Adult mouse cortical cell taxonomy revealed by single cell transcriptomics

Single-nucleus RNA-sequencing of orbitofrontal cortex in rat model of methamphetamine-induced sensitization

The behavioral sensitization, characterized by escalated behavioral responses triggered by recurrent exposure to psychostimulants, involves neurobiological mechanisms that are brain-region and cell-type specific. Enduring neuroadaptive changes have been observed in response to methamphetamine (METH) within the orbitofrontal cortex (OFC), the cell-type specific transcriptional alterations in response to METH sensitization remain understudied. In this study, we utilized Single-nucleus RNA-sequencing (snRNA-seq) to profile the gene expression changes in the OFC of a rat METH sensitization model. The analyses of differentially expressed genes (DEGs) unveiled cell-type specific transcriptional reactions associated with METH sensitization, with the most significant alterations documented in microglial cells. Bioinformatic investigations revealed that distinct functional and signaling pathways enriched in microglia-specific DEGs majorly involved in macroautophagy processes and the activation of N-methyl-D-aspartate ionotropic glutamate receptors (NMDAR). To validate the translational relevance of our findings, we analyzed our snRNA-seq data in conjunction with a transcriptomic study of individuals with opioid use disorder (OUD) and a large-scale Genome-Wide Association Studies (GWAS) from multiple externalizing phenotypes related to drug addiction. The validation analysis confirmed the consistent expression changes of key microglial DEGs in human METH addiction. Moreover, the integration with GWAS data revealed associations between addiction risk genes and the DEGs observed in specific cell types, particularly microglia and excitatory neurons. Our study highlights the importance of cell-type specific transcriptional alterations in the OFC in the context of METH sensitization and their potential translational relevance to human drug addiction.

Massive multiplexing of spatially resolved single neuron projections with axonal BARseq

Neurons in the cortex are heterogeneous, sending diverse axonal projections to multiple brain regions. Unraveling the logic of these projections requires single-neuron resolution. Although a growing number of techniques have enabled high-throughput reconstruction, these techniques are typically limited to dozens or at most hundreds of neurons per brain, requiring that statistical analyses combine data from different specimens. Here we present axonal BARseq, a high-throughput approach based on reading out nucleic acid barcodes using in situ RNA sequencing, which enables analysis of even densely labeled neurons. As a proof of principle, we have mapped the long-range projections of >8000 primary auditory cortex neurons from a single male mouse. We identified major cell types based on projection targets and axonal trajectory. The large sample size enabled us to systematically quantify the projections of intratelencephalic (IT) neurons, and revealed that individual IT neurons project to different layers in an area-dependent fashion. Axonal BARseq is a powerful technique for studying the heterogeneity of single neuronal projections at high throughput within individual brains.

ImSpiRE: image feature-aided spatial resolution enhancement method

The resolution of most spatially resolved transcriptomic technologies usually cannot attain the single-cell level, limiting their applications in biological discoveries. Here, we introduce ImSpiRE, an image feature-aided spatial resolution enhancement method for in situ capturing spatial transcriptome. Taking the information stored in histological images, ImSpiRE solves an optimal transport problem to redistribute the expression profiles of spots to construct new transcriptional profiles with enhanced resolution, together with extending the gene expression profiles into unmeasured regions. Applications to multiple datasets confirm that ImSpiRE can enhance spatial resolution to the subspot level while contributing to the discovery of tissue domains, signaling communication patterns, and spatiotemporal characterization.

Neurodevelopmental disorder-associated CYFIP2 regulates membraneless organelles and eIF2α phosphorylation via protein interactors and actin cytoskeleton

De novo variants in the Cytoplasmic FMR1-interacting protein 2 (CYFIP2) have been repeatedly associated with neurodevelopmental disorders and epilepsy, underscoring its critical role in brain development and function. While CYFIP2's role in regulating actin polymerization as part of the WAVE regulatory complex (WRC) is well-established, its additional molecular functions remain relatively unexplored. In this study, we performed unbiased quantitative proteomic analysis, revealing 278 differentially expressed proteins (DEPs) in the forebrain of Cyfip2 knock-out embryonic mice compared to wild-type mice. Unexpectedly, these DEPs, in conjunction with previously identified CYFIP2 brain interactors, included not only other WRC components but also numerous proteins associated with membraneless organelles (MLOs) involved in mRNA processing and translation within cells, including the nucleolus, stress granules, and processing bodies. Additionally, single-cell transcriptomic analysis of the Cyfip2 knock-out forebrain revealed gene expression changes linked to cellular stress responses and MLOs. We also observed morphological changes in MLOs in Cyfip2 knock-out brains and CYFIP2 knock-down cells under basal and stress conditions. Lastly, we demonstrated that CYFIP2 knock-down in cells, potentially through WRC-dependent actin regulation, suppressed the phosphorylation levels of the alpha subunit of eukaryotic translation initiation factor 2 (eIF2α), thereby enhancing protein synthesis. These results suggest a physical and functional connection between CYFIP2 and various MLO proteins and also extend CYFIP2's role within the WRC from actin regulation to influencing eIF2α phosphorylation and protein synthesis. With these dual functions, CYFIP2 may fine-tune the balance between MLO formation/dynamics and protein synthesis, a crucial aspect of proper mRNA processing and translation.

ESCHR: a hyperparameter-randomized ensemble approach for robust clustering across diverse datasets

Clustering is widely used for single-cell analysis, but current methods are limited in accuracy, robustness, ease of use, and interpretability. To address these limitations, we developed an ensemble clustering method that outperforms other methods at hard clustering without the need for hyperparameter tuning. It also performs soft clustering to characterize continuum-like regions and quantify clustering uncertainty, demonstrated here by mapping the connectivity and intermediate transitions between MNIST handwritten digits and between hypothalamic tanycyte subpopulations. This hyperparameter-randomized ensemble approach improves the accuracy, robustness, ease of use, and interpretability of single-cell clustering, and may prove useful in other fields as well.

VEGF-A-mediated venous endothelial cell proliferation results in neoangiogenesis during neuroinflammation

Newly formed leaky vessels and blood-brain barrier (BBB) damage are present in demyelinating acute and chronic lesions in multiple sclerosis (MS) and experimental autoimmune encephalomyelitis (EAE). However, the endothelial cell subtypes and signaling pathways contributing to these leaky neovessels are unclear. Here, using single-cell transcriptional profiling and in vivo validation studies, we show that venous endothelial cells express neoangiogenesis gene signatures and show increased proliferation resulting in enlarged veins and higher venous coverage in acute and chronic EAE lesions in female adult mice. These changes correlate with the upregulation of vascular endothelial growth factor A (VEGF-A) signaling. We also confirmed increased expression of neoangiogenic markers in acute and chronic human MS lesions. Treatment with a VEGF-A blocking antibody diminishes the neoangiogenic transcriptomic signatures and vascular proliferation in female adult mice with EAE, but it does not restore BBB function or ameliorate EAE pathology. Our data demonstrate that venous endothelial cells contribute to neoangiogenesis in demyelinating neuroinflammatory conditions.

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.

