Multilineage communication regulates human liver bud development from pluripotency

scQA: A dual-perspective cell type identification model for single cell transcriptome data

Single-cell RNA sequencing technologies have been pivotal in advancing the development of algorithms for clustering heterogeneous cell populations. Existing methods for utilizing scRNA-seq data to identify cell types tend to neglect the beneficial impact of dropout events and perform clustering focusing solely on quantitative perspective. Here, we introduce a novel method named scQA, notable for its ability to concurrently identify cell types and cell type-specific key genes from both qualitative and quantitative perspectives. In contrast to other methods, scQA not only identifies cell types but also extracts key genes associated with these cell types, enabling bidirectional clustering for scRNA-seq data. Through an iterative process, our approach aims to minimize the number of landmarks to approximately a dozen while maximizing the inclusion of quasi-trend-preserved genes with dropouts both qualitatively and quantitatively. It then clusters cells by employing an ingenious label propagation strategy, obviating the requirement for a predetermined number of cell types. Validated on 20 publicly available scRNA-seq datasets, scQA consistently outperforms other salient tools. Furthermore, we confirm the effectiveness and potential biological significance of the identified key genes through both external and internal validation. In conclusion, scQA emerges as a valuable tool for investigating cell heterogeneity due to its distinctive fusion of qualitative and quantitative facets, along with bidirectional clustering capabilities. Furthermore, it can be seamlessly integrated into border scRNA-seq analyses. The source codes are publicly available at https://github.com/LD-Lyndee/scQA.

CVGAE: A Self-Supervised Generative Method for Gene Regulatory Network Inference Using Single-Cell RNA Sequencing Data

Gene regulatory network (GRN) inference based on single-cell RNA sequencing data (scRNAseq) plays a crucial role in understanding the regulatory mechanisms between genes. Various computational methods have been employed for GRN inference, but their performance in terms of network accuracy and model generalization is not satisfactory, and their poor performance is caused by high-dimensional data and network sparsity. In this paper, we propose a self-supervised method for gene regulatory network inference using single-cell RNA sequencing data (CVGAE). CVGAE uses graph neural network for inductive representation learning, which merges gene expression data and observed topology into a low-dimensional vector space. The well-trained vectors will be used to calculate mathematical distance of each gene, and further predict interactions between genes. In overall framework, FastICA is implemented to relief computational complexity caused by high dimensional data, and CVGAE adopts multi-stacked GraphSAGE layers as an encoder and an improved decoder to overcome network sparsity. CVGAE is evaluated on several single cell datasets containing four related ground-truth networks, and the result shows that CVGAE achieve better performance than comparative methods. To validate learning and generalization capabilities, CVGAE is applied in few-shot environment by change the ratio of train set and test set. In condition of few-shot, CVGAE obtains comparable or superior performance.

Research progress in stem cell therapy for Wilson disease

Wilson disease (WD), also known as hepatolenticular degeneration, is an autosomal recessive disorder characterized by disorganized copper metabolism caused by mutations in the ATP7B gene. Currently, the main treatment options for WD involve medications such as d-penicillamine, trientine hydrochloride, zinc acetate, and liver transplantation. However, there are challenges that encompass issues of poor compliance, adverse effects, and limited availability of liver sources that persist. Stem cell therapy for WD is currently a promising area of research. Due to the advancement in stem cell directed differentiation technology in vitro and the availability of sufficient stem cell donors, it is expected to be a potential treatment option for the permanent correction of abnormal copper metabolism. This article discusses the research progress of stem cell therapy for WD from various sources, as well as the challenges and future prospects of the clinical application of stem cell therapy for WD.

scGraphformer: unveiling cellular heterogeneity and interactions in scRNA-seq data using a scalable graph transformer network

The precise classification of cell types from single-cell RNA sequencing (scRNA-seq) data is pivotal for dissecting cellular heterogeneity in biological research. Traditional graph neural network (GNN) models are constrained by reliance on predefined graphs, limiting the exploration of complex cell-to-cell relationships. We introduce scGraphformer, a transformer-based GNN that transcends these limitations by learning an all-encompassing cell-cell relational network directly from scRNA-seq data. Through an iterative refinement process, scGraphformer constructs a dense graph structure that captures the full spectrum of cellular interactions. This comprehensive approach enables the identification of subtle and previously obscured cellular patterns and relationships. Evaluated on multiple datasets, scGraphformer demonstrates superior performance in cell type identification compared to existing methods and showcases its scalability with large-scale datasets. Our method not only provides enhanced cell type classification ability but also reveals the underlying cell interactions, offering deeper insights into functional cellular relationships. The scGraphformer thus holds the potential to significantly advance the field of single-cell analysis and contribute to a more nuanced understanding of cellular behavior.

Approaches for Benchmarking Single-Cell Gene Regulatory Network Methods

Gene regulatory networks are powerful tools for modeling genetic interactions that control the expression of genes driving cell differentiation, and single-cell sequencing offers a unique opportunity to build these networks with high-resolution genomic data. There are many proposed computational methods to build these networks using single-cell data, and different approaches are used to benchmark these methods. However, a comprehensive discussion specifically focusing on benchmarking approaches is missing. In this article, we lay the GRN terminology, present an overview of common gold-standard studies and data sets, and define the performance metrics for benchmarking network construction methodologies. We also point out the advantages and limitations of different benchmarking approaches, suggest alternative ground truth data sets that can be used for benchmarking, and specify additional considerations in this context.

Targeting the immune privilege of tumor-initiating cells to enhance cancer immunotherapy

Tumor-initiating cells (TICs) possess the ability to evade anti-tumor immunity, potentially explaining many failures of cancer immunotherapy. Here, we identify CD49f as a prominent marker for discerning TICs in hepatocellular carcinoma (HCC), outperforming other commonly used TIC markers. CD49f-high TICs specifically recruit tumor-promoting neutrophils via the CXCL2-CXCR2 axis and create an immunosuppressive milieu in the tumor microenvironment (TME). Reciprocally, the neutrophils reprogram nearby tumor cells toward a TIC phenotype via secreting CCL4. These cells can evade CD8+ T cell-mediated killing through CCL4/STAT3-induced and CD49f-stabilized CD155 expression. Notably, while aberrant CD155 expression contributes to immune suppression, it also represents a TIC-specific vulnerability. We demonstrate that either CD155 deletion or antibody blockade significantly enhances sensitivity to anti-PD-1 therapy in preclinical HCC models. Our findings reveal a new mechanism of tumor immune evasion and provide a rationale for combining CD155 blockade with anti-PD-1/PD-L1 therapy in HCC.

Establishment of human induced pluripotent stem cell-derived hepatobiliary organoid with bile duct for pharmaceutical research use

In vitro models of the human liver are promising alternatives to animal tests for drug development. Currently, primary human hepatocytes (PHHs) are preferred for pharmacokinetic and cytotoxicity tests. However, they are unable to recapitulate the flow of bile in hepatobiliary clearance owing to the lack of bile ducts, leading to the limitation of bile analysis. To address the issue, a liver organoid culture system that has a functional bile duct network is desired. In this study, we aimed to generate human iPSC-derived hepatobiliary organoids (hHBOs) consisting of hepatocytes and bile ducts. The two-step differentiation process under 2D and semi-3D culture conditions promoted the maturation of hHBOs on culture plates, in which hepatocyte clusters were covered with monolayered biliary tubes. We demonstrated that the hHBOs reproduced the flow of bile containing a fluorescent bile acid analog or medicinal drugs from hepatocytes into bile ducts via bile canaliculi. Furthermore, the hHBOs exhibited pathophysiological responses to troglitazone, such as cholestasis and cytotoxicity. Because the hHBOs can recapitulate the function of bile ducts in hepatobiliary clearance, they are suitable as a liver disease model and would be a novel in vitro platform system for pharmaceutical research use.

