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.

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.

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.

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.

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.

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.

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.

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.

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.

Single-nucleus transcriptome sequencing reveals hepatic cell atlas in pigs

BACKGROUND

As the largest substantive organ of animals, the liver plays an essential role in the physiological processes of digestive metabolism and immune defense. However, the cellular composition of the pig liver remains poorly understood. This investigation used single-nucleus RNA sequencing technology to identify cell types from liver tissues of pigs, providing a theoretical basis for further investigating liver cell types in pigs.

RESULTS

The analysis revealed 13 cells clusters which were further identified 7 cell types including endothelial cells, T cells, hepatocytes, Kupffer cells, stellate cells, B cells, and cholangiocytes. The dominant cell types were endothelial cells, T cells and hepatocytes in the liver tissue of Dahe pigs and Dahe black pigs, which accounts for about 85.76% and 82.74%, respectively. The number of endothelial cells was higher in the liver tissue of Dahe pigs compared to Dahe black pigs, while the opposite tendency was observed for T cells. Moreover, functional enrichment analysis demonstrated that the differentially expressed genes in pig hepatic endothelial cells were significantly enriched in the protein processing in endoplasmic reticulum, MAPK signaling pathway, and FoxO signaling pathway. Functional enrichment analysis demonstrated that the differentially expressed genes in pig hepatic T cells were significantly enriched in the thyroid hormone signaling pathway, B cell receptor signaling pathway, and focal adhesion. Functional enrichment analysis demonstrated that the differentially expressed genes in pig hepatic hepatocytes were significantly enriched in the metabolic pathways.

CONCLUSIONS

In summary, this study provides a comprehensive cell atlas of porcine hepatic tissue. The number, gene expression level and functional characteristics of each cell type in pig liver tissue varied between breeds.

Self-assembly of three-dimensional liver organoids: virtual reconstruction via endocytosed polymer dots for refactoring the fine structure

In vitro culture of organoids holds considerable promise for the treatment of diseases or the provision of artificial organs. Traditional 2D differentiation from mesenchymal stem cells (MSCs) faces challenges in replicating the development of embryonic organs at the cellular level; conversely, the cultivation of 3D organoids exhibits potential for application. It is crucial for clinicians and technology researchers to acquire insights into organoid tissue differentiation, autonomous morphogenesis, as well as 3D assembly processes in vitro. In this investigation, novel 3D organoids capable of engendering complex liver-like tissues in vitro were cultured, and a class of high-luminance semiconductor polymer dots (Pdots) was employed to monitor the self-assembly process of 3D liver organoid tissues and cellular interaction and migration dynamics. Three-dimensional liver-bud (3D-LB) organoid tissues were derived through the interplay of induced MSCs, Wharton's Jelly, and human umbilical vein endothelial cells (HUVECs), and their structural characteristics were determined during the liver-bud organoid development; ultimately, the co-cultured organoid spatial cellular clusters resembling a truffle were successfully replicated. Utilizing R8-Pdots with remarkable resolution and biocompatibility, the structural elements of functional and vascularized organs derived from liver organoid tissues were adeptly reconstituted, and this investigation shall contribute to a further understanding of human hepato-developmental physiology and liver-disease modeling.

Navigating the kidney organoid: insights into assessment and enhancement of nephron function

Kidney organoids are three-dimensional structures generated from pluripotent stem cells (PSCs) that are capable of recapitulating the major structures of mammalian kidneys. As this technology is expected to be a promising tool for studying renal biology, drug discovery, and regenerative medicine, the functional capacity of kidney organoids has emerged as a critical question in the field. Kidney organoids produced using several protocols harbor key structures of native kidneys. Here, we review the current state, recent advances, and future challenges in the functional characterization of kidney organoids, strategies to accelerate and enhance kidney organoid functions, and access to PSC resources to advance organoid research. The strategies to construct physiologically relevant kidney organoids include the use of organ-on-a-chip technologies that integrate fluid circulation and improve organoid maturation. These approaches result in increased expression of the major tubular transporters and elements of mechanosensory signaling pathways suggestive of improved functionality. Nevertheless, continuous efforts remain crucial to create kidney tissue that more faithfully replicates physiological conditions for future applications in kidney regeneration medicine and their ethical use in patient care.NEW & NOTEWORTHY Kidney organoids are three-dimensional structures derived from stem cells, mimicking the major components of mammalian kidneys. Although they show great promise, their functional capacity has become a critical question. This review explores the advancements and challenges in evaluating and enhancing kidney organoid function, including the use of organ-on-chip technologies, multiomics data, and in vivo transplantation. Integrating these approaches to further enhance their physiological relevance will continue to advance disease modeling and regenerative medicine applications.

