Surface-based mapping of gene expression and probabilistic expression maps in the mouse cortex

The cytoarchitectonic landscape revealed by deep learning method facilitated precise positioning in mouse neocortex

Neocortex is a complex structure with different cortical sublayers and regions. However, the precise positioning of cortical regions can be challenging due to the absence of distinct landmarks without special preparation. To address this challenge, we developed a cytoarchitectonic landmark identification pipeline. The fluorescence micro-optical sectioning tomography method was employed to image the whole mouse brain stained by general fluorescent nucleotide dye. A fast 3D convolution network was subsequently utilized to segment neuronal somas in entire neocortex. By approach, the cortical cytoarchitectonic profile and the neuronal morphology were analyzed in 3D, eliminating the influence of section angle. And the distribution maps were generated that visualized the number of neurons across diverse morphological types, revealing the cytoarchitectonic landscape which characterizes the landmarks of cortical regions, especially the typical signal pattern of barrel cortex. Furthermore, the cortical regions of various ages were aligned using the generated cytoarchitectonic landmarks suggesting the structural changes of barrel cortex during the aging process. Moreover, we observed the spatiotemporally gradient distributions of spindly neurons, concentrated in the deep layer of primary visual area, with their proportion decreased over time. These findings could improve structural understanding of neocortex, paving the way for further exploration with this method.

Molecular biology of serotonergic systems in avian brains

Serotonin (5-hydroxytryptamine, 5-HT) is a phylogenetically conserved neurotransmitter and modulator. Neurons utilizing serotonin have been identified in the central nervous systems of all vertebrates. In the central serotonergic system of vertebrate species examined so far, serotonergic neurons have been confirmed to exist in clusters in the brainstem. Although many serotonin-regulated cognitive, behavioral, and emotional functions have been elucidated in mammals, equivalents remain poorly understood in non-mammalian vertebrates. The purpose of this review is to summarize current knowledge of the anatomical organization and molecular features of the avian central serotonergic system. In addition, selected key functions of serotonin are briefly reviewed. Gene association studies between serotonergic system related genes and behaviors in birds have elucidated that the serotonergic system is involved in the regulation of behavior in birds similar to that observed in mammals. The widespread distribution of serotonergic modulation in the central nervous system and the evolutionary conservation of the serotonergic system provide a strong foundation for understanding and comparing the evolutionary continuity of neural circuits controlling corresponding brain functions within vertebrates. The main focus of this review is the chicken brain, with this type of poultry used as a model bird. The chicken is widely used not only as a model for answering questions in developmental biology and as a model for agriculturally useful breeding, but also in research relating to cognitive, behavioral, and emotional processes. In addition to a wealth of prior research on the projection relationships of avian brain regions, detailed subdivision similarities between avian and mammalian brains have recently been identified. Therefore, identifying the neural circuits modulated by the serotonergic system in avian brains may provide an interesting opportunity for detailed comparative studies of the function of serotonergic systems in mammals.

The Allen Mouse Brain Common Coordinate Framework: A 3D Reference Atlas

Recent large-scale collaborations are generating major surveys of cell types and connections in the mouse brain, collecting large amounts of data across modalities, spatial scales, and brain areas. Successful integration of these data requires a standard 3D reference atlas. Here, we present the Allen Mouse Brain Common Coordinate Framework (CCFv3) as such a resource. We constructed an average template brain at 10 μm voxel resolution by interpolating high resolution in-plane serial two-photon tomography images with 100 μm z-sampling from 1,675 young adult C57BL/6J mice. Then, using multimodal reference data, we parcellated the entire brain directly in 3D, labeling every voxel with a brain structure spanning 43 isocortical areas and their layers, 329 subcortical gray matter structures, 81 fiber tracts, and 8 ventricular structures. CCFv3 can be used to analyze, visualize, and integrate multimodal and multiscale datasets in 3D and is openly accessible (https://atlas.brain-map.org/).

