Narayanan Kasthuri

Long-term dendritic spine stability in the adult cortex

The structural dynamics of synapses probably has a crucial role in the development and plasticity of the nervous system. In the mammalian brain, the vast majority of excitatory axo-dendritic synapses occur on dendritic specializations called ‘spines’. However, little is known about their long-term changes in the intact developing or adult animal. To address this question we developed a transcranial two-photon imaging technique to follow identified spines of layer-5 pyramidal neurons in the primary visual cortex of living transgenic mice expressing yellow fluorescent protein. Here we show that filopodia-like dendritic protrusions, extending and retracting over hours, are abundant in young animals but virtually absent from the adult. In young mice, within the ‘critical period’ for visual cortex development, ∼73% of spines remain stable over a one-month interval; most changes are associated with spine elimination. In contrast, in adult mice, the overwhelming majority of spines (∼96%) remain stable over the same interval with a half-life greater than 13 months. These results indicate that spines, initially plastic during development, become remarkably stable in the adult, providing a potential structural basis for long-term information storage.

Distinct Profiles of Myelin Distribution Along Single Axons of Pyramidal Neurons in the Neocortex

Patchy Insulation Myelin insulates neuronal axons such that their electrical signals travel faster and more efficiently. However, not all axons are myelinated equally. Tomassy et al. (p. 319; see the Perspective by Fields) obtained detailed images from two snippets of the adult mouse brain and generated three-dimensional reconstructions of individual neurons and their myelination patterns. The images show that some axons have long, unmyelinated stretches, which might offer sites for building new connections. Thus, myelination is not an all-or-none phenomenon but rather is a characteristic of what may be a specific dialogue between the neuron and the surrounding myelin-producing cells. Mouse neurons display different and distinctive patterns of myelination. [Also see Perspective by Fields] Myelin is a defining feature of the vertebrate nervous system. Variability in the thickness of the myelin envelope is a structural feature affecting the conduction of neuronal signals. Conversely, the distribution of myelinated tracts along the length of axons has been assumed to be uniform. Here, we traced high-throughput electron microscopy reconstructions of single axons of pyramidal neurons in the mouse neocortex and built high-resolution maps of myelination. We find that individual neurons have distinct longitudinal distribution of myelin. Neurons in the superficial layers displayed the most diversified profiles, including a new pattern where myelinated segments are interspersed with long, unmyelinated tracts. Our data indicate that the profile of longitudinal distribution of myelin is an integral feature of neuronal identity and may have evolved as a strategy to modulate long-distance communication in the neocortex.

Automating the Collection of Ultrathin Serial Sections for Large Volume TEM Reconstructions

TEM serial section reconstructions have proven invaluable for mapping the complex neural circuitry of tiny invertebrate animals such as C. Elegans [1], as well as small pieces of the vertebrate nervous system. If such reconstructions could be applied to larger volumes of neural tissue (on the order of many cubic millimeters) they could provide answers to many lingering questions of vertebrate neuroanatomy. Unfortunately, such reconstructions are currently limited to several thousand sections and correspondingly small tissue volumes due, in part, to the manual nature of the ultramicrotomy and tissue collection process [2]. Here we introduce a new type of ultramicrotome we are developing, an Automatic Tape-collecting Lathe Ultramicrotome (ATLUM). The ATLUM is designed to automate not only the sectioning process, but also the collection of ultrathin sections from the knife’s waterboat (currently an exclusively manual process). The ATLUM’s mechanism collects ultrathin sections onto a specially prepared copper tape that can subsequently be patternetched to generate TEM slot grids with the collected tissue already attached and ready for imaging.

The role of neuronal identity in synaptic competition

In developing mammalian muscle, axon branches of several motor neurons co-innervate the same muscle fibre. Competition among them results in the strengthening of one and the withdrawal of the rest. It is not known why one particular axon branch survives or why some competitions resolve sooner than others. Here we show that the fate of axonal branches is strictly related to the identity of the axons with which they compete. When two neurons co-innervate multiple target cells, the losing axon branches in each contest belong to the same neuron and are at nearly the same stage of withdrawal. The axonal arbor of one neuron engages in multiple sets of competitions simultaneously. Each set proceeds at a different rate and heads towards a common outcome based on the identity of the competitor. Competitive vigour at each of these sets of local competitions depends on a globally distributed resource: neurons with larger arborizations are at a competitive disadvantage when confronting neurons with smaller arborizations. An accompanying paper tests the idea that the amount of neurotransmitter released is this global resource.

