What is a cell type and how to define it?

Diversification of dentate gyrus granule cell subtypes is regulated by Nrg1 nuclear back-signaling

Neuronal heterogeneity is a defining feature of the developing mammalian brain, but the mechanisms regulating the diversification of closely related cell types remain elusive. Here, we investigated granule cell (GC) subtype composition in the dentate gyrus (DG) and the influence of a psychosis-associated V321L mutation in Neuregulin1 (Nrg1). Using morphoelectric characterization, single-nucleus gene expression, and chromatin accessibility profiling, we identified distinctions between typical GCs and a rare subtype known as semilunar granule cells (SGCs). We found that the V321L mutation, which disrupts Nrg1 nuclear back-signaling, results in overabundance of SGC-like cells. Pseudotime analyses suggest a GC-to-SGC transition potential, supported by the accessibility of SGC-enriched genes in non-SGCs. In WT mice, SGC-like gene expression increases during adolescence, coinciding with reduced Nrg1 back-signaling capacity. These results suggest that intact Nrg1 nuclear signaling represses SGC-like fate and that its developmental or pathological loss may permit acquisition of this fate. Our findings reveal a novel role of Nrg1 in maintaining DG cell-type composition and suggest that disrupted subtype regulation may contribute to disease-associated changes in the DG.

Future projections for mammalian whole-brain simulations based on technological trends in related fields

Large-scale brain simulation allows us to understand the interaction of vast numbers of neurons having nonlinear dynamics to help understand the information processing mechanisms in the brain. The scale of brain simulations continues to rise as computer performance improves exponentially. However, a simulation of the human whole brain has not yet been achieved as of 2024 due to insufficient computational performance and brain measurement data. This paper examines technological trends in supercomputers, cell type classification, connectomics, and large-scale activity measurements relevant to whole-brain simulation. Based on these trends, we attempt to predict the feasible timeframe for mammalian whole-brain simulation. Our estimates suggest that mouse whole-brain simulation at the cellular level could be realized around 2034, marmoset around 2044, and human likely later than 2044.

A suite of enhancer AAVs and transgenic mouse lines for genetic access to cortical cell types

The mammalian cortex is comprised of cells classified into types according to shared properties. Defining the contribution of each cell type to the processes guided by the cortex is essential for understanding its function in health and disease. We use transcriptomic and epigenomic cortical cell-type taxonomies from mouse and human to define marker genes and putative enhancers and create a large toolkit of transgenic lines and enhancer adeno-associated viruses (AAVs) for selective targeting of cortical cell populations. We report creation and evaluation of fifteen transgenic driver lines, two reporter lines, and >1,000 different enhancer AAV vectors covering most subclasses of cortical cells. The tools reported here have been made publicly available, and along with the scaled process of tool creation, evaluation, and modification, they will enable diverse experimental strategies toward understanding mammalian cortex and brain function.

Machine learning identification of enhancers in the rhesus macaque genome

Nonhuman primate (NHP) neuroanatomy and cognitive complexity make NHPs ideal models to study human neurobiology and disease. However, NHP circuit-function investigations are limited by the availability of molecular reagents that are effective in NHPs. This calls for reagent development approaches that prioritize NHPs. Therefore, we derived enhancers from the NHP genome. We defined cell-type-specific open chromatin regions (OCRs) in single-cell data from rhesus macaques. We trained machine-learning models to rank those OCRs according to their potential as cell-type-specific enhancers for cells in the dorsolateral prefrontal cortex (DLPFC). We packaged the top-ranked layer-3-pyramidal-neuron enhancer into AAV and injected it into the macaque DLPFC. Expression was mostly restricted to layers 2 and 3 and confirmed with light-driven activation of channelrhodopsin. These results provide a crucial tool for studying the causal functions of DLPFC and provide a roadmap for optimized gene delivery in primates.

A dynamic and multimodal framework to define microglial states

The widespread use of single-cell RNA sequencing has generated numerous purportedly distinct and novel subsets of microglia. Here, we challenge this fragmented paradigm by proposing that microglia exist along a continuum rather than as discrete entities. We identify a methodological over-reliance on computational clustering algorithms as the fundamental issue, with arbitrary cluster numbers being interpreted as biological reality. Evidence suggests that the observed transcriptional diversity stems from a combination of microglial plasticity and technical noise, resulting in terminology describing largely overlapping cellular states. We introduce a continuous model of microglial states, where cell positioning along the continuum is determined by biological aging and cell-specific molecular contexts. The model accommodates the dynamic nature of microglia. We advocate for a parsimonious approach toward classification and terminology that acknowledges the continuous spectrum of microglial states, toward a robust framework for understanding these essential immune cells of the CNS.

How radial glia progenitor lineages generate cell-type diversity in the developing cerebral cortex

The cerebral cortex is arguably the most complex organ in humans. The cortical architecture is characterized by a remarkable diversity of neuronal and glial cell types that make up its neuronal circuits. Following a precise temporally ordered program, radial glia progenitor (RGP) cells generate all cortical excitatory projection neurons and glial cell-types. Cortical excitatory projection neurons are produced either directly or via intermediate progenitors, through indirect neurogenesis. How the extensive cortical cell-type diversity is generated during cortex development remains, however, a fundamental open question. How do RGPs quantitatively and qualitatively generate all the neocortical neurons? How does direct and indirect neurogenesis contribute to the establishment of neuronal and lineage heterogeneity? Whether RGPs represent a homogeneous and/or multipotent progenitor population, or if RGPs consist of heterogeneous groups is currently also not known. In this review, we will summarize the latest findings that contributed to a deeper insight into the above key questions.

Allometry of cell types in planarians by single-cell transcriptomics

Allometry explores the relationship between an organism's body size and its various components, offering insights into ecology, physiology, metabolism, and disease. The cell is the basic unit of biological systems, and yet the study of cell-type allometry remains relatively unexplored. Single-cell RNA sequencing (scRNA-seq) provides a promising tool for investigating cell-type allometry. Planarians, capable of growing and degrowing following allometric scaling rules, serve as an excellent model for these studies. We used scRNA-seq to examine cell-type allometry in asexual planarians of different sizes, revealing that they consist of the same basic cell types but in varying proportions. Notably, the gut basal cells are the most responsive to changes in size, suggesting a role in energy storage. We capture the regulated gene modules of distinct cell types in response to body size. This research sheds light on the molecular and cellular aspects of cell-type allometry in planarians and underscores the utility of scRNA-seq in these investigations.

