What is a cell type and how to define it?

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.

A small population of stress-responsive neurons in the hypothalamus-habenula circuit mediates development of depression-like behavior in mice

Accumulating evidence has shown that various brain functions are associated with experience-activated neuronal ensembles. However, whether such neuronal ensembles are engaged in the pathogenesis of stress-induced depression remains elusive. Utilizing activity-dependent viral strategies in mice, we identified a small population of stress-responsive neurons, primarily located in the middle part of the lateral hypothalamus (mLH) and the medial part of the lateral habenula (LHbM). These neurons serve as "starter cells" to transmit stress-related information and mediate the development of depression-like behaviors during chronic stress. Starter cells in the mLH and LHbM form dominant connections, which are selectively potentiated by chronic stress. Silencing these connections during chronic stress prevents the development of depression-like behaviors, whereas activating these connections directly elicits depression-like behaviors without stress experience. Collectively, our findings dissect a core functional unit within the LH-LHb circuit that mediates the development of depression-like behaviors in mice.

VICTOR: Validation and inspection of cell type annotation through optimal regression

Single-cell RNA sequencing provides unprecedent opportunities to explore the heterogeneity and dynamics inherent in cellular biology. An essential step in the data analysis involves the automatic annotation of cells. Despite development of numerous tools for automated cell annotation, assessing the reliability of predicted annotations remains challenging, particularly for rare and unknown cell types. Here, we introduce VICTOR: Validation and inspection of cell type annotation through optimal regression. VICTOR aims to gauge the confidence of cell annotations by an elastic-net regularized regression with optimal thresholds. We demonstrated that VICTOR performed well in identifying inaccurate annotations, surpassing existing methods in diagnostic ability across various single-cell datasets, including within-platform, cross-platform, cross-studies, and cross-omics settings.

Meta-analysis of single-cell RNA sequencing co-expression in human neural organoids reveals their high variability in recapitulating primary tissue

Human neural organoids offer an exciting opportunity for studying inaccessible human-specific brain development; however, it remains unclear how precisely organoids recapitulate fetal/primary tissue biology. We characterize field-wide replicability and biological fidelity through a meta-analysis of single-cell RNA-sequencing data for first and second trimester human primary brain (2.95 million cells, 51 data sets) and neural organoids (1.59 million cells, 173 data sets). We quantify the degree primary tissue cell type marker expression and co-expression are recapitulated in organoids across 10 different protocol types. By quantifying gene-level preservation of primary tissue co-expression, we show neural organoids lie on a spectrum ranging from virtually no signal to co-expression indistinguishable from primary tissue, demonstrating a high degree of variability in biological fidelity among organoid systems. Our preserved co-expression framework provides cell type-specific measures of fidelity applicable to diverse neural organoids, offering a powerful tool for uncovering unifying axes of variation across heterogeneous neural organoid experiments.

Bidirectional valence coding in amygdala intercalated clusters: A neural substrate for the opponent-process theory of motivation

Processing emotionally meaningful stimuli and eliciting appropriate valence-specific behavior in response is a critical brain function for survival. Thus, how positive and negative valence are represented in neural circuits and how corresponding neural substrates interact to cooperatively select appropriate behavioral output are fundamental questions. In previous work, we identified that two amygdala intercalated clusters show opposite response selectivity to fear- and anxiety-inducing stimuli - negative valence (Hagihara et al., 2021). Here, we further show that the two clusters also exhibit distinctly different representations of stimuli with positive valence, demonstrating a broader role of the amygdala intercalated system beyond fear and anxiety. Together with the mutually inhibitory connectivity between the two clusters, our findings suggest that they serve as an ideal neural substrate for the integrated processing of valence for the selection of behavioral output.

BrainCellR: A precise cell type nomenclature pipeline for comparative analysis across brain single-cell datasets

Single-cell studies in neuroscience require precise cell type classification and consistent nomenclature that allows for meaningful comparisons across diverse datasets. Current approaches often lack the ability to identify fine-grained cell types and establish standardized annotations at the cluster level, hindering comprehensive understanding of the brain's cellular composition. To facilitate data integration across multiple models and datasets, we designed BrainCellR. This pipeline provides researchers with a powerful and user-friendly tool for efficient cell type classification and nomination from single-cell transcriptomic data. While initially focused on brain studies, BrainCellR is applicable to other tissues with complex cellular compositions. BrainCellR goes beyond conventional classification approaches by incorporating a standardized nomenclature system for cell types at the cluster level. This feature enables consistent and comparable annotations across different studies, promoting data integration and providing deeper insights into the complex cellular landscape of the brain. All documents for BrainCellR, including source code, user manual and tutorials, are freely available at https://github.com/WangLab-SINH/BrainCellR.

Implementation and validation of single-cell genomics experiments in neuroscience

Single-cell or single-nucleus transcriptomics is a powerful tool for identifying cell types and cell states. However, hypotheses derived from these assays, including gene expression information, require validation, and their functional relevance needs to be established. The choice of validation depends on numerous factors. Here, we present types of orthogonal and functional validation experiment to strengthen preliminary findings obtained using single-cell and single-nucleus transcriptomics as well as the challenges and limitations of these approaches.

Hierarchical annotation of eQTLs by H-eQTL enables identification of genes with cell type-divergent regulation

While context-type-specific regulation of genes is largely determined by cis-regulatory regions, attempts to identify cell type-specific eQTLs are complicated by the nested nature of cell types. We present hierarchical eQTL (H-eQTL), a network-based model for hierarchical annotation of bulk-derived eQTLs to levels of a cell type tree using single-cell chromatin accessibility data and no clustering of cells into discrete cell types. Using our model, we annotate bulk-derived eQTLs from the developing brain with high specificity to levels of a cell type hierarchy, which allows sensitive detection of genes with multiple distinct non-coding elements regulating their expression in different cell types.

BIOTIC: a Bayesian framework to integrate single-cell multi-omics for transcription factor activity inference and improve identity characterization of cells

Understanding cell destiny requires unraveling the intricate mechanism of gene regulation, where transcription factors (TFs) play a pivotal role. However, the actual contribution of TFs, that is TF activity, is not only determined by TF expression, but also accessibility of corresponding chromatin regions. Therefore, we introduce BIOTIC, an advanced Bayesian model with a well-established gene regulation structure that harnesses the power of single-cell multi-omics data to model the gene expression process under the control of regulatory elements, thereby defining the regulatory activity of TFs with variational inference. We demonstrated that the TF activity inferred by BIOTIC can serve as a characterization of cell identity, and outperforms baseline methods for the tasks of cell typing, cell development tracking, and batch effect correction. Additionally, BIOTIC trained on multi-omics data can flexibly be applied to the scenario where merely single-cell transcriptome sequencing is available, to infer TF activity and annotate the cell type by mapping the query cell into the reference TF activity space, as an emerging application of cell atlases. The structure of BIOTIC has been determined to be adaptable for the inclusion of additional biological factors, allowing for flexible and more comprehensive gene regulation analysis. BIOTIC introduces a pioneering biological-mechanism-driven framework to infer TF activity and elucidate cell identity states at gene regulatory level, paving the way for a deeper understanding of the complex interplay between TFs and gene expression in living systems.