Single-cell transcriptomic analysis of flowering regulation and vernalization in Chinese cabbage shoot apex

In Chinese cabbage development the interplay between shoot apex activity and vernalization is pivotal for flowering timing. The intricate relationship between various cell types in the shoot apex meristem and their roles in regulating flowering gene expression in Chinese cabbage is not yet fully understood. A thorough analysis of single-cell types in the Chinese cabbage shoot apex and their influence on flowering genes and vernalization is essential for deeper insight. Our study first established a single-cell transcriptomic atlas of Chinese cabbage after 25 days of non-vernalization. Analyzing 19 602 single cells, we differentiated them into 15 distinct cell clusters using established marker genes. We found that key genes in shoot apex development and flowering were primarily present in shoot meristematic cells (SMCs), companion cells (CCs), and mesophyll cells (MCs). MADS-box protein FLOWERING LOCUS C 2 (BrFLC2), a gene suppressing flowering, was observed in CCs, mirroring patterns found in Arabidopsis. By mapping developmental trajectories of SMCs, CCs, and MCs, we elucidated the evolutionary pathways of crucial genes in shoot apex development and flowering. The creation of a single-cell transcriptional atlas of the Chinese cabbage shoot apex under vernalization revealed distinct alterations in the expression of known flowering genes, such as VERNALIZATION INSENSITIVE 3 (VIN3), VERNALIZATION 1 (VRN1), VERNALIZATION 2 (VRN2), BrFLC, and FLOWERING LOCUS T (FT), which varied by cell type. Our study underscores the transformative impact of single-cell RNA sequencing (scRNA-seq) for unraveling the complex differentiation and vernalization processes in the Chinese cabbage shoot apex. These insights are pivotal for enhancing breeding strategies and cultivation management of this vital vegetable.

Graph contrastive learning as a versatile foundation for advanced scRNA-seq data analysis

Single-cell RNA sequencing (scRNA-seq) offers unprecedented insights into transcriptome-wide gene expression at the single-cell level. Cell clustering has been long established in the analysis of scRNA-seq data to identify the groups of cells with similar expression profiles. However, cell clustering is technically challenging, as raw scRNA-seq data have various analytical issues, including high dimensionality and dropout values. Existing research has developed deep learning models, such as graph machine learning models and contrastive learning-based models, for cell clustering using scRNA-seq data and has summarized the unsupervised learning of cell clustering into a human-interpretable format. While advances in cell clustering have been profound, we are no closer to finding a simple yet effective framework for learning high-quality representations necessary for robust clustering. In this study, we propose scSimGCL, a novel framework based on the graph contrastive learning paradigm for self-supervised pretraining of graph neural networks. This framework facilitates the generation of high-quality representations crucial for cell clustering. Our scSimGCL incorporates cell-cell graph structure and contrastive learning to enhance the performance of cell clustering. Extensive experimental results on simulated and real scRNA-seq datasets suggest the superiority of the proposed scSimGCL. Moreover, clustering assignment analysis confirms the general applicability of scSimGCL, including state-of-the-art clustering algorithms. Further, ablation study and hyperparameter analysis suggest the efficacy of our network architecture with the robustness of decisions in the self-supervised learning setting. The proposed scSimGCL can serve as a robust framework for practitioners developing tools for cell clustering. The source code of scSimGCL is publicly available at https://github.com/zhangzh1328/scSimGCL.

COFFEE: consensus single cell-type specific inference for gene regulatory networks

The inference of gene regulatory networks (GRNs) is crucial to understanding the regulatory mechanisms that govern biological processes. GRNs may be represented as edges in a graph, and hence, it have been inferred computationally for scRNA-seq data. A wisdom of crowds approach to integrate edges from several GRNs to create one composite GRN has demonstrated improved performance when compared with individual algorithm implementations on bulk RNA-seq and microarray data. In an effort to extend this approach to scRNA-seq data, we present COFFEE (COnsensus single cell-type speciFic inFerence for gEnE regulatory networks), a Borda voting-based consensus algorithm that integrates information from 10 established GRN inference methods. We conclude that COFFEE has improved performance across synthetic, curated, and experimental datasets when compared with baseline methods. Additionally, we show that a modified version of COFFEE can be leveraged to improve performance on newer cell-type specific GRN inference methods. Overall, our results demonstrate that consensus-based methods with pertinent modifications continue to be valuable for GRN inference at the single cell level. While COFFEE is benchmarked on 10 algorithms, it is a flexible strategy that can incorporate any set of GRN inference algorithms according to user preference. A Python implementation of COFFEE may be found on GitHub: https://github.com/lodimk2/coffee.

Single-Cell RNA Sequencing Reveals an Atlas of Hezuo Pig Testis Cells

Spermatogenesis is a complex biological process crucial for male reproduction and is characterized by intricate interactions between testicular somatic cells and germ cells. Due to the cellular heterogeneity of the testes, investigating different cell types across developmental stages has been challenging. Single-cell RNA sequencing (scRNA-seq) has emerged as a valuable approach for addressing this limitation. Here, we conducted an unbiased transcriptomic study of spermatogenesis in sexually mature 4-month-old Hezuo pigs using 10× Genomics-based scRNA-seq. A total of 16,082 cells were collected from Hezuo pig testes, including germ cells (spermatogonia (SPG), spermatocytes (SPCs), spermatids (SPTs), and sperm (SP)) and somatic cells (Sertoli cells (SCs), Leydig cells (LCs), myoid cells (MCs), endothelial cells (ECs), and natural killer (NK) cells/macrophages). Pseudo-time analysis revealed that LCs and MCs originated from common progenitors in the Hezuo pig. Functional enrichment analysis indicated that the differentially expressed genes (DEGs) in the different types of testicular germ cells were enriched in the PI3K-AKT, Wnt, HIF-1, and adherens junction signaling pathways, while the DEGs in testicular somatic cells were enriched in ECM-receptor interaction and antigen processing and presentation. Moreover, genes related to spermatogenesis, male gamete generation, sperm part, sperm flagellum, and peptide biosynthesis were expressed throughout spermatogenesis. Using immunohistochemistry, we verified several stage-specific marker genes (such as UCHL1, WT1, SOX9, and ACTA2) for SPG, SCs, and MCs. By exploring the changes in the transcription patterns of various cell types during spermatogenesis, our study provided novel insights into spermatogenesis and testicular cells in the Hezuo pig, thereby laying the foundation for the breeding and preservation of this breed.

Dimension reduction, cell clustering, and cell-cell communication inference for single-cell transcriptomics with DcjComm

Advances in single-cell transcriptomics provide an unprecedented opportunity to explore complex biological processes. However, computational methods for analyzing single-cell transcriptomics still have room for improvement especially in dimension reduction, cell clustering, and cell-cell communication inference. Herein, we propose a versatile method, named DcjComm, for comprehensive analysis of single-cell transcriptomics. DcjComm detects functional modules to explore expression patterns and performs dimension reduction and clustering to discover cellular identities by the non-negative matrix factorization-based joint learning model. DcjComm then infers cell-cell communication by integrating ligand-receptor pairs, transcription factors, and target genes. DcjComm demonstrates superior performance compared to state-of-the-art methods.

Spatial genomics: mapping human steatotic liver disease

Metabolic dysfunction-associated steatotic liver disease (MASLD, formerly known as non-alcoholic fatty liver disease) is a leading cause of chronic liver disease worldwide. MASLD can progress to metabolic dysfunction-associated steatohepatitis (MASH, formerly known as non-alcoholic steatohepatitis) with subsequent liver cirrhosis and hepatocellular carcinoma formation. The advent of current technologies such as single-cell and single-nuclei RNA sequencing have transformed our understanding of the liver in homeostasis and disease. The next frontier is contextualizing this single-cell information in its native spatial orientation. This understanding will markedly accelerate discovery science in hepatology, resulting in a further step-change in our knowledge of liver biology and pathobiology. In this Review, we discuss up-to-date knowledge of MASLD development and progression and how the burgeoning field of spatial genomics is driving exciting new developments in our understanding of human liver disease pathogenesis and therapeutic target identification.

