Single-cell spatial transcriptome reveals cell-type organization in the macaque cortex.

Elucidating the cellular organization of the cerebral cortex is critical for understanding brain structure and function. Using large-scale single-nucleus RNA sequencing and spatial transcriptomic analysis of 143 macaque cortical regions, we obtained a comprehensive atlas of 264 transcriptome-defined cortical cell types and mapped their spatial distribution across the entire cortex. We characterized the cortical layer and region preferences of glutamatergic, GABAergic, and non-neuronal cell types, as well as regional differences in cell-type composition and neighborhood complexity. Notably, we discovered a relationship between the regional distribution of various cell types and the region's hierarchical level in the visual and somatosensory systems. Cross-species comparison of transcriptomic data from human, macaque, and mouse cortices further revealed primate-specific cell types that are enriched in layer 4, with their marker genes expressed in a region-dependent manner. Our data provide a cellular and molecular basis for understanding the evolution, development, aging, and pathogenesis of the primate brain.

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.

Revealing cell-cell communication pathways with their spatially coupled gene programs.

Inference of cell-cell communication (CCC) provides valuable information in understanding the mechanisms of many important life processes. With the rise of spatial transcriptomics in recent years, many methods have emerged to predict CCCs using spatial information of cells. However, most existing methods only describe CCCs based on ligand-receptor interactions, but lack the exploration of their upstream/downstream pathways. In this paper, we proposed a new method to infer CCCs, called Intercellular Gene Association Network (IGAN). Specifically, it is for the first time that we can estimate the gene associations/network between two specific single spatially adjacent cells. By using the IGAN method, we can not only infer CCCs in an accurate manner, but also explore the upstream/downstream pathways of ligands/receptors from the network perspective, which are actually exhibited as a new panoramic cell-interaction-pathway graph, and thus provide extensive information for the regulatory mechanisms behind CCCs. In addition, IGAN can measure the CCC activity at single cell/spot resolution, and help to discover the CCC spatial heterogeneity. Interestingly, we found that CCC patterns from IGAN are highly consistent with the spatial microenvironment patterns for each cell type, which further indicated the accuracy of our method. Analyses on several public datasets validated the advantages of IGAN.

SPANN: annotating single-cell resolution spatial transcriptome data with scRNA-seq data.

MOTIVATION: The rapid development of spatial transcriptome technologies has enabled researchers to acquire single-cell-level spatial data at an affordable price. However, computational analysis tools, such as annotation tools, tailored for these data are still lacking. Recently, many computational frameworks have emerged to integrate single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics datasets. While some frameworks can utilize well-annotated scRNA-seq data to annotate spatial expression patterns, they overlook critical aspects. First, existing tools do not explicitly consider cell type mapping when aligning the two modalities. Second, current frameworks lack the capability to detect novel cells, which remains a key interest for biologists. RESULTS: To address these problems, we propose an annotation method for spatial transcriptome data called SPANN. The main tasks of SPANN are to transfer cell-type labels from well-annotated scRNA-seq data to newly generated single-cell resolution spatial transcriptome data and discover novel cells from spatial data. The major innovations of SPANN come from two aspects: SPANN automatically detects novel cells from unseen cell types while maintaining high annotation accuracy over known cell types. SPANN finds a mapping between spatial transcriptome samples and RNA data prototypes and thus conducts cell-type-level alignment. Comprehensive experiments using datasets from various spatial platforms demonstrate SPANN's capabilities in annotating known cell types and discovering novel cell states within complex tissue contexts. AVAILABILITY: The source code of SPANN can be accessed at https://github.com/ddb-qiwang/SPANN-torch. CONTACT: dengmh@math.pku.edu.cn.

Spatial transcriptomics reveals light-induced chlorenchyma cells involved in promoting shoot regeneration in tomato callus.