Graph Fourier transform for spatial omics representation and analyses of complex organs

Spatial omics technologies decipher functional components of complex organs at cellular and subcellular resolutions. We introduce Spatial Graph Fourier Transform (SpaGFT) and apply graph signal processing to a wide range of spatial omics profiling platforms to generate their interpretable representations. This representation supports spatially variable gene identification and improves gene expression imputation, outperforming existing tools in analyzing human and mouse spatial transcriptomics data. SpaGFT can identify immunological regions for B cell maturation in human lymph nodes Visium data and characterize variations in secondary follicles using in-house human tonsil CODEX data. Furthermore, it can be integrated seamlessly into other machine learning frameworks, enhancing accuracy in spatial domain identification, cell type annotation, and subcellular feature inference by up to 40%. Notably, SpaGFT detects rare subcellular organelles, such as Cajal bodies and Set1/COMPASS complexes, in high-resolution spatial proteomics data. This approach provides an explainable graph representation method for exploring tissue biology and function.

The chemokine Cxcl14 regulates interneuron differentiation in layer I of the somatosensory cortex

Spontaneous and sensory-evoked activity sculpts developing circuits. Yet, how these activity patterns intersect with cellular programs regulating the differentiation of neuronal subtypes is not well understood. Through electrophysiological and in vivo longitudinal analyses, we show that C-X-C motif chemokine ligand 14 (Cxcl14), a gene previously characterized for its association with tumor invasion, is expressed by single-bouquet cells (SBCs) in layer I (LI) of the somatosensory cortex during development. Sensory deprivation at neonatal stages markedly decreases Cxcl14 expression. Additionally, we report that loss of function of this gene leads to increased intrinsic excitability of SBCs-but not LI neurogliaform cells-and augments neuronal complexity. Furthermore, Cxcl14 loss impairs sensory map formation and compromises the in vivo recruitment of superficial interneurons by sensory inputs. These results indicate that Cxcl14 is required for LI differentiation and demonstrate the emergent role of chemokines as key players in cortical network development.

Neural Network Mechanisms Underlying General Anesthesia: Cortical and Subcortical Nuclei

General anesthesia plays a significant role in modern medicine. However, the precise mechanism of general anesthesia remains unclear, posing a key scientific challenge in anesthesiology. Advances in neuroscience techniques have enabled targeted manipulation of specific neural circuits and the capture of brain-wide neural activity at high resolution. These advances hold promise for elucidating the intricate mechanisms of action of general anesthetics. This review aims to summarize our current understanding of the role of cortical and subcortical nuclei in modulating general anesthesia, providing new evidence of cortico-cortical and thalamocortical networks in relation to anesthesia and consciousness. These insights contribute to a comprehensive understanding of the neural network mechanisms underlying general anesthesia.

Cell type-specific dysregulation of gene expression due to Chd8 haploinsufficiency during mouse cortical development

Disruptive variants in the chromodomain helicase CHD8, which acts as a transcriptional regulator during neurodevelopment, are strongly associated with risk for autism spectrum disorder (ASD). Loss of CHD8 function is hypothesized to perturb gene regulatory networks in the developing brain, thereby contributing to ASD etiology. However, insight into the cell type-specific transcriptional effects of CHD8 loss of function remains limited. We used single-cell and single-nucleus RNA-sequencing to globally profile gene expression and identify dysregulated genes in the embryonic and juvenile wild type and Chd8 +/- mouse cortex, respectively. Chd8 and other ASD risk-associated genes showed a convergent expression trajectory that was largely conserved between the mouse and human developing cortex, increasing from the progenitor zones to the cortical plate. Genes associated with risk for neurodevelopmental disorders and genes involved in neuron projection development, chromatin remodeling, signaling, and migration were dysregulated in Chd8 +/- embryonic day (E) 12.5 radial glia. Genes implicated in synaptic organization and activity were dysregulated in Chd8 +/- postnatal day (P) 25 deep- and upper-layer excitatory cortical neurons, suggesting a delay in synaptic maturation or impaired synaptogenesis due to CHD8 loss of function. Our findings reveal a complex pattern of transcriptional dysregulation in Chd8 +/- developing cortex, potentially with distinct biological impacts on progenitors and maturing neurons in the excitatory neuronal lineage.

Interleukin-21 engineering enhances NK cell activity against glioblastoma via CEBPD

Glioblastoma (GBM) is an aggressive brain cancer with limited therapeutic options. Natural killer (NK) cells are innate immune cells with strong anti-tumor activity and may offer a promising treatment strategy for GBM. We compared the anti-GBM activity of NK cells engineered to express interleukin (IL)-15 or IL-21. Using multiple in vivo models, IL-21 NK cells were superior to IL-15 NK cells both in terms of safety and long-term anti-tumor activity, with locoregionally administered IL-15 NK cells proving toxic and ineffective at tumor control. IL-21 NK cells displayed a unique chromatin accessibility signature, with CCAAT/enhancer-binding proteins (C/EBP), especially CEBPD, serving as key transcription factors regulating their enhanced function. Deletion of CEBPD resulted in loss of IL-21 NK cell potency while its overexpression increased NK cell long-term cytotoxicity and metabolic fitness. These results suggest that IL-21, through C/EBP transcription factors, drives epigenetic reprogramming of NK cells, enhancing their anti-tumor efficacy against GBM.

Chronic stress deficits in reward behaviour co-occur with low nucleus accumbens dopamine activity during reward anticipation specifically

Whilst reward pathologies are major and common in stress-related neuropsychiatric disorders, their neurobiology and treatment are poorly understood. Imaging studies in human reward pathology indicate attenuated BOLD activity in nucleus accumbens (NAc) coincident with reward anticipation but not reinforcement; potentially, this is dopamine (DA) related. In mice, chronic social stress (CSS) leads to reduced reward learning and motivation. Here, DA-sensor fibre photometry is used to investigate whether these behavioural deficits co-occur with altered NAc DA activity during reward anticipation and/or reinforcement. In CSS mice relative to controls: (1) Reduced discriminative learning of the sequence, tone-on + appetitive behaviour = tone-on + sucrose reinforcement, co-occurs with attenuated NAc DA activity throughout tone-on and sucrose reinforcement. (2) Reduced motivation during the sequence, operant behaviour = tone-on + sucrose delivery + sucrose reinforcement, co-occurs with attenuated NAc DA activity at tone-on and typical activity at sucrose reinforcement. (3) Reduced motivation during the sequence, operant behaviour = appetitive behaviour + sociosexual reinforcement, co-occurs with typical NAc DA activity at female reinforcement. Therefore, in CSS mice, low NAc DA activity co-occurs with low reward anticipation and could account for deficits in learning and motivation, with important implications for understanding human reward pathology.