Single-nucleus transcriptome reveals cell dynamic response of liver during the late chick embryonic development

The late embryonic development of the liver, a major metabolic organ, remains poorly characterized at single cell resolution. Here, we used single-nucleus RNA-sequencing (snRNA-seq) to characterize the chicken liver cells at 2 embryonic development time points (E14 and D1). We uncovered 8 cell types including hepatocytes, endothelial cells, hepatic stellate cells, erythrocytes, cholangiocytes, kupffer cells, mesothelial cells, and lymphocytes. And we discovered significant differences in the abundance of different cell types between E14 and D1. Moreover, we characterized the heterogeneity of hepatocytes, endothelial cells, and mesenchymal cells based on the gene regulatory networks of each clusters. Trajectory analyses revealed 128 genes associated with hepatocyte development and function, including apolipoprotein genes involved hepatic lipid metabolism and NADH dehydrogenase subunits involved hepatic oxidative phosphorylation. Furthermore, we identified the differentially expressed genes (DEGs) between E14 and D1 at the cellular levels, which contribute to changes in liver development and function. These DEGs were significantly enriched in PPAR signaling pathways and lipid metabolism related pathways. Our results presented the single-cell mapping of chick embryonic liver at late stages of development and demonstrated the metabolic changes across the 2 age stages at the cellular level, which can help to further study the molecular development mechanism of embryonic liver.

Understanding Macrophage Complexity in Metabolic Dysfunction-Associated Steatotic Liver Disease: Transitioning from the M1/M2 Paradigm to Spatial Dynamics

Metabolic dysfunction-associated steatotic liver disease (MASLD) encompasses metabolic dysfunction-associated fatty liver (MASL) and metabolic dysfunction-associated steatohepatitis (MASH), with MASH posing a risk of progression to cirrhosis and hepatocellular carcinoma (HCC). The global prevalence of MASLD is estimated at approximately a quarter of the population, with significant healthcare costs and implications for liver transplantation. The pathogenesis of MASLD involves intrahepatic liver cells, extrahepatic components, and immunological aspects, particularly the involvement of macrophages. Hepatic macrophages are a crucial cellular component of the liver and play important roles in liver function, contributing significantly to tissue homeostasis and swift responses during pathophysiological conditions. Recent advancements in technology have revealed the remarkable heterogeneity and plasticity of hepatic macrophage populations and their activation states in MASLD, challenging traditional classification methods like the M1/M2 paradigm and highlighting the coexistence of harmful and beneficial macrophage phenotypes that are dynamically regulated during MASLD progression. This complexity underscores the importance of considering macrophage heterogeneity in therapeutic targeting strategies, including their distinct ontogeny and functional phenotypes. This review provides an overview of macrophage involvement in MASLD progression, combining traditional paradigms with recent insights from single-cell analysis and spatial dynamics. It also addresses unresolved questions and challenges in this area.

Self-Assembled Generation of Multi-zonal Liver Organoids from Human Pluripotent Stem Cells

Distinct hepatocyte subpopulations are spatially segregated along the portal-central axis and critical to understanding metabolic homeostasis and liver injury. While several bioactive molecules have been described to play a role in directing zonal fates, including ascorbate and bilirubin, in vitro replication of zonal liver architecture has not been achieved to date. In order to evaluate hepatic zonal polarity, we developed a self-assembling zone-specific liver organoid culture by co-culturing ascorbate and bilirubin enriched hepatic progenitors derived from human induced pluripotent stem cells. We found that preconditioned hepatocyte-like cells exhibited zone-specific functions associated with urea cycle, glutathione synthesis and glutamate synthesis. Single nucleus RNA sequencing analysis of these zonally patterned organoids identifies hepatoblast differentiation trajectory that mimics periportal-, interzonal-, and pericentral human hepatocytes. Epigenetic and transcriptomic analysis showed that zonal identity is orchestrated by ascorbate or bilirubin dependent binding of histone acetyltransferase p300 (EP300) to methylcytosine dioxygenase TET1 or hypoxia-inducible factor 1-alpha (HIF1α). Transplantation of the self-assembled zonally patterned human organoids improved survival of immunodeficient rats who underwent bile duct ligation by ameliorating the hyperammonemia and hyperbilirubinemia. Overall, this multi-zonal organoid system serves as an in vitro human model to better recapitulate hepatic architecture relevant to liver development and disease.

Generation of human iPSC-derived 3D bile duct within liver organoid by incorporating human iPSC-derived blood vessel

In fetal development, tissue interaction such as the interplay between blood vessel (BV) and epithelial tissue is crucial for organogenesis. Here we recapitulate the spatial arrangement between liver epithelial tissue and the portal vein to observe the formation of intrahepatic bile ducts (BDs) from human induced pluripotent stem cells (hiPSC). We co-culture hiPSC-liver progenitors on the artificial BV consisting of immature smooth muscle cells and endothelial cells, both derived from hiPSCs. After 3 weeks, liver progenitors within hiPSC-BV-incorporated liver organoids (BVLO) differentiate to cholangiocytes and acquire epithelial characteristics, including intercellular junctions, microvilli on the apical membrane, and secretory functions. Furthermore, liver surface transplanted-BVLO temporarily attenuates cholestatic injury symptoms. Single cell RNA sequence analysis suggests that BD interact with the BV in BVLO through TGFβ and Notch pathways. Knocking out JAG1 in hiPSC-BV significantly attenuates bile duct formation, highlighting BVLO potential as a model for Alagille syndrome, a congenital biliary disease. Overall, we develop a novel 3D co-culture method that successfully establishes functional human BDs by emulating liver epithelial-BV interaction.

Enhanced Maturity and Functionality of Vascularized Human Liver Organoids through 3D Bioprinting and Pillar Plate Culture

Liver tissues, composed of hepatocytes, cholangiocytes, stellate cells, Kupffer cells, and sinusoidal endothelial cells, are differentiated from endodermal and mesodermal germ layers. By mimicking the developmental process of the liver, various differentiation protocols have been published to generate human liver organoids (HLOs) in vitro using induced pluripotent stem cells (iPSCs). However, HLOs derived solely from the endodermal germ layer often encounter technical hurdles, such as insufficient maturity and functionality, limiting their utility for disease modeling and hepatotoxicity assays. To overcome this, we separately differentiated EpCAM+ endodermal progenitor cells (EPCs) and mesoderm-derived vascular progenitor cells (VPCs) from the same human iPSC line. These cells were then mixed in BME-2 matrix and concurrently differentiated into vascular human liver organoids (vHLOs). Remarkably, vHLOs exhibited significantly higher maturity than vasculature-free HLOs, as demonstrated by increased coagulation factor secretion, albumin secretion, drug-metabolizing enzyme (DME) expression, and bile acid transportation. To enhance assay throughput and miniaturize vHLO culture, we 3D bioprinted expandable HLOs (eHLOs) in BME-2 matrix on a pillar plate platform derived from EPCs and VPCs and compared with HLOs derived from endoderm alone. Compared to HLOs cultured in a 50 μL BME-2 matrix dome in a 24-well plate, vHLOs cultured on the pillar plate exhibited superior maturity, likely due to enhanced nutrient and signaling molecule diffusion. The integration of physiologically relevant patterned liver organoids with the unique pillar plate platform enhanced the capabilities for high-throughput screening and disease modeling.

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.

