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Abstract: Aim: Experimental simulation of near‐future ocean acidification (OA) has been demonstrated to affect growth and development of echinoderm larval stages through energy allocation towards ion and pH compensatory processes. To date, it remains largely unknown how major pH regulatory systems and their energetics are affected by trans‐generational exposure to near‐future acidification levels. Methods: Here, we used the common sea star Asterias rubens in a reciprocal transplant experiment comprising different combinations of OA scenarios, to study trans‐generational plasticity using morphological and physiological endpoints. Results: Acclimation of adults to pHT 7.2 (pCO2 3500 μatm) led to reductions in feeding rates, gonad weight and fecundity. No effects were evident at moderate acidification levels (pHT 7.4; pCO2 2000 μatm). Parental pre‐acclimation to pHT 7.2 for 85 days reduced developmental rates even when larvae were raised under moderate and high pH conditions, whereas pre‐acclimation to pHT 7.4 did not alter offspring performance. Microelectrode measurements and pharmacological inhibitor studies carried out on larval stages demonstrated that maintenance of alkaline gastric pH represents a substantial energy sink under acidified conditions that may contribute up to 30% to the total energy budget. Conclusion: Parental pre‐acclimation to acidification levels that are beyond the pH that is encountered by this population in its natural habitat (eg, pHT 7.2) negatively affected larval size and development, potentially through reduced energy transfer. Maintenance of alkaline gastric pH and reductions in maternal energy reserves probably constitute the main factors for a reduced juvenile recruitment of this marine keystone species under simulated OA. [ABSTRACT FROM AUTHOR]

Large-scale gene expression studies in the mammalian brain offer the promise of understanding the topology, networks, and, ultimately, the function of its complex anatomy, opening previously unexplored avenues in neuroscience. High-throughput methods permit genome-wide searches to discover genes that are uniquely expressed in brain circuits and regions that control behavior. Previous gene expression mapping studies in model organisms have employed in situ hybridization (ISH), a technique that uses labeled nucleic acid probes to bind to specific mRNA transcripts in tissue sections. A key requirement for this effort is the development of fast and robust algorithms for anatomically mapping and quantifying gene expression for ISH. We describe a neuroinformatics pipeline for automatically mapping expression profiles of ISH data and its use to produce the first genomic scale 3D mapping of gene expression in a mammalian brain. The pipeline is fully automated and adaptable to other organisms and tissues. Our automated study of more than 20,000 genes indicates that at least 78.8 percent are expressed at some level in the adult C56BL/6J mouse brain. In addition to providing a platform for genomic scale search, high-resolution images and visualization tools for expression analysis are available at the Allen Brain Atlas web site (http://www.brain-map.org). [ABSTRACT FROM PUBLISHER]