Histone variant H2BE enhances chromatin accessibility in neurons to promote synaptic gene expression and long-term memory

Histone proteins affect gene expression through multiple mechanisms, including through exchange with histone variants. Recent findings link histone variants to neurological disorders, yet few are well studied in the brain. Most notably, widely expressed variants of H2B remain elusive. We applied recently developed antibodies, biochemical assays, and sequencing approaches to reveal broad expression of the H2B variant H2BE and defined its role in regulating chromatin structure, neuronal transcription, and mouse behavior. We find that H2BE is enriched at promoters, and a single unique amino acid allows it to dramatically enhance chromatin accessibility. Further, we show that H2BE is critical for synaptic gene expression and long-term memory. Together, these data reveal a mechanism linking histone variants to chromatin accessibility, transcriptional regulation, neuronal function, and memory. This work further identifies a widely expressed H2B variant and uncovers a single histone amino acid with profound effects on genomic structure.

High-throughput gene expression analysis with TempO-LINC sensitively resolves complex brain, lung and kidney heterogeneity at single-cell resolution

We report the development and performance of a novel genomics platform, TempO-LINC, for conducting high-throughput transcriptomic analysis on single cells and nuclei. TempO-LINC works by adding cell-identifying molecular barcodes onto highly selective and high-sensitivity gene expression probes within fixed cells, without having to first generate cDNA. Using an instrument-free combinatorial-indexing approach, all probes within the same fixed cell receive an identical barcode, enabling the reconstruction of single-cell gene expression profiles across as few as several hundred cells and up to 100,000+ cells per run. The TempO-LINC approach is easily scalable based on the number of barcodes and rounds of barcoding performed; however, for the experiments reported in this study, the assay utilized over 5.3 million unique barcodes. TempO-LINC has a robust protocol for fixing and banking cells and displays high-sensitivity gene detection from multiple diverse sample types. We show that TempO-LINC has an observed multiplet rate of less than 1.1% and a cell capture rate of ~50%. Although the assay can accurately profile the whole transcriptome (19,683 human or 21,400 mouse genes), it can be targeted to measure only actionable/informative genes and molecular pathways of interest - thereby reducing sequencing requirements. In this study, we applied TempO-LINC to profile the transcriptomes of 89,722 cells across multiple sample types, including nuclei from mouse lung, kidney and brain tissues. The data demonstrated the ability to identify and annotate at least 50 unique cell populations and positively correlate expression of cell type-specific molecular markers within them. TempO-LINC is a robust new single-cell technology that is ideal for large-scale applications/studies across thousands of samples with high data quality.

A dorsal hippocampus-prodynorphinergic dorsolateral septum-to-lateral hypothalamus circuit mediates contextual gating of feeding

Adaptive regulation of feeding depends on linkage of internal states and food outcomes with contextual cues. Human brain imaging has identified dysregulation of a hippocampal-lateral hypothalamic area (LHA) network in binge eating, but mechanistic instantiation of underlying cell-types and circuitry is lacking. Here, we identify an evolutionary conserved and discrete Prodynorphin (Pdyn)-expressing subpopulation of Somatostatin (Sst)-expressing inhibitory neurons in the dorsolateral septum (DLS) that receives primarily dorsal, but not ventral, hippocampal inputs. DLS(Pdyn) neurons inhibit LHA GABAergic neurons and confer context- and internal state-dependent calibration of feeding. Viral deletion of Pdyn in the DLS mimicked effects seen with optogenetic silencing of DLS Pdyn INs, suggesting a potential role for DYNORPHIN-KAPPA OPIOID RECEPTOR signaling in contextual regulation of food-seeking. Together, our findings illustrate how the dorsal hippocampus has evolved to recruit an ancient LHA feeding circuit module through Pdyn DLS inhibitory neurons to link contextual information with regulation of food consumption.

A general principle governing neuronal evolution reveals a human-accelerated neuron type potentially underlying the high prevalence of autism in humans

The remarkable ability of a single genome sequence to encode a diverse collection of distinct cell types, including the thousands of cell types found in the mammalian brain, is a key characteristic of multicellular life. While it has been observed that some cell types are far more evolutionarily conserved than others, the factors driving these differences in evolutionary rate remain unknown. Here, we hypothesized that highly abundant neuronal cell types may be under greater selective constraint than rarer neuronal types, leading to variation in their rates of evolution. To test this, we leveraged recently published cross-species single-nucleus RNA-sequencing datasets from three distinct regions of the mammalian neocortex. We found a strikingly consistent relationship where more abundant neuronal subtypes show greater gene expression conservation between species, which replicated across three independent datasets covering >106 neurons from six species. Based on this principle, we discovered that the most abundant type of neocortical neurons-layer 2/3 intratelencephalic excitatory neurons-has evolved exceptionally quickly in the human lineage compared to other apes. Surprisingly, this accelerated evolution was accompanied by the dramatic down-regulation of autism-associated genes, which was likely driven by polygenic positive selection specific to the human lineage. In sum, we introduce a general principle governing neuronal evolution and suggest that the exceptionally high prevalence of autism in humans may be a direct result of natural selection for lower expression of a suite of genes that conferred a fitness benefit to our ancestors while also rendering an abundant class of neurons more sensitive to perturbation.

A unified model for interpretable latent embedding of multi-sample, multi-condition single-cell data

Single-cell analysis across multiple samples and conditions requires quantitative modeling of the interplay between the continuum of cell states and the technical and biological sources of sample-to-sample variability. We introduce GEDI, a generative model that identifies latent space variations in multi-sample, multi-condition single-cell datasets and attributes them to sample-level covariates. GEDI enables cross-sample cell state mapping on par with state-of-the-art integration methods, cluster-free differential gene expression analysis along the continuum of cell states, and machine learning-based prediction of sample characteristics from single-cell data. GEDI can also incorporate gene-level prior knowledge to infer pathway and regulatory network activities in single cells. Finally, GEDI extends all these concepts to previously unexplored modalities that require joint consideration of dual measurements, such as the joint analysis of exon inclusion/exclusion reads to model alternative cassette exon splicing, or spliced/unspliced reads to model the mRNA stability landscapes of single cells.

Pyramidal neurons proportionately alter the identity and survival of specific cortical interneuron subtypes

The mammalian cerebral cortex comprises a complex neuronal network that maintains a delicate balance between excitatory neurons and inhibitory interneurons. Previous studies, including our own research, have shown that specific interneuron subtypes are closely associated with particular pyramidal neuron types, forming stereotyped local inhibitory microcircuits. However, the developmental processes that establish these precise networks are not well understood. Here we show that pyramidal neuron types are instrumental in driving the terminal differentiation and maintaining the survival of specific associated interneuron subtypes. In a wild-type cortex, the relative abundance of different interneuron subtypes aligns precisely with the pyramidal neuron types to which they synaptically target. In Fezf2 mutant cortex, characterized by the absence of layer 5 pyramidal tract neurons and an expansion of layer 6 intratelencephalic neurons, we observed a corresponding decrease in associated layer 5b interneurons and an increase in layer 6 subtypes. Interestingly, these shifts in composition are achieved through mechanisms specific to different interneuron types. While SST interneurons adjust their abundance to the change in pyramidal neuron prevalence through the regulation of programmed cell death, parvalbumin interneurons alter their identity. These findings illustrate two key strategies by which the dynamic interplay between pyramidal neurons and interneurons allows local microcircuits to be sculpted precisely. These insights underscore the precise roles of extrinsic signals from pyramidal cells in the establishment of interneuron diversity and their subsequent integration into local cortical microcircuits.