Tumour vasculature at single-cell resolution

Tumours can obtain nutrients and oxygen required to progress and metastasize through the blood supply1. Inducing angiogenesis involves the sprouting of established vessel beds and their maturation into an organized network2,3. Here we generate a comprehensive atlas of tumour vasculature at single-cell resolution, encompassing approximately 200,000 cells from 372 donors representing 31 cancer types. Trajectory inference suggested that tumour angiogenesis was initiated from venous endothelial cells and extended towards arterial endothelial cells. As neovascularization elongates (through angiogenic stages SI, SII and SIII), APLN+ tip cells at the SI stage (APLN+ TipSI) advanced to TipSIII cells with increased Notch signalling. Meanwhile, stalk cells, following tip cells, transitioned from high chemokine expression to elevated TEK (also known as Tie2) expression. Moreover, APLN+ TipSI cells not only were associated with disease progression and poor prognosis but also hold promise for predicting response to anti-VEGF therapy. Lymphatic endothelial cells demonstrated two distinct differentiation lineages: one responsible for lymphangiogenesis and the other involved in antigen presentation. In pericytes, endoplasmic reticulum stress was associated with the proangiogenic BASP1+ matrix-producing pericytes. Furthermore, intercellular communication analysis showed that neovascular endothelial cells could shape an immunosuppressive microenvironment conducive to angiogenesis. This study depicts the complexity of tumour vasculature and has potential clinical significance for anti-angiogenic therapy.

Human iPSC-liver organoid transplantation reduces fibrosis through immunomodulation

Donor organ shortages for transplantation remain a serious global concern, and alternative treatment is in high demand. Fetal cells and tissues have considerable therapeutic potential as, for example, organoid technology that uses human induced pluripotent stem cells (hiPSCs) to generate unlimited human fetal-like cells and tissues. We previously reported the in vivo vascularization of early fetal liver-like hiPSC-derived liver buds (LBs) and subsquent improved survival of recipient mice with subacute liver failure. Here, we show hiPSC-liver organoids (LOs) that recapitulate midgestational fetal liver promote de novo liver generation when grafted onto the surface of host livers in chemical fibrosis models, thereby recovering liver function. We found that fetal liver, a hematopoietic tissue, highly expressed macrophage-recruiting factors and antifibrotic M2 macrophage polarization factors compared with the adult liver, resulting in fibrosis reduction because of CD163+ M2-macrophage polarization. Next, we created midgestational fetal liver-like hiPSC-LOs by fusion of hiPSC-LBs to induce static cell-cell interactions and found that these contained complex structures such as hepatocytes, vasculature, and bile ducts after transplantation. This fusion allowed the generation of a large human tissue suitable for transplantation into immunodeficient rodent models of liver fibrosis. hiPSC-LOs showed superior liver function compared with hiPSC-LBs and improved survival and liver function upon transplantation. In addition, hiPSC-LO transplantation ameliorated chemically induced liver fibrosis, a symptom of liver cirrhosis that leads to organ dysfunction, through immunomodulatory effects, particularly on CD163+ phagocytic M2-macrophage polarization. Together, our results suggest hiPSC-LO transplantation as a promising therapeutic option for liver fibrosis.

Crosstalk between tumor and microenvironment: Insights from spatial transcriptomics

Cancer is a dynamic disease, and clonal heterogeneity plays a fundamental role in tumor development, progression, and resistance to therapies. Single-cell and spatial multimodal technologies can provide a high-resolution molecular map of underlying genomic, epigenomic, and transcriptomic alterations involved in inter- and intra-tumor heterogeneity and interactions with the microenvironment. In this review, we provide a perspective on factors driving cancer heterogeneity, tumor evolution, and clonal states. We briefly describe spatial transcriptomic technologies and summarize recent literature that sheds light on the dynamical interactions between tumor states, cell-to-cell communication, and remodeling local microenvironment.

Cell atlas of the regenerating human liver after portal vein embolization

The liver has the remarkable capacity to regenerate. In the clinic, regeneration is induced by portal vein embolization, which redirects portal blood flow, resulting in liver hypertrophy in locations with increased blood supply, and atrophy of embolized segments. Here, we apply single-cell and single-nucleus transcriptomics on healthy, hypertrophied, and atrophied patient-derived liver samples to explore cell states in the regenerating liver. Our data unveils pervasive upregulation of genes associated with developmental processes, cellular adhesion, and inflammation in post-portal vein embolization liver, disrupted portal-central hepatocyte zonation, and altered cell subtype composition of endothelial and immune cells. Interlineage crosstalk analysis reveals mesenchymal cells as an interaction hub between immune and endothelial cells, and highlights the importance of extracellular matrix proteins in liver regeneration. Moreover, we establish tissue-scale iterative indirect immunofluorescence imaging for high-dimensional spatial analysis of perivascular microenvironments, uncovering changes to tissue architecture in regenerating liver lobules. Altogether, our data is a rich resource revealing cellular and histological changes in human liver regeneration.

Self-Organization of Sinusoidal Vessels in Pluripotent Stem Cell-derived Human Liver Bud Organoids

The induction of tissue-specific vessels in in vitro living tissue systems remains challenging. Here, we directly differentiated human pluripotent stem cells into CD32b+ putative liver sinusoidal progenitors (iLSEP) by dictating developmental pathways. By devising an inverted multilayered air-liquid interface (IMALI) culture, hepatic endoderm, septum mesenchyme, arterial and sinusoidal quadruple progenitors self-organized to generate and sustain hepatocyte-like cells neighbored by divergent endothelial subsets composed of CD32blowCD31high, LYVE1+STAB1+CD32bhighCD31lowTHBD-vWF-, and LYVE1-THBD+vWF+ cells. Wnt2 mediated sinusoidal-to-hepatic intercellular crosstalk potentiates hepatocyte differentiation and branched endothelial network formation. Intravital imaging revealed iLSEP developed fully patent human vessels with functional sinusoid-like features. Organoid-derived hepatocyte- and sinusoid-derived coagulation factors enabled correction of in vitro clotting time with Factor V, VIII, IX, and XI deficient patients' plasma and rescued the severe bleeding phenotype in hemophilia A mice upon transplantation. Advanced organoid vascularization technology allows for interrogating key insights governing organ-specific vessel development, paving the way for coagulation disorder therapeutics.

Single-cell transcriptomic reveals a cell atlas and diversity of chicken amygdala responded to social hierarchy

Amygdala serves as a highly cellular, heterogeneous brain region containing excitatory and inhibitory neurons and is involved in the dopamine and serotoninergic neuron systems. An increasing number of studies have revealed the underpinned mechanism mediating social hierarchy in mammal and vertebrate, however, there are rare studies conducted on how amygdala on social hierarchy in poultry. In this study, we conducted food competition tests and determined the social hierarchy of the rooster. We performed cross-species analysis with mammalian amygdala, and found that cell types of human and rhesus monkeys were more closely related and that of chickens were more distant. We identified 26 clusters and divided them into 10 main clusters, of which GABAergic and glutamatergic neurons were associated with social behaviors. In conclusion, our results provide to serve the developmental studies of the amygdala neuron system and new insights into the underpinned mechanism of social hierarchy in roosters.

Single-nucleus RNA sequencing and lipidomics reveal characteristics of transcriptional and lipid composition in porcine longissimus dorsi muscle

BACKGROUND

Global per capita meat consumption continues to rise, especially pork. Meat quality is influenced by the content of intramuscular fat (IMF) as a key factor. The longissimus dorsi muscle of Dahe pigs (DHM, IMF: 7.98% ± 1.96%) and Dahe black pigs (DHBM, IMF: 3.30% ± 0.64%) was studied to explore cellular heterogeneity and differentially expressed genes (DEGs) associated with IMF deposition using single-nucleus RNA sequencing (snRNA-seq). The lipid composition was then analyzed using non-targeted lipidomics.

