Conservation and divergence of cortical cell organization in human and mouse revealed by MERFISH

Surface-Enhanced Raman Scattering Nanosensing and Imaging in Neuroscience

Monitoring neurochemicals and imaging the molecular content of brain tissues in vitro, ex vivo, and in vivo is essential for enhancing our understanding of neurochemistry and the causes of brain disorders. This review explores the potential applications of surface-enhanced Raman scattering (SERS) nanosensors in neurosciences, where their adoption could lead to significant progress in the field. These applications encompass detecting neurotransmitters or brain disorders biomarkers in biofluids with SERS nanosensors, and imaging normal and pathological brain tissues with SERS labeling. Specific studies highlighting in vitro, ex vivo, and in vivo analysis of brain disorders using fit-for-purpose SERS nanosensors will be detailed, with an emphasis on the ability of SERS to detect clinically pertinent levels of neurochemicals. Recent advancements in designing SERS-active nanomaterials, improving experimentation in biofluids, and increasing the usage of machine learning for interpreting SERS spectra will also be discussed. Furthermore, we will address the tagging of tissues presenting pathologies with nanoparticles for SERS imaging, a burgeoning domain of neuroscience that has been demonstrated to be effective in guiding tumor removal during brain surgery. The review also explores future research applications for SERS nanosensors in neuroscience, including monitoring neurochemistry in vivo with greater penetration using surface-enhanced spatially offset Raman scattering (SESORS), near-infrared lasers, and 2-photon techniques. The article concludes by discussing the potential of SERS for investigating the effectiveness of therapies for brain disorders and for integrating conventional neurochemistry techniques with SERS sensing.

Spatial multi-omics: deciphering technological landscape of integration of multi-omics and its applications

The emergence of spatial multi-omics has helped address the limitations of single-cell sequencing, which often leads to the loss of spatial context among cell populations. Integrated analysis of the genome, transcriptome, proteome, metabolome, and epigenome has enhanced our understanding of cell biology and the molecular basis of human diseases. Moreover, this approach offers profound insights into the interactions between intracellular and intercellular molecular mechanisms involved in the development, physiology, and pathogenesis of human diseases. In this comprehensive review, we examine current advancements in multi-omics technologies, focusing on their evolution and refinement over the past decade, including improvements in throughput and resolution, modality integration, and accuracy. We also discuss the pivotal contributions of spatial multi-omics in revealing spatial heterogeneity, constructing detailed spatial atlases, deciphering spatial crosstalk in tumor immunology, and advancing translational research and cancer therapy through precise spatial mapping.

Spatiotemporal omics for biology and medicine

The completion of the Human Genome Project has provided a foundational blueprint for understanding human life. Nonetheless, understanding the intricate mechanisms through which our genetic blueprint is involved in disease or orchestrates development across temporal and spatial dimensions remains a profound scientific challenge. Recent breakthroughs in cellular omics technologies have paved new pathways for understanding the regulation of genomic elements and the relationship between gene expression, cellular functions, and cell fate determination. The advent of spatial omics technologies, encompassing both imaging and sequencing-based methodologies, has enabled a comprehensive understanding of biological processes from a cellular ecosystem perspective. This review offers an updated overview of how spatial omics has advanced our understanding of the translation of genetic information into cellular heterogeneity and tissue structural organization and their dynamic changes over time. It emphasizes the discovery of various biological phenomena, related to organ functionality, embryogenesis, species evolution, and the pathogenesis of diseases.

Cell-cell communication: new insights and clinical implications

Multicellular organisms are composed of diverse cell types that must coordinate their behaviors through communication. Cell-cell communication (CCC) is essential for growth, development, differentiation, tissue and organ formation, maintenance, and physiological regulation. Cells communicate through direct contact or at a distance using ligand-receptor interactions. So cellular communication encompasses two essential processes: cell signal conduction for generation and intercellular transmission of signals, and cell signal transduction for reception and procession of signals. Deciphering intercellular communication networks is critical for understanding cell differentiation, development, and metabolism. First, we comprehensively review the historical milestones in CCC studies, followed by a detailed description of the mechanisms of signal molecule transmission and the importance of the main signaling pathways they mediate in maintaining biological functions. Then we systematically introduce a series of human diseases caused by abnormalities in cell communication and their progress in clinical applications. Finally, we summarize various methods for monitoring cell interactions, including cell imaging, proximity-based chemical labeling, mechanical force analysis, downstream analysis strategies, and single-cell technologies. These methods aim to illustrate how biological functions depend on these interactions and the complexity of their regulatory signaling pathways to regulate crucial physiological processes, including tissue homeostasis, cell development, and immune responses in diseases. In addition, this review enhances our understanding of the biological processes that occur after cell-cell binding, highlighting its application in discovering new therapeutic targets and biomarkers related to precision medicine. This collective understanding provides a foundation for developing new targeted drugs and personalized treatments.

Multiplexed spatial transcriptomics methods and the application of expansion microscopy

While spatial transcriptomics has undeniably revolutionized our ability to study cellular organization, it has driven the development of a great number of innovative transcriptomics methods, which can be classified into in situ sequencing (ISS) methods, in situ hybridization (ISH) techniques, and next-generation sequencing (NGS)-based sequencing with region capture. These technologies not only refine our understanding of cellular processes, but also open up new possibilities for breakthroughs in various research domains. One challenge of spatial transcriptomics experiments is the limitation of RNA detection due to optical crowding of RNA in the cells. Expansion microscopy (ExM), characterized by the controlled enlargement of biological specimens, offers a means to achieve super-resolution imaging, overcoming the diffraction limit inherent in conventional microscopy and enabling precise visualization of RNA in spatial transcriptomics methods. In this review, we elaborate on ISS, ISH and NGS-based spatial transcriptomic protocols and on how performance of these techniques can be extended by the combination of these protocols with ExM. Moving beyond the techniques and procedures, we highlight the broader implications of transcriptomics in biology and medicine. These include valuable insight into the spatial organization of gene expression in cells within tissues, aid in the identification and the distinction of cell types and subpopulations and understanding of molecular mechanisms and intercellular changes driving disease development.