Toward a probabilistic definition of neural cell types

A classical view of cell type relies on a definite set of stable properties that are critical for brain functions. Single-cell technologies led to an extensive multimodal characterization of nervous systems and perhaps achieved one of Santiago Ramón y Cajal's dreams: to unveil a comprehensive view of the brain composition. While global analyses of brain structures highlight a degree of mesoscale stereotypy, a finer-scale resolution of brain composition shows significant variance in essential neural cellular phenotypes, including morphology, gene expression, electrophysiology, and connectivity. This highlights the need for novel conceptualization of the definition of a neural "cell type." The challenge of modern neural classification is thus to integrate various distinct cellular properties into a unifying descriptor.

Single-cell genomics meets systems neuroscience: Insights from mapping the brain circuitry of stress

Responses to external and internal dangers is essential for survival and homeostatic regulation. Hypothalamic corticotropin-releasing hormone neurons (CRHNs) play a pivotal role in regulating neuroendocrine responses to fear and stress. In recent years, the application of neurogenetic tools, such as fiber photometry, chemogenetics and optogenetics, have provided new insights into the dynamic neuronal responses of CRHNs during stressful events, offering new perspectives into their functional significance in mediating neurobehavioural responses to stress. Transsynaptic viral tracers have facilitated the comprehensive mapping of neuronal inputs to CRHNs. Furthermore, the development and application of innovative single-cell genomic tools combined with viral tracing have begun to pave the way for a deeper understanding of the transcriptional profiles of neural circuit components, enabling molecular-anatomical circuit mapping. Here, I will discuss how these systems neuroscience approaches and novel single-cell genomic methods are advancing the molecular and functional mapping of stress neurocircuits, their associated challenges and future directions.

A standardized nomenclature for the rods and cones of the vertebrate retina

Vertebrate photoreceptors have been studied for well over a century, but a fixed nomenclature for referring to orthologous cell types across diverse species has been lacking. Instead, photoreceptors have been variably-and often confusingly-named according to morphology, presence/absence of 'rhodopsin', spectral sensitivity, chromophore usage, and/or the gene family of the opsin(s) they express. Here, we propose a unified nomenclature for vertebrate rods and cones that aligns with the naming systems of other retinal cell classes and that is based on the photoreceptor type's putative evolutionary history. This classification is informed by the functional, anatomical, developmental, and molecular identities of the neuron as a whole, including the expression of deeply conserved transcription factors required for development. The proposed names will be applicable across all vertebrates and indicative of the widest possible range of properties, including their postsynaptic wiring, and hence will allude to their common and species-specific roles in vision. Furthermore, the naming system is open-ended to accommodate the future discovery of as-yet unknown photoreceptor types.

Advances and applications in single-cell and spatial genomics

The applications of single-cell and spatial technologies in recent times have revolutionized the present understanding of cellular states and the cellular heterogeneity inherent in complex biological systems. These advancements offer unprecedented resolution in the examination of the functional genomics of individual cells and their spatial context within tissues. In this review, we have comprehensively discussed the historical development and recent progress in the field of single-cell and spatial genomics. We have reviewed the breakthroughs in single-cell multi-omics technologies, spatial genomics methods, and the computational strategies employed toward the analyses of single-cell atlas data. Furthermore, we have highlighted the advances made in constructing cellular atlases and their clinical applications, particularly in the context of disease. Finally, we have discussed the emerging trends, challenges, and opportunities in this rapidly evolving field.

Cell type census in cerebral cortex reveals species-specific brain function and connectivity

The cerebral cortex contains a diverse array of functional regions that are conserved across species, such as primary somatosensory and primary visual cortex. However, despite this conservation, these regions exhibit different connectivity and functions in various species. It is hypothesized that these differences arise from distinct cell types within the conserved regions. To uncover these species-specific differences, investigating gene expression at the cellular level can reveal unique cell types. In this review, we highlight recent research on the molecular mechanisms that govern the formation of specific cell types in the rodent primary somatosensory cortex. Furthermore, we explore how these conserved molecular mechanisms are observed across different brain regions in various species. These findings offer new insights into the diversity and evolutionary background of neural circuit formation in the mammalian cortex.

A Multi-Scale Neuron Morphometry Dataset from Peta-voxel Mouse Whole-Brain Images

Neuron morphology and sub-neuronal patterns offer vital insights into cell typing and the structural organization of brain networks. The community-collaborative BRAIN Initiative Cell Census Network (BICCN) project has yielded a vast amount of whole-brain imaging data. However, reconstructing multi-scale neuron morphometry at a whole-brain scale requires not only the integration of diverse hardware devices, tools, and algorithms but also a dedicated production workflow. To address these challenges, we developed a cloud-based, collaborative platform capable of handling peta-scale imaging data. Using this platform, we generated the largest multi-scale morphometry dataset from hundreds of sparsely labeled mouse brains. The morphometry dataset comprises 182,497 annotated cell bodies, 15,441 locally traced morphologies, and 1,876 fully reconstructed morphologies. We also identified sub-neuronal arborizations for both axons and dendrites, along with the primary axonal tracts connecting them. In addition, we identified 2.63 million putative boutons. All morphometric data were registered to the Allen Common Coordinate Framework (CCF) atlas. The morphometry dataset has proven to be an invaluable resource for whole-brain cross-scale morphological studies in mouse.

Segregated basal ganglia output pathways correspond to genetically divergent neuronal subclasses

The basal ganglia control multiple sensorimotor behaviors through anatomically segregated and topographically organized subcircuits with outputs to specific downstream circuits. However, it is unclear how the anatomical organization of basal ganglia output circuits relates to the molecular diversity of cell types. Here, we demonstrate that the major output nucleus of the basal ganglia, the substantia nigra pars reticulata (SNr), is comprised of transcriptomically distinct subclasses that reflect its distinct progenitor lineages. We show that these subclasses are topographically organized within the SNr, project to distinct targets in the midbrain and hindbrain, and receive inputs from different striatal subregions. Finally, we show that these mouse subclasses are also identifiable in human SNr neurons, suggesting that the genetic organization of the SNr is evolutionarily conserved. These findings provide a unifying logic for how the developmental specification of diverse SNr neurons relates to the anatomical organization of basal ganglia circuits controlling specialized downstream brain regions.

Heterogeneity-preserving discriminative feature selection for disease-specific subtype discovery

Disease-specific subtype identification can deepen our understanding of disease progression and pave the way for personalized therapies, given the complexity of disease heterogeneity. Large-scale transcriptomic, proteomic, and imaging datasets create opportunities for discovering subtypes but also pose challenges due to their high dimensionality. To mitigate this, many feature selection methods focus on selecting features that distinguish known diseases or cell states, yet often miss features that preserve heterogeneity and reveal new subtypes. To overcome this gap, we develop Preserving Heterogeneity (PHet), a statistical methodology that employs iterative subsampling and differential analysis of interquartile range, in conjunction with Fisher's method, to identify a small set of features that enhance subtype clustering quality. Here, we show that this method can maintain sample heterogeneity while distinguishing known disease/cell states, with a tendency to outperform previous differential expression and outlier-based methods, indicating its potential to advance our understanding of disease mechanisms and cell differentiation.