Exploring the Unknown: How Can We Improve Single-cell RNAseq Cell Type Annotations in Non-model Organisms?

Single-cell RNA sequencing (scRNAseq) is a powerful tool to describe cell types in multicellular organisms across the animal kingdom. In standard scRNAseq analysis pipelines, clusters of cells with similar transcriptional signatures are given cell type labels based on marker genes that infer specialized known characteristics. Since these analyses are designed for model organisms, such as humans and mice, problems arise when attempting to label cell types of distantly related, non-model species that have unique or divergent cell types. Consequently, this leads to limited discovery of novel species-specific cell types and potential mis-annotation of cell types in non-model species while using scRNAseq. To address this problem, we discuss recently published approaches that help annotate scRNAseq clusters for any non-model organism. We first suggest that annotating with an evolutionary context of cell lineages will aid in the discovery of novel cell types and provide a marker-free approach to compare cell types across distantly related species. Secondly, machine learning has greatly improved bioinformatic analyses, so we highlight some open-source programs that use reference-free approaches to annotate cell clusters. Lastly, we propose the use of unannotated genes as potential cell markers for non-model organisms, as many do not have fully annotated genomes and these data are often disregarded. Improving single-cell annotations will aid the discovery of novel cell types and enhance our understanding of non-model organisms at a cellular level. By unifying approaches to annotate cell types in non-model organisms, we can increase the confidence of cell annotation label transfer and the flexibility to discover novel cell types.

Efficient differential expression analysis of large-scale single cell transcriptomics data using dreamlet

Advances in single-cell and -nucleus transcriptomics have enabled generation of increasingly large-scale datasets from hundreds of subjects and millions of cells. These studies promise to give unprecedented insight into the cell type specific biology of human disease. Yet performing differential expression analyses across subjects remains difficult due to challenges in statistical modeling of these complex studies and scaling analyses to large datasets. Our open-source R package dreamlet (DiseaseNeurogenomics.github.io/dreamlet) uses a pseudobulk approach based on precision-weighted linear mixed models to identify genes differentially expressed with traits across subjects for each cell cluster. Designed for data from large cohorts, dreamlet is substantially faster and uses less memory than existing workflows, while supporting complex statistical models and controlling the false positive rate. We demonstrate computational and statistical performance on published datasets, and a novel dataset of 1.4M single nuclei from postmortem brains of 150 Alzheimer's disease cases and 149 controls.

scPair: Boosting single cell multimodal analysis by leveraging implicit feature selection and single cell atlases

Multimodal single-cell assays profile multiple sets of features in the same cells and are widely used for identifying and mapping cell states between chromatin and mRNA and linking regulatory elements to target genes. However, the high dimensionality of input features and shallow sequencing depth compared to unimodal assays pose challenges in data analysis. Here we present scPair, a multimodal single-cell data framework that overcomes these challenges by employing an implicit feature selection approach. scPair uses dual encoder-decoder structures trained on paired data to align cell states across modalities and predict features from one modality to another. We demonstrate that scPair outperforms existing methods in accuracy and execution time, and facilitates downstream tasks such as trajectory inference. We further show scPair can augment smaller multimodal datasets with larger unimodal atlases to increase statistical power to identify groups of transcription factors active during different stages of neural differentiation.

TLR8 agonist selgantolimod regulates Kupffer cell differentiation status and impairs HBV entry into hepatocytes via an IL-6-dependent mechanism

OBJECTIVE

Achieving HBV cure will require novel combination therapies of direct-acting antivirals and immunomodulatory agents. In this context, the toll-like receptor 8 (TLR8) agonist selgantolimod (SLGN) has been investigated in preclinical models and clinical trials for chronic hepatitis B (CHB). However, little is known regarding its action on immune effectors within the liver. Our aim was to characterise the transcriptomic changes and intercellular communication events induced by SLGN in the hepatic microenvironment.

DESIGN

We identified TLR8-expressing cell types in the human liver using publicly available single-cell RNA-seq data and established a method to isolate Kupffer cells (KCs). We characterised transcriptomic and cytokine KC profiles in response to SLGN. SLGN's indirect effect was evaluated by RNA-seq in hepatocytes treated with SLGN-conditioned media (CM) and quantification of HBV parameters following infection. Pathways mediating SLGN's effect were validated using transcriptomic data from HBV-infected patients.

RESULTS

Hepatic TLR8 expression takes place in the myeloid compartment. SLGN treatment of KCs upregulated monocyte markers (eg, S100A12) and downregulated genes associated with the KC identity (eg, SPIC). Treatment of hepatocytes with SLGN-CM downregulated NTCP and impaired HBV entry. Cotreatment with an interleukin 6-neutralising antibody reverted the HBV entry inhibition.

CONCLUSION

Our transcriptomic characterisation of SLGN sheds light into the programmes regulating KC activation. Furthermore, in addition to its previously described effect on established HBV infection and adaptive immunity, we show that SLGN impairs HBV entry. Altogether, SLGN may contribute through KCs to remodelling the intrahepatic immune microenvironment and may thus represent an important component of future combinations to cure HBV infection.

Genetically defined neuron types underlying visuomotor transformation in the superior colliculus

The superior colliculus (SC) is a conserved midbrain structure that is important for transforming visual and other sensory information into motor actions. Decades of investigations in numerous species have made the SC and its nonmammalian homologue, the optic tectum, one of the best studied structures in the brain, with rich information now available regarding its anatomical organization, its extensive inputs and outputs and its important functions in many reflexive and cognitive behaviours. Excitingly, recent studies using modern genomic and physiological approaches have begun to reveal the diverse neuronal subtypes in the SC, as well as their unique functions in visuomotor transformation. Studies have also started to uncover how subtypes of SC neurons form intricate circuits to mediate visual processing and visually guided behaviours. Here, we review these recent discoveries on the cell types and neuronal circuits underlying visuomotor transformations mediated by the SC. We also highlight the important future directions made possible by these new developments.