Callus is a reprogrammed cell mass involved in plant regeneration and gene transformation in crop engineering. Pluripotent callus cells develop into fertile shoots through shoot regeneration. The molecular basis of the shoot regeneration process in crop callus remains largely elusive. This study pioneers the exploration of the spatial transcriptome of tomato callus during shoot regeneration. The findings reveal the presence of highly heterogeneous cell populations within the callus, including epidermis, vascular tissue, shoot primordia, inner callus, and outgrowth shoots. By characterizing the spatially resolved molecular features of shoot primordia and surrounding cells, specific factors essential for shoot primordia formation are identified. Notably, chlorenchyma cells, enriched in photosynthesis-related processes, play a crucial role in promoting shoot primordia formation and subsequent shoot regeneration. Light is shown to promote shoot regeneration by inducing chlorenchyma cell development and coordinating sugar signaling. These findings significantly advance our understanding of the cellular and molecular aspects of shoot regeneration in tomato callus and demonstrate the immense potential of spatial transcriptomics in plant biology.

Graph deep learning enabled spatial domains identification for spatial transcriptomics.

Advancing spatially resolved transcriptomics (ST) technologies help biologists comprehensively understand organ function and tissue microenvironment. Accurate spatial domain identification is the foundation for delineating genome heterogeneity and cellular interaction. Motivated by this perspective, a graph deep learning (GDL) based spatial clustering approach is constructed in this paper. First, the deep graph infomax module embedded with residual gated graph convolutional neural network is leveraged to address the gene expression profiles and spatial positions in ST. Then, the Bayesian Gaussian mixture model is applied to handle the latent embeddings to generate spatial domains. Designed experiments certify that the presented method is superior to other state-of-the-art GDL-enabled techniques on multiple ST datasets. The codes and dataset used in this manuscript are summarized at https://github.com/narutoten520/SCGDL.

Unravelling infiltrating T-cell heterogeneity in kidney renal clear cell carcinoma: Integrative single-cell and spatial transcriptomic profiling.

Kidney renal clear cell carcinoma (KIRC) pathogenesis intricately involves immune system dynamics, particularly the role of T cells within the tumour microenvironment. Through a multifaceted approach encompassing single-cell RNA sequencing, spatial transcriptome analysis and bulk transcriptome profiling, we systematically explored the contribution of infiltrating T cells to KIRC heterogeneity. Employing high-density weighted gene co-expression network analysis (hdWGCNA), module scoring and machine learning, we identified a distinct signature of infiltrating T cell-associated genes (ITSGs). Spatial transcriptomic data were analysed using robust cell type decomposition (RCTD) to uncover spatial interactions. Further analyses included enrichment assessments, immune infiltration evaluations and drug susceptibility predictions. Experimental validation involved PCR experiments, CCK-8 assays, plate cloning assays, wound-healing assays and Transwell assays. Six subpopulations of infiltrating and proliferating T cells were identified in KIRC, with notable dynamics observed in mid- to late-stage disease progression. Spatial analysis revealed significant correlations between T cells and epithelial cells across varying distances within the tumour microenvironment. The ITSG-based prognostic model demonstrated robust predictive capabilities, implicating these genes in immune modulation and metabolic pathways and offering prognostic insights into drug sensitivity for 12 KIRC treatment agents. Experimental validation underscored the functional relevance of PPIB in KIRC cell proliferation, invasion and migration. Our study comprehensively characterizes infiltrating T-cell heterogeneity in KIRC using single-cell RNA sequencing and spatial transcriptome data. The stable prognostic model based on ITSGs unveils infiltrating T cells' prognostic potential, shedding light on the immune microenvironment and offering avenues for personalized treatment and immunotherapy.

Endothelial pyroptosis-driven microglial activation in choroid plexus mediates neuronal apoptosis in hemorrhagic stroke rats.