Pathogenic variants associated with speech/cognitive delay and seizures affect genes with expression biases in excitatory neurons and microglia in developing human cortex

Background & Objective

Congenital brain malformations and neurodevelopmental disorders (NDDs) are common pediatric neurological disorders and result in chronic disability. With the expansion of genetic testing, new etiologies for NDDs are continually uncovered, with as many as one third attributable to single-gene pathogenic variants. While our ability to identify pathogenic variants has continually improved, we have little understanding of the underlying cellular pathophysiology in the nervous system that results from these variants. We therefore integrated phenotypic information from subjects with monogenic diagnoses with two large, single-nucleus RNA-sequencing (snRNAseq) datasets from human cortex across developmental stages in order to investigate cell-specific biases in gene expression associated with distinct neurodevelopmental phenotypes.

Methods

Phenotypic data was gathered from 1) a single-institution cohort of 84 neonates with pathogenic single-gene variants referred to Duke Pediatric Genetics, and 2) a cohort of 4,238 patients with neurodevelopmental disorders and pathogenic single-gene variants enrolled in the Deciphering Developmental Disorders (DDD) study. Pathogenic variants were grouped into genesets by neurodevelopmental phenotype and geneset expression across cortical cell subtypes was compared within snRNAseq datasets from 86 human cortex samples spanning the 2nd trimester of gestation to adulthood.

Results

We find that pathogenic variants associated with speech/cognitive delay or seizures involve genes that are more highly expressed in cortical excitatory neurons than variants in genes not associated with these phenotypes (Speech/cognitive: p=2.25×10-7; Seizures: p=7.97×10-12). A separate set of primarily rare variants associated with speech/cognitive delay or seizures, distinct from those with excitatory neuron expression biases, demonstrated expression biases in microglia. We also found that variants associated with speech/cognitive delay and an excitatory neuron expression bias could be further parsed by the presence or absence of comorbid seizures. Variants associated with speech/cognitive delay without seizures tended to involve calcium regulatory pathways and showed greater expression in extratelencephalic neurons, while those associated with speech/cognitive delay with seizures tended to involve synaptic regulatory machinery and an intratelencephalic neuron expression bias (ANOVA by geneset p<2×10-16).

Conclusions

By combining extensive phenotype datasets from subjects with neurodevelopmental disorders with massive human cortical snRNAseq datasets across developmental stages, we identified cell-specific expression biases for genes in which pathogenic variants are associated with speech/cognitive delay and seizures. The involvement of genes with enriched expression in excitatory neurons or microglia highlights the unique role both cell types play in proper sculpting of the developing brain. Moreover, this information begins to shed light on distinct cortical cell types that are more likely to be impacted by pathogenic variants and that may mediate the symptomatology of resulting neurodevelopmental disorders.

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.

Simulating multiple variability in spatially resolved transcriptomics with scCube

A pressing challenge in spatially resolved transcriptomics (SRT) is to benchmark the computational methods. A widely-used approach involves utilizing simulated data. However, biases exist in terms of the currently available simulated SRT data, which seriously affects the accuracy of method evaluation and validation. Herein, we present scCube ( https://github.com/ZJUFanLab/scCube ), a Python package for independent, reproducible, and technology-diverse simulation of SRT data. scCube not only enables the preservation of spatial expression patterns of genes in reference-based simulations, but also generates simulated data with different spatial variability (covering the spatial pattern type, the resolution, the spot arrangement, the targeted gene type, and the tissue slice dimension, etc.) in reference-free simulations. We comprehensively benchmark scCube with existing single-cell or SRT simulators, and demonstrate the utility of scCube in benchmarking spot deconvolution, gene imputation, and resolution enhancement methods in detail through three applications.

Complete spatially resolved gene expression is not necessary for identifying spatial domains

Spatially resolved transcriptomics (SRT) technologies have revolutionized the study of tissue organization. We introduce a graph convolutional network with an attention and positive emphasis mechanism, termed BINARY, relying exclusively on binarized SRT data to accurately delineate spatial domains. BINARY outperforms existing methods across various SRT data types while using significantly less input information. Our study suggests that precise gene expression quantification may not always be essential, inspiring further exploration of the broader applications of spatially resolved binarized gene expression data.

Pathology of pain and its implications for therapeutic interventions

Pain is estimated to affect more than 20% of the global population, imposing incalculable health and economic burdens. Effective pain management is crucial for individuals suffering from pain. However, the current methods for pain assessment and treatment fall short of clinical needs. Benefiting from advances in neuroscience and biotechnology, the neuronal circuits and molecular mechanisms critically involved in pain modulation have been elucidated. These research achievements have incited progress in identifying new diagnostic and therapeutic targets. In this review, we first introduce fundamental knowledge about pain, setting the stage for the subsequent contents. The review next delves into the molecular mechanisms underlying pain disorders, including gene mutation, epigenetic modification, posttranslational modification, inflammasome, signaling pathways and microbiota. To better present a comprehensive view of pain research, two prominent issues, sexual dimorphism and pain comorbidities, are discussed in detail based on current findings. The status quo of pain evaluation and manipulation is summarized. A series of improved and innovative pain management strategies, such as gene therapy, monoclonal antibody, brain-computer interface and microbial intervention, are making strides towards clinical application. We highlight existing limitations and future directions for enhancing the quality of preclinical and clinical research. Efforts to decipher the complexities of pain pathology will be instrumental in translating scientific discoveries into clinical practice, thereby improving pain management from bench to bedside.

The cytoarchitectonic landscape revealed by deep learning method facilitated precise positioning in mouse neocortex

Neocortex is a complex structure with different cortical sublayers and regions. However, the precise positioning of cortical regions can be challenging due to the absence of distinct landmarks without special preparation. To address this challenge, we developed a cytoarchitectonic landmark identification pipeline. The fluorescence micro-optical sectioning tomography method was employed to image the whole mouse brain stained by general fluorescent nucleotide dye. A fast 3D convolution network was subsequently utilized to segment neuronal somas in entire neocortex. By approach, the cortical cytoarchitectonic profile and the neuronal morphology were analyzed in 3D, eliminating the influence of section angle. And the distribution maps were generated that visualized the number of neurons across diverse morphological types, revealing the cytoarchitectonic landscape which characterizes the landmarks of cortical regions, especially the typical signal pattern of barrel cortex. Furthermore, the cortical regions of various ages were aligned using the generated cytoarchitectonic landmarks suggesting the structural changes of barrel cortex during the aging process. Moreover, we observed the spatiotemporally gradient distributions of spindly neurons, concentrated in the deep layer of primary visual area, with their proportion decreased over time. These findings could improve structural understanding of neocortex, paving the way for further exploration with this method.