Reimagining Cortical Connectivity by Deconstructing Its Molecular Logic into Building Blocks

Comprehensive maps of neuronal connectivity provide a foundation for understanding the structure of neural circuits. In a circuit, neurons are diverse in morphology, electrophysiology, gene expression, activity, and other neuronal properties. Thus, constructing a comprehensive connectivity map requires associating various properties of neurons, including their connectivity, at cellular resolution. A commonly used approach is to use the gene expression profiles as an anchor to which all other neuronal properties are associated. Recent advances in genomics and anatomical techniques dramatically improved the ability to determine and associate the long-range projections of neurons with their gene expression profiles. These studies revealed unprecedented details of the gene-projection relationship, but also highlighted conceptual challenges in understanding this relationship. In this article, I delve into the findings and the challenges revealed by recent studies using state-of-the-art neuroanatomical and transcriptomic techniques. Building upon these insights, I propose an approach that focuses on understanding the gene-projection relationship through basic features in gene expression profiles and projections, respectively, that associate with underlying cellular processes. I then discuss how the developmental trajectories of projections and gene expression profiles create additional challenges and necessitate interrogating the gene-projection relationship across time. Finally, I explore complementary strategies that, together, can provide a comprehensive view of the gene-projection relationship.

Exploring the Role of Genetic and Environmental Features in Colorectal Cancer Development: An Agent-Based Approach

The complexity of issues in cancer research has led to the introduction of powerful computational tools to help experimental in vivo and in vitro methods. These tools, which typically focus on studying cell behavior and dynamic cell populations, range from systems of differential equations that are solved numerically to lattice models and agent-based simulations. In particular, agent-based models (ABMs) are increasingly used due to their ability to incorporate multi-scale features, ranging from the individual to the population level. This approach allows for the combination of statistically aggregated assumptions with individual heterogeneity. In this work, we present an ABM that simulates tumor progression in a colonic crypt, to provide an experimental in silico environment for testing results achieved in traditional laboratory research and developing alternative scenarios of tumor development. The model also allows some speculations about causal relationships in biologically inspired systems.

The transcriptomic landscape of spinal V1 interneurons reveals a role for En1 in specific elements of motor output

Neural circuits in the spinal cord are composed of diverse sets of interneurons that play crucial roles in shaping motor output. Despite progress in revealing the cellular architecture of the spinal cord, the extent of cell type heterogeneity within interneuron populations remains unclear. Here, we present a single-nucleus transcriptomic atlas of spinal V1 interneurons across postnatal development. We find that the core molecular taxonomy distinguishing neonatal V1 interneurons perdures into adulthood, suggesting conservation of function across development. Moreover, we identify a key role for En1, a transcription factor that marks the V1 population, in specifying one unique subset of V1Pou6f2 interneurons. Loss of En1 selectively disrupts the frequency of rhythmic locomotor output but does not disrupt flexion/extension limb movement. Beyond serving as a molecular resource for this neuronal population, our study highlights how deep neuronal profiling provides an entry point for functional studies of specialized cell types in motor output.

Continuous cell type diversification throughout the embryonic and postnatal mouse visual cortex development

The mammalian cortex is composed of a highly diverse set of cell types and develops through a series of temporally regulated events that build out the cell type and circuit foundation for cortical function. The mechanisms underlying the development of different cell types remain elusive. Single-cell transcriptomics provides the capacity to systematically study cell types across the entire temporal range of cortical development. Here, we present a comprehensive and high-resolution transcriptomic and epigenomic cell type atlas of the developing mouse visual cortex. The atlas was built from a single-cell RNA-sequencing dataset of 568,674 high-quality single-cell transcriptomes and a single-nucleus Multiome dataset of 194,545 high-quality nuclei providing both transcriptomic and chromatin accessibility profiles, densely sampled throughout the embryonic and postnatal developmental stages from E11.5 to P56. We computationally reconstructed a transcriptomic developmental trajectory map of all excitatory, inhibitory, and non-neuronal cell types in the visual cortex, identifying branching points marking the emergence of new cell types at specific developmental ages and defining molecular signatures of cellular diversification. In addition to neurogenesis, gliogenesis and early postmitotic maturation in the embryonic stage which gives rise to all the cell classes and nearly all subclasses, we find that increasingly refined cell types emerge throughout the postnatal differentiation process, including the late emergence of many cell types during the eye-opening stage (P11-P14) and the onset of critical period (P21), suggesting continuous cell type diversification at different stages of cortical development. Throughout development, we find cooperative dynamic changes in gene expression and chromatin accessibility in specific cell types, identifying both chromatin peaks potentially regulating the expression of specific genes and transcription factors potentially regulating specific peaks. Furthermore, a single gene can be regulated by multiple peaks associated with different cell types and/or different developmental stages. Collectively, our study provides the most detailed dynamic molecular map directly associated with individual cell types and specific developmental events that reveals the molecular logic underlying the continuous refinement of cell type identities in the developing visual cortex.

Causal evidence of a line attractor encoding an affective state

Continuous attractors are an emergent property of neural population dynamics that have been hypothesized to encode continuous variables such as head direction and eye position1-4. In mammals, direct evidence of neural implementation of a continuous attractor has been hindered by the challenge of targeting perturbations to specific neurons within contributing ensembles2,3. Dynamical systems modelling has revealed that neurons in the hypothalamus exhibit approximate line-attractor dynamics in male mice during aggressive encounters5. We have previously hypothesized that these dynamics may encode the variable intensity and persistence of an aggressive internal state. Here we report that these neurons also showed line-attractor dynamics in head-fixed mice observing aggression6. This allowed us to identify and manipulate line-attractor-contributing neurons using two-photon calcium imaging and holographic optogenetic perturbations. On-manifold perturbations yielded integration of optogenetic stimulation pulses and persistent activity that drove the system along the line attractor, while transient off-manifold perturbations were followed by rapid relaxation back into the attractor. Furthermore, single-cell stimulation and imaging revealed selective functional connectivity among attractor-contributing neurons. Notably, individual differences among mice in line-attractor stability were correlated with the degree of functional connectivity among attractor-contributing neurons. Mechanistic recurrent neural network modelling indicated that dense subnetwork connectivity and slow neurotransmission7 best recapitulate our empirical findings. Our work bridges circuit and manifold levels3, providing causal evidence of continuous attractor dynamics encoding an affective internal state in the mammalian hypothalamus.

Functional analysis of cell plasticity using single-cell technologies

Metazoan organisms are heterocellular systems composed of hundreds of different cell types, which arise from an isogenic genome through differentiation. Cellular 'plasticity' further enables cells to alter their fate in response to exogenous cues and is involved in a variety of processes, such as wound healing, infection, and cancer. Recent advances in cellular model systems, high-dimensional single-cell technologies, and lineage tracing have sparked a renaissance in plasticity research. Here, we discuss the definition of cell plasticity, evaluate state-of-the-art model systems and techniques to study cell-fate dynamics, and explore the application of single-cell technologies to obtain functional insights into cell plasticity in healthy and diseased tissues. The integration of advanced biomimetic model systems, single-cell technologies, and high-throughput perturbation studies is enabling a new era of research into non-genetic plasticity in metazoan systems.