The chick pallium displays divergent expression patterns of chick orthologues of mammalian neocortical deep layer-specific genes

The avian pallium is organised into clusters of neurons and does not have layered structures such as those seen in the mammalian neocortex. The evolutionary relationship between sub-regions of avian pallium and layers of mammalian neocortex remains unclear. One hypothesis, based on the similarities in neural connections of the motor output neurons that project to sub-pallial targets, proposed the cell-type homology between brainstem projection neurons in neocortex layers 5 or 6 (L5/6) and those in the avian arcopallium. Recent studies have suggested that gene expression patterns are associated with neural connection patterns, which supports the cell-type homology hypothesis. However, a limited number of genes were used in these studies. Here, we showed that chick orthologues of mammalian L5/6-specific genes, nuclear receptor subfamily 4 group A member 2 and connective tissue growth factor, were strongly expressed in the arcopallium. However, other chick orthologues of L5/6-specific genes were primarily expressed in regions other than the arcopallium. Our results do not fully support the cell-type homology hypothesis. This suggests that the cell types of brainstem projection neurons are not conserved between the avian arcopallium and the mammalian neocortex L5/6. Our findings may help understand the evolution of pallium between birds and mammals.

Concentric ring topology of mammalian cortical sectors and relevance for patterning studies

Models aiming to explain causally the evolutionary or ontogenetic emergence of the pallial isocortex and its regional/areal heterogeneity in mammals use simple or complex assumptions about the pallial structure present in basal mammals and nonmammals. The question arises: how complex is the pattern that needs to be accounted for in causal models? This topic is also paramount for comparative purposes, since some topological relationships may be explained as being ancestral, rather than newly emerged. The mouse pallium is apt to be reexamined in this context, due to the breadth of available molecular markers and correlative experimental patterning results. We center the present essay on a recapitulative glance at the classic theory of concentric mammalian allo-, meso-, and neocortex domains. In its simplest terms, this theory postulates a central neocortical island (6 layers) separated by a surrounding mesocortical ring (4-5 layers) from a peripheral allocortical ring (3 layers). These territories show additional partition into regional or areal subdivisions. There are also borderline amygdalar, claustral, and septal areas of the pallium, nuclear in structure. There has been little effort so far to contemplate the full concentric ring model in current "cortex patterning" models. In this essay, we recapitulate the ring idea in mammals (mouse) and consider a potential causal patterning scenario using topologic models. Finally, we briefly explore how far this theory may apply to pallium models proposed recently for sauropsids.

Thalamocortical Projection Neuron and Interneuron Numbers in the Visual Thalamic Nuclei of the Adult C57BL/6 Mouse

A key parameter to constrain predictive, bottom-up circuit models of a given brain domain is the number and position of the neuronal populations involved. These include not only the neurons whose bodies reside within the domain, but also the neurons in distant regions that innervate the domain. The mouse visual cortex receives its main subcortical input from the dorsal lateral geniculate nucleus (dLGN) and the lateral posterior (LP) complex of the thalamus. The latter consists of three different nuclei: lateral posterior lateral (LPL), lateral posterior medial rostral (LPMR), and lateral posterior medial caudal (LPMC), each exhibiting specific patterns of connections with the various visual cortical areas. Here, we have determined the number of thalamocortical projection neurons and interneurons in the LP complex and dLGN of the adult C57BL/6 male mouse. We combined Nissl staining and histochemical and immunolabeling methods for consistently delineating nuclei borders, and applied unbiased stereological cell counting methods. Thalamic interneurons were identified using GABA immunolabeling. The C57BL/6 dLGN contains ∼21,200 neurons, while LP complex contains ∼31,000 total neurons. The dLGN and LP are the only nuclei of the mouse dorsal thalamus containing substantial numbers GABA-immunoreactive interneurons. These interneurons, however, are scarcer than previously estimated; they are 5.6% of dLGN neurons and just 1.9% of the LP neurons. It can be thus inferred that the dLGN contains ∼20,000 and the LP complex ∼30,400 thalamocortical projection neurons (∼12,000 in LPL, 15,200 in LPMR, and 4,200 in LPMC). The present dataset is relevant for constraining models of mouse visual thalamocortical circuits, as well as for quantitative comparisons between genetically modified mouse strains, or across species.