BACKGROUND: Spontaneous intracerebral hemorrhage (ICH) is associated with alarmingly high rates of disability and mortality, and current therapeutic options are suboptimal. A critical component of ICH pathology is the initiation of a robust inflammatory response, often termed "cytokine storm," which amplifies the secondary brain injury following the initial hemorrhagic insult. The precise sources and consequences of this cytokine-driven inflammation are not fully elucidated, necessitating further investigation. METHODS: To address this knowledge gap, our study conducted a comprehensive cytokine profiling using Luminex® assays, assessing 23 key cytokines. We then employed single-cell RNA sequencing and spatial transcriptomics at three critical time points post-ICH: the hyperacute, acute, and subacute phases. Integrating these multimodal analyses allowed us to identify the cellular origins of cytokines and elucidate their mechanisms of action. RESULTS: Luminex® cytokine assays revealed a significant upregulation of IL-6 and IL-1ß levels at the 24-h post-ICH time point. Through the integration of scRNA-seq and spatial transcriptomics in the hemorrhagic hemisphere of rats, we observed a pronounced activation of cytokine-related signaling pathways within the choroid plexus. Initially, immune cell presence was sparse, but it surged 24 h post-ICH, particularly in the choroid plexus, indicating a substantial shift in the immune microenvironment. We traced the source of IL-1ß and IL-6 to endothelial cells, establishing a link to pyroptosis. Endothelial pyroptosis post-ICH induced the production of IL-1ß and IL-6, which activated microglial polarization characterized by elevated expression of Msr1, Lcn2, and Spp1 via the NF-κB pathway in the choroid plexus. Furthermore, we identified neuronal populations undergoing apoptosis, mediated by the Lcn2-SLC22A17 pathway in response to IL-1ß and IL-6 signaling. Notably, the inhibition of pyroptosis using VX-765 significantly mitigated neurological impairments. CONCLUSIONS: Our study provides evidence that endothelial pyroptosis, characterized by the release of IL-1ß and IL-6, triggers microglial polarization through NF-κB pathway activation, ultimately leading to microglia-mediated neuronal apoptosis in the choroid plexus post-ICH. These findings suggest that targeted therapeutic strategies aimed at mitigating endothelial cell pyroptosis and neutralizing inflammatory cytokines may offer neuroprotection for both microglia and neurons, presenting a promising avenue for ICH treatment.

Discovering cancer stem-like molecule, nuclear factor I X, using spatial transcriptome in gastric cancer.

Gastric cancer (GC) is characterized by significant intratumoral heterogeneity, and stem cells are promising therapeutic targets. Despite advancements in spatial transcriptome analyses, unexplored targets for addressing cancer stemness remain unknown. This study aimed to identify Nuclear Factor IX (NFIX) as a critical regulator of cancer stemness in GC and evaluate its clinicopathological significance and function. Spatial transcriptome analysis of GC was conducted. The correlation between NFIX expression, clinicopathological factors, and prognosis was assessed using immunostaining in 127 GC cases. Functional analyses of cancer cell lines validated these findings. Spatial transcriptome analysis stratified GC tissues based on genetic profiles, identified CSC-like cells, and further refined the classification to identify and highlight the significance of NFIX, as validated by Monocle 3 and CytoTRACE analyses. Knockdown experiments in cancer cell lines have demonstrated the involvement of NFIX in cancer cell proliferation and kinase activity. This study underscores the role of spatial transcriptome analysis in refining GC tissue classification and identifying therapeutic targets, highlighting NFIX as a pivotal factor. NFIX expression is correlated with poor prognosis and drives GC progression, suggesting its potential as a novel therapeutic target for personalized GC therapies.

Spatial omics technologies for understanding molecular status associated with cancer progression.

Cancer cells are generally exposed to numerous extrinsic stimulations in the tumor microenvironment. In this environment, cancer cells change their expression profiles to fight against circumstantial stresses, allowing their progression in the challenging tissue space. Technological advancements of spatial omics have had substantial influence on cancer genomics. This technical progress, especially that occurring in the spatial transcriptome, has been drastic and rapid. Here, we describe the latest spatial analytical technologies that have allowed omics feature characterization to retain their spatial and histopathological information in cancer tissues. Several spatial omics platforms have been launched, and the latest platforms finally attained single-cell level or even higher subcellular level resolution. We discuss several key papers elucidating the initial utility of the spatial analysis. In fact, spatial transcriptome analyses reveal comprehensive omics characteristics not only in cancer cells but also their surrounding cells, such as tumor infiltrating immune cells and cancer-associated fibroblasts. We also introduce several spatial omics platforms. We describe our own attempts to investigate molecular events associated with cancer progression. Furthermore, we discuss the next challenges in analyzing the multiomics status of cells, including their morphology and location. These novel technologies, in conjunction with spatial transcriptome analysis and, more importantly, with histopathology, will elucidate even novel key aspects of the intratumor heterogeneity of cancers. Such enhanced knowledge is expected to open a new path for overcoming therapeutic resistance and eventually to precisely stratify patients.