Hierarchy of Chaotic Dynamics in Random Modular Networks

We introduce a model of randomly connected neural populations and study its dynamics by means of the dynamical mean-field theory and simulations. Our analysis uncovers a rich phase diagram, featuring high- and low-dimensional chaotic phases, separated by a crossover region characterized by low values of the maximal Lyapunov exponent and participation ratio dimension, but with high values of the Lyapunov dimension that change significantly across the region. Counterintuitively, chaos can be attenuated by either adding noise to strongly modular connectivity or by introducing modularity into random connectivity. Extending the model to include a multilevel, hierarchical connectivity reveals that a loose balance between activities across levels drives the system towards the edge of chaos.

Mapping Cell Identity from scRNA-seq: A primer on computational methods

Single cell (sc) technologies mark a conceptual and methodological breakthrough in our way to study cells, the base units of life. Thanks to these technological developments, large-scale initiatives are currently ongoing aimed at mapping of all the cell types in the human body, with the ambitious aim to gain a cell-level resolution of physiological development and disease. Since its broad applicability and ease of interpretation scRNA-seq is probably the most common sc-based application. This assay uses high throughput RNA sequencing to capture gene expression profiles at the sc-level. Subsequently, under the assumption that differences in transcriptional programs correspond to distinct cellular identities, ad-hoc computational methods are used to infer cell types from gene expression patterns. A wide array of computational methods were developed for this task. However, depending on the underlying algorithmic approach and associated computational requirements, each method might have a specific range of application, with implications that are not always clear to the end user. Here we will provide a concise overview on state-of-the-art computational methods for cell identity annotation in scRNA-seq, tailored for new users and non-computational scientists. To this end, we classify existing tools in five main categories, and discuss their key strengths, limitations and range of application.

Spotiphy enables single-cell spatial whole transcriptomics across an entire section

Spatial transcriptomics (ST) has advanced our understanding of tissue regionalization by enabling the visualization of gene expression within whole-tissue sections, but current approaches remain plagued by the challenge of achieving single-cell resolution without sacrificing whole-genome coverage. Here we present Spotiphy (spot imager with pseudo-single-cell-resolution histology), a computational toolkit that transforms sequencing-based ST data into single-cell-resolved whole-transcriptome images. Spotiphy delivers the most precise cellular proportions in extensive benchmarking evaluations. Spotiphy-derived inferred single-cell profiles reveal astrocyte and disease-associated microglia regional specifications in Alzheimer's disease and healthy mouse brains. Spotiphy identifies multiple spatial domains and alterations in tumor-tumor microenvironment interactions in human breast ST data. Spotiphy bridges the information gap and enables visualization of cell localization and transcriptomic profiles throughout entire sections, offering highly informative outputs and an innovative spatial analysis pipeline for exploring complex biological systems.

Transcriptomic and spatial GABAergic neuron subtypes in zona incerta mediate distinct innate behaviors

Understanding the anatomical connection and behaviors of transcriptomic neuron subtypes is critical to delineating cell type-specific functions in the brain. Here we integrated single-nucleus transcriptomic sequencing, in vivo circuit mapping, optogenetic and chemogenetic approaches to dissect the molecular identity and function of heterogeneous GABAergic neuron populations in the zona incerta (ZI) in mice, a region involved in modulating various behaviors. By microdissecting ZI for transcriptomic and spatial gene expression analyses, our results revealed two non-overlapping Ecel1- and Pde11a-expressing GABAergic neurons with dominant expression in the rostral and medial zona incerta (ZIrEcel1 and ZImPde11a), respectively. The GABAergic projection from ZIrEcel1 to periaqueductal gray mediates self-grooming, while the GABAergic projection from ZImPde11a to the oral part of pontine reticular formation promotes transition from sleep to wakefulness. Together, our results revealed the molecular markers, spatial organization and specific neuronal circuits of two discrete GABAergic projection neuron populations in segregated subregions of the ZI that mediate distinct innate behaviors, advancing our understanding of the functional organization of the brain.

Bridge protein-mediated viral targeting of cells expressing endogenous μ-opioid G protein-coupled receptors in the mouse and monkey brain

Targeting specific cell types is essential for understanding their functional roles in the brain. Although genetic approaches enable cell-type-specific targeting in animals, their application to higher mammalian species, such as nonhuman primates, remains challenging. Here, we developed a nontransgenic method using bridge proteins to direct viral vectors to cells endogenously expressing μ-opioid receptors (MORs), a G protein-coupled receptor. The bridge protein comprises the avian viral receptor TVB, the MOR ligand β-endorphin (βed), and an interdomain linker. EnvB-enveloped viruses bind to the TVB component, followed by the interaction of βed with MORs, triggering viral infection in MOR-expressing cells. We optimized the secretion signals, domain arrangements, and interdomain linkers of the bridge proteins to maximize viral targeting efficiency and specificity. Alternative configurations incorporating different ligands and viral receptors also induced viral infection in MOR-expressing cells. The optimized βed-f2-TVB bridge protein with EnvB-pseudotyped lentiviruses induced infection in MOR-expressing cells in the striatum of mice and monkeys. An intersectional approach combining βed-f2-TVB with a neuron-specific promoter refined cell-type specificity. This study establishes the foundation for the rational bridge protein design and the feasibility of targeting G protein-coupled receptors beyond tyrosine kinase receptors, thereby expanding targetable cell types in the brain and throughout the body.