Analysis of human neuronal cells carrying ASTN2 deletion associated with psychiatric disorders

Recent genetic studies have found common genomic risk variants among psychiatric disorders, strongly suggesting the overlaps in their molecular and cellular mechanism. Our research group identified the variant in ASTN2 as one of the candidate risk factors across these psychiatric disorders by whole-genome copy number variation analysis. However, the alterations in the human neuronal cells resulting from ASTN2 variants identified in patients remain unknown. To address this, we used patient-derived and genome-edited iPS cells with ASTN2 deletion; cells were further differentiated into neuronal cells. A comprehensive gene expression analysis using genome-edited iPS cells with variants on both alleles revealed that the expression level of ZNF558, a gene specifically expressed in human forebrain neural progenitor cells, was greatly reduced in ASTN2-deleted neuronal cells. Furthermore, the expression of the mitophagy-related gene SPATA18, which is repressed by ZNF558, and mitophagy activity were increased in ASTN2-deleted neuronal cells. These phenotypes were also detected in neuronal cells differentiated from patient-derived iPS cells with heterozygous ASTN2 deletion. Our results suggest that ASTN2 deletion is related to the common pathogenic mechanism of psychiatric disorders by regulating mitophagy via ZNF558.

A molecular and cellular perspective on human brain evolution and tempo

The evolution of the modern human brain was accompanied by distinct molecular and cellular specializations, which underpin our diverse cognitive abilities but also increase our susceptibility to neurological diseases. These features, some specific to humans and others shared with related species, manifest during different stages of brain development. In this multi-stage process, neural stem cells proliferate to produce a large and diverse progenitor pool, giving rise to excitatory or inhibitory neurons that integrate into circuits during further maturation. This process unfolds over varying time scales across species and has progressively become slower in the human lineage, with differences in tempo correlating with differences in brain size, cell number and diversity, and connectivity. Here we introduce the terms 'bradychrony' and 'tachycrony' to describe slowed and accelerated developmental tempos, respectively. We review how recent technical advances across disciplines, including advanced engineering of in vitro models, functional comparative genetics and high-throughput single-cell profiling, are leading to a deeper understanding of how specializations of the human brain arise during bradychronic neurodevelopment. Emerging insights point to a central role for genetics, gene-regulatory networks, cellular innovations and developmental tempo, which together contribute to the establishment of human specializations during various stages of neurodevelopment and at different points in evolution.

Large-scale neurophysiology and single-cell profiling in human neuroscience

Advances in large-scale single-unit human neurophysiology, single-cell RNA sequencing, spatial transcriptomics and long-term ex vivo tissue culture of surgically resected human brain tissue have provided an unprecedented opportunity to study human neuroscience. In this Perspective, we describe the development of these paradigms, including Neuropixels and recent brain-cell atlas efforts, and discuss how their convergence will further investigations into the cellular underpinnings of network-level activity in the human brain. Specifically, we introduce a workflow in which functionally mapped samples of human brain tissue resected during awake brain surgery can be cultured ex vivo for multi-modal cellular and functional profiling. We then explore how advances in human neuroscience will affect clinical practice, and conclude by discussing societal and ethical implications to consider. Potential findings from the field of human neuroscience will be vast, ranging from insights into human neurodiversity and evolution to providing cell-type-specific access to study and manipulate diseased circuits in pathology. This Perspective aims to provide a unifying framework for the field of human neuroscience as we welcome an exciting era for understanding the functional cytoarchitecture of the human brain.

Single-cell genomics and regulatory networks for 388 human brains

Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multiomics datasets into a resource comprising >2.8 million nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550,000 cell type-specific regulatory elements and >1.4 million single-cell expression quantitative trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.

A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex

The molecular organization of the human neocortex historically has been studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally defined spatial domains that move beyond classic cytoarchitecture. We used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex. Integration with paired single-nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we mapped the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains.

A complementary approach for neocortical cytoarchitecture inspection with cellular resolution imaging at whole brain scale

Cytoarchitecture, the organization of cells within organs and tissues, serves as a crucial anatomical foundation for the delineation of various regions. It enables the segmentation of the cortex into distinct areas with unique structural and functional characteristics. While traditional 2D atlases have focused on cytoarchitectonic mapping of cortical regions through individual sections, the intricate cortical gyri and sulci demands a 3D perspective for unambiguous interpretation. In this study, we employed fluorescent micro-optical sectioning tomography to acquire architectural datasets of the entire macaque brain at a resolution of 0.65 μm × 0.65 μm × 3 μm. With these volumetric data, the cortical laminar textures were remarkably presented in appropriate view planes. Additionally, we established a stereo coordinate system to represent the cytoarchitectonic information as surface-based tomograms. Utilizing these cytoarchitectonic features, we were able to three-dimensionally parcel the macaque cortex into multiple regions exhibiting contrasting architectural patterns. The whole-brain analysis was also conducted on mice that clearly revealed the presence of barrel cortex and reflected biological reasonability of this method. Leveraging these high-resolution continuous datasets, our method offers a robust tool for exploring the organizational logic and pathological mechanisms of the brain's 3D anatomical structure.

Structural characterization of a polymorphic repeat at the CACNA1C schizophrenia locus

Genetic variation within intron 3 of the CACNA1C calcium channel gene is associated with schizophrenia and bipolar disorder, but analysis of the causal variants and their effect is complicated by a nearby variable-number tandem repeat (VNTR). Here, we used 155 long-read genome assemblies from 78 diverse individuals to delineate the structure and population variability of the CACNA1C intron 3 VNTR. We categorized VNTR sequences into 7 Types of structural alleles using sequence differences among repeat units. Only 12 repeat units at the 5' end of the VNTR were shared across most Types, but several Types were related through a series of large and small duplications. The most diverged Types were rare and present only in individuals with African ancestry, but the multiallelic structural polymorphism Variable Region 2 was present across populations at different frequencies, consistent with expansion of the VNTR preceding the emergence of early hominins. VR2 was in complete linkage disequilibrium with fine-mapped schizophrenia variants (SNPs) from genome-wide association studies (GWAS). This risk haplotype was associated with decreased CACNA1C gene expression in brain tissues profiled by the GTEx project. Our work suggests that sequence variation within a human-specific VNTR affects gene expression, and provides a detailed characterization of new alleles at a flagship neuropsychiatric locus.