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 used transcriptomic and epigenomic cortical cell type taxonomies from mouse and human to define marker genes and putative enhancers and created a large toolkit of transgenic lines and enhancer AAVs for selective targeting of cortical cell populations. We report evaluation of fifteen new transgenic driver lines, two new reporter lines, and >800 different enhancer AAVs covering most subclasses of cortical cells. The tools reported here as well as the scaled process of tool creation and modification enable diverse experimental strategies towards understanding mammalian cortex and brain function.

Segregated basal ganglia output pathways correspond to genetically divergent neuronal subclasses

The basal ganglia control multiple sensorimotor behaviors though 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 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 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.

Cell differentiation controls iron assimilation in a choanoflagellate

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 understand their roles in essential biogeochemical cycles1,2. 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 types3-6. 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 for improved growth through the expression of a cytochrome b561 iron reductase (cytb561a). This gene is an ortholog of the mammalian duodenal cytochrome b561 (DCYTB) that reduces ferric cations prior to their uptake in gut epithelia7 and is part of an iron utilization toolkit that choanoflagellates and their closest living relatives, the animals, inherited from a last common eukaryotic ancestor. In a database of oceanic metagenomes8,9, the abundance of cytb561a transcripts from choanoflagellates positively correlates with upwellings, which are a major source of ferric colloids in marine environments10. As this predominant form of iron11,12 is largely inaccessible to cell-walled microbes13,14, choanoflagellates and other phagotrophic eukaryotes may serve critical ecological roles by first acquiring ferric colloids through phagocytosis and then cycling this essential nutrient through iron utilization pathways13-15. These 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 multicellularity16-22.

Unsupervised pattern identification in spatial gene expression atlas reveals mouse brain regions beyond established ontology

The rapid growth of large-scale spatial gene expression data demands efficient and reliable computational tools to extract major trends of gene expression in their native spatial context. Here, we used stability-driven unsupervised learning (i.e., staNMF) to identify principal patterns (PPs) of 3D gene expression profiles and understand spatial gene distribution and anatomical localization at the whole mouse brain level. Our subsequent spatial correlation analysis systematically compared the PPs to known anatomical regions and ontology from the Allen Mouse Brain Atlas using spatial neighborhoods. We demonstrate that our stable and spatially coherent PPs, whose linear combinations accurately approximate the spatial gene data, are highly correlated with combinations of expert-annotated brain regions. These PPs yield a brain ontology based purely on spatial gene expression. Our PP identification approach outperforms principal component analysis and typical clustering algorithms on the same task. Moreover, we show that the stable PPs reveal marked regional imbalance of brainwide genetic architecture, leading to region-specific marker genes and gene coexpression networks. Our findings highlight the advantages of stability-driven machine learning for plausible biological discovery from dense spatial gene expression data, streamlining tasks that are infeasible by conventional manual approaches.

Cancer cell states: Lessons from ten years of single-cell RNA-sequencing of human tumors

Human tumors are intricate ecosystems composed of diverse genetic clones and malignant cell states that evolve in a complex tumor micro-environment. Single-cell RNA-sequencing (scRNA-seq) provides a compelling strategy to dissect this intricate biology and has enabled a revolution in our ability to understand tumor biology over the last ten years. Here we reflect on this first decade of scRNA-seq in human tumors and highlight some of the powerful insights gleaned from these studies. We first focus on computational approaches for robustly defining cancer cell states and their diversity and highlight some of the most common patterns of gene expression intra-tumor heterogeneity (eITH) observed across cancer types. We then discuss ambiguities in the field in defining and naming such eITH programs. Finally, we highlight critical developments that will facilitate future research and the broader implementation of these technologies in clinical settings.

Flexible use of conserved motif vocabularies constrains genome access in cell type evolution

Cell types evolve into a hierarchy with related types grouped into families. How cell type diversification is constrained by the stable separation between families over vast evolutionary times remains unknown. Here, integrating single-nucleus multiomic sequencing and deep learning, we show that hundreds of sequence features (motifs) divide into distinct sets associated with accessible genomes of specific cell type families. This division is conserved across highly divergent, early-branching animals including flatworms and cnidarians. While specific interactions between motifs delineate cell type relationships within families, surprisingly, these interactions are not conserved between species. Consistently, while deep learning models trained on one species can predict accessibility of other species' sequences, their predictions frequently rely on distinct, but synonymous, motif combinations. We propose that long-term stability of cell type families is maintained through genome access specified by conserved motif sets, or 'vocabularies', whereas cell types diversify through flexible use of motifs within each set.

2P-NucTag: on-demand phototagging for molecular analysis of functionally identified cortical neurons

Neural circuits are characterized by genetically and functionally diverse cell types. A mechanistic understanding of circuit function is predicated on linking the genetic and physiological properties of individual neurons. However, it remains highly challenging to map the transcriptional properties to functionally heterogeneous neuronal subtypes in mammalian cortical circuits in vivo. Here, we introduce a high-throughput two-photon nuclear phototagging (2P-NucTag) approach optimized for on-demand and indelible labeling of single neurons via a photoactivatable red fluorescent protein following in vivo functional characterization in behaving mice. We demonstrate the utility of this function-forward pipeline by selectively labeling and transcriptionally profiling previously inaccessible 'place' and 'silent' cells in the mouse hippocampus. Our results reveal unexpected differences in gene expression between these hippocampal pyramidal neurons with distinct spatial coding properties. Thus, 2P-NucTag opens a new way to uncover the molecular principles that govern the functional organization of neural circuits.

Linking brain activity across scales with simultaneous opto- and electrophysiology

The brain enables adaptive behavior via the dynamic coordination of diverse neuronal signals across spatial and temporal scales: from fast action potential patterns in microcircuits to slower patterns of distributed activity in brain-wide networks. Understanding principles of multiscale dynamics requires simultaneous monitoring of signals in multiple, distributed network nodes. Combining optical and electrical recordings of brain activity is promising for collecting data across multiple scales and can reveal aspects of coordinated dynamics invisible to standard, single-modality approaches. We review recent progress in combining opto- and electrophysiology, focusing on mouse studies that shed new light on the function of single neurons by embedding their activity in the context of brain-wide activity patterns. Optical and electrical readouts can be tailored to desired scales to tackle specific questions. For example, fast dynamics in single cells or local populations recorded with multi-electrode arrays can be related to simultaneously acquired optical signals that report activity in specified subpopulations of neurons, in non-neuronal cells, or in neuromodulatory pathways. Conversely, two-photon imaging can be used to densely monitor activity in local circuits while sampling electrical activity in distant brain areas at the same time. The refinement of combined approaches will continue to reveal previously inaccessible and under-appreciated aspects of coordinated brain activity.