The adult ovary at single cell resolution: an expert review

The ovaries play a crucial role in both the endocrine health and fertility of adult women. The fundamental functional units of the ovaries, primordial follicles, form during fetal development and constitute the ovarian reserve. Ovaries age prematurely in comparison to other organs, with the quality of oocytes declining steeply prior to the entire reserve becoming depleted, usually around age 50. Despite the pivotal role of ovaries in women's overall health, surprisingly little is known about the mechanisms controlling follicle dormancy, growth activation, atresia, maturation, and oocyte quality. Understanding ovarian function on a cellular and molecular level is increasingly important for several reasons. First, the global trend of women delaying childbirth creates a growing population of patients wishing to conceive when the quality and quantity of their oocytes are already critically low. Second, conditions affecting the ovaries, such as polycystic ovary syndrome and endometriosis, are widespread, yet diagnosis and treatment still present challenges. Lastly, advancements in cancer therapies have increased the number of cancer survivors who contend with late complications affecting fertility and hormonal balance. Clearly, a better understanding of diseases, aging, and toxicity in ovaries is needed for the development of novel treatments, preventive therapies, and safer pharmaceuticals. Human ovaries are notoriously difficult to obtain for research due to their pivotal role in women's health, and the highly heterogeneous distribution of follicles within the tissue combined with monthly cyclical changes present further challenges. Single-cell profiling techniques are creating new opportunities, enabling the characterization of small amounts of tissue with unprecedented resolution. Here, we review the literature on single-cell characterization of adult, reproductive-age ovaries. The majority of the 46 identified studies have focused on oocytes discarded during assisted reproduction, with only a handful focusing on ovarian tissue. The overwhelming focus of the studies is on follicles and oocytes, although the somatic cell niche in the ovary undoubtedly plays an important role in endocrine function and follicle biology. Altogether, the studies reveal unexpected diversity and heterogeneity among ovarian somatic and germ cells, highlighting the prevailing knowledge gaps in basic ovarian biology. As the most common outcome for a follicle is atresia, it is possible that part of the cell diversity relates to the biology of follicles destined to degenerate. The absence of spatial coordinates in single-cell studies further complicates the interpretation of the roles and significance of the various reported cell clusters. Accomplishing a representative ovarian single-cell atlas will require merging these studies. However, direct comparisons are challenging due to nonuniform nomenclature, differing tissue sources, varying meta-data reporting, and lack of gold standards in technical approaches. Although these reports establish a single-cell draft of adult-fertile age human ovaries, more detailed metadata and better quality reporting will be essential for the development of a robust ovarian cell atlas in health and disease.

Comparative single-cell transcriptomic atlases of drosophilid brains suggest glial evolution during ecological adaptation

To explore how brains change upon species evolution, we generated single-cell transcriptomic atlases of the central brains of three closely related but ecologically distinct drosophilids: the generalists Drosophila melanogaster and Drosophila simulans, and the noni fruit specialist Drosophila sechellia. The global cellular composition of these species' brains is well-conserved, but we predicted a few cell types with different frequencies, notably perineurial glia of the blood-brain barrier, which we validate in vivo. Gene expression analysis revealed that distinct cell types evolve at different rates and patterns, with glial populations exhibiting the greatest divergence between species. Compared to the D. melanogaster brain, cellular composition and gene expression patterns are more divergent in D. sechellia than in D. simulans-despite their similar phylogenetic distance from D. melanogaster-indicating that the specialization of D. sechellia is reflected in the structure and function of its brain. Expression changes in D. sechellia include several metabolic signaling genes, suggestive of adaptations to its novel source of nutrition. Additional single-cell transcriptomic analysis on D. sechellia revealed genes and cell types responsive to dietary supplement with noni, pointing to glia as sites for both physiological and genetic adaptation to this fruit. Our atlases represent the first comparative datasets for "whole" central brains and provide a comprehensive foundation for studying the evolvability of nervous systems in a well-defined phylogenetic and ecological framework.

Fascial Manual Medicine: The Concept of Fascial Continuum

Fascial tissue ubiquitously pervades the body system, becoming the target of many disciplines that use manual techniques for patient treatment. It is a much-debated topic as there is currently no univocal definition among different authors. Due to the non-discontinuity of the fascia, we can speak of a fascial continuum; this principle is the basis of the osteopathic perspective. This vision, which seems banal, is not always applied in manual fascial medicine, where, often, it is conditioned by a reductionist (layers) and mechanistic (compartments) approach, forgetting that the body is not a machine but an organism. This continuity teaches that manual treatment does not only reverberate in the area where the operator's hands rest but creates a series of local and systemic adaptations. This narrative review revises the concept of the fascial continuum by highlighting that fascia is a tissue system (different tissues working in harmony), multi-organ (capable of behaving like an organ), whose macroscopic functional expression (movement) and microscopic (with cellular adaptations) derives from a nanoscopic coherence (electromagnetic behaviors). This means that the body acts as a unit, and makes the manual approach never local but always systemic. The aim of the article is to highlight the fact that the fascial continuum is a single biological entity (solid and fluid), and that manual fascial medicine does not approach a single segment, but the entire person.

Inhibitory specificity from a connectomic census of mouse visual cortex

Mammalian cortex features a vast diversity of neuronal cell types, each with characteristic anatomical, molecular and functional properties1. Synaptic connectivity shapes how each cell type participates in the cortical circuit, but mapping connectivity rules at the resolution of distinct cell types remains difficult. Here we used millimetre-scale volumetric electron microscopy2 to investigate the connectivity of all inhibitory neurons across a densely segmented neuronal population of 1,352 cells spanning all layers of mouse visual cortex, producing a wiring diagram of inhibition with more than 70,000 synapses. Inspired by classical neuroanatomy, we classified inhibitory neurons based on targeting of dendritic compartments and developed an excitatory neuron classification based on dendritic reconstructions with whole-cell maps of synaptic input. Single-cell connectivity showed a class of disinhibitory specialist that targets basket cells. Analysis of inhibitory connectivity onto excitatory neurons found widespread specificity, with many interneurons exhibiting differential targeting of spatially intermingled subpopulations. Inhibitory targeting was organized into 'motif groups', diverse sets of cells that collectively target both perisomatic and dendritic compartments of the same excitatory targets. Collectively, our analysis identified new organizing principles for cortical inhibition and will serve as a foundation for linking contemporary multimodal neuronal atlases with the cortical wiring diagram.

Cell differentiation controls iron assimilation in the choanoflagellate Salpingoeca rosetta

Marine microeukaryotes have evolved diverse cellular features that link their life histories to surrounding environments. How those dynamic life histories intersect with the ecological functions of microeukaryotes remains a frontier to understanding their roles in critical biogeochemical cycles. Choanoflagellates, phagotrophs that cycle nutrients through filter feeding, provide models to explore this intersection, for many choanoflagellate species transition between life history stages by differentiating into distinct cell types. Here, we report that cell differentiation in the marine choanoflagellate Salpingoeca rosetta endows one of its cell types with the ability to utilize insoluble ferric colloids. These colloids are a predominant form of iron in marine environments and are largely inaccessible to cell-walled microbes. Therefore, choanoflagellates and other phagotrophic eukaryotes may serve critical ecological roles by cycling this essential nutrient through iron utilization pathways. We found that S. rosetta can utilize these ferric colloids via the expression of a cytochrome b561 iron reductase (cytb561a). This gene and its mammalian ortholog, the duodenal cytochrome b561 (DCYTB) that reduces ferric cations for uptake in gut epithelia, belong to a subgroup of cytochrome b561 proteins with distinct biochemical features that contribute to iron reduction activity. Overall, our findings provide insight into the ecological roles choanoflagellates perform and inform reconstructions of early animal evolution where functionally distinct cell types became an integrated whole at the origin of animal multicellularity.