A petavoxel fragment of human cerebral cortex reconstructed at nanoscale resolution

To fully understand how the human brain works, knowledge of its structure at high resolution is needed. Presented here is a computationally intensive reconstruction of the ultrastructure of a cubic millimeter of human temporal cortex that was surgically removed to gain access to an underlying epileptic focus. It contains about 57,000 cells, about 230 millimeters of blood vessels, and about 150 million synapses and comprises 1.4 petabytes. Our analysis showed that glia outnumber neurons 2:1, oligodendrocytes were the most common cell, deep layer excitatory neurons could be classified on the basis of dendritic orientation, and among thousands of weak connections to each neuron, there exist rare powerful axonal inputs of up to 50 synapses. Further studies using this resource may bring valuable insights into the mysteries of the human brain.

Molecular and cellular mechanisms of selective vulnerability in neurodegenerative diseases

The selective vulnerability of specific neuronal subtypes is a hallmark of neurodegenerative diseases. In this Review, I summarize our current understanding of the brain regions and cell types that are selectively vulnerable in different neurodegenerative diseases and describe the proposed underlying cell-autonomous and non-cell-autonomous mechanisms. I highlight how recent methodological innovations - including single-cell transcriptomics, CRISPR-based screens and human cell-based models of disease - are enabling new breakthroughs in our understanding of selective vulnerability. An understanding of the molecular mechanisms that determine selective vulnerability and resilience would shed light on the key processes that drive neurodegeneration and point to potential therapeutic strategies to protect vulnerable cell populations.

Spot-Based Global Registration for Subpixel Stitching of Single-Molecule Resolution Images for Tissue-Scale Spatial Transcriptomics

Single-molecule imaging at the tissue scale has revolutionized our understanding of biology by providing unprecedented insight into the molecular expression of individual cells and their spatial organization within tissues. However, achieving precise image stitching at the single-molecule level remains a challenge, primarily due to heterogeneous background signals and dim labeling signals in single-molecule images. This paper introduces Spot-Based Global Registration (SBGR), a novel strategy that shifts the focus from raw images to identified molecular spots for high-resolution image alignment. The use of spot-based data enables straightforward and robust evaluation of the credibility of estimated translations and stitching performance. The method outperforms existing image-based stitching methods, achieving subpixel accuracy (83 ± 36 nm) with exceptional consistency. Furthermore, SBGR incorporates a mechanism to surgically remove duplicate spots in overlapping regions, maximizing information recovery from duplicate measurements. In conclusion, SBGR emerges as a robust and accurate solution for stitching single-molecule resolution images in tissue-scale spatial transcriptomics, offering versatility and potential for high-resolution spatial analysis.

Induced pluripotent stem cells (iPSCs): molecular mechanisms of induction and applications

The induced pluripotent stem cell (iPSC) technology has transformed in vitro research and holds great promise to advance regenerative medicine. iPSCs have the capacity for an almost unlimited expansion, are amenable to genetic engineering, and can be differentiated into most somatic cell types. iPSCs have been widely applied to model human development and diseases, perform drug screening, and develop cell therapies. In this review, we outline key developments in the iPSC field and highlight the immense versatility of the iPSC technology for in vitro modeling and therapeutic applications. We begin by discussing the pivotal discoveries that revealed the potential of a somatic cell nucleus for reprogramming and led to successful generation of iPSCs. We consider the molecular mechanisms and dynamics of somatic cell reprogramming as well as the numerous methods available to induce pluripotency. Subsequently, we discuss various iPSC-based cellular models, from mono-cultures of a single cell type to complex three-dimensional organoids, and how these models can be applied to elucidate the mechanisms of human development and diseases. We use examples of neurological disorders, coronavirus disease 2019 (COVID-19), and cancer to highlight the diversity of disease-specific phenotypes that can be modeled using iPSC-derived cells. We also consider how iPSC-derived cellular models can be used in high-throughput drug screening and drug toxicity studies. Finally, we discuss the process of developing autologous and allogeneic iPSC-based cell therapies and their potential to alleviate human diseases.

Directed and acyclic synaptic connectivity in the human layer 2-3 cortical microcircuit

The computational capabilities of neuronal networks are fundamentally constrained by their specific connectivity. Previous studies of cortical connectivity have mostly been carried out in rodents; whether the principles established therein also apply to the evolutionarily expanded human cortex is unclear. We studied network properties within the human temporal cortex using samples obtained from brain surgery. We analyzed multineuron patch-clamp recordings in layer 2-3 pyramidal neurons and identified substantial differences compared with rodents. Reciprocity showed random distribution, synaptic strength was independent from connection probability, and connectivity of the supragranular temporal cortex followed a directed and mostly acyclic graph topology. Application of these principles in neuronal models increased dimensionality of network dynamics, suggesting a critical role for cortical computation.

Charting the cellular biogeography in colitis reveals fibroblast trajectories and coordinated spatial remodeling

Gut inflammation involves contributions from immune and non-immune cells, whose interactions are shaped by the spatial organization of the healthy gut and its remodeling during inflammation. The crosstalk between fibroblasts and immune cells is an important axis in this process, but our understanding has been challenged by incomplete cell-type definition and biogeography. To address this challenge, we used multiplexed error-robust fluorescence in situ hybridization (MERFISH) to profile the expression of 940 genes in 1.35 million cells imaged across the onset and recovery from a mouse colitis model. We identified diverse cell populations, charted their spatial organization, and revealed their polarization or recruitment in inflammation. We found a staged progression of inflammation-associated tissue neighborhoods defined, in part, by multiple inflammation-associated fibroblasts, with unique expression profiles, spatial localization, cell-cell interactions, and healthy fibroblast origins. Similar signatures in ulcerative colitis suggest conserved human processes. Broadly, we provide a framework for understanding inflammation-induced remodeling in the gut and other tissues.

q-Diffusion leverages the full dimensionality of gene coexpression in single-cell transcriptomics

Unlocking the full dimensionality of single-cell RNA sequencing data (scRNAseq) is the next frontier to a richer, fuller understanding of cell biology. We introduce q-diffusion, a framework for capturing the coexpression structure of an entire library of genes, improving on state-of-the-art analysis tools. The method is demonstrated via three case studies. In the first, q-diffusion helps gain statistical significance for differential effects on patient outcomes when analyzing the CALGB/SWOG 80405 randomized phase III clinical trial, suggesting precision guidance for the treatment of metastatic colorectal cancer. Secondly, q-diffusion is benchmarked against existing scRNAseq classification methods using an in vitro PBMC dataset, in which the proposed method discriminates IFN-γ stimulation more accurately. The same case study demonstrates improvements in unsupervised cell clustering with the recent Tabula Sapiens human atlas. Finally, a local distributional segmentation approach for spatial scRNAseq, driven by q-diffusion, yields interpretable structures of human cortical tissue.

