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The largest survey of gene expression ever performed in the adult human brain reveals highly stereotyped transcriptional patterning across individuals. The most stably patterned genes are enriched for neuronal annotations, disease associations, drug targets and correspond to resting state functional networks.

Laser microdissection and microarrays are used to assess 900 precise subdivisions of the brains from three healthy men with 60,000 gene expression probes; the resulting atlas allows comparisons between humans and other animals, and will facilitate studies of human neurological and psychiatric diseases.

The affected cellular populations during Alzheimer’s disease progression remain understudied. Here the authors use a cohort of 84 donors, quantitative neuropathology and multimodal datasets from the BRAIN Initiative. Their pseudoprogression analysis revealed two disease phases.

A high-resolution gene expression atlas of prenatal and postnatal brain development of rhesus monkey charts global transcriptional dynamics in relation to brain maturation, while comparative analysis reveals human-specific gene trajectories; candidate risk genes associated with human neurodevelopmental disorders tend to be co-expressed in disease-specific patterns in the developing monkey neocortex.

 A transcriptomic cell-type atlas of the whole adult mouse brain with ~5,300 clusters built from single-cell and spatial transcriptomic datasets with more than eight million cells reveals remarkable cell type diversity across the brain and unique cell type characteristics of different brain regions. 

A spatially resolved transcriptional atlas of the mid-gestational developing human brain has been created using laser-capture microdissection and microarray technology, providing a comprehensive reference resource which also enables new hypotheses about the nature of human brain evolution and the origins of neurodevelopmental disorders.

The authors use integrative deconvolution of gene expression data to reveal core transcriptional features of CNS cell classes in humans, and identify cell-class-specific transcriptional differences in disease, among CNS regions, and between species.

The expression of each of the roughly 22,000 genes of the mouse genome has been mapped, at cellular resolution, across all major structures of the mouse brain, revealing that 80% of all genes appear to be expressed in the brain.

Here the authors analyzed 3.7 petavoxels of 3D imaging data from 204 mouse brains, aiming to comprehensively characterize diverse morphological and modular patterns conserved across six spatial scales of mouse brain anatomy, ranging from the whole-brain scale to synaptic levels.

Combined patch clamp recording, biocytin staining and single-cell RNA-sequencing of human neurocortical neurons shows an expansion of glutamatergic neuron types relative to mouse that characterizes the greater complexity of the human neocortex.

Single-cell transcriptomics of more than 20,000 cells from two functionally distinct areas of the mouse neocortex identifies 133 transcriptomic types, and provides a foundation for understanding the diversity of cortical cell types.

An examination of motor cortex in humans, marmosets and mice reveals a generally conserved cellular makeup that is likely to extend to many mammalian species, but also differences in gene expression, DNA methylation and chromatin state that lead to species-dependent specializations.

Multi-modal analysis is used to generate a 3D atlas of the upper limb area of the mouse primary motor cortex, providing a framework for future studies of motor control circuitry.

RNA-sequencing analysis of cells in the human cortex enabled identification of diverse cell types, revealing well-conserved architecture and homologous cell types as well as extensive differences when compared with datasets covering the analogous region of the mouse brain.

mBrainAligner is a cross-modal registration platform for whole mouse brains imaged with different modalities. In addition, a fluorescence micro-optical sectioning tomography-based mouse brain atlas has been generated.

Reconstructing the full shape of neurons is a major informatics challenge as it requires handling huge whole-brain imaging datasets. Here the authors present an open-source virtual reality annotation system for precise and efficient data production of neuronal shapes reconstructed from whole brains.

This Resource presents a method to define connectivity types of neurons based on a spatially registered large database containing more than 20,000 neuronal reconstructions. A brain connectivity map is also generated using such connectivity features.

The next step after sequencing a genome is to figure out how the cell actually uses it as an instruction manual. A large international consortium has examined 1% of the genome for what part is transcribed, where proteins are bound, what the chromatin structure looks like, and how the sequence compares to that of other organisms.

This paper discusses how experimental and computational studies integrating multimodal data, such as RNA expression, connectivity and neural activity, are advancing our understanding of the architecture, mechanisms and function of cortical circuits.

Sparse labelling and whole-brain imaging are used to reconstruct and classify brain-wide complete morphologies of 1,741 individual neurons in the mouse brain, revealing a dependence on both brain region and transcriptomic profile.

The authors describe an integrated atlas of the diverse cell types in the mouse primary motor cortex.

Neocortical circuits exhibit diverse cell types that can be difficult to build into computational models. Here the authors employ a genetic algorithm-based parameter optimization to generate multi-compartment Hodgkin-Huxley models for diverse cell types in the Allen Cell Types Database.