IMPORTANCE

This study examines how cell differentiation in a choanoflagellate enables the uptake of iron, an essential nutrient. Choanoflagellates are widespread, aquatic microeukaryotes that are the closest living relatives of animals. Similar to their animal relatives, we found that the model choanoflagellate, S. rosetta, divides metabolic functions between distinct cell types. One cell type uses an iron reductase to acquire ferric colloids, a key source of iron in the ocean. We also observed that S. rosetta has three variants of this reductase, each with distinct biochemical properties that likely lead to differences in how they reduce iron. These reductases are variably distributed across ocean regions, suggesting a role for choanoflagellates in cycling iron in marine environments.

Gradients of Cell Recognition Molecules Wire Visuomotor Transformation

Converting sensory information into motor commands is fundamental to most of our actions 1,2 . In Drosophila , visuomotor transformations are mediated by Visual Projection Neurons (VPNs) 3,4 . These neurons encode object location and motion to drive directional behaviors through a synaptic gradient mechanism 5 . However, the molecular origins of such graded connectivity remain unknown. We addressed this question in a VPN cell type called LPLC2 6 , which integrates looming motion and transforms it into an escape response through two separate dorsoventral synaptic gradients at its inputs and outputs. We identified two corresponding dorsoventral expression gradients of cell recognition molecules within the LPLC2 population that regulate this synaptic connectivity. Dpr13 determines synaptic outputs of LPLC2 axons by interacting with its binding partner, DIP-ε, expressed in the Giant Fiber - a neuron that mediates escape 7 . Similarly, Beat-VI regulates synaptic inputs onto LPLC2 dendrites by interacting with Side-II expressed in upstream motion-detecting neurons. Behavioral, physiological, and molecular experiments demonstrate that these coordinated molecular gradients regulate synaptic connectivity, enabling the accurate transformation of visual features into motor commands. As continuous variation in gene expression within a neuronal type is also observed in the mammalian brain 8 , graded expression of cell recognition molecules may represent a common mechanism underlying synaptic specificity.

Distinct gene regulatory dynamics drive skeletogenic cell fate convergence during vertebrate embryogenesis

Cell type repertoires have expanded extensively in metazoan animals, with some clade-specific cells being crucial to evolutionary success. A prime example are the skeletogenic cells of vertebrates. Depending on anatomical location, these cells originate from three different precursor lineages, yet they converge developmentally towards similar cellular phenotypes. Furthermore, their 'skeletogenic competency' arose at distinct evolutionary timepoints, thus questioning to what extent different skeletal body parts rely on truly homologous cell types. Here, we investigate how lineage-specific molecular properties are integrated at the gene regulatory level, to allow for skeletogenic cell fate convergence. Using single-cell functional genomics, we find that distinct transcription factor profiles are inherited from the three precursor states and incorporated at lineage-specific enhancer elements. This lineage-specific regulatory logic suggests that these regionalized skeletogenic cells are distinct cell types, rendering them amenable to individualized selection, to define adaptive morphologies and biomaterial properties in different parts of the vertebrate skeleton.

All the single cells: Single-cell transcriptomics/epigenomics experimental design and analysis considerations for glial biologists

Single-cell transcriptomics, epigenomics, and other 'omics applied at single-cell resolution can significantly advance hypotheses and understanding of glial biology. Omics technologies are revealing a large and growing number of new glial cell subtypes, defined by their gene expression profile. These subtypes have significant implications for understanding glial cell function, cell-cell communications, and glia-specific changes between homeostasis and conditions such as neurological disease. For many, the training in how to analyze, interpret, and understand these large datasets has been through reading and understanding literature from other fields like biostatistics. Here, we provide a primer for glial biologists on experimental design and analysis of single-cell RNA-seq datasets. Our goal is to further the understanding of why decisions are made about datasets and to enhance biologists' ability to interpret and critique their work and the work of others. We review the steps involved in single-cell analysis with a focus on decision points and particular notes for glia. The goal of this primer is to ensure that single-cell 'omics experiments continue to advance glial biology in a rigorous and replicable way.

Organizational Principles of the Primate Cerebral Cortex at the Single-Cell Level

The primate cerebral cortex, the major organ for cognition, consists of an immense number of neurons. However, the organizational principles governing these neurons remain unclear. By accessing the single-cell spatial transcriptome of over 25 million neuron cells across the entire macaque cortex, it is discovered that the distribution of neurons within cortical layers is highly non-random. Strikingly, over three-quarters of these neurons are located in distinct neuronal clusters. Within these clusters, different cell types tend to collaborate rather than function independently. Typically, excitatory neuron clusters mainly consist of excitatory-excitatory combinations, while inhibitory clusters primarily contain excitatory-inhibitory combinations. Both cluster types have roughly equal numbers of neurons in each layer. Importantly, most excitatory and inhibitory neuron clusters form spatial partnerships, indicating a balanced local neuronal network and correlating with specific functional regions. These organizational principles are conserved across mouse cortical regions. These findings suggest that different brain regions of the cortex may exhibit similar mechanisms at the neuronal population level.

Fast, flexible analysis of differences in cellular composition with crumblr

Changes in cell type composition play an important role in human health and disease. Recent advances in single-cell technology have enabled the measurement of cell type composition at increasing cell lineage resolution across large cohorts of individuals. Yet this raises new challenges for statistical analysis of these compositional data to identify changes in cell type frequency. We introduce crumblr (DiseaseNeurogenomics.github.io/crumblr), a scalable statistical method for analyzing count ratio data using precision-weighted linear mixed models incorporating random effects for complex study designs. Uniquely, crumblr performs statistical testing at multiple levels of the cell lineage hierarchy using a multivariate approach to increase power over tests of one cell type. In simulations, crumblr increases power compared to existing methods while controlling the false positive rate. We demonstrate the application of crumblr to published single-cell RNA-seq datasets for aging, tuberculosis infection in T cells, bone metastases from prostate cancer, and SARS-CoV-2 infection.