Data in behavioral research is often quantified with event-logging software, generating large data sets containing detailed information about subjects, recipients, and the duration of behaviors. Exploring and analyzing such large data sets can be challenging without tools to visualize behavioral interactions between individuals or transitions between behavioral states, yet software that can adequately visualize complex behavioral data sets is rare. TIBA (The Interactive Behavior Analyzer) is a web application for behavioral data visualization, which provides a series of interactive visualizations, including the temporal occurrences of behavioral events, the number and direction of interactions between individuals, the behavioral transitions and their respective transitional frequencies, as well as the visual and algorithmic comparison of the latter across data sets. It can therefore be applied to visualize behavior across individuals, species, or contexts. Several filtering options (selection of behaviors and individuals) together with options to set node and edge properties (in the network drawings) allow for interactive customization of the output drawings, which can also be downloaded afterwards. TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. The web application and usage instructions are available at tiba.inf.uni-konstanz.de. The source code is publicly available on GitHub: github.com/LSI-UniKonstanz/tiba., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Kraus et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Diffuse hemispheric gliomas, H3G34R/V-mutant (DHG-H3G34), are lethal brain tumors lacking targeted therapies. They originate from interneuronal precursors; however, leveraging this origin for therapeutic insights remains unexplored. Here, we delineate a cellular hierarchy along the interneuron lineage development continuum, revealing that DHG-H3G34 mirror spatial patterns of progenitor streams surrounding interneuron nests, as seen during human brain development. Integrating these findings with genome-wide CRISPR-Cas9 screens identifies genes upregulated in interneuron lineage progenitors as major dependencies. Among these, CDK6 emerges as a targetable vulnerability: DHG-H3G34 tumor cells show enhanced sensitivity to CDK4/6 inhibitors and a CDK6-specific degrader, promoting a shift toward more mature interneuron-like states, reducing tumor growth, and prolonging xenograft survival. Notably, a patient with progressive DHG-H3G34 treated with a CDK4/6 inhibitor achieved 17 months of stable disease. This study underscores interneuronal progenitor-like states, organized in characteristic niches, as a distinct vulnerability in DHG-H3G34, highlighting CDK6 as a promising clinically actionable target., Competing Interests: Declaration of interests M.G.F. is a consultant for Twentyeight-Seven Therapeutics and Blueprint Medicines. M.M. is an SAB member for Cygnal Therapeutics. K.L.L. is founder and equity holder of Travera Inc. and receives consulting fees from BMS, Integragen, and Rarecyte, and research support from Lilly, BMS, and Amgen. D.H. has acted as an advisor for Novartis in relation to ribociclib., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Throughout an organism's life, a multitude of complex and interdependent biological systems transition through biophysical processes that serve as indicators of the underlying biological states. Inferring these latent, unobserved states is a goal of modern biology and neuroscience. However, in many experimental setups, we can at best obtain discrete snapshots of the system at different times and for different individuals. This challenge is particularly relevant in the study of Alzheimer's Disease (AD) progression, where we observe the aggregation of pathology in brain donors, but the underlying disease state is unknown. This paper proposes a biophysically motivated Bayesian framework (B-BIND: Biophysical Bayesian Inference for Neurodegenerative Dynamics), where the disease state is modeled and continuously inferred from observed quantifications of multiple AD pathological proteins. Inspired by biophysical models, we describe pathological burden as an exponential process. The progression of AD is modeled by assigning a latent score, termed pseudotime, to each pathological state, creating a pseudotemporal order of donors based on their pathological burden. We study the theoretical properties of the model using linearization to reveal convergence and identifiability properties. We provide Markov chain Monte Carlo estimation algorithms, illustrating the effectiveness of our approach with multiple simulation studies across various data conditions. Applying this methodology to data from the Seattle Alzheimer's Disease Brain Cell Atlas, we infer the pseudotime ordering of donors. Finally, we analyze the information within each pathological feature to refine the model, focusing on the most informative pathologies. This framework lays the groundwork for continuous pseudotime modeling in the analysis of neurodegenerative diseases.

Animal behaviour is often modelled as networks, where, for example, the nodes are individuals of a group and the edges represent behaviour within this group. Different types of behaviours or behavioural categories are then modelled as different yet connected networks which form a multilayer network. Recent developments show the potential and benefit of multilayer networks for animal behaviour research as well as the potential benefit of stereoscopic 3D immersive environments for the interactive visualisation, exploration and analysis of animal behaviour multilayer networks. However, so far animal behaviour research is mainly supported by libraries or software on 2D desktops. Here, we explore the domain-specific requirements for (stereoscopic) 3D environments. Based on those requirements, we provide a proof of concept to visualise, explore and analyse animal behaviour multilayer networks in immersive environments., (© 2024 the author(s), published by De Gruyter, Berlin/Boston.)