Multimodal Nature of the Single-cell Primate Brain Atlas: Morphology, Transcriptome, Electrophysiology, and Connectivity

Primates exhibit complex brain structures that augment cognitive function. The neocortex fulfills high-cognitive functions through billions of connected neurons. These neurons have distinct transcriptomic, morphological, and electrophysiological properties, and their connectivity principles vary. These features endow the primate brain atlas with a multimodal nature. The recent integration of next-generation sequencing with modified patch-clamp techniques is revolutionizing the way to census the primate neocortex, enabling a multimodal neuronal atlas to be established in great detail: (1) single-cell/single-nucleus RNA-seq technology establishes high-throughput transcriptomic references, covering all major transcriptomic cell types; (2) patch-seq links the morphological and electrophysiological features to the transcriptomic reference; (3) multicell patch-clamp delineates the principles of local connectivity. Here, we review the applications of these technologies in the primate neocortex and discuss the current advances and tentative gaps for a comprehensive understanding of the primate neocortex.

Benchmarking spatial clustering methods with spatially resolved transcriptomics data

Spatial clustering, which shares an analogy with single-cell clustering, has expanded the scope of tissue physiology studies from cell-centroid to structure-centroid with spatially resolved transcriptomics (SRT) data. Computational methods have undergone remarkable development in recent years, but a comprehensive benchmark study is still lacking. Here we present a benchmark study of 13 computational methods on 34 SRT data (7 datasets). The performance was evaluated on the basis of accuracy, spatial continuity, marker genes detection, scalability, and robustness. We found existing methods were complementary in terms of their performance and functionality, and we provide guidance for selecting appropriate methods for given scenarios. On testing additional 22 challenging datasets, we identified challenges in identifying noncontinuous spatial domains and limitations of existing methods, highlighting their inadequacies in handling recent large-scale tasks. Furthermore, with 145 simulated data, we examined the robustness of these methods against four different factors, and assessed the impact of pre- and postprocessing approaches. Our study offers a comprehensive evaluation of existing spatial clustering methods with SRT data, paving the way for future advancements in this rapidly evolving field.

Single-cell genomics and regulatory networks for 388 human brains

Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet, little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multi-omics datasets into a resource comprising >2.8M nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550K cell-type-specific regulatory elements and >1.4M single-cell expression-quantitative-trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.

Mapping human tissues with highly multiplexed RNA in situ hybridization

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

Comparative neuroimaging of sex differences in human and mouse brain anatomy

In vivo neuroimaging studies have established several reproducible volumetric sex differences in the human brain, but the causes of such differences are hard to parse. While mouse models are useful for understanding the cellular and mechanistic bases of sex-specific brain development, there have been no attempts to formally compare human and mouse neuroanatomical sex differences to ascertain how well they translate. Addressing this question would shed critical light on the use of the mouse as a translational model for sex differences in the human brain and provide insights into the degree to which sex differences in brain volume are conserved across mammals. Here, we use structural magnetic resonance imaging to conduct the first comparative neuroimaging study of sex-specific neuroanatomy of the human and mouse brain. In line with previous findings, we observe that in humans, males have significantly larger and more variable total brain volume; these sex differences are not mirrored in mice. After controlling for total brain volume, we observe modest cross-species congruence in the volumetric effect size of sex across 60 homologous regions (r=0.30). This cross-species congruence is greater in the cortex (r=0.33) than non-cortex (r=0.16). By incorporating regional measures of gene expression in both species, we reveal that cortical regions with greater cross-species congruence in volumetric sex differences also show greater cross-species congruence in the expression profile of 2835 homologous genes. This phenomenon differentiates primary sensory regions with high congruence of sex effects and gene expression from limbic cortices where congruence in both these features was weaker between species. These findings help identify aspects of sex-biased brain anatomy present in mice that are retained, lost, or inverted in humans. More broadly, our work provides an empirical basis for targeting mechanistic studies of sex-specific brain development in mice to brain regions that best echo sex-specific brain development in humans.

Drug targeting in psychiatric disorders - how to overcome the loss in translation?

In spite of major efforts and investment in development of psychiatric drugs, many clinical trials have failed in recent decades, and clinicians still prescribe drugs that were discovered many years ago. Although multiple reasons have been discussed for the drug development deadlock, we focus here on one of the major possible biological reasons: differences between the characteristics of drug targets in preclinical models and the corresponding targets in patients. Importantly, based on technological advances in single-cell analysis, we propose here a framework for the use of available and newly emerging knowledge from single-cell and spatial omics studies to evaluate and potentially improve the translational predictivity of preclinical models before commencing preclinical and, in particular, clinical studies. We believe that these recommendations will improve preclinical models and the ability to assess drugs in clinical trials, reducing failure rates in expensive late-stage trials and ultimately benefitting psychiatric drug discovery and development.

NMDA glutamate receptor antagonist MK-801 induces proteome changes in adult human brain slices which are partially counteracted by haloperidol and clozapine

Deciphering the molecular pathways associated with N-methyl-D-aspartate receptor (NMDAr) hypofunction and its interaction with antipsychotics is necessary to advance our understanding of the basis of schizophrenia, as well as our capacity to treat this disease. In this regard, the development of human brain-derived models that are amenable to studying the neurobiology of schizophrenia may contribute to filling the gaps left by the widely employed animal models. Here, we assessed the proteomic changes induced by the NMDA glutamate receptor antagonist MK-801 on human brain slice cultures obtained from adult donors submitted to respective neurosurgery. Initially, we demonstrated that MK-801 diminishes NMDA glutamate receptor signaling in human brain slices in culture. Next, using mass-spectrometry-based proteomics and systems biology in silico analyses, we found that MK-801 led to alterations in proteins related to several pathways previously associated with schizophrenia pathophysiology, including ephrin, opioid, melatonin, sirtuin signaling, interleukin 8, endocannabinoid, and synaptic vesicle cycle. We also evaluated the impact of both typical and atypical antipsychotics on MK-801-induced proteome changes. Interestingly, the atypical antipsychotic clozapine showed a more significant capacity to counteract the protein alterations induced by NMDAr hypofunction than haloperidol. Finally, using our dataset, we identified potential modulators of the MK-801-induced proteome changes, which may be considered promising targets to treat NMDAr hypofunction in schizophrenia. This dataset is publicly available and may be helpful in further studies aimed at evaluating the effects of MK-801 and antipsychotics in the human brain.

