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Just how related are reptilian and mammalian brains? Tosches et al. used single-cell transcriptomics to study turtle, lizard, mouse, and human brain samples. They assessed how the mammalian six-layered cortex might be derived from the reptilian three-layered cortex. Despite a lack of correspondence between layers, mammalian astrocytes and adult neural stem cells shared evolutionary origins. General classes of interneuron types were represented across the evolutionary span, although subtypes were species-specific. Pieces of the much-folded mammalian hippocampus were represented as adjacent fields in the reptile brains. Science , this issue p. 881 Transcriptomics tracks the mix of evolutionary derivation and species-specific elaboration that generates brains from reptiles to mammals. Computations in the mammalian cortex are carried out by glutamatergic and γ-aminobutyric acid–releasing (GABAergic) neurons forming specialized circuits and areas. Here we asked how these neurons and areas evolved in amniotes. We built a gene expression atlas of the pallium of two reptilian species using large-scale single-cell messenger RNA sequencing. The transcriptomic signature of glutamatergic neurons in reptilian cortex suggests that mammalian neocortical layers are made of new cell types generated by diversification of ancestral gene-regulatory programs. By contrast, the diversity of reptilian cortical GABAergic neurons indicates that the interneuron classes known in mammals already existed in the common ancestor of all amniotes.

Morphological neuron classification helps to reveal the functional characteristics and information transmission mechanisms of the nervous system. However, existing methods that use geometric feature extraction or image-based transformation do not consider the 3D properties of neurons, often resulting in a significant loss of valuable morphological information. To address this, we propose a spiking point cloud neural network (SPCNNet) model to improve classification performance, which is capable of directly processing 3D point clouds and applying spike signals to represent morphological features and classify neurons. A neuronal representation strategy is designed to convert original SWC data into 3D point clouds, and encode real-valued point cloud data into spike trains for further processing by the spiking neural networks. Furthermore, the SPCNNet model with spike-based deep learning algorithm learns the spatial features of neurons for classification tasks. In experiment, we analyzed the impact of different SPCNNet parameters on neuron classification performance, including the number of sampled points, simulation duration and batch size. We also conducted ablation experiments to verify the effectiveness of the proposed method. Experimental results demonstrate that our SPCNNet method precisely represents neuronal morphologies and achieves superior performance on the two NeuroMorpho datasets, with classification accuracies of 84.76% and 85.42% respectively. Compared with other mainstream machine learning methods, our spike-driven method is more plausible for solving complex morphological neuron classification problems on NeuMorph dataset.

The Neuron morphology classification is a critical task in neuroscience research, as the morphological features of neurons are closely linked to the functional characteristics of neural circuits. However, traditional classification methods often struggle with the complexity and diversity of neuronal morphologies. To address this, we propose PRT-net, a network architecture specifically designed for neuron morphology classification. By incorporating innovative data augmentation strategies and a contrastive learning framework, PRT-net effectively improves classification performance and model generalization. PRT-net leverages Complex Residual Structures and TreeLSTM to efficiently model the local features and global dependencies of neuron morphology. To address issues of data scarcity and imbalance, we designed a tailored data augmentation strategy that simulates diverse morphological variations, enhancing model robustness. Experiments conducted on three public datasets—BIL, JML, and ACT—demonstrate that PRT-net achieves classification accuracies of 78.45%, 67.11%, and 58.95%, respectively, significantly surpassing existing state-of-the-art methods. Notably, it achieves improvements of 2.9 and 3.3 percentage points on the JML and ACT datasets, respectively. Through the introduction of multiple evaluation metrics, we comprehensively analyze the classification and clustering performance of the model, validating its strong adaptability to complex data distributions. This study provides an efficient solution for neuron morphology classification, advancing research in this domain.

The primary motor cortex (M1) is essential for voluntary fine-motor control and is functionally conserved across mammals 1 . Here, using high-throughput transcriptomic and epigenomic profiling of more than 450,000 single nuclei in humans, marmoset monkeys and mice, we demonstrate a broadly conserved cellular makeup of this region, with similarities that mirror evolutionary distance and are consistent between the transcriptome and epigenome. The core conserved molecular identities of neuronal and non-neuronal cell types allow us to generate a cross-species consensus classification of cell types, and to infer conserved properties of cell types across species. Despite the overall conservation, however, many species-dependent specializations are apparent, including differences in cell-type proportions, gene expression, DNA methylation and chromatin state. Few cell-type marker genes are conserved across species, revealing a short list of candidate genes and regulatory mechanisms that are responsible for conserved features of homologous cell types, such as the GABAergic chandelier cells. This consensus transcriptomic classification allows us to use patch–seq (a combination of whole-cell patch-clamp recordings, RNA sequencing and morphological characterization) to identify corticospinal Betz cells from layer 5 in non-human primates and humans, and to characterize their highly specialized physiology and anatomy. These findings highlight the robust molecular underpinnings of cell-type diversity in M1 across mammals, and point to the genes and regulatory pathways responsible for the functional identity of cell types and their species-specific adaptations. 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.