Biophysically interpretable inference of cell types from multimodal sequencing data

Multimodal, single-cell genomics technologies enable simultaneous measurement of multiple facets of DNA and RNA processing in the cell. This creates opportunities for transcriptome-wide, mechanistic studies of cellular processing in heterogeneous cell populations, such as regulation of cell fate by transcriptional stochasticity or tumor proliferation through aberrant splicing dynamics. However, current methods for determining cell types or 'clusters' in multimodal data often rely on ad hoc approaches to balance or integrate measurements, and assumptions ignoring inherent properties of the data. To enable interpretable and consistent cell cluster determination, we present meK-means (mechanistic K-means) which integrates modalities through a unifying model of transcription to learn underlying, shared biophysical states. With meK-means we can cluster cells with nascent and mature mRNA measurements, utilizing the causal, physical relationships between these modalities. This identifies shared transcription dynamics across cells, which induce the observed molecule counts, and provides an alternative definition for 'clusters' through the governing parameters of cellular processes.

Joint inference of discrete cell types and continuous type-specific variability in single-cell datasets with MMIDAS

Reproducible definition and identification of cell types is essential to enable investigations into their biological function and to understand their relevance in the context of development, disease and evolution. Current approaches model variability in data as continuous latent factors, followed by clustering as a separate step, or immediately apply clustering on the data. We show that such approaches can suffer from qualitative mistakes in identifying cell types robustly, particularly when the number of such cell types is in the hundreds or even thousands. Here we propose an unsupervised method, Mixture Model Inference with Discrete-coupled AutoencoderS (MMIDAS), which combines a generalized mixture model with a multi-armed deep neural network to jointly infer the discrete type and continuous type-specific variability. Using four recent datasets of brain cells spanning different technologies, species and conditions, we demonstrate that MMIDAS can identify reproducible cell types and infer cell type-dependent continuous variability in both unimodal and multimodal datasets.

scATAcat: cell-type annotation for scATAC-seq data

Cells whose accessibility landscape has been profiled with scATAC-seq cannot readily be annotated to a particular cell type. In fact, annotating cell-types in scATAC-seq data is a challenging task since, unlike in scRNA-seq data, we lack knowledge of 'marker regions' which could be used for cell-type annotation. Current annotation methods typically translate accessibility to expression space and rely on gene expression patterns. We propose a novel approach, scATAcat, that leverages characterized bulk ATAC-seq data as prototypes to annotate scATAC-seq data. To mitigate the inherent sparsity of single-cell data, we aggregate cells that belong to the same cluster and create pseudobulk. To demonstrate the feasibility of our approach we collected a number of datasets with respective annotations to quantify the results and evaluate performance for scATAcat. scATAcat is available as a python package at https://github.com/aybugealtay/scATAcat.

Movement-related increases in subthalamic activity optimize locomotion

The subthalamic nucleus (STN) is traditionally thought to restrict movement. Lesion or prolonged STN inhibition increases movement vigor and propensity, while optogenetic excitation has opposing effects. However, STN neurons often exhibit movement-related increases in firing. To address this paradox, STN activity was recorded and manipulated in head-fixed mice at rest and during self-initiated and self-paced treadmill locomotion. We found that (1) most STN neurons (type 1) exhibit locomotion-dependent increases in activity, with half firing preferentially during the propulsive phase of the contralateral locomotor cycle; (2) a minority of STN neurons exhibit dips in activity or are uncorrelated with movement; (3) brief optogenetic inhibition of the lateral STN (where type 1 neurons are concentrated) slows and prematurely terminates locomotion; and (4) in Q175 Huntington's disease mice, abnormally brief, low-velocity locomotion is associated with type 1 hypoactivity. Together, these data argue that movement-related increases in STN activity contribute to optimal locomotor performance.

Lack of basic rationale in epithelial-mesenchymal transition and its related concepts

Epithelial-mesenchymal transition (EMT) is defined as a cellular process during which epithelial cells acquire mesenchymal phenotypes and behavior following the downregulation of epithelial features. EMT and its reversed process, the mesenchymal-epithelial transition (MET), and the special form of EMT, the endothelial-mesenchymal transition (EndMT), have been considered as mainstream concepts and general rules driving developmental and pathological processes, particularly cancer. However, discrepancies and disputes over EMT and EMT research have also grown over time. EMT is defined as transition between two cellular states, but it is unanimously agreed by EMT researchers that (1) neither the epithelial and mesenchymal states nor their regulatory networks have been clearly defined, (2) no EMT markers or factors can represent universally epithelial and mesenchymal states, and thus (3) EMT cannot be assessed on the basis of one or a few EMT markers. In contrast to definition and proposed roles of EMT, loss of epithelial feature does not cause mesenchymal phenotype, and EMT does not contribute to embryonic mesenchyme and neural crest formation, the key developmental events from which the EMT concept was derived. EMT and MET, represented by change in cell shapes or adhesiveness, or symbolized by EMT factors, are biased interpretation of the overall change in cellular property and regulatory networks during development and cancer progression. Moreover, EMT and MET are consequences rather than driving factors of developmental and pathological processes. The true meaning of EMT in some developmental and pathological processes, such as fibrosis, needs re-evaluation. EMT is believed to endow malignant features, such as migration, stemness, etc., to cancer cells. However, the core property of cancer (tumorigenic) cells is neural stemness, and the core EMT factors are components of the regulatory networks of neural stemness. Thus, EMT in cancer progression is misattribution of the roles of neural stemness to the unknown mesenchymal state. Similarly, neural crest EMT is misattribution of intrinsic property of neural crest cells to the unknown mesenchymal state. Lack of basic rationale in EMT and related concepts urges re-evaluation of their significance as general rules for understanding developmental and pathological processes, and re-evaluation of their significance in scientific research.