Phenotype remodelling of HNSCC cells in the muscle invasion environment.

BACKGROUND: Tumour invading muscle in head and neck squamous cell carcinoma (HNSCC) is often associated with destructive growth and poor prognosis. However, the phenotypic functions and pathological mechanisms of muscle-invasive cancer cells in tumour progress remains unknown. In this study, we aimed to investigate the phenotypic functions of muscle-invasive cancer cells of HNSCC and their potential crosstalk with tumour microenvironment. METHODS: We obtained scRNA-seq data (SC) from GSE103322 (N = 18) and GSE181919 (N = 37), spatial RNA-seq data (ST) from GSE208253 and GSE181300 (N = 4), transcriptomics of human HNSCC samples from GSE42743 (N = 12) and GSE41613 (N = 97). Utilizing the TCGA-HNSC dataset, we conducted univariate and multivariate Cox analyses to investigate the prognostic impact of muscle-invasion in HNSCC, with validation in an additional cohort. Through Stutility and AUCell approaches, we identified and characterized muscle-invasive cancer cell clusters, including their functional phenotypes and gene-specific profiles. Integration of SC and ST data was achieved using Seurat analysis, multimodal intersection analysis, and spatial deconvolution. The results were further validated via in vitro and in vivo experiments. RESULTS: Our analyses of the TCGA-HNSC cohort revealed the presence of muscle-invasion was associated with a poor prognosis. By combining ST and SC, we identified muscle-invasive cancer cells exhibiting epithelial-to-mesenchymal transition (EMT) and myoepithelial-like transcriptional programs, which were correlated with a poor prognosis. Furthermore, we identified G0S2 as a novel marker of muscle-invasive malignant cells that potentially promotes EMT and the acquisition of myoepithelium-like phenotypes. These findings were validated through in vitro assays and chorioallantoic membranes experiments. Additionally, we demonstrated that G0S2-overexpressing cancer cells might attract human ECs via VEGF signalling. Subsequent in vitro and in vivo experiments revealed G0S2 plays key roles in promoting the proliferation and invasion of cancer cells. CONCLUSIONS: In this study, we profiled the transcriptional programs of muscle-invasive HNSCC cell populations and characterized their EMT and myoepithelial-like phenotypes. Furthermore, our findings highlight the presence of muscle-invasion as a predictive marker for HNSCC patients. G0S2 as one of the markers of muscle-invasive cancer cells is involved in HNSCC intravasation, probably via VEGF signalling.

Metabolic heterogeneity in clear cell renal cell carcinoma revealed by single-cell RNA sequencing and spatial transcriptomics.

BACKGROUND: Clear cell renal cell carcinoma is a prototypical tumor characterized by metabolic reprogramming, which extends beyond tumor cells to encompass diverse cell types within the tumor microenvironment. Nonetheless, current research on metabolic reprogramming in renal cell carcinoma mostly focuses on either tumor cells alone or conducts analyses of all cells within the tumor microenvironment as a mixture, thereby failing to precisely identify metabolic changes in different cell types within the tumor microenvironment. METHODS: Gathering 9 major single-cell RNA sequencing databases of clear cell renal cell carcinoma, encompassing 195 samples. Spatial transcriptomics data were selected to conduct metabolic activity analysis with spatial localization. Developing scMet program to convert RNA-seq data into scRNA-seq data for downstream analysis. RESULTS: Diverse cellular entities within the tumor microenvironment exhibit distinct infiltration preferences across varying histological grades and tissue origins. Higher-grade tumors manifest pronounced immunosuppressive traits. The identification of tumor cells in the RNA splicing state reveals an association between the enrichment of this particular cellular population and an unfavorable prognostic outcome. The energy metabolism of CD8+ T cells is pivotal not only for their cytotoxic effector functions but also as a marker of impending cellular exhaustion. Sphingolipid metabolism evinces a correlation with diverse macrophage-specific traits, particularly M2 polarization. The tumor epicenter is characterized by heightened metabolic activity, prominently marked by elevated tricarboxylic acid cycle and glycolysis while the pericapsular milieu showcases a conspicuous enrichment of attributes associated with vasculogenesis, inflammatory responses, and epithelial-mesenchymal transition. The scMet facilitates the transformation of RNA sequencing datasets sourced from TCGA into scRNA sequencing data, maintaining a substantial degree of correlation. CONCLUSIONS: The tumor microenvironment of clear cell renal cell carcinoma demonstrates significant metabolic heterogeneity across various cell types and spatial dimensions. scMet exhibits a notable capability to transform RNA sequencing data into scRNA sequencing data with a high degree of correlation.