RESULTS

A total of seven cell subpopulations were identified, including myocytes, fibroblast/fibro/adipogenic progenitors (FAPs), satellite cells, endothelial cells, macrophages, pericytes, and adipocytes. Among them, FAPs and adipocytes were more focused because they could be associated with lipid deposition. 1623 DEGs in the FAPs subpopulation of DHBM were up-regulated compared with DHM, while 1535 were down-regulated. These DEGs enriched in the glycolysis/gluconeogenesis pathway. 109 DEGs were up-regulated and 806 were down-regulated in the adipocyte subpopulation of DHBM compared with DHM, which were mainly enriched in the PPAR signaling pathway and fatty acid (FA) biosynthesis. The expression level of PPARG, ABP4, LEP, and ACSL1 genes in DHM was higher than that in DHBM. Lipidomics reveals porcine lipid composition characteristics of muscle tissue. A total of 41 lipid classes and 2699 lipid species were identified in DHM and DHBM groups. The top ten relative peak areas of lipid classes in DHM and DHBM were triglyceride (TG), phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylserine (PS), diglyceride (DG), cardiolipin (CL), ceramides (Cer), Simple Glc series (Hex1Cer), sphingomyelin (phSM), and phosphatidylinositol (PI). The relative peak areas of 35 lipid species in DHM were lower than DHBM, and 28 lipid species that were higher. There was a significant increase in the TG fatty acyl chains C6:0, C17:0, and C11:4, and a significant decrease in C16:0, C18:1, C18:2, and C22:4 in DHBM (p < 0.05).

CONCLUSIONS

C16:0 FA may downregulate the expression level of PPARG gene, which leads to the downregulation of fat metabolism-related genes such as ACSL, PLIN2, and FABP4 in DHBM compared with DHM. This may be the reason that the lipid deposition ability of Dahe pigs is stronger than that of Dahe black pigs, which need further investigation.

Advances in Single-Cell Transcriptome Sequencing and Spatial Transcriptome Sequencing in Plants

"Omics" typically involves exploration of the structure and function of the entire composition of a biological system at a specific level using high-throughput analytical methods to probe and analyze large amounts of data, including genomics, transcriptomics, proteomics, and metabolomics, among other types. Genomics characterizes and quantifies all genes of an organism collectively, studying their interrelationships and their impacts on the organism. However, conventional transcriptomic sequencing techniques target population cells, and their results only reflect the average expression levels of genes in population cells, as they are unable to reveal the gene expression heterogeneity and spatial heterogeneity among individual cells, thus masking the expression specificity between different cells. Single-cell transcriptomic sequencing and spatial transcriptomic sequencing techniques analyze the transcriptome of individual cells in plant or animal tissues, enabling the understanding of each cell's metabolites and expressed genes. Consequently, statistical analysis of the corresponding tissues can be performed, with the purpose of achieving cell classification, evolutionary growth, and physiological and pathological analyses. This article provides an overview of the research progress in plant single-cell and spatial transcriptomics, as well as their applications and challenges in plants. Furthermore, prospects for the development of single-cell and spatial transcriptomics are proposed.

Exploration of the shared pathways and common biomarker in adamantinomatous craniopharyngioma and type 2 diabetes using integrated bioinformatics analysis

Craniopharyngiomas are rare tumors of the central nervous system that typically present with symptoms such as headache and visual impairment, and those reflecting endocrine abnormalities, which seriously affect the quality of life of patients. Patients with craniopharyngiomas are at higher cardiometabolic risk, defined as conditions favoring the development of type 2 diabetes and cardiovascular disease. However, the underlying common pathogenic mechanisms of craniopharyngiomas and type 2 diabetes are not clear. Especially due to the difficulty of conducting in vitro or in vivo experiments on craniopharyngioma, we thought the common pathway analysis between craniopharyngioma and type 2 diabetes based on bioinformatics is a powerful and feasible method. In the present study, using public datasets (GSE94349, GSE68015, GSE38642 and GSE41762) obtained from the GEO database, the gene expression associated with adamantinomatous craniopharyngioma, a subtype of craniopharyngioma, and type 2 diabetes were analyzed using a bioinformatic approach. We found 11 hub genes using a protein-protein interaction network analysis. Of these, seven (DKK1, MMP12, KRT14, PLAU, WNT5B, IKBKB, and FGF19) were also identified by least absolute shrinkage and selection operator analysis. Finally, single-gene validation and receptor operating characteristic analysis revealed that four of these genes (MMP12, PLAU, KRT14, and DKK1) may be involved in the common pathogenetic mechanism of adamantinomatous craniopharyngioma and type 2 diabetes. In addition, we have characterized the differences in immune cell infiltration that characterize these two diseases, providing a reference for further research.

Single-cell RNA-seq reveals the effects of the FecB mutation on the transcriptome profile in ovine cumulus cells

Genetic variations in the ovine ovulation rate, which are associated with the FecB mutation, provide useful models by which to explore the mechanisms regulating the development of mammalian antral follicles. In order to study the effects of the FecB mutation on cumulus cell differentiation, preovulatory follicles were aspirated and cumulus cells were isolated from three FecB genotypes (homozygous, heterozygous and wild type) of Small Tail Han (STH) sheep superstimulated with FSH. Transcriptome information from tens of thousands of cumulus cells was determined with the 10 × Genomics single-cell RNA-seq technology. Under the superovulation treatment, the observed number of preovulatory follicles in the ovaries of FecB carriers was still significantly higher than that in the wild-type (P < 0.05). The expression patterns of cumulus cells differed between FecB carriers and wild-type ewes. The screened cumulus cells could also be further divided into different cell clusters, and the differentiation states and fates of each group of cumulus cells also remained different, which supports the notion that heterogeneity in gene expression is prevalent in single cells. The oxidative phosphorylation pathway was significantly enriched in differentially expressed genes among the cell differentiation branch nodes of cumulus cells and among the differentially expressed genes of cumulus cells from the three genotypes. Combined with the important role of oxidative phosphorylation in the maturation of COCs, we suggest that the oxidative phosphorylation pathway of cumulus cells plays a crucial role in the differentiation process of cumulus cells and the mutation effect of the FecB gene.

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

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

The effect of sevoflurane exposure on cell-type-specific changes in the prefrontal cortex in young mice

Sevoflurane, the predominant pediatric anesthetic, has been linked to neurotoxicity in young mice, although the underlying mechanisms remain unclear. This study focuses on investigating the impact of neonatal sevoflurane exposure on cell-type-specific alterations in the prefrontal cortex (PFC) of young mice. Neonatal mice were subjected to either control treatment (60% oxygen balanced with nitrogen) or sevoflurane anesthesia (3% sevoflurane in 60% oxygen balanced with nitrogen) for 2 hours on postnatal days (PNDs) 6, 8, and 10. Behavioral tests and single-nucleus RNA sequencing (snRNA-seq) of the PFC were conducted from PNDs 31 to 37. Mechanistic exploration included clustering analysis, identification of differentially expressed genes (DEGs), enrichment analyses, single-cell trajectory analysis, and genome-wide association studies (GWAS). Sevoflurane anesthesia resulted in sociability and cognition impairments in mice. Novel specific marker genes identified 8 distinct cell types in the PFC. Most DEGs between the control and sevoflurane groups were unique to specific cell types. Re-defining 15 glutamatergic neuron subclusters based on layer identity revealed their altered expression profiles. Notably, sevoflurane disrupted the trajectory from oligodendrocyte precursor cells (OPCs) to oligodendrocytes (OLs). Validation of disease-relevant candidate genes across the main cell types demonstrated their association with social dysfunction and working memory impairment. Behavioral results and snRNA-seq collectively elucidated the cellular atlas in the PFC of young male mice, providing a foundation for further mechanistic studies on developmental neurotoxicity induced by anesthesia.

Macrophages communicate with mesangial cells through the CXCL12/DPP4 axis in lupus nephritis pathogenesis

Lupus nephritis (LN) occurs in 50% of cases of systemic lupus erythematosus (SLE) and is one of the most serious complications that can occur during lupus progression. Mesangial cells (MCs) are intrinsic cells in the kidney that can regulate capillary blood flow, phagocytose apoptotic cells, and secrete vasoactive substances and growth factors. Previous studies have shown that various types of inflammatory cells can activate MCs for hyperproliferation, leading to disruption of the filtration barrier and impairment of renal function in LN. Here, we characterized the heterogeneity of kidney cells of LN mice by single-nucleus RNA sequencing (snRNA-seq) and revealed the interaction between macrophages and MCs through the CXC motif chemokine ligand 12 (CXCL12)/dipeptidyl peptidase 4 (DPP4) axis. In culture, macrophages modulated the proliferation and migration of MCs through this ligand-receptor interaction. In LN mice, treatment with linagliptin, a DPP4 inhibitor, effectively inhibited MC proliferation and reduced urinary protein levels. Together, our findings indicated that targeting the CXCL12/DPP4 axis with linagliptin treatment may serve as a novel strategy for the treatment of LN via the CXCL12/DPP4 axis.