ScLSTM: single-cell type detection by siamese recurrent network and hierarchical clustering

MOTIVATION

Categorizing cells into distinct types can shed light on biological tissue functions and interactions, and uncover specific mechanisms under pathological conditions. Since gene expression throughout a population of cells is averaged out by conventional sequencing techniques, it is challenging to distinguish between different cell types. The accumulation of single-cell RNA sequencing (scRNA-seq) data provides the foundation for a more precise classification of cell types. It is crucial building a high-accuracy clustering approach to categorize cell types since the imbalance of cell types and differences in the distribution of scRNA-seq data affect single-cell clustering and visualization outcomes.

RESULT

To achieve single-cell type detection, we propose a meta-learning-based single-cell clustering model called ScLSTM. Specifically, ScLSTM transforms the single-cell type detection problem into a hierarchical classification problem based on feature extraction by the siamese long-short term memory (LSTM) network. The similarity matrix derived from the improved sigmoid kernel is mapped to the siamese LSTM feature space to analyze the differences between cells. ScLSTM demonstrated superior classification performance on 8 scRNA-seq data sets of different platforms, species, and tissues. Further quantitative analysis and visualization of the human breast cancer data set validated the superiority and capability of ScLSTM in recognizing cell types.

Sctensor detects many-to-many cell-cell interactions from single cell RNA-sequencing data

BACKGROUND

Complex biological systems are described as a multitude of cell-cell interactions (CCIs). Recent single-cell RNA-sequencing studies focus on CCIs based on ligand-receptor (L-R) gene co-expression but the analytical methods are not appropriate to detect many-to-many CCIs.

RESULTS

In this work, we propose scTensor, a novel method for extracting representative triadic relationships (or hypergraphs), which include ligand-expression, receptor-expression, and related L-R pairs.

CONCLUSIONS

Through extensive studies with simulated and empirical datasets, we have shown that scTensor can detect some hypergraphs that cannot be detected using conventional CCI detection methods, especially when they include many-to-many relationships. scTensor is implemented as a freely available R/Bioconductor package.

Human vascularized bile duct-on-a chip: a multi-cellular micro-physiological system for studying cholestatic liver disease

Exploring the pathogenesis of and developing therapies for cholestatic liver diseases such as primary sclerosing cholangitis (PSC) remains challenging, partly due to a paucity ofin vitromodels that capture the complex environments contributing to disease progression and partly due to difficulty in obtaining cholangiocytes. Here we report the development of a human vascularized bile duct-on-a-chip (VBDOC) that uses cholangiocyte organoids derived from normal bile duct tissue and human vascular endothelial cells to model bile ducts and blood vessels structurally and functionally in three dimensions. Cholangiocytes in the duct polarized, formed mature tight junctions and had permeability properties comparable to those measured inex vivosystems. The flow of blood and bile was modeled by perfusion of the cell-lined channels, and cholangiocytes and endothelial cells displayed differential responses to flow. We also showed that the device can be constructed with biliary organoids from cells isolated from both bile duct tissue and the bile of PSC patients. Cholangiocytes in the duct became more inflammatory under the stimulation of IL-17A, which induced peripheral blood mononuclear cells and differentiated Th17 cells to transmigrate across the vascular channel. In sum, this human VBDOC recapitulated the vascular-biliary interface structurally and functionally and represents a novel multicellular platform to study inflammatory and fibrotic cholestatic liver diseases.