Re-analysis of single cell and spatial transcriptomics data reveals B cell landscape in gastric cancer microenvironment and its potential crosstalk with tumor cells for clinical prognosis.

BACKGROUND: At present, immunotherapy has become a powerful treatment for advanced gastric cancer (AGC), but not all patients can benefit from it. According to the latest research, the impact of B cell subpopulations on the immune microenvironment of gastric cancer (GC) is unknown. Exploring whether the interaction between B cells and tumor cells in GC affects the effectiveness of immunotherapy has attracted our interest. METHODS: This study involved the re-analysis of single-cell RNA (scRNA) and spatial transcriptomics (ST) data from publicly available datasets. The focus was on investigating the subpopulations and differentiation trajectories of B cells in the gastric cancer (GC) tumor immune microenvironment (TIME). Spatial transcriptomics (ST) and multiple immunofluorescence (mIF) revealed a clear co-localization pattern between B cells and tumor cells. Multiple immunotherapy datasets were collected to identify unique immunotherapy biomarkers. The unique immunotherapeutic potential of targeting CCL28 was validated through a mouse gastric cancer model. In addition, flow cytometry revealed changes in the tumor immune microenvironment targeting CCL28. RESULTS: The re-analysis of ST data from multiple cancer types revealed a co-localization pattern between B cells and tumor cells. A significant number of IgA plasma cells were identified in the GC TIME. Five different tumor-infiltrating B cell subpopulations and two unique B cell differentiation trajectories were characterized, along with seven GC-related states. By analyzing the communication between GC cells and B cells, it was further discovered that tumor cells can influence and recruit plasma cells through CCL28-CCR10 signaling. Additionally, there was a crosstalk between GC cells and B cells. Finally, we identified the LAMA/CD44 signaling axis as a potential prognostic marker for immunotherapy through a large amount of immunotherapy data. We also validated through various animal tumor models that targeting CCL28 can significantly promote CD8+T cell infiltration and function in the TME by regulating B cell and plasma cell functions, and has the ability to synergize immunotherapy. CONCLUSION: The co-localization and crosstalk between GC cells and B cells significantly affect the efficacy of immunotherapy, and inhibiting the CCL28-CCR10 signal axis is a potential immunotherapy target for GC. Meanwhile, LAMA/CD44 pair may be a potential adverse indicator for immunotherapy and tumor prognosis.

Spatiotemporal transcriptome atlas reveals gene regulatory patterns during the organogenesis of the rapid growing bamboo shoots.

Bamboo with its remarkable growth rate and economic significance, offers an ideal system to investigate the molecular basis of organogenesis in rapidly growing plants, particular in monocots, where gene regulatory networks governing the maintenance and differentiation of shoot apical and intercalary meristems remain a subject of controversy. We employed both spatial and single-nucleus transcriptome sequencing on 10× platform to precisely dissect the gene functions in various tissues and early developmental stages of bamboo shoots. Our comprehensive analysis reveals distinct cell trajectories during shoot development, uncovering critical genes and pathways involved in procambium differentiation, intercalary meristem formation, and vascular tissue development. Spatial and temporal expression patterns of key regulatory genes, particularly those related to hormone signaling and lipid metabolism, strongly support the hypothesis that intercalary meristem origin from surrounded parenchyma cells. Specific gene expressions in intercalary meristem exhibit regular and dispersed distribution pattern, offering clues for understanding the intricate molecular mechanisms that drive the rapid growth of bamboo shoots. The single-nucleus and spatial transcriptome analysis reveal a comprehensive landscape of gene activity, enhancing the understanding of the molecular architecture of organogenesis and providing valuable resources for future genomic and genetic studies relying on identities of specific cell types.