A Spatial Transcriptomics Browser for Discovering Gene Expression Landscapes across Microscopic Tissue Sections

A crucial feature of life is its spatial organization and compartmentalization on the molecular, cellular, and tissue levels. Spatial transcriptomics (ST) technology has opened a new chapter of the sequencing revolution, emerging rapidly with transformative effects across biology. This technique produces extensive and complex sequencing data, raising the need for computational methods for their comprehensive analysis and interpretation. We developed the ST browser web tool for the interactive discovery of ST images, focusing on different functional aspects such as single gene expression, the expression of functional gene sets, as well as the inspection of the spatial patterns of cell-cell interactions. As a unique feature, our tool applies self-organizing map (SOM) machine learning to the ST data. Our SOM data portrayal method generates individual gene expression landscapes for each spot in the ST image, enabling its downstream analysis with high resolution. The performance of the spatial browser is demonstrated by disentangling the intra-tumoral heterogeneity of melanoma and the microarchitecture of the mouse brain. The integration of machine-learning-based SOM portrayal into an interactive ST analysis environment opens novel perspectives for the comprehensive knowledge mining of the organization and interactions of cellular ecosystems.

Integrative spatial analysis reveals a multi-layered organization of glioblastoma

Glioma contains malignant cells in diverse states. Here, we combine spatial transcriptomics, spatial proteomics, and computational approaches to define glioma cellular states and uncover their organization. We find three prominent modes of organization. First, gliomas are composed of small local environments, each typically enriched with one major cellular state. Second, specific pairs of states preferentially reside in proximity across multiple scales. This pairing of states is consistent across tumors. Third, these pairwise interactions collectively define a global architecture composed of five layers. Hypoxia appears to drive the layers, as it is associated with a long-range organization that includes all cancer cell states. Accordingly, tumor regions distant from any hypoxic/necrotic foci and tumors that lack hypoxia such as low-grade IDH-mutant glioma are less organized. In summary, we provide a conceptual framework for the organization of cellular states in glioma, highlighting hypoxia as a long-range tissue organizer.

Single-cell transcriptomic landscape of peripheral blood cells provides insights into adaptation of red-eared sliders (Trachemys scripta elegans)

Red-eared sliders (Trachemys scripta elegans), as one of the 100 most threatening aliens, have stronger immunity than the native species in response to environmental stress. Blood cells are an important component of immunity in the body. However, the blood cell researches of turtle are still in the traditional blood cell classification and morphological structure observation. Furthermore, turtle granulocytes cannot be accurately identified using traditional methods. Single-cell RNA sequencing techniques have been successfully implemented to study cells based on the mRNA expression patterns of each cell. The present study profiled the transcriptomes of peripheral blood cells in red-eared sliders to construct a single-cell transcriptional landscape of the different cell types and explored environmental adaptation mechanism from the perspective of hematology. All 14 transcriptionally distinct clusters (platelets, erythrocytes1, erythrocytes2, CSF1R monocytes, POF1B monocytes, neutrophils, GATA2high basophils, GATA2low basophils, CD4 T cells, CD7 T cells, B cells, ACKR4 cells, serotriflin cells, and ficolin cells) were identified in the peripheral blood cells of the red-eared sliders. In particular, a subtype of erythrocytes (erythrocytes1) that expressed immune signals was identified. Peripheral blood cells were grouped into three lineages: platelet, erythroid/lymphoid, and myeloid cell lineages. Furthermore, based on differentiation trajectory and up-regulated gene expression, ACKR4 cells were newly identified as lymphocytes, and serotriflin and ficolin cells as granulocytes. The single-cell transcriptional atlas of the peripheral blood cells in red-eared sliders provided in the present study will offer a comprehensive transcriptome reference for the exploration of physiological and pathological hematology in this species.

Directed Differentiation of Human Induced Pluripotent Stem Cells to Heart Valve Cells

BACKGROUND

A main obstacle in current valvular heart disease research is the lack of high-quality homogeneous functional heart valve cells. Human induced pluripotent stem cells (hiPSCs)-derived heart valve cells may help with this dilemma. However, there are no well-established protocols to induce hiPSCs to differentiate into functional heart valve cells, and the networks that mediate the differentiation have not been fully elucidated.

METHODS

To generate heart valve cells from hiPSCs, we sequentially activated the Wnt, BMP4, VEGF (vascular endothelial growth factor), and NFATc1 signaling pathways using CHIR-99021, BMP4, VEGF-165, and forskolin, respectively. The transcriptional and functional similarity of hiPSC-derived heart valve cells compared with primary heart valve cells were characterized. Longitudinal single-cell RNA sequencing was used to uncover the trajectory, switch genes, pathways, and transcription factors of the differentiation.

RESULTS

An efficient protocol was developed to induce hiPSCs to differentiate into functional hiPSC-derived valve endothelial-like cells and hiPSC-derived valve interstitial-like cells. After 6-day differentiation and CD144 magnetic bead sorting, ≈70% CD144+ cells and 30% CD144- cells were obtained. On the basis of single-cell RNA sequencing data, the CD144+ cells and CD144- cells were found to be highly similar to primary heart valve endothelial cells and primary heart valve interstitial cells in gene expression profile. Furthermore, CD144+ cells had the typical function of primary heart valve endothelial cells, including tube formation, uptake of low-density lipoprotein, generation of endothelial nitric oxide synthase, and response to shear stress. Meanwhile, CD144- cells could secret collagen and matrix metalloproteinases, and differentiate into osteogenic or adipogenic lineages like primary heart valve interstitial cells. Therefore, we identified CD144+ cells and CD144- cells as hiPSC-derived valve endothelial-like cells and hiPSC-derived valve interstitial-like cells, respectively. Using single-cell RNA sequencing analysis, we demonstrated that the trajectory of heart valve cell differentiation was consistent with embryonic valve development. We identified the main switch genes (NOTCH1, HEY1, and MEF2C), signaling pathways (TGF-β, Wnt, and NOTCH), and transcription factors (MSX1, SP5, and MECOM) that mediated the differentiation. Finally, we found that hiPSC-derived valve interstitial-like cells might derive from hiPSC-derived valve endothelial-like cells undergoing endocardial-mesenchymal transition.

CONCLUSIONS

In summary, this is the first study to report an efficient strategy to generate functional hiPSC-derived valve endothelial-like cells and hiPSC-derived valve interstitial-like cells from hiPSCs, as well as to elucidate the differentiation trajectory and transcriptional dynamics of hiPSCs differentiated into heart valve cells.

Integrative analysis of single-nucleus RNA-seq and bulk RNA-seq reveals germline cells development dynamics and niches in the Pacific oyster gonad

Gametogenesis drives the maturation of germ cell precursors into functional gametes, facilitated by interactions with the niche environment. However, the molecular mechanisms, especially in invertebrates, remain incompletely understood. In this study, the gonadal microenvironment and gametogenic processes in the Pacific oyster, a model for diffuse gonadal organization and periodic gametogenesis, are investigated. We combine single-nucleus RNA-seq and bulk RNA-seq to analyze gonadal microenvironments in oysters. Twenty-three male and nineteen female gonadal cell clusters are identified, revealing four male and three female germ cell types, alongside follicular cells in females and Sertoli/Leydig cells in males. The NOTCH and BMP (bone morphogenetic protein) signaling pathways play a significant role in the male germline niche, suggesting similarities with mammalian germ cell microenvironment. This study offers valuable insights into germ cell developmental transitions and microenvironmental characteristics.