Simplified neuron models, such as generalized leaky integrate-and-fire (GLIF) models, are extensively used in network modeling. Here the authors systematically generate and compare GLIF models of varying complexity for their ability to classify cell types in the Allen Cell Types Database and faithfully reproduce spike trains.

Navigating the complex structure of the brain poses a challenge to neuroscientists. Here, the authors have trained an AI (DeepSlice) that can automatically register brain images with speed and accuracy, thus simplifying this process.

The BRAIN Initiative Cell Census Network has constructed a multimodal cell census and atlas of the mammalian primary motor cortex in a landmark effort towards understanding brain cell-type diversity, neural circuit organization and brain function.

The Seattle Alzheimer’s Disease Brain Cell Atlas (SEA-AD) is a multifaceted open-data resource that is designed to identify cellular and molecular pathologies that underlie Alzheimer’s disease. Integrating neuropathology, single-cell and spatial genomics, and longitudinal clinical metadata, SEA-AD is a unique resource for studying the pathogenesis of Alzheimer’s disease and related dementias.

Large three-dimensional images are commonly generated through biological experimentation. Here the authors report software tools for exploration of three-dimensional images along with applications to assist in imaging, microsurgery, visualization and annotation of large image data sets.

In mouse, an axonal connectivity map showing the wiring patterns across the entire brain has been created using an EGFP-expressing adeno-associated virus tracing technique, providing the first such whole-brain map for a vertebrate species.

Gouwens et al. established a morpho-electrical taxonomy of cell types for the mouse visual cortex via unsupervised clustering analysis of multiple quantitative features from 1,938 neurons available online at the Allen Cell Types Database.

Gene expression patterns have been associated with functional activity patterns in the brain. Here the authors determine how gene expression patterns in the human brain supports brain phenotypes obtained from resting state fMRI imaging, identifying brain regions and genes relevant to autism.

This paper describes an integrated approach for neuroimaging data acquisition, analysis and sharing. Building on methodological advances from the Human Connectome Project (HCP) and elsewhere, the HCP-style paradigm applies to new and existing data sets that meet core requirements and may accelerate progress in understanding the brain in health and disease.

The authors applied a correlation-based metric, ‘differential stability’ (DS), to assess reproducibility of gene expression patterning across individual brains, revealing mesoscale genetic organization. The highest DS genes were enriched for brain-related biological annotations, disease associations and drug targets, and their anatomical expression pattern correlated with resting state functional connectivity.

This resource describes a collection of neurons from a variety of light microscopy-based datasets, which can serve as a gold standard for testing automated tracing algorithms, as shown by comparison of the performance of 35 algorithms.

Automated tracing algorithms can extract neuronal morphology from fluorescent or brightfield images. UltraTracer scales up the capability of existing tracing algorithms to handle datasets of ever-increasing size.

Aligning multimodal data for cell type research is a challenging problem in neuroscience. Coupled autoencoders, a deep neural network-based methodology, can be used to effectively address this task.

This resource article describes a bioinformatical tool that, accessing an extensive gene expression database, allows the definition and identification of new brain structures based on gene expression patterns.

This Resource paper describes a set of five new reporter mice, derived from Rosa26, driving Cre-dependent strong and ubiquitous expression of fluorescent proteins. In particular, the new mice show clear and specific expression patterns in the adult brain. The mice are available through Jackson Laboratories, and growing expression datasets can be accessed at http://transgenicmouse.alleninstitute.org/.

Mammalian cortex comprises a variety of cells, but the extent of this cellular diversity is unknown. The authors defined cell types in the primary visual cortex of adult mice using single-cell transcriptomics. This revealed 49 cell types, including 23 GABAergic, 19 glutamatergic and 7 non-neuronal types.

To understand the function of cortical circuits, it is necessary to catalog their cellular diversity. Past attempts to do so using anatomical, physiological or molecular features of cortical cells have not resulted in a unified taxonomy of neuronal or glial cell types, partly due to limited data. Single-cell transcriptomics is enabling, for the first time, systematic high-throughput measurements of cortical cells and generation of datasets that hold the promise of being complete, accurate and permanent. Statistical analyses of these data reveal clusters that often correspond to cell types previously defined by morphological or physiological criteria and that appear conserved across cortical areas and species. To capitalize on these new methods, we propose the adoption of a transcriptome-based taxonomy of cell types for mammalian neocortex. This classification should be hierarchical and use a standardized nomenclature. It should be based on a probabilistic definition of a cell type and incorporate data from different approaches, developmental stages and species. A community-based classification and data aggregation model, such as a knowledge graph, could provide a common foundation for the study of cortical circuits. This community-based classification, nomenclature and data aggregation could serve as an example for cell type atlases in other parts of the body.