Bone marrow cells contribute to seven different endothelial cell populations in the heart.

Understanding the mechanisms underlying vascular regeneration in the heart is crucial for developing novel therapeutic strategies for myocardial ischemia. This study investigates the contribution of bone marrow-derived cells to endothelial cell populations in the heart, and their role in cardiac function and coronary circulation following repetitive ischemia (RI). Chimeric rats were created by transplanting BM cells from GFP female rats into irradiated male recipients. After engraftment chimeras were subjected to RI for 17 days. Vascular growth was assessed from recovery of cardiac function and increases in myocardial blood flow during LAD occlusion. After sorting GFP+ BM cells from heart and bone of Control and RI rats, single-cell RNA sequencing was implemented to determine the fate of BM cells. Our in vivo RI model demonstrated an improvement in cardiac function and myocardial blood flow after 17 days of RI with increased capillary density in the rats subjected to RI compared to Controls. Single-cell RNA sequencing of bone marrow cells isolated from rats' hearts identified distinct endothelial cell (EC) subpopulations. These ECs exhibited heterogeneous gene expression profiles and were enriched for markers of capillary, artery, lymphatic, venous, and immune ECs. Furthermore, BM-derived ECs in the RI group showed an angiogenic profile, characterized by upregulated genes associated with blood vessel development and angiogenesis. This study elucidates the heterogeneity of bone marrow-derived endothelial cells in the heart and their response to repetitive ischemia, laying the groundwork for targeting specific subpopulations for therapeutic angiogenesis in myocardial ischemia.

The Novel-B-Cell-Related Gene Signature Predicts the Prognosis and Immune Status of Patients with Esophageal Carcinoma.

BACKGROUND: The current understanding of the prognostic significance of B cells and their role in the tumor microenvironment (TME) in esophageal carcinoma (ESCA) is limited. METHODS: We conducted a screening for B-cell-related genes through the analysis of single-cell transcriptome data. Subsequently, we developed a B-cell-related gene signature (BRGrisk) using LASSO regression analysis. Patients from The Cancer Genome Atlas cohort were divided into a training cohort and a test cohort. Patients were categorized into high- and low-risk groups based on their median BRGrisk scores. The overall survival was assessed using the Kaplan-Meier method, and a nomogram based on BRGrisk was constructed. Immune infiltration profiles between the risk groups were also compared. RESULTS: The BRGrisk prognostic model indicated significantly worse outcomes for patients with high BRGrisk scores (p < 0.001). The BRGrisk-based nomogram exhibited good prognostic performance. Analysis of immune infiltration revealed that patients in the high-BRGrisk group had notably higher levels of immune cell infiltration and were more likely to be in an immunoresponsive state. Enrichment analysis showed a strong correlation between the prognostic gene signature and cancer-related pathways. IC50 results indicated that patients in the low-BRGrisk group were more responsive to common drugs compared to those in the high-BRGrisk group. CONCLUSIONS: This study presents a novel BRGrisk that can be used to stratify the prognosis of ESCA patients and may offer guidance for personalized treatment strategies aimed at improving prognosis.

Integrated analysis of single-cell RNA-seq, bulk RNA-seq, Mendelian randomization, and eQTL reveals T cell-related nomogram model and subtype classification in rheumatoid arthritis.

Objective: Rheumatoid arthritis (RA) is a systemic disease that attacks the joints and causes a heavy economic burden on humans worldwide. T cells regulate RA progression and are considered crucial targets for therapy. Therefore, we aimed to integrate multiple datasets to explore the mechanisms of RA. Moreover, we established a T cell-related diagnostic model to provide a new method for RA immunotherapy. Methods: scRNA-seq and bulk-seq datasets for RA were obtained from the Gene Expression Omnibus (GEO) database. Various methods were used to analyze and characterize the T cell heterogeneity of RA. Using Mendelian randomization (MR) and expression quantitative trait loci (eQTL), we screened for potential pathogenic T cell marker genes in RA. Subsequently, we selected an optimal machine learning approach by comparing the nine types of machine learning in predicting RA to identify T cell-related diagnostic features to construct a nomogram model. Patients with RA were divided into different T cell-related clusters using the consensus clustering method. Finally, we performed immune cell infiltration and clinical correlation analyses of T cell-related diagnostic features. Results: By analyzing the scRNA-seq dataset, we obtained 10,211 cells that were annotated into 7 different subtypes based on specific marker genes. By integrating the eQTL from blood and RA GWAS, combined with XGB machine learning, we identified a total of 8 T cell-related diagnostic features (MIER1, PPP1CB, ICOS, GADD45A, CD3D, SLFN5, PIP4K2A, and IL6ST). Consensus clustering analysis showed that RA could be classified into two different T-cell patterns (Cluster 1 and Cluster 2), with Cluster 2 having a higher T-cell score than Cluster 1. The two clusters involved different pathways and had different immune cell infiltration states. There was no difference in age or sex between the two different T cell patterns. In addition, ICOS and IL6ST were negatively correlated with age in RA patients. Conclusion: Our findings elucidate the heterogeneity of T cells in RA and the communication role of these cells in an RA immune microenvironment. The construction of T cell-related diagnostic models provides a resource for guiding RA immunotherapeutic strategies.