Streamlining spatial omics data analysis with Pysodb

Advances in spatial omics technologies have improved the understanding of cellular organization in tissues, leading to the generation of complex and heterogeneous data and prompting the development of specialized tools for managing, loading and visualizing spatial omics data. The Spatial Omics Database (SODB) was established to offer a unified format for data storage and interactive visualization modules. Here we detail the use of Pysodb, a Python-based tool designed to enable the efficient exploration and loading of spatial datasets from SODB within a Python environment. We present seven case studies using Pysodb, detailing the interaction with various computational methods, ensuring reproducibility of experimental data and facilitating the integration of new data and alternative applications in SODB. The approach offers a reference for method developers by outlining label and metadata availability in representative spatial data that can be loaded by Pysodb. The tool is supplemented by a website ( https://protocols-pysodb.readthedocs.io/ ) with detailed information for benchmarking analysis, and allows method developers to focus on computational models by facilitating data processing. This protocol is designed for researchers with limited experience in computational biology. Depending on the dataset complexity, the protocol typically requires ~12 h to complete.

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

Spatial omics technologies are capable of deciphering detailed components of complex organs or tissue in cellular and subcellular resolution. A robust, interpretable, and unbiased representation method for spatial omics is necessary to illuminate novel investigations into biological functions, whereas a mathematical theory deficiency still exists. We present SpaGFT (Spatial Graph Fourier Transform), which provides a unique analytical feature representation of spatial omics data and elucidates molecular signatures linked to critical biological processes within tissues and cells. It outperformed existing tools in spatially variable gene prediction and gene expression imputation across human/mouse Visium data. Integrating SpaGFT representation into existing machine learning frameworks can enhance up to 40% accuracy of spatial domain identification, cell type annotation, cell-to-spot alignment, and subcellular hallmark inference. SpaGFT identified immunological regions for B cell maturation in human lymph node Visium data, characterized secondary follicle variations from in-house human tonsil CODEX data, and detected extremely rare subcellular organelles such as Cajal body and Set1/COMPASS. This new method lays the groundwork for a new theoretical model in explainable AI, advancing our understanding of tissue organization and function.

High resolution spatial profiling of kidney injury and repair using RNA hybridization-based in situ sequencing

Emerging spatially resolved transcriptomics technologies allow for the measurement of gene expression in situ at cellular resolution. We apply direct RNA hybridization-based in situ sequencing (dRNA HybISS, Cartana part of 10xGenomics) to compare male and female healthy mouse kidneys and the male kidney injury and repair timecourse. A pre-selected panel of 200 genes is used to identify cell state dynamics patterns during injury and repair. We develop a new computational pipeline, CellScopes, for the rapid analysis, multi-omic integration and visualization of spatially resolved transcriptomic datasets. The resulting dataset allows us to resolve 13 kidney cell types within distinct kidney niches, dynamic alterations in cell state over the course of injury and repair and cell-cell interactions between leukocytes and kidney parenchyma. At late timepoints after injury, C3+ leukocytes are enriched near pro-inflammatory, failed-repair proximal tubule cells. Integration of snRNA-seq dataset from the same injury and repair samples also allows us to impute the spatial localization of genes not directly measured by dRNA HybISS.

Role of H3K4 monomethylation in gene regulation

Methylation of histone H3 on the lysine-4 residue (H3K4me) is found throughout the eukaryotic domain, and its initial discovery as a conserved epigenetic mark of active transcription from yeast to mammalian cells has contributed to the histone code hypothesis. However, recent studies have raised questions on whether the different forms of H3K4me play a direct role in gene regulation or are simply by-products of the transcription process. Here, we review the often-conflicting experimental evidence, focusing on the monomethylation of lysine 4 on histone H3 that has been linked to the transcriptional state of enhancers in metazoans. We suggest that this epigenetic mark acts in a context-dependent manner to directly facilitate the transcriptional output of the genome and the establishment of cellular identity.

Protracted neuronal recruitment in the temporal lobes of young children

The temporal lobe of the human brain contains the entorhinal cortex (EC). This region of the brain is a highly interconnected integrative hub for sensory and spatial information; it also has a key role in episodic memory formation and is the main source of cortical hippocampal inputs1-4. The human EC continues to develop during childhood5, but neurogenesis and neuronal migration to the EC are widely considered to be complete by birth. Here we show that the human temporal lobe contains many young neurons migrating into the postnatal EC and adjacent regions, with a large tangential stream persisting until the age of around one year and radial dispersal continuing until around two to three years of age. By contrast, we found no equivalent postnatal migration in rhesus macaques (Macaca mulatta). Immunostaining and single-nucleus RNA sequencing of ganglionic eminence germinal zones, the EC stream and the postnatal EC revealed that most migrating cells in the EC stream are derived from the caudal ganglionic eminence and become LAMP5+RELN+ inhibitory interneurons. These late-arriving interneurons could continue to shape the processing of sensory and spatial information well into postnatal life, when children are actively interacting with their environment. The EC is one of the first regions of the brain to be affected in Alzheimer's disease, and previous work has linked cognitive decline to the loss of LAMP5+RELN+ cells6,7. Our investigation reveals that many of these cells arrive in the EC through a major postnatal migratory stream in early childhood.

Deciphering microglia phenotypes in health and disease

Microglia are the major immune cells of the central nervous system (CNS) that perform numerous adaptive functions required for normal CNS development and homeostasis but are also linked to neurodegenerative and behavioral diseases. Microglia development and function are strongly influenced by brain environmental signals that are integrated at the level of transcriptional enhancers to drive specific programs of gene expression. Here, we describe a conceptual framework for how lineage-determining and signal-dependent transcription factors interact to select and regulate the ensembles of enhancers that determine microglia development and function. We then highlight recent findings that advance these concepts and conclude with a consideration of open questions that represent some of the major hurdles to be addressed in the future.

