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How have complex brains evolved from simple circuits? Here we investigated brain region evolution at cell-type resolution in the cerebellar nuclei, the output structures of the cerebellum. Using single-nucleus RNA sequencing in mice, chickens, and humans, as well as STARmap spatial transcriptomic analysis and whole-central nervous system projection tracing, we identified a conserved cell-type set containing two region-specific excitatory neuron classes and three region-invariant inhibitory neuron classes. This set constitutes an archetypal cerebellar nucleus that was repeatedly duplicated to form new regions. The excitatory cell class that preferentially funnels information to lateral frontal cortices in mice becomes predominant in the massively expanded human lateral nucleus. Our data suggest a model of brain region evolution by duplication and divergence of entire cell-type sets.

Which neural circuits undergo synaptic changes when an animal learns? Although it is widely accepted that changes in synaptic strength underlie many forms of learning and memory, it remains challenging to connect changes in synaptic strength at specific neural pathways to specific behaviors and memories. Here we introduce SYNPLA (synaptic proximity ligation assay), a synapse-specific, high-throughput, and potentially brain-wide method capable of detecting circuit-specific learning-induced synaptic plasticity.

Author(s): Esther Klingler [sup.1] , Ugo Tomasello [sup.1] , Julien Prados [sup.2] , Justus M. Kebschull [sup.3] [sup.6] , Alessandro Contestabile [sup.1] , Gregorio L. Galiñanes [sup.1] , Sabine Fièvre [...], Interconnectivity between neocortical areas is critical for sensory integration and sensorimotor transformations.sup.1-6. These functions are mediated by heterogeneous inter-areal cortical projection neurons (ICPN), which send axon branches across cortical areas as well as to subcortical targets.sup.7-9. Although ICPN are anatomically diverse.sup.10-14, they are molecularly homogeneous.sup.15, and how the diversity of their anatomical and functional features emerge during development remains largely unknown. Here we address this question by linking the connectome and transcriptome in developing single ICPN of the mouse neocortex using a combination of multiplexed analysis of projections by sequencing.sup.16,17 (MAPseq, to identify single-neuron axonal projections) and single-cell RNA sequencing (to identify corresponding gene expression). Focusing on neurons of the primary somatosensory cortex (S1), we reveal a protracted unfolding of the molecular and functional differentiation of motor cortex-projecting ([Formula omitted]) ICPN compared with secondary somatosensory cortex-projecting ([Formula omitted]) ICPN. We identify SOX11 as a temporally differentially expressed transcription factor in [Formula omitted] versus [Formula omitted] ICPN. Postnatal manipulation of SOX11 expression in S1 impaired sensorimotor connectivity and disrupted selective exploratory behaviours in mice. Together, our results reveal that within a single cortical area, different subtypes of ICPN have distinct postnatal paces of molecular differentiation, which are subsequently reflected in distinct circuit connectivities and functions. Dynamic differences in the expression levels of a largely generic set of genes, rather than fundamental differences in the identity of developmental genetic programs, may thus account for the emergence of intra-type diversity in cortical neurons. Combined analysis of the connectome and transcriptome in the mouse cortex indicates that dynamic differences in expression levels of largely generic sets of genes regulate differential targeting within neuronal subtypes.

Author(s): Yunyun Han [1, 2, 3]; Justus M. Kebschull [4, 5]; Robert A. A. Campbell [3]; Devon Cowan [3]; Fabia Imhof [3]; Anthony M. Zador (corresponding author) [5]; Thomas D. [...], Neocortical areas communicate through extensive axonal projections, but the logic of information transfer remains poorly understood, because the projections of individual neurons have not been systematically characterized. It is not known whether individual neurons send projections only to single cortical areas or distribute signals across multiple targets. Here we determine the projection patterns of 591 individual neurons in the mouse primary visual cortex using whole-brain fluorescence-based axonal tracing and high-throughput DNA sequencing of genetically barcoded neurons (MAPseq). Projections were highly diverse and divergent, collectively targeting at least 18 cortical and subcortical areas. Most neurons targeted multiple cortical areas, often in non-random combinations, suggesting that sub-classes of intracortical projection neurons exist. Our results indicate that the dominant mode of intracortical information transfer is not based on one neuronone target area mapping. Instead, signals carried by individual cortical neurons are shared across subsets of target areas, and thus concurrently contribute to multiple functional pathways.

Which neural circuits undergo synaptic changes when an animal learns? Although it is widely accepted that changes in synaptic strength underlie many forms of learning and memory, it remains challenging to connect changes in synaptic strength at specific neural pathways to specific behaviors and memories. Here we introduce SYNPLA (synaptic proximity ligation assay), a synapsespecific, high-throughput, and potentially brain-wide method capable of detecting circuit-specific learning-induced synaptic plasticity. [ABSTRACT FROM AUTHOR]

Spatial transcriptomics (ST) technologies enable high throughput gene expression characterization within thin tissue sections. However, comparing spatial observations across sections, samples, and technologies remains challenging. To address this challenge, we developed STalign to align ST datasets in a manner that accounts for partially matched tissue sections and other local non-linear distortions using diffeomorphic metric mapping. We apply STalign to align ST datasets within and across technologies as well as to align ST datasets to a 3D common coordinate framework. We show that STalign achieves high gene expression and cell-type correspondence across matched spatial locations that is significantly improved over landmark-based affine alignments. Applying STalign to align ST datasets of the mouse brain to the 3D common coordinate framework from the Allen Brain Atlas, we highlight how STalign can be used to lift over brain region annotations and enable the interrogation of compositional heterogeneity across anatomical structures. STalign is available as an open-source Python toolkit at https://github.com/JEFworks-Lab/STalign and as supplementary software with additional documentation and tutorials available at https://jef.works/STalign., Competing Interests: Competing financial interests MIM is a founder of AnatomyWorks. This arrangement has been reviewed and approved by the Johns Hopkins University in accordance with its conflict-of-interest policies. The other authors declare that they have no competing financial interests.