Combining single-cell and bulk RNA sequencing, NK cell marker genes reveal a prognostic and immune status in pancreatic ductal adenocarcinoma.

The NK cell is an important component of the tumor microenvironment of pancreatic ductal adenocarcinoma (PDAC), also plays a significant role in PDAC development. This study aimed to explore the relationship between NK cell marker genes and prognosis, immune response of PDAC patients. By scRNA-seq data, we found the proportion of NK cells were significantly downregulated in PDAC and 373 NK cell marker genes were screened out. By TCGA database, we enrolled 7 NK cell marker genes to construct the signature for predicting prognosis in PDAC patients. Cox analysis identified the signature as an independent factor for pancreatic cancer. Subsequently, the predictive power of signature was validated by 6 GEO datasets and had an excellent evaluation. Our analysis of relationship between the signature and patients' immune status revealed that the signature has a strong correlation with immunocyte infiltration, inflammatory reaction, immune checkpoint inhibitors (ICIs) response. The NK cell marker genes are closely related to the prognosis and immune capacity of PDAC patients, and they have potential value as a therapeutic target.

Single-cell transcriptome sequencing reveals SPP1-CD44-mediated macrophage-tumor cell interactions drive chemoresistance in TNBC.

Triple-negative breast cancer (TNBC) is often considered one of the most aggressive subtypes of breast cancer, characterized by a high recurrence rate and low overall survival (OS). It is notorious for posing challenges related to drug resistance. While there has been progress in TNBC research, the mechanisms underlying chemotherapy resistance in TNBC remain largely elusive. We collect single-cell RNA sequencing (scRNA-seq) data from five TNBC patients susceptible to chemotherapy and five resistant cases. Comprehensive analyses involving copy number variation (CNV), pseudotime trajectory, cell-cell interactions, pseudospace analysis, as well as transcription factor and functional enrichment are conducted specifically on macrophages and malignant cells. Furthermore, we performed validation experiments on clinical samples using multiplex immunofluorescence. We identified a subset of SPP1+ macrophages that secrete SPP1 signals interacting with CD44 on malignant cell surfaces, potentially activating the PDE3B pathway within malignant cells via the integrin pathway, leading to chemotherapy resistance. The abnormally enhanced SPP1 signal between macrophages and malignant cells may serve as a factor promoting chemotherapy resistance in TNBC patients. Therefore, SPP1+ macrophages could potentially serve as a therapeutic target to reduce chemotherapy resistance.

Genomic characteristics of adipose-derived stromal cells induced into neurons based on single-cell RNA sequencing.

Adipose-derived stromal cells (ADSCs) can be induced to differentiate into neurons, representing the most promising avenue for cell therapy. However, the molecular mechanism and genomic characteristics of the differentiation of ADSCs into neurons remain poorly understood. In this study, cells from the adult ADSCs group, induction 1h, 3h, 5h, 6h, and 8h groups were selected for single-cell RNA sequencing (scRNA-Seq). Samples from these seven-time points were sequenced and analyzed. The expression of neuron marker genes, including NES, MAP2, TMEM59L, PTK2B, CHN1, DNM1, NRSN2, FBLN2, SCAMP1, SLC1A1, DLG4, CDK5, and ENO2, was found to be low in the ADSCs group, but highly expressed in differentiated cell clusters. The expression of stem cell marker genes, including CCND1, IL1B, MMP1, MMP3, MYO10, and BMP2, was the highest in the ADSCs cluster. This expression decreased significantly with the extension of induction time. Gene ontology (GO) enrichment analysis of upregulated genes in the induced samples showed that the biological processes related to neuronal differentiation and development, such as neuronal differentiation, projection, and apoptosis, were significantly upregulated with a longer induction time during cell cluster differentiation. The results of the cell communication analysis demonstrated the gradual formation of complex neural network connections between ADSC-derived neurons through receptor and ligand pairs at 5h after the induction of differentiation.

Single-cell transcriptomics by clinical course of Mycobacterium avium complex pulmonary disease.