Boundary Learning by Optimization with Topological Constraints

Recent studies have shown that machine learning can improve the accuracy of detecting object boundaries in images. In the standard approach, a boundary detector is trained by minimizing its pixel-level disagreement with human boundary tracings. This naive metric is problematic because it is overly sensitive to boundary locations. This problem is solved by metrics provided with the Berkeley Segmentation Dataset, but these can be insensitive to topo-logical differences, such as gaps in boundaries. Furthermore, the Berkeley metrics have not been useful as cost functions for supervised learning. Using concepts from digital topology, we propose a new metric called the warping error that tolerates disagreements over boundary location, penalizes topological disagreements, and can be used directly as a cost function for learning boundary detection, in a method that we call Boundary Learning by Optimization with Topological Constraints (BLOTC). We trained boundary detectors on electron microscopic images of neurons, using both BLOTC and standard training. BLOTC produced substantially better performance on a 1.2 million pixel test set, as measured by both the warping error and the Rand index evaluated on segmentations generated from the boundary labelings. We also find our approach yields significantly better segmentation performance than either gPb-OWT-UCM or multiscale normalized cut, as well as Boosted Edge Learning trained directly on our data.

High-contrast en bloc staining of neuronal tissue for field emission scanning electron microscopy

Conventional heavy metal poststaining methods on thin sections lend contrast but often cause contamination. To avoid this problem, we tested several en bloc staining techniques to contrast tissue in serial sections mounted on solid substrates for examination by field emission scanning electron microscopy (FESEM). Because FESEM section imaging requires that specimens have higher contrast and greater electrical conductivity than transmission electron microscopy (TEM) samples, our technique uses osmium impregnation (OTO) to make the samples conductive while heavily staining membranes for segmentation studies. Combining this step with other classic heavy metal en bloc stains, including uranyl acetate (UA), lead aspartate, copper sulfate and lead citrate, produced clean, highly contrasted TEM and scanning electron microscopy (SEM) samples of insect, fish and mammalian nervous systems. This protocol takes 7–15 d to prepare resin-embedded tissue, cut sections and produce serial section images.

Stacked Endoplasmic Reticulum Sheets Are Connected by Helicoidal Membrane Motifs

The endoplasmic reticulum (ER) often forms stacked membrane sheets, an arrangement that is likely required to accommodate a maximum of membrane-bound polysomes for secretory protein synthesis. How sheets are stacked is unknown. Here, we used improved staining and automated ultrathin sectioning electron microscopy methods to analyze stacked ER sheets in neuronal cells and secretory salivary gland cells of mice. Our results show that stacked ER sheets form a continuous membrane system in which the sheets are connected by twisted membrane surfaces with helical edges of left- or right-handedness. The three-dimensional structure of tightly stacked ER sheets resembles a parking garage, in which the different levels are connected by helicoidal ramps. A theoretical model explains the experimental observations and indicates that the structure corresponds to a minimum of elastic energy of sheet edges and surfaces. The structure allows the dense packing of ER sheets in the restricted space of a cell.

The rise of the 'projectome'

Although new super-resolution imaging techniques provide valuable biological insights, some applications, such as determining the organization of neural projections in the brain, are better served by comprehensive imaging of very large samples at lower resolution.

Large-scale automatic reconstruction of neuronal processes from electron microscopy images.

Automated sample preparation and electron microscopy enables acquisition of very large image data sets. These technical advances are of special importance to the field of neuroanatomy, as 3D reconstructions of neuronal processes at the nm scale can provide new insight into the fine grained structure of the brain. Segmentation of large-scale electron microscopy data is the main bottleneck in the analysis of these data sets. In this paper we present a pipeline that provides state-of-the art reconstruction performance while scaling to data sets in the GB-TB range. First, we train a random forest classifier on interactive sparse user annotations. The classifier output is combined with an anisotropic smoothing prior in a Conditional Random Field framework to generate multiple segmentation hypotheses per image. These segmentations are then combined into geometrically consistent 3D objects by segmentation fusion. We provide qualitative and quantitative evaluation of the automatic segmentation and demonstrate large-scale 3D reconstructions of neuronal processes from a 27,000 μm(3) volume of brain tissue over a cube of 30 μm in each dimension corresponding to 1000 consecutive image sections. We also introduce Mojo, a proofreading tool including semi-automated correction of merge errors based on sparse user scribbles.

Pervasive Synaptic Branch Removal in the Mammalian Neuromuscular System at Birth

VIDEO ABSTRACT Using light and serial electron microscopy, we show profound refinements in motor axonal branching and synaptic connectivity before and after birth. Embryonic axons become maximally connected just before birth when they innervate ∼10-fold more muscle fibers than in maturity. In some developing muscles, axons innervate almost every muscle fiber. At birth, each neuromuscular junction is coinnervated by approximately ten highly intermingled axons (versus one in adults). Extensive die off of terminal branches occurs during the first several postnatal days, leading to much sparser arbors that still span the same territory. Despite the extensive pruning, total axoplasm per neuron increases as axons elongate, thicken, and add more synaptic release sites on their remaining targets. Motor axons therefore initially establish weak connections with nearly all available postsynaptic targets but, beginning at birth, massively redistribute synaptic resources, concentrating many more synaptic sites on many fewer muscle fibers. Analogous changes in connectivity may occur in the CNS.