Cortical Layer-Dependent Signaling in Cognition: Three Computational Modes of the Canonical Circuit

The cerebral cortex performs computations via numerous six-layer modules. The operational dynamics of these modules were studied primarily in early sensory cortices using bottom-up computation for response selectivity as a model, which has been recently revolutionized by genetic approaches in mice. However, cognitive processes such as recall and imagery require top-down generative computation. The question of whether the layered module operates similarly in top-down generative processing as in bottom-up sensory processing has become testable by advances in the layer identification of recorded neurons in behaving monkeys. This review examines recent advances in laminar signaling in these two computations, using predictive coding computation as a common reference, and shows that each of these computations recruits distinct laminar circuits, particularly in layer 5, depending on the cognitive demands. These findings highlight many open questions, including how different interareal feedback pathways, originating from and terminating at different layers, convey distinct functional signals.

Mining alternative splicing patterns in scRNA-seq data using scASfind

Single-cell RNA-seq (scRNA-seq) is widely used for transcriptome profiling, but most analyses focus on gene-level events, with less attention devoted to alternative splicing. Here, we present scASfind, a novel computational method to allow for quantitative analysis of cell type-specific splicing events using full-length scRNA-seq data. ScASfind utilizes an efficient data structure to store the percent spliced-in value for each splicing event. This makes it possible to exhaustively search for patterns among all differential splicing events, allowing us to identify marker events, mutually exclusive events, and events involving large blocks of exons that are specific to one or more cell types.

Compound models and Pearson residuals for single-cell RNA-seq data without UMIs

Recent work employed Pearson residuals from Poisson or negative binomial models to normalize UMI data. To extend this approach to non-UMI data, we model the additional amplification step with a compound distribution: we assume that sequenced RNA molecules follow a negative binomial distribution, and are then replicated following an amplification distribution. We show how this model leads to compound Pearson residuals, which yield meaningful gene selection and embeddings of Smart-seq2 datasets. Further, we suggest that amplification distributions across several sequencing protocols can be described by a broken power law. The resulting compound model captures previously unexplained overdispersion and zero-inflation patterns in non-UMI data.

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.

Pyramidal neurons proportionately alter the identity and survival of specific cortical interneuron subtypes

The mammalian cerebral cortex comprises a complex neuronal network that maintains a delicate balance between excitatory neurons and inhibitory interneurons. Previous studies, including our own research, have shown that specific interneuron subtypes are closely associated with particular pyramidal neuron types, forming stereotyped local inhibitory microcircuits. However, the developmental processes that establish these precise networks are not well understood. Here we show that pyramidal neuron types are instrumental in driving the terminal differentiation and maintaining the survival of specific associated interneuron subtypes. In a wild-type cortex, the relative abundance of different interneuron subtypes aligns precisely with the pyramidal neuron types to which they synaptically target. In Fezf2 mutant cortex, characterized by the absence of layer 5 pyramidal tract neurons and an expansion of layer 6 intratelencephalic neurons, we observed a corresponding decrease in associated layer 5b interneurons and an increase in layer 6 subtypes. Interestingly, these shifts in composition are achieved through mechanisms specific to different interneuron types. While SST interneurons adjust their abundance to the change in pyramidal neuron prevalence through the regulation of programmed cell death, parvalbumin interneurons alter their identity. These findings illustrate two key strategies by which the dynamic interplay between pyramidal neurons and interneurons allows local microcircuits to be sculpted precisely. These insights underscore the precise roles of extrinsic signals from pyramidal cells in the establishment of interneuron diversity and their subsequent integration into local cortical microcircuits.

A brief history of somatostatin interneuron taxonomy or: how many somatostatin subtypes are there, really?

We provide a brief (and unabashedly biased) overview of the pre-transcriptomic history of somatostatin interneuron taxonomy, followed by a chronological summary of the large-scale, NIH-supported effort over the last ten years to generate a comprehensive, single-cell RNA-seq-based taxonomy of cortical neurons. Focusing on somatostatin interneurons, we present the perspective of experimental neuroscientists trying to incorporate the new classification schemes into their own research while struggling to keep up with the ever-increasing number of proposed cell types, which seems to double every two years. We suggest that for experimental analysis, the most useful taxonomic level is the subdivision of somatostatin interneurons into ten or so "supertypes," which closely agrees with their more traditional classification by morphological, electrophysiological and neurochemical features. We argue that finer subdivisions ("t-types" or "clusters"), based on slight variations in gene expression profiles but lacking clear phenotypic differences, are less useful to researchers and may actually defeat the purpose of classifying neurons to begin with. We end by stressing the need for generating novel tools (mouse lines, viral vectors) for genetically targeting distinct supertypes for expression of fluorescent reporters, calcium sensors and excitatory or inhibitory opsins, allowing neuroscientists to chart the input and output synaptic connections of each proposed subtype, reveal the position they occupy in the cortical network and examine experimentally their roles in sensorimotor behaviors and cognitive brain functions.

In situ Patch-seq analysis of microglia reveals a lack of stress genes as found in FACS-isolated microglia

We applied the patch-seq technique to harvest transcripts from individual microglial cells from cortex, hippocampus and corpus callosum of acute brain slices from adult mice. After recording membrane currents with the patch-clamp technique, the cytoplasm was collected via the pipette and underwent adapted SMART-seq2 preparation with subsequent sequencing. On average, 4138 genes were detected in 113 cells from hippocampus, corpus callosum and cortex, including microglia markers such as Tmem119, P2ry12 and Siglec-H. Comparing our dataset to previously published single cell mRNA sequencing data from FACS-isolated microglia indicated that two clusters of cells were absent in our patch-seq dataset. Pathway analysis of marker genes in FACS-specific clusters revealed association with microglial activation and stress response. This indicates that under normal conditions microglia in situ lack transcripts associated with a stress-response, and that the microglia-isolation procedure by mechanical dissociation and FACS triggers the expression of genes related to activation and stress.

Long-range inhibition from prelimbic to cingulate areas of the medial prefrontal cortex enhances network activity and response execution

It is well established that the medial prefrontal cortex (mPFC) exerts top-down control of many behaviors, but little is known regarding how cross-talk between distinct areas of the mPFC influences top-down signaling. We performed virus-mediated tracing and functional studies in male mice, homing in on GABAergic projections whose axons are located mainly in layer 1 and that connect two areas of the mPFC, namely the prelimbic area (PrL) with the cingulate area 1 and 2 (Cg1/2). We revealed the identity of the targeted neurons that comprise two distinct types of layer 1 GABAergic interneurons, namely single-bouquet cells (SBCs) and neurogliaform cells (NGFs), and propose that this connectivity links GABAergic projection neurons with cortical canonical circuits. In vitro electrophysiological and in vivo calcium imaging studies support the notion that the GABAergic projection neurons from the PrL to the Cg1/2 exert a crucial role in regulating the activity in the target area by disinhibiting layer 5 output neurons. Finally, we demonstrated that recruitment of these projections affects impulsivity and mechanical responsiveness, behaviors which are known to be modulated by Cg1/2 activity.

