Explore the vast range of titles available.

An open-source library of analytical tools for mapping large-scale patterns of brain activity using cluster computing finds structure in two-photon imaging data from mouse and whole-brain light-sheet functional imaging data from behaving larval zebrafish. Vladimirov et al ., also in this issue, describes the light-sheet functional imaging system used here. Understanding brain function requires monitoring and interpreting the activity of large networks of neurons during behavior. Advances in recording technology are greatly increasing the size and complexity of neural data. Analyzing such data will pose a fundamental bottleneck for neuroscience. We present a library of analytical tools called Thunder built on the open-source Apache Spark platform for large-scale distributed computing. The library implements a variety of univariate and multivariate analyses with a modular, extendable structure well-suited to interactive exploration and analysis development. We demonstrate how these analyses find structure in large-scale neural data, including whole-brain light-sheet imaging data from fictively behaving larval zebrafish, and two-photon imaging data from behaving mouse. The analyses relate neuronal responses to sensory input and behavior, run in minutes or less and can be used on a private cluster or in the cloud. Our open-source framework thus holds promise for turning brain activity mapping efforts into biological insights.

The endoplasmic reticulum (ER) forms a dynamic network that contacts other cellular membranes to regulate stress responses, calcium signalling and lipid transfer. Here, using high-resolution volume electron microscopy, we find that the ER forms a previously unknown association with keratin intermediate filaments and desmosomal cell–cell junctions. Peripheral ER assembles into mirror image-like arrangements at desmosomes and exhibits nanometre proximity to keratin filaments and the desmosome cytoplasmic plaque. ER tubules exhibit stable associations with desmosomes, and perturbation of desmosomes or keratin filaments alters ER organization, mobility and expression of ER stress transcripts. These findings indicate that desmosomes and the keratin cytoskeleton regulate the distribution, function and dynamics of the ER network. Overall, this study reveals a previously unknown subcellular architecture defined by the structural integration of ER tubules with an epithelial intercellular junction. Bharathan et al. discover that the endoplasmic reticulum associates with keratin intermediate filaments and desmosomal cell–cell junctions, and that desmosomes and the keratin cytoskeleton regulate the distribution, dynamics and function of the endoplasmic reticulum network.

Cells contain hundreds of organelles and macromolecular assemblies. Obtaining a complete understanding of their intricate organization requires the nanometre-level, three-dimensional reconstruction of whole cells, which is only feasible with robust and scalable automatic methods. Here, to support the development of such methods, we annotated up to 35 different cellular organelle classes—ranging from endoplasmic reticulum to microtubules to ribosomes—in diverse sample volumes from multiple cell types imaged at a near-isotropic resolution of 4 nm per voxel with focused ion beam scanning electron microscopy (FIB-SEM) 1 . We trained deep learning architectures to segment these structures in 4 nm and 8 nm per voxel FIB-SEM volumes, validated their performance and showed that automatic reconstructions can be used to directly quantify previously inaccessible metrics including spatial interactions between cellular components. We also show that such reconstructions can be used to automatically register light and electron microscopy images for correlative studies. We have created an open data and open-source web repository, ‘OpenOrganelle’, to share the data, computer code and trained models, which will enable scientists everywhere to query and further improve automatic reconstruction of these datasets. Focused ion beam scanning electron microscopy (FIB-SEM) combined with deep-learning-based segmentation is used to produce three-dimensional reconstructions of complete cells and tissues, in which up to 35 different organelle classes are annotated.

Understanding cellular architecture is essential for understanding biology. Electron microscopy (EM) uniquely visualizes cellular structures with nanometre resolution. However, traditional methods, such as thin-section EM or EM tomography, have limitations in that they visualize only a single slice or a relatively small volume of the cell, respectively. Focused ion beam-scanning electron microscopy (FIB-SEM) has demonstrated the ability to image small volumes of cellular samples with 4-nm isotropic voxels 1 . Owing to advances in the precision and stability of FIB milling, together with enhanced signal detection and faster SEM scanning, we have increased the volume that can be imaged with 4-nm voxels by two orders of magnitude. Here we present a volume EM atlas at such resolution comprising ten three-dimensional datasets for whole cells and tissues, including cancer cells, immune cells, mouse pancreatic islets and Drosophila neural tissues. These open access data (via OpenOrganelle 2 ) represent the foundation of a field of high-resolution whole-cell volume EM and subsequent analyses, and we invite researchers to explore this atlas and pose questions. Open-access 3D images of whole cells and tissues with combined finer resolution and larger sample size are enabled by advances in focused ion beam-scanning electron microscopy.

In this thesis, I present a set of interlocking biological and technological developments: in Chapter 2, I will argue that a class of glial cells in the larval zebrafish is structurally and functionally homologous to astrocytes, which have been intensely-studied in mammals. This finding is of interest because the larval zebrafish is a model organism that enables a uniquely expansive set of high-throughput, high-resolution experiments for probing the vertebrate nervous system, but these advantages have hitherto been leveraged exclusively for studying neuronal cells, with no consideration for glia. Chapter 3 presents evidence that zebrafish glia can participate in behaviorally relevant computation, which builds on and demonstrates the arguments of Chapter 2. The biological data I present in Chapters 2 and 3 would not be possible without an ecosystem of tools for gathering and analyzing functional imaging data via light microscopy. Chapter 4 presents a sorely-needed addition to the functional imaging toolkit: a simple technique for vastly accelerating data analysis of large functional imaging datasets.