Spatial transcriptomics reveals gene interactions and signaling pathway dynamics in rat embryos with anorectal malformation.

Anorectal malformation (ARM) is a prevalent early pregnancy digestive tract anomaly. The intricate anatomy of the embryonic cloaca region makes it challenging for traditional high-throughput sequencing methods to capture location-specific information. Spatial transcriptomics was used to sequence libraries of frozen sections from embryonic rats at gestational days (GD) 14 to 16, covering both normal and ARM cases. Bioinformatics analyses and predictions were performed using methods such as WGCNA, GSEA, and PROGENy. Immunofluorescence staining was used to verify gene expression levels. Gene expression data was obtained with anatomical annotations of clusters, focusing on the cloaca region's location-specific traits. WGCNA revealed gene modules linked to normal and ARM cloacal anatomy development, with cooperation between modules on GD14 and GD15. Differential gene expression profiles and functional enrichment were presented. Notably, protein levels of Pcsk9, Hmgb2, and Sod1 were found to be downregulated in the GD15 ARM hindgut. The PROGENy algorithm predicted the activity and interplay of common signaling pathways in embryonic sections, highlighting their synergistic and complementary effects. A competing endogenous RNA (ceRNA) regulatory network was constructed from whole transcriptome data. Spatial transcriptomics provided location-specific cloaca region gene expression. Diverse bioinformatics analyses deepened our understanding of ARM's molecular interactions, guiding future research and providing insights into gene regulation in ARM development.

Measuring the composition of the tumor microenvironment with transcriptome analysis: past, present and future.

Interactions between tumor cells and immune cells in the tumor microenvironment (TME) play a vital role the mechanisms of immune evasion, by which cancer cells escape immune elimination. Thus, the characterization and quantification of different components in the TME is a hot topic in molecular biology and drug discovery. Since the development of transcriptome sequencing in bulk tissue, single cells and spatial dimensions, there are increasing methods emerging to deconvolute and subtype the TME. This review discusses and compares such computational strategies and downstream subtyping analyses. Integrative analyses of the transcriptome with other data, such as epigenetics and T-cell receptor sequencing, are needed to obtain comprehensive knowledge of the dynamic TME.

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Plant biotechnology research with single-cell transcriptome: recent advancements and prospects.

KEY MESSAGE: Single-cell transcriptomic techniques have emerged as powerful tools in plant biology, offering high-resolution insights into gene expression at the individual cell level. This review highlights the rapid expansion of single-cell technologies in plants, their potential in understanding plant development, and their role in advancing plant biotechnology research. Single-cell techniques have emerged as powerful tools to enhance our understanding of biological systems, providing high-resolution transcriptomic analysis at the single-cell level. In plant biology, the adoption of single-cell transcriptomics has seen rapid expansion of available technologies and applications. This review article focuses on the latest advancements in the field of single-cell transcriptomic in plants and discusses the potential role of these approaches in plant development and expediting plant biotechnology research in the near future. Furthermore, inherent challenges and limitations of single-cell technology are critically examined to overcome them and enhance our knowledge and understanding.

Using Single-Cell and Spatial Transcriptomes to Understand Stem Cell Lineage Specification During Early Embryo Development.

Embryonic development and stem cell differentiation provide a paradigm to understand the molecular regulation of coordinated cell fate determination and the architecture of tissue patterning. Emerging technologies such as single-cell RNA sequencing and spatial transcriptomics are opening new avenues to dissect cell organization, the divergence of morphological and molecular properties, and lineage allocation. Rapid advances in experimental and computational tools have enabled researchers to make many discoveries and revisit old hypotheses. In this review, we describe the use of single-cell RNA sequencing in studies of molecular trajectories and gene regulation networks for stem cell lineages, while highlighting the integratedexperimental and computational analysis of single-cell and spatial transcriptomes in the molecular annotation of tissue lineages and development during postimplantation gastrulation.