In situ transcriptomic techniques promise a holistic view of tissue organization and cell-cell interactions. There has been a surge of multiplexed RNA in situ mapping techniques but their application to human tissues has been limited due to their large size, general lower tissue quality and high autofluorescence. Here we report DART-FISH, a padlock probe-based technology capable of profiling hundreds to thousands of genes in centimeter-sized human tissue sections. We introduce an omni-cell type cytoplasmic stain that substantially improves the segmentation of cell bodies. Our enzyme-free isothermal decoding procedure allows us to image 121 genes in large sections from the human neocortex in <10 h. We successfully recapitulated the cytoarchitecture of 20 neuronal and non-neuronal subclasses. We further performed in situ mapping of 300 genes on a diseased human kidney, profiled >20 healthy and pathological cell states, and identified diseased niches enriched in transcriptionally altered epithelial cells and myofibroblasts., (© 2024. The Author(s).)

Alzheimer's disease (AD) is an age-associated neurodegenerative disorder characterized by progressive neuronal loss and pathological accumulation of the misfolded proteins amyloid-β and tau 1,2 . Neuroinflammation mediated by microglia and brain-resident macrophages plays a crucial role in AD pathogenesis 1-5 , though the mechanisms by which age, genes, and other risk factors interact remain largely unknown. Somatic mutations accumulate with age and lead to clonal expansion of many cell types, contributing to cancer and many non-cancer diseases 6,7 . Here we studied somatic mutation in normal aged and AD brains by three orthogonal methods and in three independent AD cohorts. Analysis of bulk RNA sequencing data from 866 samples from different brain regions revealed significantly higher (~two-fold) overall burdens of somatic single-nucleotide variants (sSNVs) in AD brains compared to age-matched controls. Molecular-barcoded deep (>1000X) gene panel sequencing of 311 prefrontal cortex samples showed enrichment of sSNVs and somatic insertions and deletions (sIndels) in cancer driver genes in AD brain compared to control, with recurrent, and often multiple, mutations in genes implicated in clonal hematopoiesis (CH) 8,9 . Pathogenic sSNVs were enriched in CSF1R+ microglia of AD brains, and the high proportion of microglia (up to 40%) carrying some sSNVs in cancer driver genes suggests mutation-driven microglial clonal expansion (MiCE). Analysis of single-nucleus RNA sequencing (snRNAseq) from temporal neocortex of 62 additional AD cases and controls exhibited nominally increased mosaic chromosomal alterations (mCAs) associated with CH 10,11 . Microglia carrying mCA showed upregulated pro-inflammatory genes, resembling the transcriptomic features of disease-associated microglia (DAM) in AD. Our results suggest that somatic driver mutations in microglia are common with normal aging but further enriched in AD brain, driving MiCE with inflammatory and DAM signatures. Our findings provide the first insights into microglial clonal dynamics in AD and identify potential new approaches to AD diagnosis and therapy., Competing Interests: C.A.W. is a paid consultant (cash, no equity) to Third Rock Ventures and Flagship Pioneering (cash, no equity) and is on the Clinical Advisory Board (cash and equity) of Maze Therapeutics. No research support is received. These companies did not fund and had no role in the conception or performance of this research project. All other authors have no competing interests to declare.

The mammalian brain is composed of diverse neuron types that play different functional roles. Recent single-cell RNA sequencing approaches have led to a whole brain taxonomy of transcriptomically-defined cell types, yet cell type definitions that include multiple cellular properties can offer additional insights into a neuron's role in brain circuits. While the Patch-seq method can investigate how transcriptomic properties relate to the local morphological and electrophysiological properties of cell types, linking transcriptomic identities to long-range projections is a major unresolved challenge. To address this, we collected coordinated Patch-seq and whole brain morphology data sets of excitatory neurons in mouse visual cortex. From the Patch-seq data, we defined 16 integrated morpho-electric-transcriptomic (MET)-types; in parallel, we reconstructed the complete morphologies of 300 neurons. We unified the two data sets with a multi-step classifier, to integrate cell type assignments and interrogate cross-modality relationships. We find that transcriptomic variations within and across MET-types correspond with morphological and electrophysiological phenotypes. In addition, this variation, along with the anatomical location of the cell, can be used to predict the projection targets of individual neurons. We also shed new light on infragranular cell types and circuits, including cell-type-specific, interhemispheric projections. With this approach, we establish a comprehensive, integrated taxonomy of excitatory neuron types in mouse visual cortex and create a system for integrated, high-dimensional cell type classification that can be extended to the whole brain and potentially across species.