Comparing the Expression of Genes Related to Serotonin (5-HT) in C57BL/6J Mice and Humans Based on Data Available at the Allen Mouse Brain Atlas and Allen Human Brain Atlas

Brain atlases are tools based on comprehensive studies used to locate biological characteristics (structures, connections, proteins, and gene expression) in different regions of the brain. These atlases have been disseminated to the point where tools have been created to store, manage, and share the information they contain. This study used the data published by the Allen Mouse Brain Atlas (2004) for mice (C57BL/6J) and Allen Human Brain Atlas (2010) for humans (6 donors) to compare the expression of serotonin-related genes. Genes of interest were searched for manually in each case (in situ hybridization for mice and microarrays for humans), normalized expression data (z-scores) were extracted, and the results were graphed. Despite the differences in methodology, quantification, and subjects used in the process, a high degree of similarity was found between expression data. Here we compare expression in a way that allows the use of translational research methods to infer and validate knowledge. This type of study allows part of the relationship between structures and functions to be identified, by examining expression patterns and comparing levels of expression in different states, anatomical correlations, and phenotypes between different species. The study concludes by discussing the importance of knowing, managing, and disseminating comprehensive, open-access studies in neuroscience.

Brain transcriptome atlases: a computational perspective

The immense complexity of the mammalian brain is largely reflected in the underlying molecular signatures of its billions of cells. Brain transcriptome atlases provide valuable insights into gene expression patterns across different brain areas throughout the course of development. Such atlases allow researchers to probe the molecular mechanisms which define neuronal identities, neuroanatomy, and patterns of connectivity. Despite the immense effort put into generating such atlases, to answer fundamental questions in neuroscience, an even greater effort is needed to develop methods to probe the resulting high-dimensional multivariate data. We provide a comprehensive overview of the various computational methods used to analyze brain transcriptome atlases.

Analysis of spatial-temporal gene expression patterns reveals dynamics and regionalization in developing mouse brain

Allen Brain Atlas (ABA) provides a valuable resource of spatial/temporal gene expressions in mammalian brains. Despite rich information extracted from this database, current analyses suffer from several limitations. First, most studies are either gene-centric or region-centric, thus are inadequate to capture the superposition of multiple spatial-temporal patterns. Second, standard tools of expression analysis such as matrix factorization can capture those patterns but do not explicitly incorporate spatial dependency. To overcome those limitations, we proposed a computational method to detect recurrent patterns in the spatial-temporal gene expression data of developing mouse brains. We demonstrated that regional distinction in brain development could be revealed by localized gene expression patterns. The patterns expressed in the forebrain, medullary and pontomedullary, and basal ganglia are enriched with genes involved in forebrain development, locomotory behavior, and dopamine metabolism respectively. In addition, the timing of global gene expression patterns reflects the general trends of molecular events in mouse brain development. Furthermore, we validated functional implications of the inferred patterns by showing genes sharing similar spatial-temporal expression patterns with Lhx2 exhibited differential expression in the embryonic forebrains of Lhx2 mutant mice. These analysis outcomes confirm the utility of recurrent expression patterns in studying brain development.

A high-resolution spatiotemporal atlas of gene expression of the developing mouse brain

To provide a temporal framework for the genoarchitecture of brain development, we generated in situ hybridization data for embryonic and postnatal mouse brain at seven developmental stages for ∼2,100 genes, which were processed with an automated informatics pipeline and manually annotated. This resource comprises 434,946 images, seven reference atlases, an ontogenetic ontology, and tools to explore coexpression of genes across neurodevelopment. Gene sets coinciding with developmental phenomena were identified. A temporal shift in the principles governing the molecular organization of the brain was detected, with transient neuromeric, plate-based organization of the brain present at E11.5 and E13.5. Finally, these data provided a transcription factor code that discriminates brain structures and identifies the developmental age of a tissue, providing a foundation for eventual genetic manipulation or tracking of specific brain structures over development. The resource is available as the Allen Developing Mouse Brain Atlas (http://developingmouse.brain-map.org).