Diffusion on PCA-UMAP Manifold: The Impact of Data Structure Preservation to Denoise High-Dimensional Single-Cell RNA Sequencing Data

Single-cell transcriptomics (scRNA-seq) is revolutionizing biological research, yet it faces challenges such as inefficient transcript capture and noise. To address these challenges, methods like neighbor averaging or graph diffusion are used. These methods often rely on k-nearest neighbor graphs from low-dimensional manifolds. However, scRNA-seq data suffer from the 'curse of dimensionality', leading to the over-smoothing of data when using imputation methods. To overcome this, sc-PHENIX employs a PCA-UMAP diffusion method, which enhances the preservation of data structures and allows for a refined use of PCA dimensions and diffusion parameters (e.g., k-nearest neighbors, exponentiation of the Markov matrix) to minimize noise introduction. This approach enables a more accurate construction of the exponentiated Markov matrix (cell neighborhood graph), surpassing methods like MAGIC. sc-PHENIX significantly mitigates over-smoothing, as validated through various scRNA-seq datasets, demonstrating improved cell phenotype representation. Applied to a multicellular tumor spheroid dataset, sc-PHENIX identified known extreme phenotype states, showcasing its effectiveness. sc-PHENIX is open-source and available for use and modification.

Control of innate olfactory valence by segregated cortical amygdala circuits

Animals perform innate behaviors that are stereotyped responses to specific evolutionarily relevant stimuli in the absence of prior learning or experience. These behaviors can be reduced to an axis of valence, whereby specific odors evoke approach or avoidance. The cortical amygdala (plCoA) mediates innate attraction and aversion to odor. However, little is known about how this brain area gives rise to behaviors of opposing motivational valence. Here, we sought to define the circuit features of plCoA that give rise to innate olfactory behaviors of valence. We characterized the physiology, gene expression, and projections of this structure, identifying a divergent, topographic organization that selectively controls innate attraction and avoidance to odor. First, we examined odor-evoked responses in these areas and found sparse encoding of odor identity, but not valence. We next considered a topographic organization and found that optogenetic stimulation of the anterior and posterior domains of plCoA elicits attraction and avoidance, respectively, suggesting a functional axis for valence. Using single cell and spatial RNA sequencing, we identified the molecular cell types in plCoA, revealing an anteroposterior gradient in cell types, whereby anterior glutamatergic neurons preferentially express Slc17a6 and posterior neurons express Slc17a7. Activation of these respective cell types recapitulates appetitive and aversive valence behaviors, and chemogenetic inhibition reveals partial necessity for valence responses to innate appetitive or aversive odors. Finally, we identified topographically organized circuits defined by projections, whereby anterior neurons preferentially project to medial amygdala, and posterior neurons preferentially project to nucleus accumbens, which are respectively sufficient and necessary for innate negative and positive olfactory valence. Together, these data advance our understanding of how the olfactory system generates stereotypic, hardwired attraction and avoidance, and supports a model whereby distinct, topographically distributed plCoA populations direct innate olfactory valence responses by signaling to divergent valence-specific targets, linking upstream olfactory identity to downstream valence behaviors, through a population code. This represents a novel circuit motif in which valence encoding is represented not by the firing properties of individual neurons, but by population level identity encoding that is routed through divergent targets to mediate distinct valence.

Where Top-Down Meets Bottom-Up: Cell-Type Specific Connectivity Map of the Whisker System

Sensorimotor computation integrates bottom-up world state information with top-down knowledge and task goals to form action plans. In the rodent whisker system, a prime model of active sensing, evidence shows neuromodulatory neurotransmitters shape whisker control, affecting whisking frequency and amplitude. Since neuromodulatory neurotransmitters are mostly released from subcortical nuclei and have long-range projections that reach the rest of the central nervous system, mapping the circuits of top-down neuromodulatory control of sensorimotor nuclei will help to systematically address the mechanisms of active sensing. Therefore, we developed a neuroinformatic target discovery pipeline to mine the Allen Institute's Mouse Brain Connectivity Atlas. Using network connectivity analysis, we identified new putative connections along the whisker system and anatomically confirmed the existence of 42 previously unknown monosynaptic connections. Using this data, we updated the sensorimotor connectivity map of the mouse whisker system and developed the first cell-type-specific map of the network. The map includes 157 projections across 18 principal nuclei of the whisker system and neuromodulatory neurotransmitter-releasing. Performing a graph network analysis of this connectome, we identified cell-type specific hubs, sources, and sinks, provided anatomical evidence for monosynaptic inhibitory projections into all stages of the ascending pathway, and showed that neuromodulatory projections improve network-wide connectivity. These results argue that beyond the modulatory chemical contributions to information processing and transfer in the whisker system, the circuit connectivity features of the neuromodulatory networks position them as nodes of sensory and motor integration.

Transcriptomic insights into multiple system atrophy from a PLP-α-synuclein transgenic mouse model

Multiple system atrophy (MSA) is a rare, neurodegenerative disorder with rapid motor and non-motor symptom progression. MSA is characterized by protein aggregations of α-synuclein found in the cytoplasm of oligodendrocytes. Despite this pathological hallmark, there is still little known about the cause of this disease, resulting in poor treatment options and quality of life post-diagnosis. In this study, we investigated differentially expressed genes (DEGs) via RNA-sequencing of brain samples from a validated PLP-α-synuclein transgenic mouse model, identifying a total of 40 DEGs in the PLP group compared to wild-type (WT), with top detected genes being Gm15446, Mcm6, Aldh7a1 and Gm3435. We observed a significant enrichment of immune pathways and endothelial cell genes among the upregulated genes, whereas downregulated genes were significantly enriched for oligodendrocyte and neuronal genes. We then calculated possible overlap of these DEGs with previously profiled human MSA RNA, resulting in the identification of significant downregulation of the Tsr2 gene. Identifying key gene expression profiles specific to MSA patients is crucial to further understanding the cause, and possible prevention, of this rapidly progressive neurodegenerative disorder.

Spatial Deconvolution of Cell Types and Cell States at Scale Utilizing TACIT

Identifying cell types and states remains a time-consuming, error-prone challenge for spatial biology. While deep learning is increasingly used, it is difficult to generalize due to variability at the level of cells, neighborhoods, and niches in health and disease. To address this, we developed TACIT, an unsupervised algorithm for cell annotation using predefined signatures that operates without training data. TACIT uses unbiased thresholding to distinguish positive cells from background, focusing on relevant markers to identify ambiguous cells in multiomic assays. Using five datasets (5,000,000-cells; 51-cell types) from three niches (brain, intestine, gland), TACIT outperformed existing unsupervised methods in accuracy and scalability. Integrating TACIT-identified cell types with a novel Shiny app revealed new phenotypes in two inflammatory gland diseases. Finally, using combined spatial transcriptomics and proteomics, we discovered under- and overrepresented immune cell types and states in regions of interest, suggesting multimodality is essential for translating spatial biology to clinical applications.

scHolography: a computational method for single-cell spatial neighborhood reconstruction and analysis

Spatial transcriptomics has transformed our ability to study tissue complexity. However, it remains challenging to accurately dissect tissue organization at single-cell resolution. Here we introduce scHolography, a machine learning-based method designed to reconstruct single-cell spatial neighborhoods and facilitate 3D tissue visualization using spatial and single-cell RNA sequencing data. scHolography employs a high-dimensional transcriptome-to-space projection that infers spatial relationships among cells, defining spatial neighborhoods and enhancing analyses of cell-cell communication. When applied to both human and mouse datasets, scHolography enables quantitative assessments of spatial cell neighborhoods, cell-cell interactions, and tumor-immune microenvironment. Together, scHolography offers a robust computational framework for elucidating 3D tissue organization and analyzing spatial dynamics at the cellular level.