Slc43a2+ T cell metastasis from spleen to brain in RGNNV infected teleost

The origin of T cells in the teleost's brain is unclear. While viewing the central nervous system (CNS) as immune privileged has been widely accepted, previous studies suggest that T cells residing in the thymus but not in the spleen of the teleost play an essential role in communicating with the peripheral organs. Here, we identified nine T cell subpopulations in the thymus and spleen of orange-spotted grouper (Epinephelus coioices) through single-cell RNA-sequencing analysis. After viral CNS infection with red-spotted grouper nervous necrosis virus (RGNNV), the number of slc43a2+ T cells synchronously increased in the spleen and brain. During the infection tests in asplenic zebrafish (tlx1▲ zebrafish model), no increase in the number of slc43a2+ T cells was observed in the brain. Single-cell transcriptomic analysis indicated that slc43a2+ T cells mature and functionally differentiate within the spleen and then migrate into the brain to trigger an immune response. This study suggests a novel route for T cell migration from the spleen to the brain during viral infection in fish.

Comparative transcriptomic and phenotypic analysis of induced pluripotent stem cell hepatocyte-like cells and primary human hepatocytes

Primary human hepatocytes (PHHs) are used extensively for in vitro liver cultures to study hepatic functions. However, limited availability and invasive retrieval prevent their widespread use. Induced pluripotent stem cells exhibit significant potential since they can be obtained non-invasively and differentiated into hepatic lineages, such as hepatocyte-like cells (iHLCs). However, there are concerns about their fetal phenotypic characteristics and their hepatic functions compared to PHHs in culture. Therefore, we performed an RNA-sequencing (RNA-seq) analysis to understand pathways that are either up- or downregulated in each cell type. Analysis of the RNA-seq data showed an upregulation in the bile secretion pathway where genes such as AQP9 and UGT1A1 were higher expressed in PHHs compared to iHLCs by 455- and 15-fold, respectively. Upon immunostaining, bile canaliculi were shown to be present in PHHs. The TCA cycle in PHHs was upregulated compared to iHLCs. Cellular analysis showed a 2-2.5-fold increase in normalized urea production in PHHs compared to iHLCs. In addition, drug metabolism pathways, including cytochrome P450 (CYP450) and UDP-glucuronosyltransferase enzymes, were upregulated in PHHs compared to iHLCs. Of note, CYP2E1 gene expression was significantly higher (21,810-fold) in PHHs. Acetaminophen and ethanol were administered to PHH and iHLC cultures to investigate differences in biotransformation. CYP450 activity of baseline and toxicant-treated samples was significantly higher in PHHs compared to iHLCs. Our analysis revealed that iHLCs have substantial differences from PHHs in critical hepatic functions. These results have highlighted the differences in gene expression and hepatic functions between PHHs and iHLCs to motivate future investigation.

scAnnoX: an R package integrating multiple public tools for single-cell annotation

Background

Single-cell annotation plays a crucial role in the analysis of single-cell genomics data. Despite the existence of numerous single-cell annotation algorithms, a comprehensive tool for integrating and comparing these algorithms is also lacking.

Methods

This study meticulously investigated a plethora of widely adopted single-cell annotation algorithms. Ten single-cell annotation algorithms were selected based on the classification of either reference dataset-dependent or marker gene-dependent approaches. These algorithms included SingleR, Seurat, sciBet, scmap, CHETAH, scSorter, sc.type, cellID, scCATCH, and SCINA. Building upon these algorithms, we developed an R package named scAnnoX for the integration and comparative analysis of single-cell annotation algorithms.

Results

The development of the scAnnoX software package provides a cohesive framework for annotating cells in scRNA-seq data, enabling researchers to more efficiently perform comparative analyses among the cell type annotations contained in scRNA-seq datasets. The integrated environment of scAnnoX streamlines the testing, evaluation, and comparison processes among various algorithms. Among the ten annotation tools evaluated, SingleR, Seurat, sciBet, and scSorter emerged as top-performing algorithms in terms of prediction accuracy, with SingleR and sciBet demonstrating particularly superior performance, offering guidance for users. Interested parties can access the scAnnoX package at https://github.com/XQ-hub/scAnnoX.

Mechanical convergence in mixed populations of mammalian epithelial cells

Tissues consist of cells with different molecular and/or mechanical properties. Measuring the forces and stresses in mixed-cell populations is essential for understanding the mechanisms by which tissue development, homeostasis, and disease emerge from the cooperation of distinct cell types. However, many previous studies have primarily focused their mechanical measurements on dissociated cells or aggregates of a single-cell type, leaving the mechanics of mixed-cell populations largely unexplored. In the present study, we aimed to elucidate the influence of interactions between different cell types on cell mechanics by conducting in situ mechanical measurements on a monolayer of mammalian epithelial cells. Our findings revealed that while individual cell types displayed varying magnitudes of traction and intercellular stress before mixing, these mechanical values shifted in the mixed monolayer, becoming nearly indistinguishable between the cell types. Moreover, by analyzing a mixed-phase model of active tissues, we identified physical conditions under which such mechanical convergence is induced. Overall, the present study underscores the importance of in situ mechanical measurements in mixed-cell populations to deepen our understanding of the mechanics of multicellular systems.

Comparative analysis of cell-cell communication at single-cell resolution

Inference of cell-cell communication from single-cell RNA sequencing data is a powerful technique to uncover intercellular communication pathways, yet existing methods perform this analysis at the level of the cell type or cluster, discarding single-cell-level information. Here we present Scriabin, a flexible and scalable framework for comparative analysis of cell-cell communication at single-cell resolution that is performed without cell aggregation or downsampling. We use multiple published atlas-scale datasets, genetic perturbation screens and direct experimental validation to show that Scriabin accurately recovers expected cell-cell communication edges and identifies communication networks that can be obscured by agglomerative methods. Additionally, we use spatial transcriptomic data to show that Scriabin can uncover spatial features of interaction from dissociated data alone. Finally, we demonstrate applications to longitudinal datasets to follow communication pathways operating between timepoints. Our approach represents a broadly applicable strategy to reveal the full structure of niche-phenotype relationships in health and disease.

Single-cell RNA-seq reveals a link of ovule abortion and sugar transport in Camellia oleifera

Camellia oleifera is the most important woody oil crop in China. Seed number per fruit is an important yield trait in C. oleifera. Ovule abortion is generally observed in C. oleifera and significantly decreases the seed number per fruit. However, the mechanisms of ovule abortion remain poorly understood at present. Single-cell RNA sequencing (scRNA-seq) was performed using mature ovaries of two C. oleifera varieties with different ovule abortion rates (OARs). In total, 20,526 high-quality cells were obtained, and 18 putative cell clusters were identified. Six cell types including female gametophyte, protoxylem, protophloem, procambium, epidermis, and parenchyma cells were identified from three main tissue types of ovule, placenta, and pericarp inner layer. A comparative analysis on scRNA-seq data between high- and low-OAR varieties demonstrated that the overall expression of CoSWEET and CoCWINV in procambium cells, and CoSTP in the integument was significantly upregulated in the low-OAR variety. Both the infertile ovule before pollination and the abortion ovule producing after compatible pollination might be attributed to selective abortion caused by low sugar levels in the apoplast around procambium cells and a low capability of hexose uptake in the integument. Here, the first single-cell transcriptional landscape is reported in woody crop ovaries. Our investigation demonstrates that ovule abortion may be related to sugar transport in placenta and ovules and sheds light on further deciphering the mechanism of regulating sugar transport and the improvement of seed yield in C. oleifera.