Probing perceptual decisions in rodents

The study of perceptual decision-making offers insight into how the brain uses complex, sometimes ambiguous information to guide actions. Understanding the underlying processes and their neural bases requires that one pair recordings and manipulations of neural activity with rigorous psychophysics. Though this research has been traditionally performed in primates, it seems increasingly promising to pursue it at least partly in mice and rats. However, rigorous psychophysical methods are not yet as developed for these rodents as they are for primates. Here we give a brief overview of the sensory capabilities of rodents and of their cortical areas devoted to sensation and decision. We then review methods of psychophysics, focusing on the technical issues that arise in their implementation in rodents. These methods represent a rich set of challenges and opportunities.

Convergence of pontine and proprioceptive streams onto multimodal cerebellar granule cells

Cerebellar granule cells constitute the majority of neurons in the brain and are the primary conveyors of sensory and motor-related mossy fiber information to Purkinje cells. The functional capability of the cerebellum hinges on whether individual granule cells receive mossy fiber inputs from multiple precerebellar nuclei or are instead unimodal; this distinction is unresolved. Using cell-type-specific projection mapping with synaptic resolution, we observed the convergence of separate sensory (upper body proprioceptive) and basilar pontine pathways onto individual granule cells and mapped this convergence across cerebellar cortex. These findings inform the long-standing debate about the multimodality of mammalian granule cells and substantiate their associative capacity predicted in the Marr-Albus theory of cerebellar function. We also provide evidence that the convergent basilar pontine pathways carry corollary discharges from upper body motor cortical areas. Such merging of related corollary and sensory streams is a critical component of circuit models of predictive motor control.

DOI:http://dx.doi.org/10.7554/eLife.00400.001

Learning a new motor skill, from riding a bicycle to eating with chopsticks, involves the cerebellum—a structure located at the base of the brain underneath the cerebral hemispheres. Although its name translates as ‘little brain' in Latin, the cerebellum contains more neurons than all other regions of the mammalian brain combined.

Most cerebellar neurons are granule cells which, although numerous, are simple neurons with an average of only four excitatory inputs, from axons called mossy fibers. These inputs are diverse in nature, originating from virtually every sensory system and from command centers at multiple levels of the motor hierarchy. However, it is unclear whether individual granule cells receive inputs from only a single sensory source or can instead mix modalities. This distinction has important implications for the functional capabilities of the cerebellum.

Now, Huang et al. have addressed this question by mapping, at extremely high resolution, the projections of two pathways onto individual granule cells—one carrying sensory feedback from the upper body (the proprioceptive stream), and another carrying motor-related information (the pontine stream). Using a combination of genetic and viral techniques to label the pathways, Huang and co-workers identified regions where the two types of fiber terminated in close proximity. They then showed that around 40% of proprioceptive granule cells formed junctions, or synapses, with two (or more) fibers carrying different types of input. These cells were not uniformly distributed throughout the cerebellum but tended to occur in ‘hotspots’.

Lastly, Huang et al. examined the type of information conveyed by the sensory and motor-related input streams whenever they contacted a single granule cell. They confirmed that when the sensory input consisted of feedback from the upper body, the motor input consisted of copies of motor commands related to the same body region. Because it is thought that the cerebellum converts sensory information into representations of the body's movements, directing motor commands to these same circuits may allow the cerebellum to predict the consequences of a planned movement prior to, or without, the actual movement occurring.

The work of Huang et al. provides evidence to support the previously controversial idea that granule cells in the mammalian cerebellum receive both sensory and motor-related inputs. The labeling technique that they used could also be deployed to study the inputs to the cerebellum in greater detail, which should yield new insights into the functioning of this part of the brain.

DOI:http://dx.doi.org/10.7554/eLife.00400.002

Large-scale neuroinformatics for in situ hybridization data in the mouse brain

Large-scale databases of the brain are providing content to the neuroscience community through molecular, cellular, functional, and connectomic data. Organization, presentation, and maintenance requirements are substantial given the complexity, diverse modalities, resolution, and scale. In addition to microarrays, magnetic resonance imaging, and RNA sequencing, several in situ hybridization databases have been constructed due to their value in spatially localizing cellular expression. Scalable techniques for processing and presenting these data for maximum utility in viewing and analysis are key for end user value. We describe methods and use cases for the Allen Brain Atlas resources of the adult and developing mouse.