STEM enables mapping of single-cell and spatial transcriptomics data with transfer learning

Profiling spatial variations of cellular composition and transcriptomic characteristics is important for understanding the physiology and pathology of tissues. Spatial transcriptomics (ST) data depict spatial gene expression but the currently dominating high-throughput technology is yet not at single-cell resolution. Single-cell RNA-sequencing (SC) data provide high-throughput transcriptomic information at the single-cell level but lack spatial information. Integrating these two types of data would be ideal for revealing transcriptomic landscapes at single-cell resolution. We develop the method STEM (SpaTially aware EMbedding) for this purpose. It uses deep transfer learning to encode both ST and SC data into a unified spatially aware embedding space, and then uses the embeddings to infer SC-ST mapping and predict pseudo-spatial adjacency between cells in SC data. Semi-simulation and real data experiments verify that the embeddings preserved spatial information and eliminated technical biases between SC and ST data. We apply STEM to human squamous cell carcinoma and hepatic lobule datasets to uncover the localization of rare cell types and reveal cell-type-specific gene expression variation along a spatial axis. STEM is powerful for mapping SC and ST data to build single-cell level spatial transcriptomic landscapes, and can provide mechanistic insights into the spatial heterogeneity and microenvironments of tissues.

MENDER: fast and scalable tissue structure identification in spatial omics data

Tissue structure identification is a crucial task in spatial omics data analysis, for which increasingly complex models, such as Graph Neural Networks and Bayesian networks, are employed. However, whether increased model complexity can effectively lead to improved performance is a notable question in the field. Inspired by the consistent observation of cellular neighborhood structures across various spatial technologies, we propose Multi-range cEll coNtext DEciphereR (MENDER), for tissue structure identification. Applied on datasets of 3 brain regions and a whole-brain atlas, MENDER, with biology-driven design, offers substantial improvements over modern complex models while automatically aligning labels across slices, despite using much less running time than the second-fastest. MENDER's identification power allows the uncovering of previously overlooked spatial domains that exhibit strong associations with brain aging. MENDER's scalability makes it freely appliable on a million-level brain spatial atlas. MENDER's discriminative power enables the differentiation of breast cancer patient subtypes obscured by single-cell analysis.

Spatial Omics: Navigating Neuroscience Research into the New Era

The human brain's complexity is underpinned by billions of neurons and trillions of synapses, necessitating coordinated activities across diverse cell types. Conventional techniques like in situ hybridization and immunohistochemistry, while valuable, face limitations in resolution and comprehensiveness when analyzing neuron types. Advances in spatial omics technologies, especially those integrating transcriptomics and proteomics, have revolutionized our understanding of brain tissue organization. These technologies, such as FISH-based, in situ sequencing-based (ISS), and next-generation sequencing (NGS)-based methods, provide detailed spatial context, overcoming previous limitations. FISH techniques, including smFISH and its variants like seqFISH and MERFISH, offer high-resolution spatial gene expression data. ISS approaches leverage padlock probes and rolling circle amplification to yield spatial transcriptome information. NGS-based methods, such as spatial transcriptomics and spatial-epigenomics, integrate spatial barcodes with single-cell sequencing, enabling comprehensive profiling of gene expression and epigenetic states in tissues. These innovations have propelled insights into neural development and disease, identifying cellular heterogeneity and molecular alterations in conditions like Alzheimer's and major depression. Despite challenges in cost, speed, and data analysis, spatial omics technologies continue to evolve, promising deeper insights into the molecular mechanisms of the brain and neurodegenerative diseases.

Directed Differentiation of Neurons from Human iPSCs for Modeling Neurological Disorders

Human-induced pluripotent stem cell (hiPSC) technology has enabled comprehensive human cell-based disease modeling in vitro. Due to limited accessibility of primary human neurons as well as species-specific divergence between human and rodent brain tissues, hiPSC-derived neurons have become a popular tool for studying neuronal biology in a dish. Here, we provide methods for transcription factor-driven directed differentiation of neurons from hiPSCs via a neural progenitor cell (NPC) intermediate. Doxycycline-inducible expression of neuron fate-determining transcription factors neurogenin 2 (NGN2) and achaete-scute homolog 1 (ASCL1) enables rapid and controllable differentiation of human neurons for disease modeling applications. The provided method is also designed to improve the reproducibility of human neuron differentiation by reducing the batch-to-batch variation of NPC differentiation and lentiviral transduction.

SingleScan: a comprehensive resource for single-cell sequencing data processing and mining

Single-cell sequencing has shed light on previously inaccessible biological questions from different fields of research, including organism development, immune function, and disease progression. The number of single-cell-based studies increased dramatically over the past decade. Several new methods and tools have been continuously developed, making it extremely tricky to navigate this research landscape and develop an up-to-date workflow to analyze single-cell sequencing data, particularly for researchers seeking to enter this field without computational experience. Moreover, choosing appropriate tools and optimal parameters to meet the demands of researchers represents a major challenge in processing single-cell sequencing data. However, a specific resource for easy access to detailed information on single-cell sequencing methods and data processing pipelines is still lacking. In the present study, an online resource called SingleScan was developed to curate all up-to-date single-cell transcriptome/genome analyzing tools and pipelines. All the available tools were categorized according to their main tasks, and several typical workflows for single-cell data analysis were summarized. In addition, spatial transcriptomics, which is a breakthrough molecular analysis method that enables researchers to measure all gene activity in tissue samples and map the site of activity, was included along with a portion of single-cell and spatial analysis solutions. For each processing step, the available tools and specific parameters used in published articles are provided and how these parameters affect the results is shown in the resource. All information used in the resource was manually extracted from related literature. An interactive website was designed for data retrieval, visualization, and download. By analyzing the included tools and literature, users can gain insights into the trends of single-cell studies and easily grasp the specific usage of a specific tool. SingleScan will facilitate the analysis of single-cell sequencing data and promote the development of new tools to meet the growing and diverse needs of the research community. The SingleScan database is publicly accessible via the website at http://cailab.labshare.cn/SingleScan .

Cell-type-specific 3D-genome organization and transcription regulation in the brain

3D organization of the genome plays a critical role in regulating gene expression. However, it remains unclear how chromatin organization differs among different cell types in the brain. Here we used genome-scale DNA and RNA imaging to investigate 3D-genome organization in transcriptionally distinct cell types in the primary motor cortex of the mouse brain. We uncovered a wide spectrum of differences in the nuclear architecture and 3D-genome organization among different cell types, ranging from the physical size of the cell nucleus to the active-inactive chromatin compartmentalization and radial positioning of chromatin loci within the nucleus. These cell-type-dependent variations in nuclear architecture and chromatin organization exhibited strong correlation with both total transcriptional activity of the cell and transcriptional regulation of cell-type-specific marker genes. Moreover, we found that the methylated-DNA-binding protein MeCP2 regulates transcription in a divergent manner, depending on the nuclear radial positions of chromatin loci, through modulating active-inactive chromatin compartmentalization.