Cluster replicability in single-cell and single-nucleus atlases of the mouse brain

Single-cell RNA sequencing has advanced our understanding of cellular heterogeneity. Ensuring the replicability of identified cell clusters across studies is essential for determining their biological robustness. We assess the replicability of cell clusters identified in two large mouse brain atlases, one generated using single-cell RNA sequencing and the other with single nuclei. Both profile over 4 million cells and group them into over 5000 clusters. Using transcriptome-wide neighbor voting, we identify 2009 reciprocally matched cluster pairs with consistent spatial localization and coordinated gene expression, which were also observed in datasets from multiple species. Reciprocal clusters are enriched in the cerebellum, where lower diversity aids replicability, while the hypothalamus's heterogeneity limits agreement. Distinguishing close clusters is much more challenging than differentiating a cluster from most others, especially when using marker genes. By incorporating replicability data, we provide a stronger foundation for investigating the newly identified clusters and their biological meaning.

Consequences of training data composition for deep learning models in single-cell biology

Foundation models for single-cell transcriptomics have the potential to augment (or replace) purpose-built tools for a variety of common analyses, especially when data are sparse. Recent work with large language models has shown that training data composition greatly shapes performance; however, to date, single-cell foundation models have ignored this aspect, opting instead to train on the largest possible corpus. We systematically investigate the consequences of training dataset composition on the behavior of deep learning models of single-cell transcriptomics, focusing on human hematopoiesis as a tractable model system and including cells from adult and developing tissues, disease states, and perturbation atlases. We find that (1) these models generalize poorly to unseen cell types, (2) adding malignant cells to a healthy cell training corpus does not necessarily improve modeling of unseen malignant cells, and (3) including an embryonic stem cell differentiation atlas during training improves performance on out-of-distribution tasks. Our results emphasize the importance of diverse training data and suggest strategies to optimize future single-cell foundation models.

Divergence in cellular markers observed in single-cell transcriptomics datasets between cultured primary trabecular meshwork cells and tissues

The trabecular meshwork within the outflow apparatus is critical in maintaining intraocular pressure homeostasis. In vitro studies employing primary cell cultures of the human trabecular meshwork (hTM) have conventionally served as surrogates for investigating the pathobiology of TM dysfunction. Despite its abundant use, translation of outcomes from in vitro studies to ex vivo and/or in vivo studies remains a challenge. Given the cell heterogeneity, performing single-cell RNA sequencing comparing primary hTM cell cultures to hTM tissue may provide important insights on cellular identity and translatability, as such an approach has not been reported before. In this study, we assembled a total of 14 primary hTM in vitro samples across passages 1-4, including 4 samples from individuals diagnosed with glaucoma. This dataset offers a comprehensive transcriptomic resource of primary hTM in vitro scRNA-seq data to study global changes in gene expression in comparison to cells in tissue in situ. We have performed extensive preprocessing and quality control, allowing the research community to access and utilize this public resource.

A Quantitative Approach to Mapping Mitochondrial Specialization and Plasticity

Mitochondria are a diverse family of organelles that specialize to accomplish complimentary functions 1-3. All mitochondria share general features, but not all mitochondria are created equal 4.Here we develop a quantitative pipeline to define the degree of molecular specialization among different mitochondrial phenotypes - or mitotypes. By distilling hundreds of validated mitochondrial genes/proteins into 149 biologically interpretable MitoPathway scores (MitoCarta 3.0 5) the simple mitotyping pipeline allows investigators to quantify and interpret mitochondrial diversity and plasticity from transcriptomics or proteomics data across a variety of natural and experimental contexts. We show that mouse and human multi-organ mitotypes segregate along two main axes of mitochondrial specialization, contrasting anabolic (liver) and catabolic (brain) tissues. In cultured primary human fibroblasts exhibiting robust time-dependent and treatment-induced metabolic plasticity 6-8, we demonstrate how the mitotype of a given cell type recalibrates i) over time in parallel with hallmarks of aging, and ii) in response to genetic, pharmacological, and metabolic perturbations. Investigators can now use MitotypeExplorer.org and the associated code to visualize, quantify and interpret the multivariate space of mitochondrial biology.

A Quantitative Approach to Mapping Mitochondrial Specialization and Plasticity

Mitochondria are a diverse family of organelles that specialize to accomplish complimentary functions1-3. All mitochondria share general features, but not all mitochondria are created equal4. Here we develop a quantitative pipeline to define the degree of molecular specialization among different mitochondrial phenotypes - or mitotypes. By distilling hundreds of validated mitochondrial genes/proteins into 149 biologically interpretable MitoPathway scores (MitoCarta 3.05) the simple mitotyping pipeline allows investigators to quantify and interpret mitochondrial diversity and plasticity from transcriptomics or proteomics data across a variety of natural and experimental contexts. We show that mouse and human multi-organ mitotypes segregate along two main axes of mitochondrial specialization, contrasting anabolic (liver) and catabolic (brain) tissues. In cultured primary human fibroblasts exhibiting robust time-dependent and treatment-induced metabolic plasticity6-8, we demonstrate how the mitotype of a given cell type recalibrates i) over time in parallel with hallmarks of aging, and ii) in response to genetic, pharmacological, and metabolic perturbations. Investigators can now use MitotypeExplorer.org and the associated code to visualize, quantify and interpret the multivariate space of mitochondrial biology.

The promising role of proteomes and metabolomes in defining the single-cell landscapes of plants

The plant community has a strong track record of RNA sequencing technology deployment, which combined with the recent advent of spatial platforms (e.g. 10× genomics) has resulted in an explosion of single-cell and nuclei datasets that can be positioned in an in situ context within tissues (e.g. a cell atlas). In the genomics era, application of proteomics technologies in the plant sciences has always trailed behind that of RNA sequencing technologies, largely due in part to upfront cost, ease-of-use, and access to expertise. Conversely, the use of early analytical tools for characterizing small molecules (metabolites) from plant systems predates nucleic acid sequencing and proteomics analysis, as the search for plant-based natural products has played a significant role in improving human health throughout history. As the plant sciences field now aims to fully define cell states, cell-specific regulatory networks, metabolic asymmetry and phenotypes, the measurement of proteins and metabolites at the single-cell level will be paramount. As a result of these efforts, the plant community will unlock exciting opportunities to accelerate discovery and drive toward meaningful translational outcomes.