Improvements in Maturity and Stability of 3D iPSC-Derived Hepatocyte-like Cell Cultures

Induced pluripotent stem cell (iPSC) technology enables differentiation of human hepatocytes or hepatocyte-like cells (iPSC-HLCs). Advances in 3D culturing platforms enable the development of more in vivo-like liver models that recapitulate the complex liver architecture and functionality better than traditional 2D monocultures. Moreover, within the liver, non-parenchymal cells (NPCs) are critically involved in the regulation and maintenance of hepatocyte metabolic function. Thus, models combining 3D culture and co-culturing of various cell types potentially create more functional in vitro liver models than 2D monocultures. Here, we report the establishment of 3D cultures of iPSC-HLCs alone and in co-culture with human umbilical vein endothelial cells (HUVECs) and adipose tissue-derived mesenchymal stem/stromal cells (hASCs). The 3D cultures were performed as spheroids or on microfluidic chips utilizing various biomaterials. Our results show that both 3D spheroid and on-chip culture enhance the expression of mature liver marker genes and proteins compared to 2D. Among the spheroid models, we saw the best functionality in iPSC-HLC monoculture spheroids. On the contrary, in the chip system, the multilineage model outperformed the monoculture chip model. Additionally, the optical projection tomography (OPT) and electrical impedance tomography (EIT) system revealed changes in spheroid size and electrical conductivity during spheroid culture, suggesting changes in cell-cell connections. Altogether, the present study demonstrates that iPSC-HLCs can successfully be cultured in 3D as spheroids and on microfluidic chips, and co-culturing iPSC-HLCs with NPCs enhances their functionality. These 3D in vitro liver systems are promising human-derived platforms usable in various liver-related studies, specifically when using patient-specific iPSCs.

Inferring circadian gene regulatory relationships from gene expression data with a hybrid framework

BACKGROUND

The central biological clock governs numerous facets of mammalian physiology, including sleep, metabolism, and immune system regulation. Understanding gene regulatory relationships is crucial for unravelling the mechanisms that underlie various cellular biological processes. While it is possible to infer circadian gene regulatory relationships from time-series gene expression data, relying solely on correlation-based inference may not provide sufficient information about causation. Moreover, gene expression data often have high dimensions but a limited number of observations, posing challenges in their analysis.

METHODS

In this paper, we introduce a new hybrid framework, referred to as Circadian Gene Regulatory Framework (CGRF), to infer circadian gene regulatory relationships from gene expression data of rats. The framework addresses the challenges of high-dimensional data by combining the fuzzy C-means clustering algorithm with dynamic time warping distance. Through this approach, we efficiently identify the clusters of genes related to the target gene. To determine the significance of genes within a specific cluster, we employ the Wilcoxon signed-rank test. Subsequently, we use a dynamic vector autoregressive method to analyze the selected significant gene expression profiles and reveal directed causal regulatory relationships based on partial correlation.

CONCLUSION

The proposed CGRF framework offers a comprehensive and efficient solution for understanding circadian gene regulation. Circadian gene regulatory relationships are inferred from the gene expression data of rats based on the Aanat target gene. The results show that genes Pde10a, Atp7b, Prok2, Per1, Rhobtb3 and Dclk1 stand out, which have been known to be essential for the regulation of circadian activity. The potential relationships between genes Tspan15, Eprs, Eml5 and Fsbp with a circadian rhythm need further experimental research.

Predicting gene regulatory links from single-cell RNA-seq data using graph neural networks

Single-cell RNA-sequencing (scRNA-seq) has emerged as a powerful technique for studying gene expression patterns at the single-cell level. Inferring gene regulatory networks (GRNs) from scRNA-seq data provides insight into cellular phenotypes from the genomic level. However, the high sparsity, noise and dropout events inherent in scRNA-seq data present challenges for GRN inference. In recent years, the dramatic increase in data on experimentally validated transcription factors binding to DNA has made it possible to infer GRNs by supervised methods. In this study, we address the problem of GRN inference by framing it as a graph link prediction task. In this paper, we propose a novel framework called GNNLink, which leverages known GRNs to deduce the potential regulatory interdependencies between genes. First, we preprocess the raw scRNA-seq data. Then, we introduce a graph convolutional network-based interaction graph encoder to effectively refine gene features by capturing interdependencies between nodes in the network. Finally, the inference of GRN is obtained by performing matrix completion operation on node features. The features obtained from model training can be applied to downstream tasks such as measuring similarity and inferring causality between gene pairs. To evaluate the performance of GNNLink, we compare it with six existing GRN reconstruction methods using seven scRNA-seq datasets. These datasets encompass diverse ground truth networks, including functional interaction networks, Loss of Function/Gain of Function data, non-specific ChIP-seq data and cell-type-specific ChIP-seq data. Our experimental results demonstrate that GNNLink achieves comparable or superior performance across these datasets, showcasing its robustness and accuracy. Furthermore, we observe consistent performance across datasets of varying scales. For reproducibility, we provide the data and source code of GNNLink on our GitHub repository: https://github.com/sdesignates/GNNLink.