Neocortical long-range inhibition promotes cortical synchrony and sleep

Behavioral states such as sleep and wake are highly correlated with specific patterns of rhythmic activity in the cortex. During low arousal states such as slow wave sleep, the cortex is synchronized and dominated by low frequency rhythms coordinated across multiple regions. Although recent evidence suggests that GABAergic inhibitory neurons are key players in cortical state modulation, the in vivo circuit mechanisms coordinating synchronized activity among local and distant neocortical networks are not well understood. Here, we show that somatostatin and chondrolectin co-expressing cells (Sst-Chodl cells), a sparse and unique class of neocortical inhibitory neurons, are selectively active during low arousal states and are largely silent during periods of high arousal. In contrast to other neocortical inhibitory neurons, we show these neurons have long-range axons that project across neocortical areas. Activation of Sst-Chodl cells is sufficient to promote synchronized cortical states characteristic of low arousal, with increased spike co-firing and low frequency brain rhythms, and to alter behavioral states by promoting sleep. Contrary to the prevailing belief that sleep is exclusively driven by subcortical mechanisms, our findings reveal that these long-range inhibitory neurons not only track changes in behavioral state but are sufficient to induce both sleep-like cortical states and sleep behavior, establishing a crucial circuit component in regulating behavioral states.

Variation and convergence in the morpho-functional properties of the mammalian neocortex

Man's natural inclination to classify and hierarchize the living world has prompted neurophysiologists to explore possible differences in brain organisation between mammals, with the aim of understanding the diversity of their behavioural repertoires. But what really distinguishes the human brain from that of a platypus, an opossum or a rodent? In this review, we compare the structural and electrical properties of neocortical neurons in the main mammalian radiations and examine their impact on the functioning of the networks they form. We discuss variations in overall brain size, number of neurons, length of their dendritic trees and density of spines, acknowledging their increase in humans as in most large-brained species. Our comparative analysis also highlights a remarkable consistency, particularly pronounced in marsupial and placental mammals, in the cell typology, intrinsic and synaptic electrical properties of pyramidal neuron subtypes, and in their organisation into functional circuits. These shared cellular and network characteristics contribute to the emergence of strikingly similar large-scale physiological and pathological brain dynamics across a wide range of species. These findings support the existence of a core set of neural principles and processes conserved throughout mammalian evolution, from which a number of species-specific adaptations appear, likely allowing distinct functional needs to be met in a variety of environmental contexts.

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

The mammalian cortex is comprised of cells with different morphological, physiological, and molecular properties that can be classified according to shared properties into cell types. Defining the contribution of each cell type to the computational and cognitive processes that are guided by the cortex is essential for understanding its function in health and disease. We use transcriptomic and epigenomic cortical cell type taxonomies from mice and humans to define marker genes and enhancers, and to build genetic tools for cortical cell types. Here, we present a large toolkit for selective targeting of cortical populations, including mouse transgenic lines and recombinant adeno-associated virus (AAV) vectors containing genomic enhancers. We report evaluation of fifteen new transgenic driver lines and over 680 different enhancer AAVs covering all major subclasses of cortical cells, with many achieving a high degree of specificity, comparable with existing transgenic lines. We find that the transgenic lines based on marker genes can provide exceptional specificity and completeness of cell type labeling, but frequently require generation of a triple-transgenic cross for best usability/specificity. On the other hand, enhancer AAVs are easy to screen and use, and can be easily modified to express diverse cargo, such as recombinases. However, their use depends on many factors, such as viral titer and route of administration. The tools reported here as well as the scaled process of tool creation provide an unprecedented resource that should enable diverse experimental strategies towards understanding mammalian cortex and brain function.

Deciphering the genetic code of neuronal type connectivity through bilinear modeling

Understanding how different neuronal types connect and communicate is critical to interpreting brain function and behavior. However, it has remained a formidable challenge to decipher the genetic underpinnings that dictate the specific connections formed between neuronal types. To address this, we propose a novel bilinear modeling approach that leverages the architecture similar to that of recommendation systems. Our model transforms the gene expressions of presynaptic and postsynaptic neuronal types, obtained from single-cell transcriptomics, into a covariance matrix. The objective is to construct this covariance matrix that closely mirrors a connectivity matrix, derived from connectomic data, reflecting the known anatomical connections between these neuronal types. When tested on a dataset of Caenorhabditis elegans, our model achieved a performance comparable to, if slightly better than, the previously proposed spatial connectome model (SCM) in reconstructing electrical synaptic connectivity based on gene expressions. Through a comparative analysis, our model not only captured all genetic interactions identified by the SCM but also inferred additional ones. Applied to a mouse retinal neuronal dataset, the bilinear model successfully recapitulated recognized connectivity motifs between bipolar cells and retinal ganglion cells, and provided interpretable insights into genetic interactions shaping the connectivity. Specifically, it identified unique genetic signatures associated with different connectivity motifs, including genes important to cell-cell adhesion and synapse formation, highlighting their role in orchestrating specific synaptic connections between these neurons. Our work establishes an innovative computational strategy for decoding the genetic programming of neuronal type connectivity. It not only sets a new benchmark for single-cell transcriptomic analysis of synaptic connections but also paves the way for mechanistic studies of neural circuit assembly and genetic manipulation of circuit wiring.

ARX regulates cortical interneuron differentiation and migration

Mutations in aristaless-related homeobox ( ARX ) are associated with neurodevelopmental disorders including developmental epilepsies, intellectual disabilities, and autism spectrum disorders, with or without brain malformations. Aspects of these disorders have been linked to abnormal cortical interneuron (cIN) development and function. To further understand ARX's role in cIN development, multiple Arx mutant mouse lines were interrogated. We found that ARX is critical for controlling cIN numbers and distribution, especially, in the developing marginal zone (MZ). Single cell transcriptomics and ChIP-seq, combined with functional studies, revealed ARX directly or indirectly regulates genes involved in proliferation and the cell cycle (e.g., Bub3 , Cspr3 ), fate specification (e.g., Nkx2.1 , Maf , Mef2c ), and migration (e.g., Nkx2.1 , Lmo1 , Cxcr4 , Nrg1 , ErbB4 ). Our data suggest that the MZ stream defects primarily result from disordered cell-cell communication. Together our findings provide new insights into the mechanisms underlying cIN development and migration and how they are disrupted in several disorders.