Conserved and divergent gene regulatory programs of the mammalian neocortex

Divergence of cis-regulatory elements drives species-specific traits1, but how this manifests in the evolution of the neocortex at the molecular and cellular level remains unclear. Here we investigated the gene regulatory programs in the primary motor cortex of human, macaque, marmoset and mouse using single-cell multiomics assays, generating gene expression, chromatin accessibility, DNA methylome and chromosomal conformation profiles from a total of over 200,000 cells. From these data, we show evidence that divergence of transcription factor expression corresponds to species-specific epigenome landscapes. We find that conserved and divergent gene regulatory features are reflected in the evolution of the three-dimensional genome. Transposable elements contribute to nearly 80% of the human-specific candidate cis-regulatory elements in cortical cells. Through machine learning, we develop sequence-based predictors of candidate cis-regulatory elements in different species and demonstrate that the genomic regulatory syntax is highly preserved from rodents to primates. Finally, we show that epigenetic conservation combined with sequence similarity helps to uncover functional cis-regulatory elements and enhances our ability to interpret genetic variants contributing to neurological disease and traits.

Molecularly defined and spatially resolved cell atlas of the whole mouse brain

In mammalian brains, millions to billions of cells form complex interaction networks to enable a wide range of functions. The enormous diversity and intricate organization of cells have impeded our understanding of the molecular and cellular basis of brain function. Recent advances in spatially resolved single-cell transcriptomics have enabled systematic mapping of the spatial organization of molecularly defined cell types in complex tissues1-3, including several brain regions (for example, refs. 1-11). However, a comprehensive cell atlas of the whole brain is still missing. Here we imaged a panel of more than 1,100 genes in approximately 10 million cells across the entire adult mouse brains using multiplexed error-robust fluorescence in situ hybridization12 and performed spatially resolved, single-cell expression profiling at the whole-transcriptome scale by integrating multiplexed error-robust fluorescence in situ hybridization and single-cell RNA sequencing data. Using this approach, we generated a comprehensive cell atlas of more than 5,000 transcriptionally distinct cell clusters, belonging to more than 300 major cell types, in the whole mouse brain with high molecular and spatial resolution. Registration of this atlas to the mouse brain common coordinate framework allowed systematic quantifications of the cell-type composition and organization in individual brain regions. We further identified spatial modules characterized by distinct cell-type compositions and spatial gradients featuring gradual changes of cells. Finally, this high-resolution spatial map of cells, each with a transcriptome-wide expression profile, allowed us to infer cell-type-specific interactions between hundreds of cell-type pairs and predict molecular (ligand-receptor) basis and functional implications of these cell-cell interactions. These results provide rich insights into the molecular and cellular architecture of the brain and a foundation for functional investigations of neural circuits and their dysfunction in health and disease.

SPACEL: deep learning-based characterization of spatial transcriptome architectures

Spatial transcriptomics (ST) technologies detect mRNA expression in single cells/spots while preserving their two-dimensional (2D) spatial coordinates, allowing researchers to study the spatial distribution of the transcriptome in tissues; however, joint analysis of multiple ST slices and aligning them to construct a three-dimensional (3D) stack of the tissue still remain a challenge. Here, we introduce spatial architecture characterization by deep learning (SPACEL) for ST data analysis. SPACEL comprises three modules: Spoint embeds a multiple-layer perceptron with a probabilistic model to deconvolute cell type composition for each spot in a single ST slice; Splane employs a graph convolutional network approach and an adversarial learning algorithm to identify spatial domains that are transcriptomically and spatially coherent across multiple ST slices; and Scube automatically transforms the spatial coordinate systems of consecutive slices and stacks them together to construct a 3D architecture of the tissue. Comparisons against 19 state-of-the-art methods using both simulated and real ST datasets from various tissues and ST technologies demonstrate that SPACEL outperforms the others for cell type deconvolution, for spatial domain identification, and for 3D alignment, thus showcasing SPACEL as a valuable integrated toolkit for ST data processing and analysis.

Insights gained from single-cell analysis of chimeric antigen receptor T-cell immunotherapy in cancer

Advances in chimeric antigen receptor (CAR)-T cell therapy have significantly improved clinical outcomes of patients with relapsed or refractory hematologic malignancies. However, progress is still hindered as clinical benefit is only available for a fraction of patients. A lack of understanding of CAR-T cell behaviors in vivo at the single-cell level impedes their more extensive application in clinical practice. Mounting evidence suggests that single-cell sequencing techniques can help perfect the receptor design, guide gene-based T cell modification, and optimize the CAR-T manufacturing conditions, and all of them are essential for long-term immunosurveillance and more favorable clinical outcomes. The information generated by employing these methods also potentially informs our understanding of the numerous complex factors that dictate therapeutic efficacy and toxicities. In this review, we discuss the reasons why CAR-T immunotherapy fails in clinical practice and what this field has learned since the milestone of single-cell sequencing technologies. We further outline recent advances in the application of single-cell analyses in CAR-T immunotherapy. Specifically, we provide an overview of single-cell studies focusing on target antigens, CAR-transgene integration, and preclinical research and clinical applications, and then discuss how it will affect the future of CAR-T cell therapy.

Functional genomics and systems biology in human neuroscience

Neuroscience research has entered a phase of key discoveries in the realm of neurogenomics owing to strong financial and intellectual support for resource building and tool development. The previous challenge of tissue heterogeneity has been met with the application of techniques that can profile individual cells at scale. Moreover, the ability to perturb genes, gene regulatory elements and neuronal activity in a cell-type-specific manner has been integrated with gene expression studies to uncover the functional underpinnings of the genome at a systems level. Although these insights have necessarily been grounded in model systems, we now have the opportunity to apply these approaches in humans and in human tissue, thanks to advances in human genetics, brain imaging and tissue collection. We acknowledge that there will probably always be limits to the extent to which we can apply the genomic tools developed in model systems to human neuroscience; however, as we describe in this Perspective, the neuroscience field is now primed with an optimal foundation for tackling this ambitious challenge. The application of systems-level network analyses to these datasets will facilitate a deeper appreciation of human neurogenomics that cannot otherwise be achieved from directly observable phenomena.