The development and diversity of neuronal subtypes in the human hypothalamus has been insufficiently characterized. To address this, we integrated transcriptomic data from 241,096 cells (126,840 newly generated) in the prenatal and adult human hypothalamus to reveal a temporal trajectory from proliferative stem cell populations to mature hypothalamic cell types. Iterative clustering of the adult neurons identified 108 robust transcriptionally distinct neuronal subtypes representing 10 hypothalamic nuclei. Pseudotime trajectories provided insights into the genes driving formation of these nuclei. Comparisons to single-cell transcriptomic data from the mouse hypothalamus suggested extensive conservation of neuronal subtypes despite certain differences in species-enriched gene expression. The uniqueness of hypothalamic neuronal lineages was examined developmentally by comparing excitatory lineages present in cortex and inhibitory lineages in ganglionic eminence, revealing both distinct and shared drivers of neuronal maturation across the human forebrain. These results provide a comprehensive transcriptomic view of human hypothalamus development through gestation and adulthood at cellular resolution.

Enhanced cognitive function in humans is hypothesized to result from cortical expansion and increased cellular diversity. However, the mechanisms that drive these phenotypic innovations remain poorly understood, in part because of the lack of high-quality cellular resolution data in human and non-human primates. Here, we take advantage of single-cell expression data from the middle temporal gyrus of five primates (human, chimp, gorilla, macaque and marmoset) to identify 57 homologous cell types and generate cell type-specific gene co-expression networks for comparative analysis. Although orthologue expression patterns are generally well conserved, we find 24% of genes with extensive differences between human and non-human primates (3,383 out of 14,131), which are also associated with multiple brain disorders. To assess the functional significance of gene expression differences in an evolutionary context, we evaluate changes in network connectivity across meta-analytic co-expression networks from 19 animals. We find that a subset of these genes has deeply conserved co-expression across all non-human animals, and strongly divergent co-expression relationships in humans (139 out of 3,383, <1% of primate orthologues). Genes with human-specific cellular expression and co-expression profiles (such as NHEJ1, GTF2H2, C2 and BBS5) typically evolve under relaxed selective constraints and may drive rapid evolutionary change in brain function., (© 2023. The Author(s).)

Delineating the gene-regulatory programs underlying complex cell types is fundamental for understanding brain function in health and disease. Here, we comprehensively examined human brain cell epigenomes by probing DNA methylation and chromatin conformation at single-cell resolution in 517 thousand cells (399 thousand neurons and 118 thousand non-neurons) from 46 regions of three adult male brains. We identified 188 cell types and characterized their molecular signatures. Integrative analyses revealed concordant changes in DNA methylation, chromatin accessibility, chromatin organization, and gene expression across cell types, cortical areas, and basal ganglia structures. We further developed single-cell methylation barcodes that reliably predict brain cell types using the methylation status of select genomic sites. This multimodal epigenomic brain cell atlas provides new insights into the complexity of cell-type-specific gene regulation in adult human brains.

Quantifying neuron morphology and distribution at the whole-brain scale is essential to understand the structure and diversity of cell types. It is exceedingly challenging to reuse recent technologies of single-cell labeling and whole-brain imaging to study human brains. We propose adaptive cell tomography (ACTomography), a low-cost, high-throughput, and high-efficacy tomography approach, based on adaptive targeting of individual cells. We established a platform to inject dyes into cortical neurons in surgical tissues of 18 patients with brain tumors or other conditions and one donated fresh postmortem brain. We collected three-dimensional images of 1746 cortical neurons, of which 852 neurons were reconstructed to quantify local dendritic morphology, and mapped to standard atlases. In our data, human neurons are more diverse across brain regions than by subject age or gender. The strong stereotypy within cohorts of brain regions allows generating a statistical tensor field of neuron morphology to characterize anatomical modularity of a human brain.