Neuronal cell populations in circumoral nerve ring of sea cucumber Apostichopus japonicus: Ultrastructure and transcriptional profile

The echinoderm nervous system has been studied as a model for understanding the evolution of the chordate nervous system. Neuronal cells are essential groups that release a 'cocktail' of messenger molecules providing a spectrum of biological actions in the nervous system. Among echinoderms, most evidence on neuronal cell types has been obtained from starfish and sea urchin. In sea cucumbers, most research has focused on the location of neuronal cells, whereas their transcriptional features have rarely been investigated. Here, we observed the ultrastructure of neuronal cells in the sea cucumber, Apostichopus japonicus. The transcriptional profile of neuronal cells from the circumoral nerve ring (CNR) was investigated using single-cell RNA sequencing (scRNA-seq), and a total of six neuronal cell types were identified. 26 neuropeptide precursor genes (NPPs) and 28 G-protein-coupled receptors (GPCR) were expressed in the six neuronal cell types, comprising five NPP/NP-GPCR pairs. Unsupervised pseudotime analysis of neuronal cells showed their different differentiation status. We also located the neuronal cells in the CNR by immunofluorescence (IF) and identified the potential hub genes of key cell populations. This broad resource serves as a valuable support in the development of cell-specific markers for accurate cell-type identification in sea cucumbers. It also contributes to facilitating comparison across species, providing a deeper understanding of the evolutionary processes of neuronal cells.

A Fusion Learning Model Based on Deep Learning for Single-Cell RNA Sequencing Data Clustering

Single-cell RNA sequencing (scRNA-seq) technology provides a means for studying biology from a cellular perspective. The fundamental goal of scRNA-seq data analysis is to discriminate single-cell types using unsupervised clustering. Few single-cell clustering algorithms have taken into account both deep and surface information, despite the recent slew of suggestions. Consequently, this article constructs a fusion learning framework based on deep learning, namely scGASI. For learning a clustering similarity matrix, scGASI integrates data affinity recovery and deep feature embedding in a unified scheme based on various top feature sets. Next, scGASI learns the low-dimensional latent representation underlying the data using a graph autoencoder to mine the hidden information residing in the data. To efficiently merge the surface information from raw area and the deeper potential information from underlying area, we then construct a fusion learning model based on self-expression. scGASI uses this fusion learning model to learn the similarity matrix of an individual feature set as well as the clustering similarity matrix of all feature sets. Lastly, gene marker identification, visualization, and clustering are accomplished using the clustering similarity matrix. Extensive verification on actual data sets demonstrates that scGASI outperforms many widely used clustering techniques in terms of clustering accuracy.

scEM: A New Ensemble Framework for Predicting Cell Type Composition Based on scRNA-Seq Data

With the advent of single-cell RNA sequencing (scRNA-seq) technology, many scRNA-seq data have become available, providing an unprecedented opportunity to explore cellular composition and heterogeneity. Recently, many computational algorithms for predicting cell type composition have been developed, and these methods are typically evaluated on different datasets and performance metrics using diverse techniques. Consequently, the lack of comprehensive and standardized comparative analysis makes it difficult to gain a clear understanding of the strengths and weaknesses of these methods. To address this gap, we reviewed 20 cutting-edge unsupervised cell type identification methods and evaluated these methods comprehensively using 24 real scRNA-seq datasets of varying scales. In addition, we proposed a new ensemble cell-type identification method, named scEM, which learns the consensus similarity matrix by applying the entropy weight method to the four representative methods are selected. The Louvain algorithm is adopted to obtain the final classification of individual cells based on the consensus matrix. Extensive evaluation and comparison with 11 other similarity-based methods under real scRNA-seq datasets demonstrate that the newly developed ensemble algorithm scEM is effective in predicting cellular type composition.

Association cortical areas in the mouse contain a large population of fast-spiking GABAergic neurons that do not express parvalbumin

GABAergic neurons represent 10-15% of the neuronal population of the cortex but exert a powerful control over information flow in cortical circuits. The largest GABAergic class in the neocortex is represented by the parvalbumin-expressing fast-spiking neurons, which provide powerful somatic inhibition to their postsynaptic targets. Recently, the density of parvalbumin interneurons has been shown to be lower in associative areas of the mouse cortex as compared with sensory and motor areas. Modelling work based on these quantifications linked the low-density of parvalbumin interneurons with specific computations of associative cortices. However, it is still unknown whether the total GABAergic population of association cortices is smaller or whether another GABAergic type can compensate for the low density of parvalbumin interneurons. In the present study, we investigated these hypotheses using a combination of neuroanatomy, mouse genetics and neurophysiology. We found that the GABAergic population of association areas is comparable with that of primary sensory areas, and it is enriched of fast-spiking neurons that do not express parvalbumin and were not accounted for by previous quantifications. We developed an intersectional viral strategy to demonstrate that the population of fast-spiking neurons is comparable across cortical regions. Our results provide quantifications of the density of fast-spiking GABAergic neurons and offers new biological constrains to refine current models of cortical computations.

Single-cell long-read sequencing-based mapping reveals specialized splicing patterns in developing and adult mouse and human brain

RNA isoforms influence cell identity and function. However, a comprehensive brain isoform map was lacking. We analyze single-cell RNA isoforms across brain regions, cell subtypes, developmental time points and species. For 72% of genes, full-length isoform expression varies along one or more axes. Splicing, transcription start and polyadenylation sites vary strongly between cell types, influence protein architecture and associate with disease-linked variation. Additionally, neurotransmitter transport and synapse turnover genes harbor cell-type variability across anatomical regions. Regulation of cell-type-specific splicing is pronounced in the postnatal day 21-to-postnatal day 28 adolescent transition. Developmental isoform regulation is stronger than regional regulation for the same cell type. Cell-type-specific isoform regulation in mice is mostly maintained in the human hippocampus, allowing extrapolation to the human brain. Conversely, the human brain harbors additional cell-type specificity, suggesting gain-of-function isoforms. Together, this detailed single-cell atlas of full-length isoform regulation across development, anatomical regions and species reveals an unappreciated degree of isoform variability across multiple axes.

Higher-order thalamocortical circuits are specified by embryonic cortical progenitor types in the mouse brain

The sensory cortex receives synaptic inputs from both first-order and higher-order thalamic nuclei. First-order inputs relay simple stimulus properties from the periphery, whereas higher-order inputs relay more complex response properties, provide contextual feedback, and modulate plasticity. Here, we reveal that a cortical neuron's higher-order input is determined by the type of progenitor from which it is derived during embryonic development. Within layer 4 (L4) of the mouse primary somatosensory cortex, neurons derived from intermediate progenitors receive stronger higher-order thalamic input and exhibit greater higher-order sensory responses. These effects result from differences in dendritic morphology and levels of the transcription factor Lhx2, which are specified by the L4 neuron's progenitor type. When this mechanism is disrupted, cortical circuits exhibit altered higher-order responses and sensory-evoked plasticity. Therefore, by following distinct trajectories, progenitor types generate diversity in thalamocortical circuitry and may provide a general mechanism for differentially routing information through the cortex.