A Data-Distribution and Successive Spline Points based discretization approach for evolving gene regulatory networks from scRNA-Seq time-series data using Cartesian Genetic Programming

The inference of gene regulatory networks (GRNs) is a widely addressed problem in Systems Biology. GRNs can be modeled as Boolean networks, which is the simplest approach for this task. However, Boolean models need binarized data. Several approaches have been developed for the discretization of gene expression data (GED). Also, the advance of data extraction technologies, such as single-cell RNA-Sequencing (scRNA-Seq), provides a new vision of gene expression and brings new challenges for dealing with its specificities, such as a large occurrence of zero data. This work proposes a new discretization approach for dealing with scRNA-Seq time-series data, named Distribution and Successive Spline Points Discretization (DSSPD), which considers the data distribution and a proper preprocessing step. Here, Cartesian Genetic Programming (CGP) is used to infer GRNs using the results of DSSPD. The proposal is compared with CGP with the standard data handling and five state-of-the-art algorithms on curated models and experimental data. The results show that the proposal improves the results of CGP in all tested cases and outperforms the state-of-the-art algorithms in most cases.

scAMAC: self-supervised clustering of scRNA-seq data based on adaptive multi-scale autoencoder

Cluster assignment is vital to analyzing single-cell RNA sequencing (scRNA-seq) data to understand high-level biological processes. Deep learning-based clustering methods have recently been widely used in scRNA-seq data analysis. However, existing deep models often overlook the interconnections and interactions among network layers, leading to the loss of structural information within the network layers. Herein, we develop a new self-supervised clustering method based on an adaptive multi-scale autoencoder, called scAMAC. The self-supervised clustering network utilizes the Multi-Scale Attention mechanism to fuse the feature information from the encoder, hidden and decoder layers of the multi-scale autoencoder, which enables the exploration of cellular correlations within the same scale and captures deep features across different scales. The self-supervised clustering network calculates the membership matrix using the fused latent features and optimizes the clustering network based on the membership matrix. scAMAC employs an adaptive feedback mechanism to supervise the parameter updates of the multi-scale autoencoder, obtaining a more effective representation of cell features. scAMAC not only enables cell clustering but also performs data reconstruction through the decoding layer. Through extensive experiments, we demonstrate that scAMAC is superior to several advanced clustering and imputation methods in both data clustering and reconstruction. In addition, scAMAC is beneficial for downstream analysis, such as cell trajectory inference. Our scAMAC model codes are freely available at https://github.com/yancy2024/scAMAC.

COFFEE: Consensus Single Cell-Type Specific Inference for Gene Regulatory Networks

The inference of gene regulatory networks (GRNs) is crucial to understanding the regulatory mechanisms that govern biological processes. GRNs may be represented as edges in a graph, and hence have been inferred computationally for scRNA-seq data. A wisdom of crowds approach to integrate edges from several GRNs to create one composite GRN has demonstrated improved performance when compared to individual algorithm implementations on bulk RNA-seq and microarray data. In an effort to extend this approach to scRNA-seq data, we present COFFEE (COnsensus single cell-type speciFic inFerence for gEnE regulatory networks), a Borda voting based consensus algorithm that integrates information from 10 established GRN inference methods. We conclude that COFFEE has improved performance across synthetic, curated and experimental datasets when compared to baseline methods. Additionally, we show that a modified version of COFFEE can be leveraged to improve performance on newer cell-type specific GRN inference methods. Overall, our results demonstrate that consensus based methods with pertinent modifications continue to be valuable for GRN inference at the single cell level.

Hallmarks of stemness in mammalian tissues

All adult tissues experience wear and tear. Most tissues can compensate for cell loss through the activity of resident stem cells. Although the cellular maintenance strategies vary greatly between different adult (read: postnatal) tissues, the function of stem cells is best defined by their capacity to replace lost tissue through division. We discuss a set of six complementary hallmarks that are key enabling features of this basic function. These include longevity and self-renewal, multipotency, transplantability, plasticity, dependence on niche signals, and maintenance of genome integrity. We discuss these hallmarks in the context of some of the best-understood adult stem cell niches.

scCAN: Clustering With Adaptive Neighbor-Based Imputation Method for Single-Cell RNA-Seq Data

Single-cell RNA sequencing (scRNA-seq) is widely used to study cellular heterogeneity in different samples. However, due to technical deficiencies, dropout events often result in zero gene expression values in the gene expression matrix. In this paper, we propose a new imputation method called scCAN, based on adaptive neighborhood clustering, to estimate the zero value of dropouts. Our method continuously updates cell-cell similarity information by simultaneously learning similarity relationships, clustering structures, and imposing new rank constraints on the Laplacian matrix of the similarity matrix, improving the imputation of dropout zero values. To evaluate the performance of this method, we used four simulated and eight real scRNA-seq data for downstream analyses, including cell clustering, recovered gene expression, and reconstructed cell trajectories. Our method improves the performance of the downstream analysis and is better than other imputation methods.

Heterogeneity-Preserving Discriminative Feature Selection for Subtype Discovery

The discovery of subtypes is pivotal for disease diagnosis and targeted therapy, considering the diverse responses of different cells or patients to specific treatments. Exploring the heterogeneity within disease or cell states provides insights into disease progression mechanisms and cell differentiation. The advent of high-throughput technologies has enabled the generation and analysis of various molecular data types, such as single-cell RNA-seq, proteomic, and imaging datasets, at large scales. While presenting opportunities for subtype discovery, these datasets pose challenges in finding relevant signatures due to their high dimensionality. Feature selection, a crucial step in the analysis pipeline, involves choosing signatures that reduce the feature size for more efficient downstream computational analysis. Numerous existing methods focus on selecting signatures that differentiate known diseases or cell states, yet they often fall short in identifying features that preserve heterogeneity and reveal subtypes. To identify features that can capture the diversity within each class while also maintaining the discrimination of known disease states, we employed deep metric learning-based feature embedding to conduct a detailed exploration of the statistical properties of features essential in preserving heterogeneity. Our analysis revealed that features with a significant difference in interquartile range (IQR) between classes possess crucial subtype information. Guided by this insight, we developed a robust statistical method, termed PHet (Preserving Heterogeneity) that performs iterative subsampling differential analysis of IQR and Fisher's method between classes, identifying a minimal set of heterogeneity-preserving discriminative features to optimize subtype clustering quality. Validation using public single-cell RNA-seq and microarray datasets showcased PHet's effectiveness in preserving sample heterogeneity while maintaining discrimination of known disease/cell states, surpassing the performance of previous outlier-based methods. Furthermore, analysis of a single-cell RNA-seq dataset from mouse tracheal epithelial cells revealed, through PHet-based features, the presence of two distinct basal cell subtypes undergoing differentiation toward a luminal secretory phenotype. Notably, one of these subtypes exhibited high expression of BPIFA1. Interestingly, previous studies have linked BPIFA1 secretion to the emergence of secretory cells during mucociliary differentiation of airway epithelial cells. PHet successfully pinpointed the basal cell subtype associated with this phenomenon, a distinction that pre-annotated markers and dispersion-based features failed to make due to their admixed feature expression profiles. These findings underscore the potential of our method to deepen our understanding of the mechanisms underlying diseases and cell differentiation and contribute significantly to personalized medicine.

Deciphering driver regulators of cell fate decisions from single-cell transcriptomics data with CEFCON

Single-cell technologies enable the dynamic analyses of cell fate mapping. However, capturing the gene regulatory relationships and identifying the driver factors that control cell fate decisions are still challenging. We present CEFCON, a network-based framework that first uses a graph neural network with attention mechanism to infer a cell-lineage-specific gene regulatory network (GRN) from single-cell RNA-sequencing data, and then models cell fate dynamics through network control theory to identify driver regulators and the associated gene modules, revealing their critical biological processes related to cell states. Extensive benchmarking tests consistently demonstrated the superiority of CEFCON in GRN construction, driver regulator identification, and gene module identification over baseline methods. When applied to the mouse hematopoietic stem cell differentiation data, CEFCON successfully identified driver regulators for three developmental lineages, which offered useful insights into their differentiation from a network control perspective. Overall, CEFCON provides a valuable tool for studying the underlying mechanisms of cell fate decisions from single-cell RNA-seq data.