Robust discovery of gene regulatory networks from single-cell gene expression data by Causal Inference Using Composition of Transactions

Gene regulatory networks (GRNs) drive organism structure and functions, so the discovery and characterization of GRNs is a major goal in biological research. However, accurate identification of causal regulatory connections and inference of GRNs using gene expression datasets, more recently from single-cell RNA-seq (scRNA-seq), has been challenging. Here we employ the innovative method of Causal Inference Using Composition of Transactions (CICT) to uncover GRNs from scRNA-seq data. The basis of CICT is that if all gene expressions were random, a non-random regulatory gene should induce its targets at levels different from the background random process, resulting in distinct patterns in the whole relevance network of gene-gene associations. CICT proposes novel network features derived from a relevance network, which enable any machine learning algorithm to predict causal regulatory edges and infer GRNs. We evaluated CICT using simulated and experimental scRNA-seq data in a well-established benchmarking pipeline and showed that CICT outperformed existing network inference methods representing diverse approaches with many-fold higher accuracy. Furthermore, we demonstrated that GRN inference with CICT was robust to different levels of sparsity in scRNA-seq data, the characteristics of data and ground truth, the choice of association measure and the complexity of the supervised machine learning algorithm. Our results suggest aiming at directly predicting causality to recover regulatory relationships in complex biological networks substantially improves accuracy in GRN inference.

NG-SEM: an effective non-Gaussian structural equation modeling framework for gene regulatory network inference from single-cell RNA-seq data

Inference of gene regulatory network (GRN) from gene expression profiles has been a central problem in systems biology and bioinformatics in the past decades. The tremendous emergency of single-cell RNA sequencing (scRNA-seq) data brings new opportunities and challenges for GRN inference: the extensive dropouts and complicated noise structure may also degrade the performance of contemporary gene regulatory models. Thus, there is an urgent need to develop more accurate methods for gene regulatory network inference in single-cell data while considering the noise structure at the same time. In this paper, we extend the traditional structural equation modeling (SEM) framework by considering a flexible noise modeling strategy, namely we use the Gaussian mixtures to approximate the complex stochastic nature of a biological system, since the Gaussian mixture framework can be arguably served as a universal approximation for any continuous distributions. The proposed non-Gaussian SEM framework is called NG-SEM, which can be optimized by iteratively performing Expectation-Maximization algorithm and weighted least-squares method. Moreover, the Akaike Information Criteria is adopted to select the number of components of the Gaussian mixture. To probe the accuracy and stability of our proposed method, we design a comprehensive variate of control experiments to systematically investigate the performance of NG-SEM under various conditions, including simulations and real biological data sets. Results on synthetic data demonstrate that this strategy can improve the performance of traditional Gaussian SEM model and results on real biological data sets verify that NG-SEM outperforms other five state-of-the-art methods.

Integrative analysis of single-cell RNA-seq and ATAC-seq reveals heterogeneity of induced pluripotent stem cell-derived hepatic organoids

To gain deeper insights into transcriptomes and epigenomes of organoids, liver organoids from two states (expandable and more differentiated) were subjected to single-cell RNA-seq (scRNA-seq) and single-cell ATAC-seq (scATAC-seq) analyses. Mitochondrial gene expression was higher in differentiated than in non-differentiated hepatocytes, with ATAC-seq peaks increasing near the mitochondrial control region. Differentiation of liver organoids resulted in the expression of transcription factors that act as enhancers and repressors. In addition, epigenetic mechanisms regulating the expression of alpha-fetoprotein (AFP) and albumin (ALB) differed in liver organoids and adult liver. Knockdown of PDX1, an essential transcription factor for pancreas development, led to the hepatic maturation of liver organoids through regulation of AFP and ALB expression. This integrative analysis of the transcriptomes and epigenomes of liver organoids at the single-cell level may contribute to a better understanding of the regulatory networks during liver development and the further development of mature in vitro human liver models.