Building a learnable universal coordinate system for single-cell atlas with a joint-VAE model

A universal coordinate system that can ensemble the huge number of cells and capture their heterogeneities is of vital importance for constructing large-scale cell atlases as references for molecular and cellular studies. Studies have shown that cells exhibit multifaceted heterogeneities in their transcriptomic features at multiple resolutions. This nature of complexity makes it hard to design a fixed coordinate system through a combination of known features. It is desirable to build a learnable universal coordinate model that can capture major heterogeneities and serve as a controlled generative model for data augmentation. We developed UniCoord, a specially-tuned joint-VAE model to represent single-cell transcriptomic data in a lower-dimensional latent space with high interpretability. Each latent dimension can represent either discrete or continuous feature, and either supervised by prior knowledge or unsupervised. The latent dimensions can be easily reconfigured to generate pseudo transcriptomic profiles with desired properties. UniCoord can also be used as a pre-trained model to analyze new data with unseen cell types and thus can serve as a feasible framework for cell annotation and comparison. UniCoord provides a prototype for a learnable universal coordinate framework to enable better analysis and generation of cells with highly orchestrated functions and heterogeneities.

Scalable spatial single-cell transcriptomics and translatomics in 3D thick tissue blocks

Characterizing the transcriptional and translational gene expression patterns at the single-cell level within their three-dimensional (3D) tissue context is essential for revealing how genes shape tissue structure and function in health and disease. However, most existing spatial profiling techniques are limited to 5-20 μm thin tissue sections. Here, we developed Deep-STARmap and Deep-RIBOmap, which enable 3D in situ quantification of thousands of gene transcripts and their corresponding translation activities, respectively, within 200-μm thick tissue blocks. This is achieved through scalable probe synthesis, hydrogel embedding with efficient probe anchoring, and robust cDNA crosslinking. We first utilized Deep-STARmap in combination with multicolor fluorescent protein imaging for simultaneous molecular cell typing and 3D neuron morphology tracing in the mouse brain. We also demonstrate that 3D spatial profiling facilitates comprehensive and quantitative analysis of tumor-immune interactions in human skin cancer.

In Situ Synthesis and Assembly of Functional Materials and Devices in Living Systems

Integrating functional materials and devices with living systems enables novel methods for recording, manipulating, or augmenting organisms not accessible by traditional chemical, optical, or genetic approaches. (The term "device" refers to the fundamental components of complex electronic systems, such as transistors, capacitors, conductors, and electrodes.) Typically, these advanced materials and devices are synthesized, either through chemical or physical reactions, outside the biological systems (ex situ) before they are integrated. This is due in part to the more limited repertoire of biocompatible chemical transformations available for assembling functional materials in vivo. Given that most of the assembled bulk materials are impermeable to cell membranes and cannot go through the blood-brain barrier (BBB), the external synthesis poses challenges when trying to interface these materials and devices with cells precisely and in a timely manner and at the micro- and nanoscale─a crucial requirement for modulating cellular functions. In contrast to presynthesis in a separate location, in situ assembly, wherein small molecules or building blocks are directly assembled into functional materials within a biological system at the desired site of action, has offered a potential solution for spatiotemporal and genetic control of material synthesis and assembly. In this Account, we highlight recent advances in spatially and temporally targeted functional material synthesis and assembly in living cells, tissues and animals and provide perspective on how they may enable novel probing, modulation, or augmentation of fundamental biology. We discuss several strategies, starting from the traditional nontargeted methods to targeted assembly of functional materials and devices based on the endogenous markers of the biological system. We then focus on genetically targeted assembly of functional materials, which employs enzymatic catalysis centers expressed in living systems to assemble functional materials in specific molecular-defined cell types. We introduce the recent efforts of our group to modulate membrane capacitance and neuron excitability using in situ synthesized electrically functional polymers in a genetically targetable manner. These advances demonstrate the promise of in situ synthesis and assembly of functional materials and devices, including the optogenetic polymerization developed by our lab, to interface with cells in a cellular- or subcellular-specific manner by incorporating genetic and/or optical control over material assembly. Finally, we discuss remaining challenges, areas for improvement, potential applications to other biological systems, and novel methods for the in situ synthesis of functional materials that could be elevated by incorporating genetic or material design strategies. As researchers expand the toolkit of biocompatible in situ functional material synthetic techniques, we anticipate that these advancements could potentially offer valuable tools for exploring biological systems and developing therapeutic solutions.

Genetic approaches to elucidating cortical and hippocampal GABAergic interneuron diversity

GABAergic interneurons (INs) in the mammalian forebrain represent a diverse population of cells that provide specialized forms of local inhibition to regulate neural circuit activity. Over the last few decades, the development of a palette of genetic tools along with the generation of single-cell transcriptomic data has begun to reveal the molecular basis of IN diversity, thereby providing deep insights into how different IN subtypes function in the forebrain. In this review, we outline the emerging picture of cortical and hippocampal IN speciation as defined by transcriptomics and developmental origin and summarize the genetic strategies that have been utilized to target specific IN subtypes, along with the technical considerations inherent to each approach. Collectively, these methods have greatly facilitated our understanding of how IN subtypes regulate forebrain circuitry via cell type and compartment-specific inhibition and thus have illuminated a path toward potential therapeutic interventions for a variety of neurocognitive disorders.

Interneuron diversity in the human dorsal striatum

Deciphering the striatal interneuron diversity is key to understanding the basal ganglia circuit and to untangling the complex neurological and psychiatric diseases affecting this brain structure. We performed snRNA-seq and spatial transcriptomics of postmortem human caudate nucleus and putamen samples to elucidate the diversity and abundance of interneuron populations and their inherent transcriptional structure in the human dorsal striatum. We propose a comprehensive taxonomy of striatal interneurons with eight main classes and fourteen subclasses, providing their full transcriptomic identity and spatial expression profile as well as additional quantitative FISH validation for specific populations. We have also delineated the correspondence of our taxonomy with previous standardized classifications and shown the main transcriptomic and class abundance differences between caudate nucleus and putamen. Notably, based on key functional genes such as ion channels and synaptic receptors, we found matching known mouse interneuron populations for the most abundant populations, the recently described PTHLH and TAC3 interneurons. Finally, we were able to integrate other published datasets with ours, supporting the generalizability of this harmonized taxonomy.

Multiplexed imaging to reveal tissue dendritic cell spatial localisation and function

Dendritic cells (DCs) play a pivotal role in immune surveillance, acting as sentinels that coordinate immune responses within tissues. Although differences in the identity and functional states of DC subpopulations have been identified through multiparametric flow cytometry and single-cell RNA sequencing, these methods do not provide information about the spatial context in which the cells are located. This knowledge is crucial for understanding tissue organisation and cellular cross-talk. Recent developments in multiplex imaging techniques can now offer insights into this complex spatial and functional landscape. This review provides a concise overview of these imaging methodologies, emphasising their application in identifying DCs to delineate their tissue-specific functions and aiding newcomers in navigating this field.