A mammalian brain is composed of numerous cell types organized in an intricate manner to form functional neural circuits. Single-cell RNA sequencing allows systematic identification of cell types based on their gene expression profiles and has revealed many distinct cell populations in the brain 1 , 2 . Single-cell epigenomic profiling 3 , 4 further provides information on gene-regulatory signatures of different cell types. Understanding how different cell types contribute to brain function, however, requires knowledge of their spatial organization and connectivity, which is not preserved in sequencing-based methods that involve cell dissociation. Here we used a single-cell transcriptome-imaging method, multiplexed error-robust fluorescence in situ hybridization (MERFISH) 5 , to generate a molecularly defined and spatially resolved cell atlas of the mouse primary motor cortex. We profiled approximately 300,000 cells in the mouse primary motor cortex and its adjacent areas, identified 95 neuronal and non-neuronal cell clusters, and revealed a complex spatial map in which not only excitatory but also most inhibitory neuronal clusters adopted laminar organizations. Intratelencephalic neurons formed a largely continuous gradient along the cortical depth axis, in which the gene expression of individual cells correlated with their cortical depths. Furthermore, we integrated MERFISH with retrograde labelling to probe projection targets of neurons of the mouse primary motor cortex and found that their cortical projections formed a complex network in which individual neuronal clusters project to multiple target regions and individual target regions receive inputs from multiple neuronal clusters. As part of the BICCN consortium, the authors used a single-cell transcriptomic imaging method to produce a highly defined atlas of cell types across the mouse primary motor cortex.

Cortical neurons exhibit extreme diversity in gene expression as well as in morphological and electrophysiological properties 1 , 2 . Most existing neural taxonomies are based on either transcriptomic 3 , 4 or morpho-electric 5 , 6 criteria, as it has been technically challenging to study both aspects of neuronal diversity in the same set of cells 7 . Here we used Patch-seq 8 to combine patch-clamp recording, biocytin staining, and single-cell RNA sequencing of more than 1,300 neurons in adult mouse primary motor cortex, providing a morpho-electric annotation of almost all transcriptomically defined neural cell types. We found that, although broad families of transcriptomic types (those expressing Vip , Pvalb , Sst and so on) had distinct and essentially non-overlapping morpho-electric phenotypes, individual transcriptomic types within the same family were not well separated in the morpho-electric space. Instead, there was a continuum of variability in morphology and electrophysiology, with neighbouring transcriptomic cell types showing similar morpho-electric features, often without clear boundaries between them. Our results suggest that neuronal types in the neocortex do not always form discrete entities. Instead, neurons form a hierarchy that consists of distinct non-overlapping branches at the level of families, but can form continuous and correlated transcriptomic and morpho-electrical landscapes within families. Single-cell transcriptomic, morphological and electrophysiological characteristics are combined to classify more than 1,300 neurons from mouse motor cortex.

Autonomous regulation of the intestine requires the combined activity of functionally distinct neurons of the enteric nervous system (ENS). However, the variety of enteric neuron types and how they emerge during development remain largely unknown. Here, we define a molecular taxonomy of 12 enteric neuron classes within the myenteric plexus of the mouse small intestine using single-cell RNA sequencing. We present cell–cell communication features and histochemical markers for motor neurons, sensory neurons and interneurons, together with transgenic tools for class-specific targeting. Transcriptome analysis of the embryonic ENS uncovers a novel principle of neuronal diversification, where two neuron classes arise through a binary neurogenic branching and all other identities emerge through subsequent postmitotic differentiation. We identify generic and class-specific transcriptional regulators and functionally connect Pbx3 to a postmitotic fate transition. Our results offer a conceptual and molecular resource for dissecting ENS circuits and predicting key regulators for directed differentiation of distinct enteric neuron classes. Imaging and transcriptomic approaches to investigate mouse enteric nervous system diversity and development reveal a new classification of intestinal myenteric neurons and a novel principle of neuronal diversification by postmitotic transitions.

