training. A large-scale discriminative learning algorithm is developed for supporting multi-class classifier training and achieving accurate inter-group discrimination and effective intra-group separation. Our discriminative learning algorithm can significantly enhance the discrimination power of the classifiers and dramatically reduce the computational cost for large-scale classifier training

Molecular mapping of virus-infected cells with immunogold and metal-tagging transmission electron microscopy

Transmission electron microscopy (TEM) has been essential to study virus-cell interactions. The architecture of viral replication factories, the principles of virus assembly and the components of virus egress pathways are known thanks to the contribution of TEM methods. Specially, when studying viruses in cells, methodologies for labeling proteins and other macromolecules are important tools to correlate morphology with function. In this review, we present the most widely used labeling method for TEM, immunogold, together with a lesser known technique, metal-tagging transmission electron microscopy (METTEM) and how they can contribute to study viral infections. Immunogold uses the power of antibodies and electron dense, colloidal gold particles while METTEM uses metallothionein (MT), a metal-binding protein as a clonable tag. MT molecules build gold nano-clusters inside cells when these are incubated with gold salts. We describe the necessary controls to confirm that signals are specific, the advantages and limitations of both methods, and show some examples of immunogold and METTEM of cells infected with viruses.This work has been funded by grant RTI2018-094445-B100 (MCIU/AEI/FEDER, UE) from the Ministry of Science and Innovation of Spain (C.R.). We are grateful to Ms. Sara Y. Fernández-Sánchez for critically reading the manuscript. We acknowledge support of the publication fee by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI).S