Making Ramón y Cajal proud: Development of cell identity and diversity in the cerebral cortex

Since the beautiful images of Santiago Ramón y Cajal provided a first glimpse into the immense diversity and complexity of cell types found in the cerebral cortex, neuroscience has been challenged and inspired to understand how these diverse cells are generated and how they interact with each other to orchestrate the development of this remarkable tissue. Some fundamental questions drive the field's quest to understand cortical development: what are the mechanistic principles that govern the emergence of neuronal diversity? How do extrinsic and intrinsic signals integrate with physical forces and activity to shape cell identity? How do the diverse populations of neurons and glia influence each other during development to guarantee proper integration and function? The advent of powerful new technologies to profile and perturb cortical development at unprecedented resolution and across a variety of modalities has offered a new opportunity to integrate past knowledge with brand new data. Here, we review some of this progress using cortical excitatory projection neurons as a system to draw out general principles of cell diversification and the role of cell-cell interactions during cortical development.

Neurogliaform Cells Exhibit Laminar-specific Responses in the Visual Cortex and Modulate Behavioral State-dependent Cortical Activity

Neurogliaform cells are a distinct type of GABAergic cortical interneurons known for their 'volume transmission' output property. However, their activity and function within cortical circuits remain unclear. Here, we developed two genetic tools to target these neurons and examine their function in the primary visual cortex. We found that the spontaneous activity of neurogliaform cells positively correlated with locomotion. Silencing these neurons increased spontaneous activity during locomotion and impaired visual responses in L2/3 pyramidal neurons. Furthermore, the contrast-dependent visual response of neurogliaform cells varies with their laminar location and is constrained by their morphology and input connectivity. These findings demonstrate the importance of neurogliaform cells in regulating cortical behavioral state-dependent spontaneous activity and indicate that their functional engagement during visual stimuli is influenced by their laminar positioning and connectivity.

Joint inference of discrete cell types and continuous type-specific variability in single-cell datasets with MMIDAS

Reproducible definition and identification of cell types is essential to enable investigations into their biological function, and understanding their relevance in the context of development, disease and evolution. Current approaches model variability in data as continuous latent factors, followed by clustering as a separate step, or immediately apply clustering on the data. We show that such approaches can suffer from qualitative mistakes in identifying cell types robustly, particularly when the number of such cell types is in the hundreds or even thousands. Here, we propose an unsupervised method, MMIDAS, which combines a generalized mixture model with a multi-armed deep neural network, to jointly infer the discrete type and continuous type-specific variability. Using four recent datasets of brain cells spanning different technologies, species, and conditions, we demonstrate that MMIDAS can identify reproducible cell types and infer cell type-dependent continuous variability in both uni-modal and multi-modal datasets.

Computational Tools for Neuronal Morphometric Analysis: A Systematic Search and Review

Morphometry is fundamental for studying and correlating neuronal morphology with brain functions. With increasing computational power, it is possible to extract morphometric characteristics automatically, including features such as length, volume, and number of neuron branches. However, to the best of our knowledge, there is no mapping of morphometric tools yet. In this context, we conducted a systematic search and review to identify and analyze tools within the scope of neuron analysis. Thus, the work followed a well-defined protocol and sought to answer the following research questions: What open-source tools are available for neuronal morphometric analysis? What morphometric characteristics are extracted by these tools? For this, aiming for greater robustness and coverage, the study was based on the paper analysis as well as the study of documentation and tests with the tools available in repositories. We analyzed 1,586 papers and mapped 23 tools, where NeuroM, L-Measure, and NeuroMorphoVis extract the most features. Furthermore, we contribute to the body of knowledge with the unprecedented presentation of 150 unique morphometric features whose terminologies were categorized and standardized. Overall, the study contributes to advancing the understanding of the complex mechanisms underlying the brain.

Deciphering brain cellular and behavioral mechanisms: Insights from single-cell and spatial RNA sequencing

The brain is a complex computing system composed of a multitude of interacting neurons. The computational outputs of this system determine the behavior and perception of every individual. Each brain cell expresses thousands of genes that dictate the cell's function and physiological properties. Therefore, deciphering the molecular expression of each cell is of great significance for understanding its characteristics and role in brain function. Additionally, the positional information of each cell can provide crucial insights into their involvement in local brain circuits. In this review, we briefly overview the principles of single-cell RNA sequencing and spatial transcriptomics, the potential issues and challenges in their data processing, and their applications in brain research. We further outline several promising directions in neuroscience that could be integrated with single-cell RNA sequencing, including neurodevelopment, the identification of novel brain microstructures, cognition and behavior, neuronal cell positioning, molecules and cells related to advanced brain functions, sleep-wake cycles/circadian rhythms, and computational modeling of brain function. We believe that the deep integration of these directions with single-cell and spatial RNA sequencing can contribute significantly to understanding the roles of individual cells or cell types in these specific functions, thereby making important contributions to addressing critical questions in those fields. This article is categorized under: RNA Evolution and Genomics > Computational Analyses of RNA RNA in Disease and Development > RNA in Development RNA in Disease and Development > RNA in Disease.

Layer Va neurons, as major presynaptic partners of corticospinal neurons, play critical roles in skilled movements

Corticospinal neurons (CSNs) are located in the cortex and projecting into the spinal cord. The activation of CSNs, which is associated with skilled motor behaviors, induces the activation of interneurons in the spinal cord. Eventually, motor neuron activation is induced by corticospinal circuits to coordinate muscle activation. Therefore, elucidating how the activation of CSNs in the brain is regulated is necessary for understanding the roles of CSNs in skilled motor behaviors. However, the presynaptic partners of CSNs in the brain remain to be identified. Here, we performed transsynaptic rabies virus-mediated brain-wide mapping to identify presynaptic partners of CSNs (pre-CSNs). We found that pre-CSNs are located in all cortical layers, but major pre-CSNs are located in layer Va. A small population of pre-CSNs are also located outside the cortex, such as in the thalamus. Inactivation of layer Va neurons in Tlx3-Cre mice results in deficits in skilled reaching and grasping behaviors, suggesting that, similar to CSNs, layer Va neurons are critical for skilled movements. Finally, we examined whether the connectivity of CSNs is altered after spinal cord injury (SCI). We found that unlike connections between CNSs and postsynaptic neurons, connections between pre-CSNs and CSNs do not change after SCI.