The basic plan of the retina is conserved across vertebrates, yet species differ profoundly in their visual needs 1 . Retinal cell types may have evolved to accommodate these varied needs, but this has not been systematically studied. Here we generated and integrated single-cell transcriptomic atlases of the retina from 17 species: humans, two non-human primates, four rodents, three ungulates, opossum, ferret, tree shrew, a bird, a reptile, a teleost fish and a lamprey. We found high molecular conservation of the six retinal cell classes (photoreceptors, horizontal cells, bipolar cells, amacrine cells, retinal ganglion cells (RGCs) and Müller glia), with transcriptomic variation across species related to evolutionary distance. Major subclasses were also conserved, whereas variation among cell types within classes or subclasses was more pronounced. However, an integrative analysis revealed that numerous cell types are shared across species, based on conserved gene expression programmes that are likely to trace back to an early ancestral vertebrate. The degree of variation among cell types increased from the outer retina (photoreceptors) to the inner retina (RGCs), suggesting that evolution acts preferentially to shape the retinal output. Finally, we identified rodent orthologues of midget RGCs, which comprise more than 80% of RGCs in the human retina, subserve high-acuity vision, and were previously believed to be restricted to primates 2 . By contrast, the mouse orthologues have large receptive fields and comprise around 2% of mouse RGCs. Projections of both primate and mouse orthologous types are overrepresented in the thalamus, which supplies the primary visual cortex. We suggest that midget RGCs are not primate innovations, but are descendants of evolutionarily ancient types that decreased in size and increased in number as primates evolved, thereby facilitating high visual acuity and increased cortical processing of visual information. Single-cell and single-nucleus transcriptomic analysis of retina from 17 vertebrate species shows high conservation of retinal cell types and suggests that midget retinal ganglion cells in primates evolved from orthologous cells in ancestral mammals.

Alzheimer’s disease is the leading cause of dementia worldwide, but the cellular pathways that underlie its pathological progression across brain regions remain poorly understood 1 – 3 . Here we report a single-cell transcriptomic atlas of six different brain regions in the aged human brain, covering 1.3 million cells from 283 post-mortem human brain samples across 48 individuals with and without Alzheimer’s disease. We identify 76 cell types, including region-specific subtypes of astrocytes and excitatory neurons and an inhibitory interneuron population unique to the thalamus and distinct from canonical inhibitory subclasses. We identify vulnerable populations of excitatory and inhibitory neurons that are depleted in specific brain regions in Alzheimer’s disease, and provide evidence that the Reelin signalling pathway is involved in modulating the vulnerability of these neurons. We develop a scalable method for discovering gene modules, which we use to identify cell-type-specific and region-specific modules that are altered in Alzheimer’s disease and to annotate transcriptomic differences associated with diverse pathological variables. We identify an astrocyte program that is associated with cognitive resilience to Alzheimer’s disease pathology, tying choline metabolism and polyamine biosynthesis in astrocytes to preserved cognitive function late in life. Together, our study develops a regional atlas of the ageing human brain and provides insights into cellular vulnerability, response and resilience to Alzheimer’s disease pathology. A regional atlas of the ageing human brain—spanning six distinct anatomical regions from individuals with and without Alzheimer’s dementia—provides insights into cellular vulnerability, response and resilience to Alzheimer’s disease pathology

Connections between neurons can be mapped by acquiring and analysing electron microscopic brain images. In recent years, this approach has been applied to chunks of brains to reconstruct local connectivity maps that are highly informative 1 – 6 , but nevertheless inadequate for understanding brain function more globally. Here we present a neuronal wiring diagram of a whole brain containing 5 × 10 7 chemical synapses 7 between 139,255 neurons reconstructed from an adult female Drosophila melanogaster 8 , 9 . The resource also incorporates annotations of cell classes and types, nerves, hemilineages and predictions of neurotransmitter identities 10 – 12 . Data products are available for download, programmatic access and interactive browsing and have been made interoperable with other fly data resources. We derive a projectome—a map of projections between regions—from the connectome and report on tracing of synaptic pathways and the analysis of information flow from inputs (sensory and ascending neurons) to outputs (motor, endocrine and descending neurons) across both hemispheres and between the central brain and the optic lobes. Tracing from a subset of photoreceptors to descending motor pathways illustrates how structure can uncover putative circuit mechanisms underlying sensorimotor behaviours. The technologies and open ecosystem reported here set the stage for future large-scale connectome projects in other species. FlyWire presents a neuronal wiring diagram of the whole fly brain with annotations for cell types, classes, nerves, hemilineages and predicted neurotransmitters, with data products and an open ecosystem to facilitate exploration and browsing.