Recent advances in single-cell transcriptomics have illuminated the diverse neuronal and glial cell types within the human brain. However, the regulatory programs governing cell identity and function remain unclear. Using a single-nucleus assay for transposase-accessible chromatin using sequencing (snATAC-seq), we explored open chromatin landscapes across 1.1 million cells in 42 brain regions from three adults. Integrating this data unveiled 107 distinct cell types and their specific utilization of 544,735 candidate cis-regulatory DNA elements (cCREs) in the human genome. Nearly a third of the cCREs demonstrated conservation and chromatin accessibility in the mouse brain cells. We reveal strong links between specific brain cell types and neuropsychiatric disorders including schizophrenia, bipolar disorder, Alzheimer's disease (AD), and major depression, and have developed deep learning models to predict the regulatory roles of noncoding risk variants in these disorders.

In situ transcriptomic techniques promise a holistic view of tissue organization and cell-cell interactions. Recently there has been a surge of multiplexed RNA in situ techniques but their application to human tissues and clinical biopsies has been limited due to their large size, general lower tissue quality and high background autofluorescence. Here we report DART-FISH, a versatile padlock probe-based technology capable of profiling hundreds to thousands of genes in centimeter-sized human tissue sections at cellular resolution. We introduced an omni-cell type cytoplasmic stain, dubbed RiboSoma that substantially improves the segmentation of cell bodies. We developed a computational decoding-by-deconvolution workflow to extract gene spots even in the presence of optical crowding. Our enzyme-free isothermal decoding procedure allowed us to image 121 genes in a large section from the human neocortex in less than 10 hours, where we successfully recapitulated the cytoarchitecture of 20 neuronal and non-neuronal subclasses. Additionally, we demonstrated the detection of transcripts as short as 461 nucleotides, including neuropeptides and discovered new cortical layer markers. We further performed in situ mapping of 300 genes on a diseased human kidney, profiled >20 healthy and pathological cell states, and identified diseased niches enriched in transcriptionally altered epithelial cells and myofibroblasts.

Characterizing cellular diversity at different levels of biological organization and across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also essential to manipulate cell types in controlled ways and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data-generating centers, data archives, and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain with demonstration of prototype feasibility for human and nonhuman primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed by the BICCN, and to accessing and using these data and extensive resources, including the BRAIN Cell Data Center (BCDC), which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted toward Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: AR is a co-founder and equity holder of Celsius Therapeutics, an equity holder in Immunitas Therapeutics and, until 31 July 2020, was a scientific advisory board member of Thermo Fisher Scientific, Syros Pharmaceuticals, Asimov, and Neogene Therapeutics. From 1 August 2020, AR is an employee of Genentech and has equity in Roche. AR is a named inventor on multiple patents related to single cell and spatial genomics filed by or issued to the Broad Institute., (Copyright: © 2023 Hawrylycz et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Temporal lobe epilepsy is the fourth most common neurological disorder, with about 40% of patients not responding to pharmacological treatment. Increased cellular loss is linked to disease severity and pathological phenotypes such as heightened seizure propensity. While the hippocampus is the target of therapeutic interventions, the impact of the disease at the cellular level remains unclear. Here, we show that hippocampal granule cells change with disease progression as measured in living, resected hippocampal tissue excised from patients with epilepsy. We show that granule cells increase excitability and shorten response latency while also enlarging in cellular volume and spine density. Single-nucleus RNA sequencing combined with simulations ascribes the changes to three conductances: BK, Cav2.2, and Kir2.1. In a network model, we show that these changes related to disease progression bring the circuit into a more excitable state, while reversing them produces a less excitable, "early-disease-like" state., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)