The Gene Expression Landscape of Disease Genes

Fine-mapping and gene-prioritisation techniques applied to the latest Genome-Wide Association Study (GWAS) results have prioritised hundreds of genes as causally associated with disease. Here we leverage these recently compiled lists of high-confidence causal genes to interrogate where in the body disease genes operate. Specifically, we combine GWAS summary statistics, gene prioritisation results and gene expression RNA-seq data from 46 tissues and 204 cell types in relation to 16 major diseases (including 8 cancers). In tissues and cell types with well-established relevance to the disease, the prioritised genes typically have higher absolute and relative (i.e. tissue/cell specific) expression compared to non-prioritised 'control' genes. Examples include brain tissues in psychiatric disorders (P-value < 1×10-7), microglia cells in Alzheimer's Disease (P-value = 9.8×10-3) and colon mucosa in colorectal cancer (P-value < 1×10-3). We also observe significantly higher expression for disease genes in multiple tissues and cell types with no established links to the corresponding disease. While some of these results may be explained by cell types that span multiple tissues, such as macrophages in brain, blood, lung and spleen in relation to Alzheimer's disease (P-values < 1×10-3), the cause for others is unclear and motivates further investigation that may provide novel insights into disease etiology. For example, mammary tissue in Type 2 Diabetes (P-value < 1×10-7); reproductive tissues such as breast, uterus, vagina, and prostate in Coronary Artery Disease (P-value < 1×10-4); and motor neurons in psychiatric disorders (P-value < 3×10-4). In the GTEx dataset, tissue type is the major predictor of gene expression but the contribution of each predictor (tissue, sample, subject, batch) varies widely among disease-associated genes. Finally, we highlight genes with the highest levels of gene expression in relevant tissues to guide functional follow-up studies. Our results could offer novel insights into the tissues and cells involved in disease initiation, inform drug target and delivery strategies, highlighting potential off-target effects, and exemplify the relative performance of different statistical tests for linking disease genes with tissue and cell type gene expression.

Optimization of AAV vectors for transactivator-regulated enhanced gene expression within targeted neuronal populations

Adeno-associated virus (AAV) vectors are potential tools for cell-type-selective gene delivery to the central nervous system. Although cell-type-specific enhancers and promoters have been identified for AAV systems, there is limited information regarding the effects of AAV genomic components on the selectivity and efficiency of gene expression. Here, we offer an alternative strategy to provide specific and efficient gene delivery to a targeted neuronal population by optimizing recombinant AAV genomic components, named TAREGET (TransActivator-Regulated Enhanced Gene Expression within Targeted neuronal populations). We established this strategy in oxytocinergic neurons and showed that the TAREGET enabled sufficient gene expression to label long-projecting axons in wild-type mice. Its application to other cell types, including serotonergic and dopaminergic neurons, was also demonstrated. These results demonstrate that optimization of AAV expression cassettes can improve the specificity and efficiency of cell-type-specific gene expression and that TAREGET can renew previously established cell-type-specific promoters with improved performance.

scRank infers drug-responsive cell types from untreated scRNA-seq data using a target-perturbed gene regulatory network

Cells respond divergently to drugs due to the heterogeneity among cell populations. Thus, it is crucial to identify drug-responsive cell populations in order to accurately elucidate the mechanism of drug action, which is still a great challenge. Here, we address this problem with scRank, which employs a target-perturbed gene regulatory network to rank drug-responsive cell populations via in silico drug perturbations using untreated single-cell transcriptomic data. We benchmark scRank on simulated and real datasets, which shows the superior performance of scRank over existing methods. When applied to medulloblastoma and major depressive disorder datasets, scRank identifies drug-responsive cell types that are consistent with the literature. Moreover, scRank accurately uncovers the macrophage subpopulation responsive to tanshinone IIA and its potential targets in myocardial infarction, with experimental validation. In conclusion, scRank enables the inference of drug-responsive cell types using untreated single-cell data, thus providing insights into the cellular-level impacts of therapeutic interventions.

Evo-devo applied to sleep research: an approach whose time has come

Sleep occurs in all animals but its amount, form, and timing vary considerably between species and between individuals. Currently, little is known about the basis for these differences, in part, because we lack a complete understanding of the brain circuitry controlling sleep-wake states and markers for the cell types which can identify similar circuits across phylogeny. Here, I explain the utility of an "Evo-devo" approach for comparative studies of sleep regulation and function as well as for sleep medicine. This approach focuses on the regulation of evolutionary ancient transcription factors which act as master controllers of cell-type specification. Studying these developmental transcription factor cascades can identify novel cell clusters which control sleep and wakefulness, reveal the mechanisms which control differences in sleep timing, amount, and expression, and identify the timepoint in evolution when different sleep-wake control neurons appeared. Spatial transcriptomic studies, which identify cell clusters based on transcription factor expression, will greatly aid this approach. Conserved developmental pathways regulate sleep in mice, Drosophila, and C. elegans. Members of the LIM Homeobox (Lhx) gene family control the specification of sleep and circadian neurons in the forebrain and hypothalamus. Increased Lhx9 activity may account for increased orexin/hypocretin neurons and reduced sleep in Mexican cavefish. Other transcription factor families specify sleep-wake circuits in the brainstem, hypothalamus, and basal forebrain. The expression of transcription factors allows the generation of specific cell types for transplantation approaches. Furthermore, mutations in developmental transcription factors are linked to variation in sleep duration in humans, risk for restless legs syndrome, and sleep-disordered breathing. This paper is part of the "Genetic and other molecular underpinnings of sleep, sleep disorders, and circadian rhythms including translational approaches" collection.

The ENCODE mouse postnatal developmental time course identifies regulatory programs of cell types and cell states

Postnatal genomic regulation significantly influences tissue and organ maturation but is under-studied relative to existing genomic catalogs of adult tissues or prenatal development in mouse. The ENCODE4 consortium generated the first comprehensive single-nucleus resource of postnatal regulatory events across a diverse set of mouse tissues. The collection spans seven postnatal time points, mirroring human development from childhood to adulthood, and encompasses five core tissues. We identified 30 cell types, further subdivided into 69 subtypes and cell states across adrenal gland, left cerebral cortex, hippocampus, heart, and gastrocnemius muscle. Our annotations cover both known and novel cell differentiation dynamics ranging from early hippocampal neurogenesis to a new sex-specific adrenal gland population during puberty. We used an ensemble Latent Dirichlet Allocation strategy with a curated vocabulary of 2,701 regulatory genes to identify regulatory "topics," each of which is a gene vector, linked to cell type differentiation, subtype specialization, and transitions between cell states. We find recurrent regulatory topics in tissue-resident macrophages, neural cell types, endothelial cells across multiple tissues, and cycling cells of the adrenal gland and heart. Cell-type-specific topics are enriched in transcription factors and microRNA host genes, while chromatin regulators dominate mitosis topics. Corresponding chromatin accessibility data reveal dynamic and sex-specific regulatory elements, with enriched motifs matching transcription factors in regulatory topics. Together, these analyses identify both tissue-specific and common regulatory programs in postnatal development across multiple tissues through the lens of the factors regulating transcription.