The advent of direct electron detectors has enabled the routine use of single-particle cryo-electron microscopy (EM) approaches to determine structures of a variety of protein complexes at near-atomic resolution. Here, we report the development of methods to account for local variations in defocus and beam-induced drift, and the implementation of a data-driven dose compensation scheme that significantly improves the extraction of high-resolution information recorded during exposure of the specimen to the electron beam. These advances enable determination of a cryo-EM density map for β-galactosidase bound to the inhibitor phenylethyl β-D-thiogalactopyranoside where the ordered regions are resolved at a level of detail seen in X-ray maps at ∼ 1.5 Å resolution. Using this density map in conjunction with constrained molecular dynamics simulations provides a measure of the local flexibility of the non-covalently bound inhibitor and offers further opportunities for structure-guided inhibitor design.

The mammalian hippocampus, comprised of serially connected subfields, participates in diverse behavioral and cognitive functions. It has been postulated that parallel circuitry embedded within hippocampal subfields may underlie such functional diversity. We sought to identify, delineate, and manipulate this putatively parallel architecture in the dorsal subiculum, the primary output subfield of the dorsal hippocampus. Population and single-cell RNA-seq revealed that the subiculum can be divided into two spatially adjacent subregions associated with prominent differences in pyramidal cell gene expression. Pyramidal cells occupying these two regions differed in their long-range inputs, local wiring, projection targets, and electrophysiological properties. Leveraging gene-expression differences across these regions, we use genetically restricted neuronal silencing to show that these regions differentially contribute to spatial working memory. This work provides a coherent molecular-, cellular-, circuit-, and behavioral-level demonstration that the hippocampus embeds structurally and functionally dissociable streams within its serial architecture.

Intracellular recording allows measurement and perturbation of the membrane potential of identified neurons with sub-millisecond and sub-millivolt precision. This gives intracellular recordings a unique capacity to provide rich information about individual cells (e.g., high-resolution characterization of inputs, outputs, excitability, and structure). Hence, such recordings can elucidate the mechanisms that underlie fundamental phenomena, such as brain state, sparse coding, gating, gain modulation, and learning. Technical developments have increased the range of behaviors during which intracellular recording methods can be employed, such as in freely moving animals and head-fixed animals actively performing tasks, including in virtual environments. Such advances, and the combination of intracellular recordings with genetic and imaging techniques, have enabled investigation of the mechanisms that underlie neural computations during natural and trained behaviors.

Large electron microscopy image datasets for connectomics are typically composed of thousands to millions of partially overlapping two-dimensional images (tiles), which must be registered into a coherent volume prior to further analysis. A common registration strategy is to find matching features between neighboring and overlapping image pairs, followed by a numerical estimation of optimal image deformation using a so-called solver program. 
Existing solvers are inadequate for large data volumes, and inefficient for small-scale image registration. 
In this work, an efficient and accurate matrix-based solver method is presented. A linear system is constructed that combines minimization of feature-pair square distances with explicit constraints in a regularization term. In absence of reliable priors for regularization, we show how to construct a rigid-model approximation to use as prior. The linear system is solved using available computer programs, whose performance on typical registration tasks we briefly compare, and to which future scale-up is delegated. Our method is applied to the joint alignment of 2.67 million images, with more than 200 million point-pairs and has been used for successfully aligning the first full adult fruit fly brain.

Zika virus (ZIKV) is an emerging flavivirus that caused thousands of human infections in recent years. Compared to other human flaviviruses, ZIKV replication is not well understood. Using fluorescent, transmission electron, and focused ion beam-scanning electron microscopy, we examined ZIKV replication dynamics in Vero 76 cells and in the brains of infected laboratory mice. We observed the progressive development of a perinuclear flaviviral replication factory both in vitro and in vivo. In vitro, we illustrated the ZIKV lifecycle from particle cell entry to egress. ZIKV particles assembled and aggregated in an induced convoluted membrane structure and ZIKV strain-specific membranous vesicles. While most mature virus particles egressed via membrane budding, some particles also likely trafficked through late endosomes and egressed through membrane abscission. Interestingly, we consistently observed a novel sheet-like virus particle array consisting of a single layer of ZIKV particles. Our study further defines ZIKV replication and identifies a novel hallmark of ZIKV infection.

True physiological imaging of subcellular dynamics requires studying cells within their parent organisms, where all the environmental cues that drive gene expression, and hence the phenotypes that we actually observe, are present. A complete understanding also requires volumetric imaging of the cell and its surroundings at high spatiotemporal resolution, without inducing undue stress on either. We combined lattice light-sheet microscopy with adaptive optics to achieve, across large multicellular volumes, noninvasive aberration-free imaging of subcellular processes, including endocytosis, organelle remodeling during mitosis, and the migration of axons, immune cells, and metastatic cancer cells in vivo. The technology reveals the phenotypic diversity within cells across different organisms and developmental stages and may offer insights into how cells harness their intrinsic variability to adapt to different physiological environments.

Lim et al. (2018) use live imaging in Drosophila embryos to show that enhancers can drive transcription from promoters on another chromosome when they are in close proximity. In addition, they show that multiple promoters can access the same enhancer without competition, potentially sharing a pool of factors in a transcriptional "hub."

Micro-crystal electron diffraction (MicroED) combines the efficiency of electron scattering with diffraction to allow structure determination from nano-sized crystalline samples in cryoelectron microscopy (cryo-EM). It has been used to solve structures of a diverse set of biomolecules and materials, in some cases to sub-atomic resolution. However, little is known about the damaging effects of the electron beam on samples during such measurements. We assess global and site-specific damage from electron radiation on nanocrystals of proteinase K and of a prion hepta-peptide and find that the dynamics of electron-induced damage follow well-established trends observed in X-ray crystallography. Metal ions are perturbed, disulfide bonds are broken, and acidic side chains are decarboxylated while the diffracted intensities decay exponentially with increasing exposure. A better understanding of radiation damage in MicroED improves our assessment and processing of all types of cryo-EM data.

Tremendous progress has been made since Neuron published our Primer on genetic dissection of neural circuits 10 years ago. Since then, cell-type-specific anatomical, neurophysiological, and perturbation studies have been carried out in a multitude of invertebrate and vertebrate organisms, linking neurons and circuits to behavioral functions. New methods allow systematic classification of cell types and provide genetic access to diverse neuronal types for studies of connectivity and neural coding during behavior. Here we evaluate key advances over the past decade and discuss future directions.

We consider the problem of optimizing general convex objective functions with nonnegativity constraints. Using the Karush-Kuhn-Tucker (KKT) conditions for the nonnegativity constraints we will derive fast multiplicative update rules for several problems of interest in signal processing, including non-negative deconvolution, point-process smoothing, ML estimation for Poisson Observations, nonnegative least squares and nonnegative matrix factorization (NMF). Our algorithm can also account for temporal and spatial structure and regularization. We will analyze the performance of our algorithm on simultaneously recorded neuronal calcium imaging and electrophysiology data.