A transcriptomic atlas of mouse neocortical layers

In the mammalian cortex, neurons and glia form a patterned structure across six layers whose complex cytoarchitectonic arrangement is likely to contribute to cognition. We sequenced transcriptomes from layers 1-6b of different areas (primary and secondary) of the adult (postnatal day 56) mouse somatosensory cortex to understand the transcriptional levels and functional repertoires of coding and noncoding loci for cells constituting these layers. A total of 5,835 protein-coding genes and 66 noncoding RNA loci are differentially expressed ("patterned") across the layers, on the basis of a machine-learning model (naive Bayes) approach. Layers 2-6b are each associated with specific functional and disease annotations that provide insights into their biological roles. This new resource (http://genserv.anat.ox.ac.uk/layers) greatly extends currently available resources, such as the Allen Mouse Brain Atlas and microarray data sets, by providing quantitative expression levels, by being genome-wide, by including novel loci, and by identifying candidate alternatively spliced transcripts that are differentially expressed across layers.

Functional genomics of the brain: uncovering networks in the CNS using a systems approach

The central nervous system (CNS) is undoubtedly the most complex human organ system in terms of its diverse functions, cellular composition, and connections. Attempts to capture this diversity experimentally were the foundation on which the field of neurobiology was built. Until now though, techniques were either painstakingly slow or insufficient in capturing this heterogeneity. In addition, the combination of multiple layers of information needed for a complete picture of neuronal diversity from the epigenome to the proteome requires an even more complex compilation of data. In this era of high-throughput genomics though, the ability to isolate and profile neurons and brain tissue has increased tremendously and now requires less effort. Both microarrays and next-generation sequencing have identified neuronal transcriptomes and signaling networks involved in normal brain development, as well as in disease. However, the expertise needed to organize and prioritize the resultant data remains substantial. A combination of supervised organization and unsupervised analyses are needed to fully appreciate the underlying structure in these datasets. When utilized effectively, these analyses have yielded striking insights into a number of fundamental questions in neuroscience on topics ranging from the evolution of the human brain to neuropsychiatric and neurodegenerative disorders. Future studies will incorporate these analyses with behavioral and physiological data from patients to more efficiently move toward personalized therapeutics.

Molecular analysis of neocortical layer structure in the ferret

Molecular markers that distinguish specific layers of rodent neocortex are increasingly employed to study cortical development and the physiology of cortical circuits. The extent to which these markers represent general features of neocortical cell type identity across mammals, however, is unknown. To assess the conservation of layer markers more broadly, we isolated orthologs for 15 layer-enriched genes in the ferret, a carnivore with a large, gyrencephalic brain, and analyzed their patterns of neocortical gene expression. Our major findings are: 1) Many but not all layer markers tested show similar patterns of layer-specific gene expression between mouse and ferret cortex, supporting the view that layer-specific cell type identity is conserved at a molecular level across mammalian superorders; 2) Our panel of deep layer markers (ER81/ETV1, SULF2, PCP4, FEZF2/ZNF312, CACNA1H, KCNN2/SK2, SYT6, FOXP2, CTGF) provides molecular evidence that the specific stratifications of layers 5 and 6 into 5a, 5b, 6a, and 6b are also conserved between rodents and carnivores; 3) Variations in layer-specific gene expression are more pronounced across areas of ferret cortex than between homologous areas of mouse and ferret cortex; 4) This variation of area gene expression was clearest with the superficial layer markers studied (SERPINE2, MDGA1, CUX1, UNC5D, RORB/NR1F2, EAG2/KCNH5). Most dramatically, the layer 4 markers RORB and EAG2 disclosed a molecular sublamination to ferret visual cortex and demonstrated a molecular dissociation among the so-called agranular areas of the neocortex. Our findings establish molecular markers as a powerful complement to cytoarchitecture for neocortical layer and cell-type comparisons across mammals.