Mapping the topography of spatial gene expression with interpretable deep learning

Spatially resolved transcriptomics technologies provide high-throughput measurements of gene expression in a tissue slice, but the sparsity of these data complicates analysis of spatial gene expression patterns. We address this issue by deriving a topographic map of a tissue slice-analogous to a map of elevation in a landscape-using a quantity called the isodepth. Contours of constant isodepths enclose domains with distinct cell type composition, while gradients of the isodepth indicate spatial directions of maximum change in expression. We develop GASTON (gradient analysis of spatial transcriptomics organization with neural networks), an unsupervised and interpretable deep learning algorithm that simultaneously learns the isodepth, spatial gradients and piecewise linear expression functions that model both continuous gradients and discontinuous variation in gene expression. We show that GASTON accurately identifies spatial domains and marker genes across several tissues, gradients of neuronal differentiation and firing in the brain, and gradients of metabolism and immune activity in the tumor microenvironment.

Transcriptomic neuron types vary topographically in function and morphology

Neuronal phenotypic traits such as morphology, connectivity and function are dictated, to a large extent, by a specific combination of differentially expressed genes. Clusters of neurons in transcriptomic space correspond to distinct cell types and in some cases-for example, Caenorhabditis elegans neurons1 and retinal ganglion cells2-4-have been shown to share morphology and function. The zebrafish optic tectum is composed of a spatial array of neurons that transforms visual inputs into motor outputs. Although the visuotopic map is continuous, subregions of the tectum are functionally specialized5,6. Here, to uncover the cell-type architecture of the tectum, we transcriptionally profiled its neurons, revealing more than 60 cell types that are organized in distinct anatomical layers. We measured the visual responses of thousands of tectal neurons by two-photon calcium imaging and matched them with their transcriptional profiles. Furthermore, we characterized the morphologies of transcriptionally identified neurons using specific transgenic lines. Notably, we found that neurons that are transcriptionally similar can diverge in shape, connectivity and visual responses. Incorporating the spatial coordinates of neurons within the tectal volume revealed functionally and morphologically defined anatomical subclusters within individual transcriptomic clusters. Our findings demonstrate that extrinsic, position-dependent factors expand the phenotypic repertoire of genetically similar neurons.

Fast, flexible analysis of differences in cellular composition with crumblr

Changes in cell type composition play an important role in human health and disease. Recent advances in single-cell technology have enabled the measurement of cell type composition at increasing cell lineage resolution across large cohorts of individuals. Yet this raises new challenges for statistical analysis of these compositional data to identify changes in cell type frequency. We introduce crumblr (DiseaseNeurogenomics.github.io/crumblr), a scalable statistical method for analyzing count ratio data using precision-weighted linear mixed models incorporating random effects for complex study designs. Uniquely, crumblr performs statistical testing at multiple levels of the cell lineage hierarchy using a multivariate approach to increase power over tests of one cell type. In simulations, crumblr increases power compared to existing methods while controlling the false positive rate. We demonstrate the application of crumblr to published single-cell RNA-seq datasets for aging, tuberculosis infection in T cells, bone metastases from prostate cancer, and SARS-CoV-2 infection.

Dissecting the cellular architecture and genetic circuitry of the soybean seed

Seeds are complex structures composed of three regions, embryo, endosperm, and seed coat, with each further divided into subregions that consist of tissues, cell layers, and cell types. Although the seed is well characterized anatomically, much less is known about the genetic circuitry that dictates its spatial complexity. To address this issue, we profiled mRNAs from anatomically distinct seed subregions at several developmental stages. Analyses of these profiles showed that all subregions express similar diverse gene numbers and that the small gene numbers expressed subregion specifically provide information about the biological processes that occur in these seed compartments. In parallel, we profiled RNAs in individual nuclei and identified nuclei clusters representing distinct cell identities. Integrating single-nucleus RNA and subregion mRNA transcriptomes allowed most cell identities to be assigned to specific subregions and cell types and/or cell states. The number of cell identities exceeds the number of anatomically distinguishable cell types, emphasizing the spatial complexity of seeds. We defined gene coexpression networks that underlie distinct biological processes during seed development. We showed that network distribution among subregions and cell identities is highly variable. Some networks operate in single subregions and/or cell identities, and many coexpression networks operate in multiple subregions and/or cell identities. We also showed that single subregions and cell identities possess several networks. Together, our studies provide unique insights into the biological processes and genetic circuitry that underlie the spatial landscape of the seed.

Benchmarking cross-species single-cell RNA-seq data integration methods: towards a cell type tree of life

Cross-species single-cell RNA-seq data hold immense potential for unraveling cell type evolution and transferring knowledge between well-explored and less-studied species. However, challenges arise from interspecific genetic variation, batch effects stemming from experimental discrepancies and inherent individual biological differences. Here, we benchmarked nine data-integration methods across 20 species, encompassing 4.7 million cells, spanning eight phyla and the entire animal taxonomic hierarchy. Our evaluation reveals notable differences between the methods in removing batch effects and preserving biological variance across taxonomic distances. Methods that effectively leverage gene sequence information capture underlying biological variances, while generative model-based approaches excel in batch effect removal. SATURN demonstrates robust performance across diverse taxonomic levels, from cross-genus to cross-phylum, emphasizing its versatility. SAMap excels in integrating species beyond the cross-family level, especially for atlas-level cross-species integration, while scGen shines within or below the cross-class hierarchy. As a result, our analysis offers recommendations and guidelines for selecting suitable integration methods, enhancing cross-species single-cell RNA-seq analyses and advancing algorithm development.

Accurate quantification of nascent and mature RNAs from single-cell and single-nucleus RNA-seq

In single-cell and single-nucleus RNA sequencing (RNA-seq), the coexistence of nascent (unprocessed) and mature (processed) messenger RNA (mRNA) poses challenges in accurate read mapping and the interpretation of count matrices. The traditional transcriptome reference, defining the "region of interest" in bulk RNA-seq, restricts its focus to mature mRNA transcripts. This restriction leads to two problems: reads originating outside of the "region of interest" are prone to mismapping within this region, and additionally, such external reads cannot be matched to specific transcript targets. Expanding the "region of interest" to encompass both nascent and mature mRNA transcript targets provides a more comprehensive framework for RNA-seq analysis. Here, we introduce the concept of distinguishing flanking k-mers (DFKs) to improve mapping of sequencing reads. We have developed an algorithm to identify DFKs, which serve as a sophisticated "background filter", enhancing the accuracy of mRNA quantification. This dual strategy of an expanded region of interest coupled with the use of DFKs enhances the precision in quantifying both mature and nascent mRNA molecules, as well as in delineating reads of ambiguous status.

Cross-species single-nucleus analysis reveals the potential role of whole-genome duplication in the evolution of maize flower development

BACKGROUND

The evolution and development of flowers are biologically essential and of broad interest. Maize and sorghum have similar morphologies and phylogeny while harboring different inflorescence architecture. The difference in flower architecture between these two species is likely due to spatiotemporal gene expression regulation, and they are a good model for researching the evolution of flower development.