Identification of a tumour immune barrier in the HCC microenvironment that determines the efficacy of immunotherapy.

BACKGROUND & AIMS: The tumour microenvironment (TME) is a crucial mediator of cancer progression and therapeutic outcome. The TME subtype correlates with patient response to immunotherapy in multiple cancers. Most previous studies have focused on the role of different cellular components in the TME associated with immunotherapy efficacy. However, the specific structure of the TME and its role in immunotherapy efficacy remain largely unknown. METHODS: We combined spatial transcriptomics with single-cell RNA-sequencing and multiplexed immunofluorescence to identify the specific spatial structures in the TME that determine the efficacy of immunotherapy in patients with hepatocellular carcinoma (HCC) receiving anti-PD-1 treatment. RESULTS: We identified a tumour immune barrier (TIB) structure, a spatial niche composed of SPP1+ macrophages and cancer-associated fibroblasts (CAFs) located near the tumour boundary, which is associated with the efficacy of immune checkpoint blockade. Furthermore, we dissected ligand‒receptor networks among malignant cells, SPP1+ macrophages, and CAFs; that is, the hypoxic microenvironment promotes SPP1 expression, and SPP1+ macrophages interact with CAFs to stimulate extracellular matrix remodelling and promote TIB structure formation, thereby limiting immune infiltration in the tumour core. Preclinically, the blockade of SPP1 or macrophage-specific deletion of Spp1 in mice led to enhanced efficacy of anti-PD-1 treatment in mouse liver cancer, accompanied by reduced CAF infiltration and increased cytotoxic T-cell infiltration. CONCLUSIONS: We identified that the TIB structure formed by the interaction of SPP1+ macrophages and CAFs is related to immunotherapy efficacy. Therefore, disruption of the TIB structure by blocking SPP1 may be considered a relevant therapeutic approach to enhance the therapeutic effect of immune checkpoint blockade in HCC. IMPACT AND IMPLICATIONS: Only a limited number of patients with hepatocellular carcinoma (HCC) benefit from tumour immunotherapy, which significantly hinders its application. Herein, we used multiomics to identify the spatial structure of the tumour immune barrier (TIB), which is formed by the interaction of SPP1+ macrophages and cancer-associated fibroblasts in the HCC microenvironment. This structure constrains immunotherapy efficacy by limiting immune cell infiltration into malignant regions. Preclinically, we revealed that blocking SPP1 or macrophage-specific deletion of Spp1 in mice could destroy the TIB structure and sensitize HCC cells to immunotherapy. These results provide the first key steps towards finding more effective therapies for HCC and have implications for physicians, scientists, and drug developers in the field of HCC.

Liver in infections: a single-cell and spatial transcriptomics perspective.

The liver is an immune organ that plays a vital role in the detection, capture, and clearance of pathogens and foreign antigens that invade the human body. During acute and chronic infections, the liver transforms from a tolerant to an active immune state. The defence mechanism of the liver mainly depends on a complicated network of intrahepatic and translocated immune cells and non-immune cells. Therefore, a comprehensive liver cell atlas in both healthy and diseased states is needed for new therapeutic target development and disease intervention improvement. With the development of high-throughput single-cell technology, we can now decipher heterogeneity, differentiation, and intercellular communication at the single-cell level in sophisticated organs and complicated diseases. In this concise review, we aimed to summarise the advancement of emerging high-throughput single-cell technologies and re-define our understanding of liver function towards infections, including hepatitis B virus, hepatitis C virus, Plasmodium, schistosomiasis, endotoxemia, and corona virus disease 2019 (COVID-19). We also unravel previously unknown pathogenic pathways and disease mechanisms for the development of new therapeutic targets. As high-throughput single-cell technologies mature, their integration into spatial transcriptomics, multiomics, and clinical data analysis will aid in patient stratification and in developing effective treatment plans for patients with or without liver injury due to infectious diseases.