Organization of corticocortical and thalamocortical top-down inputs in the primary visual cortex

Unified visual perception requires integration of bottom-up and top-down inputs in the primary visual cortex (V1), yet the organization of top-down inputs in V1 remains unclear. Here, we used optogenetics-assisted circuit mapping to identify how multiple top-down inputs from higher-order cortical and thalamic areas engage V1 excitatory and inhibitory neurons. Top-down inputs overlap in superficial layers yet segregate in deep layers. Inputs from the medial secondary visual cortex (V2M) and anterior cingulate cortex (ACA) converge on L6 Pyrs, whereas ventrolateral orbitofrontal cortex (ORBvl) and lateral posterior thalamic nucleus (LP) inputs are processed in parallel in Pyr-type-specific subnetworks (Pyr←ORBvl and Pyr←LP) and drive mutual inhibition between them via local interneurons. Our study deepens understanding of the top-down modulation mechanisms of visual processing and establishes that V2M and ACA inputs in L6 employ integrated processing distinct from the parallel processing of LP and ORBvl inputs in L5.

SpatialPrompt: spatially aware scalable and accurate tool for spot deconvolution and domain identification in spatial transcriptomics

Efficiently mapping of cell types in situ remains a major challenge in spatial transcriptomics. Most spot deconvolution tools ignore spatial coordinate information and perform extremely slow on large datasets. Here, we introduce SpatialPrompt, a spatially aware and scalable tool for spot deconvolution and domain identification. SpatialPrompt integrates gene expression, spatial location, and single-cell RNA sequencing (scRNA-seq) dataset as reference to accurately infer cell-type proportions of spatial spots. SpatialPrompt uses non-negative ridge regression and graph neural network to efficiently capture local microenvironment information. Our extensive benchmarking analysis on Visium, Slide-seq, and MERFISH datasets demonstrated superior performance of SpatialPrompt over 15 existing tools. On mouse hippocampus dataset, SpatialPrompt achieves spot deconvolution and domain identification within 2 minutes for 50,000 spots. Overall, domain identification using SpatialPrompt was 44 to 150 times faster than existing methods. We build a database housing 40 plus curated scRNA-seq datasets for seamless integration with SpatialPrompt for spot deconvolution.

Spatial transcriptomic brain imaging reveals the effects of immunomodulation therapy on specific regional brain cells in a mouse dementia model

Increasing evidence of brain-immune crosstalk raises expectations for the efficacy of novel immunotherapies in Alzheimer's disease (AD), but the lack of methods to examine brain tissues makes it difficult to evaluate therapeutics. Here, we investigated the changes in spatial transcriptomic signatures and brain cell types using the 10x Genomics Visium platform in immune-modulated AD models after various treatments. To proceed with an analysis suitable for barcode-based spatial transcriptomics, we first organized a workflow for segmentation of neuroanatomical regions, establishment of appropriate gene combinations, and comprehensive review of altered brain cell signatures. Ultimately, we investigated spatial transcriptomic changes following administration of immunomodulators, NK cell supplements and an anti-CD4 antibody, which ameliorated behavior impairment, and designated brain cells and regions showing probable associations with behavior changes. We provided the customized analytic pipeline into an application named STquantool. Thus, we anticipate that our approach can help researchers interpret the real action of drug candidates by simultaneously investigating the dynamics of all transcripts for the development of novel AD therapeutics.

Spotless, a reproducible pipeline for benchmarking cell type deconvolution in spatial transcriptomics

Spatial transcriptomics (ST) technologies allow the profiling of the transcriptome of cells while keeping their spatial context. Since most commercial untargeted ST technologies do not yet operate at single-cell resolution, computational methods such as deconvolution are often used to infer the cell type composition of each sequenced spot. We benchmarked 11 deconvolution methods using 63 silver standards, 3 gold standards, and 2 case studies on liver and melanoma tissues. We developed a simulation engine called synthspot to generate silver standards from single-cell RNA-sequencing data, while gold standards are generated by pooling single cells from targeted ST data. We evaluated methods based on their performance, stability across different reference datasets, and scalability. We found that cell2location and RCTD are the top-performing methods, but surprisingly, a simple regression model outperforms almost half of the dedicated spatial deconvolution methods. Furthermore, we observe that the performance of all methods significantly decreased in datasets with highly abundant or rare cell types. Our results are reproducible in a Nextflow pipeline, which also allows users to generate synthetic data, run deconvolution methods and optionally benchmark them on their dataset (https://github.com/saeyslab/spotless-benchmark).

Benchmarking mapping algorithms for cell-type annotating in mouse brain by integrating single-nucleus RNA-seq and Stereo-seq data

Limited gene capture efficiency and spot size of spatial transcriptome (ST) data pose significant challenges in cell-type characterization. The heterogeneity and complexity of cell composition in the mammalian brain make it more challenging to accurately annotate ST data from brain. Many algorithms attempt to characterize subtypes of neuron by integrating ST data with single-nucleus RNA sequencing (snRNA-seq) or single-cell RNA sequencing. However, assessing the accuracy of these algorithms on Stereo-seq ST data remains unresolved. Here, we benchmarked 9 mapping algorithms using 10 ST datasets from four mouse brain regions in two different resolutions and 24 pseudo-ST datasets from snRNA-seq. Both actual ST data and pseudo-ST data were mapped using snRNA-seq datasets from the corresponding brain regions as reference data. After comparing the performance across different areas and resolutions of the mouse brain, we have reached the conclusion that both robust cell-type decomposition and SpatialDWLS demonstrated superior robustness and accuracy in cell-type annotation. Testing with publicly available snRNA-seq data from another sequencing platform in the cortex region further validated our conclusions. Altogether, we developed a workflow for assessing suitability of mapping algorithm that fits for ST datasets, which can improve the efficiency and accuracy of spatial data annotation.

Optogenetic Determination of Dynamic and Cell-Type-Specific Inhibitory Reversal Potentials

The reversal potential refers to the membrane potential at which the net current flow through a channel reverses direction. The reversal potential is determined by transmembrane ion gradients and, in turn, determines how the channel's activity will affect the membrane potential. Traditional investigation into the reversal potential of inhibitory ligand-gated ion channels (EInh) has relied upon the activation of endogenous receptors, such as the GABA-A receptor (GABAAR). There are, however, challenges associated with activating endogenous receptors, including agonist delivery, isolating channel responses, and the effects of receptor saturation and desensitization. Here, we demonstrate the utility of using a light-gated anion channel, stGtACR2, to probe EInh in the rodent brain. Using mice of both sexes, we demonstrate that the properties of this optically activated channel make it a suitable proxy for studying GABAAR receptor-mediated inhibition. We validate this agonist-independent optogenetic strategy in vitro and in vivo and further show how it can accurately capture differences in EInh dynamics following manipulations of endogenous ion fluxes. This allows us to explore distinct resting EInh differences across genetically defined neuronal subpopulations. Using this approach to challenge ion homeostasis mechanisms in neurons, we uncover cell-specific EInh dynamics that are supported by the differential expression of endogenous ion handling mechanisms. Our findings therefore establish an effective optical strategy for revealing novel aspects of inhibitory reversal potentials and thereby expand the repertoire of optogenetics.