RESULTS

In this study, we generated single nucleus and spatial RNA-seq data for maize ear, tassel, and sorghum inflorescence. By combining single nucleus and spatial transcriptome, we can track the spatial expression of single nucleus cluster marker genes and map single nucleus clusters to spatial positions. This ability provides great power to annotate the single nucleus clusters. Combining the cell cluster resolved transcriptome comparison with genome alignment, our analysis suggested that maize ear and tassel inflorescence diversity is associated with the maize-specific whole genome duplication. Taking sorghum as the outgroup, it is likely that the loss of gene expression profiling contributes to the inflorescence diversity between tassel and ear, resulting in the unisexual flower architecture of maize. The sequence of highly expressed genes in the tassel is more conserved than the highly expressed genes in the ear.

CONCLUSION

This study provides a high-resolution atlas of gene activity during inflorescence development and helps to unravel the potential evolution associated with the differentiation of the ear and tassel in maize.

The cell-type underpinnings of the human functional cortical connectome

The functional properties of the human brain arise, in part, from the vast assortment of cell types that pattern the cerebral cortex. The cortical sheet can be broadly divided into distinct networks, which are embedded into processing streams, or gradients, that extend from unimodal systems through higher-order association territories. Here using microarray data from the Allen Human Brain Atlas and single-nucleus RNA-sequencing data from multiple cortical territories, we demonstrate that cell-type distributions are spatially coupled to the functional organization of cortex, as estimated through functional magnetic resonance imaging. Differentially enriched cells follow the spatial topography of both functional gradients and associated large-scale networks. Distinct cellular fingerprints were evident across networks, and a classifier trained on postmortem cell-type distributions was able to predict the functional network allegiance of cortical tissue samples. These data indicate that the in vivo organization of the cortical sheet is reflected in the spatial variability of its cellular composition.

From Inhibition to Exciting Science

I am lucky to be part of the hippocampus story, if not from the beginning but at least in its formative decades. Being part of this community is a true privilege. As I try to illustrate below, science is made by scientists. My fierce competitors over the years have become my close friends. I hope the field of hippocampus research will stay that way forever.

Homebuilt Imaging-Based Spatial Transcriptomics: Tertiary Lymphoid Structures as a Case Example

Spatial transcriptomics methods provide insight into the cellular heterogeneity and spatial architecture of complex, multicellular systems. Combining molecular and spatial information provides important clues to study tissue architecture in development and disease. Here, we present a comprehensive do-it-yourself (DIY) guide to perform such experiments at reduced costs leveraging open-source approaches. This guide spans the entire life cycle of a project, from its initial definition to experimental choices, wet lab approaches, instrumentation, and analysis. As a concrete example, we focus on tertiary lymphoid structures (TLS), which we use to develop typical questions that can be addressed by these approaches.

Cell types or cell states? An investigation of adrenergic and mesenchymal cell phenotypes in neuroblastoma

Neuroblastoma exhibits two cellular phenotypes: therapy-sensitive adrenergic (ADRN) and therapy-resistant mesenchymal (MES). To understand treatment response, it is important to elucidate how these phenotypes impact the dynamics of cancer cell populations and whether they represent distinct cell types or dynamic cell states. Here, we use an integrated experimental and mathematical modeling approach. We experimentally measure the fractions of ADRN and MES phenotypes under baseline (untreated) conditions and under repeated treatment cycles. We develop evolutionary game theoretic models predicting how the populations would respond if ADRN and MES phenotypes (1) are distinct cell types or (2) represent dynamic cell states and fit these models to the experimental data. We find that, although cells may undergo an ADRN to MES phenotypic switch under treatment, the best-fit model sees ADRN and MES as distinct cell types. Differential proliferation and survival of these two cell types, and not cell-state switching, drive therapeutic response.

Flexible and robust cell type annotation for highly multiplexed tissue images

Identifying cell types in highly multiplexed images is essential for understanding tissue spatial organization. Current cell type annotation methods often rely on extensive reference images and manual adjustments. In this work, we present a tool, Robust Image-Based Cell Annotator (RIBCA), that enables accurate, automated, unbiased, and fine-grained cell type annotation for images with a wide range of antibody panels, without requiring additional model training or human intervention. Our tool has successfully annotated over 3 million cells, revealing the spatial organization of various cell types across more than 40 different human tissues. It is open-source and features a modular design, allowing for easy extension to additional cell types.

Should Artificial Intelligence Play a Durable Role in Biomedical Research and Practice?

During the last decade, artificial intelligence (AI) was applied to nearly all domains of human activity, including scientific research. It is thus warranted to ask whether AI thinking should be durably involved in biomedical research. This problem was addressed by examining three complementary questions (i) What are the major barriers currently met by biomedical investigators? It is suggested that during the last 2 decades there was a shift towards a growing need to elucidate complex systems, and that this was not sufficiently fulfilled by previously successful methods such as theoretical modeling or computer simulation (ii) What is the potential of AI to meet the aforementioned need? it is suggested that recent AI methods are well-suited to perform classification and prediction tasks on multivariate systems, and possibly help in data interpretation, provided their efficiency is properly validated. (iii) Recent representative results obtained with machine learning suggest that AI efficiency may be comparable to that displayed by human operators. It is concluded that AI should durably play an important role in biomedical practice. Also, as already suggested in other scientific domains such as physics, combining AI with conventional methods might generate further progress and new applications, involving heuristic and data interpretation.

Chemogenetic Modulation of Preoptic Gabre Neurons Decreases Body Temperature and Heart Rate

The preoptic area of the hypothalamus is critical for regulation of brain-body interaction, including circuits that control vital signs such as body temperature and heart rate. The preoptic area contains approximately 70 molecularly distinct cell types. The Gabre gene is expressed in a subset of preoptic area cell types. It encodes the GABA receptor ε-subunit, which is thought to confer resistance to anesthetics at the molecular level, but the function of Gabre cells in the brain remains largely unknown. We generated and have extensively characterized a Gabre-cre knock-in mouse line and used chemogenetic tools to interrogate the function of Gabre cells in the preoptic area. Comparison with macaque GABRE expression revealed the conserved character of Gabre cells in the preoptic area. In awake mice, we found that chemogenetic activation of Gabre neurons in the preoptic area reduced body temperature, whereas chemogenetic inhibition had no effect. Furthermore, chemogenetic inhibition of Gabre neurons in the preoptic area decreased the heart rate, whereas chemogenetic activation had no effect under isoflurane anesthesia. These findings suggest an important role of preoptic Gabre neurons in maintaining vital signs such as body temperature and heart rate during wakefulness and under anesthesia.