Mycobacterium avium complex pulmonary disease (MAC-PD) has a heterogeneous clinical course. However, immune profiles associated with MAC-PD clinical course are limited. We performed single-cell RNA sequencing of peripheral blood mononuclear cells from 21 MAC-PD patients divided into three clinical courses: group A, spontaneous culture conversion; group B, stable disease without antibiotic treatment; and group C, progressive disease with antibiotic treatment. A lower proportion of NK cells and higher proportion of monocytes were noted in group C compared to combined groups A and B. The proportion of classical monocytes was higher in group C compared to groups A and B, while the proportion of non-classical monocytes decreased. EGR1, HSPA1A, HSPA1B, and CD83 were up-regulated in spontaneous culture conversion group A compared to progressive disease group C. Up-regulation of MYOM2 and LILRA4 and down-regulation of MT-ATP8, CD83, and CCL3L1 was found in progressive disease group C. PCBP1, FOS, RGCC, S100B, G0S2, AREG, and LYN were highly expressed in favorable treatment response compared to unfavorable response. Our findings may offer a comprehensive understanding of the host immune profiles that influence a particular MAC-PD clinical course and could suggest an immunological mechanism associated with the disease progression of MAC-PD.

Predicting intercellular communication based on metabolite-related ligand-receptor interactions with MRCLinkdb.

BACKGROUND: Metabolite-associated cell communications play critical roles in maintaining human biological function. However, most existing tools and resources focus only on ligand-receptor interaction pairs where both partners are proteinaceous, neglecting other non-protein molecules. To address this gap, we introduce the MRCLinkdb database and algorithm, which aggregates and organizes data related to non-protein L-R interactions in cell-cell communication, providing a valuable resource for predicting intercellular communication based on metabolite-related ligand-receptor interactions. RESULTS: Here, we manually curated the metabolite-ligand-receptor (ML-R) interactions from the literature and known databases, ultimately collecting over 790 human and 670 mouse ML-R interactions. Additionally, we compiled information on over 1900 enzymes and 260 transporter entries associated with these metabolites. We developed Metabolite-Receptor based Cell Link Database (MRCLinkdb) to store these ML-R interactions data. Meanwhile, the platform also offers extensive information for presenting ML-R interactions, including fundamental metabolite information and the overall expression landscape of metabolite-associated gene sets (such as receptor, enzymes, and transporter proteins) based on single-cell transcriptomics sequencing (covering 35 human and 26 mouse tissues, 52 human and 44 mouse cell types) and bulk RNA-seq/microarray data (encompassing 62 human and 39 mouse tissues). Furthermore, MRCLinkdb introduces a web server dedicated to the analysis of intercellular communication based on ML-R interactions. MRCLinkdb is freely available at https://www.cellknowledge.com.cn/mrclinkdb/ . CONCLUSIONS: In addition to supplementing ligand-receptor databases, MRCLinkdb may provide new perspectives for decoding the intercellular communication and advancing related prediction tools based on ML-R interactions.

DeepGRNCS: deep learning-based framework for jointly inferring gene regulatory networks across cell subpopulations.

Inferring gene regulatory networks (GRNs) allows us to obtain a deeper understanding of cellular function and disease pathogenesis. Recent advances in single-cell RNA sequencing (scRNA-seq) technology have improved the accuracy of GRN inference. However, many methods for inferring individual GRNs from scRNA-seq data are limited because they overlook intercellular heterogeneity and similarities between different cell subpopulations, which are often present in the data. Here, we propose a deep learning-based framework, DeepGRNCS, for jointly inferring GRNs across cell subpopulations. We follow the commonly accepted hypothesis that the expression of a target gene can be predicted based on the expression of transcription factors (TFs) due to underlying regulatory relationships. We initially processed scRNA-seq data by discretizing data scattering using the equal-width method. Then, we trained deep learning models to predict target gene expression from TFs. By individually removing each TF from the expression matrix, we used pre-trained deep model predictions to infer regulatory relationships between TFs and genes, thereby constructing the GRN. Our method outperforms existing GRN inference methods for various simulated and real scRNA-seq datasets. Finally, we applied DeepGRNCS to non-small cell lung cancer scRNA-seq data to identify key genes in each cell subpopulation and analyzed their biological relevance. In conclusion, DeepGRNCS effectively predicts cell subpopulation-specific GRNs. The source code is available at https://github.com/Nastume777/DeepGRNCS.

Single-Cell RNA Sequencing Reveals Transcriptional Signatures and Cell-Cell Communication in Diabetic Retinopathy.

BACKGROUND: Diabetic retinopathy (DR) is a major cause of vision loss in workingage individuals worldwide. Cell-to-cell communication between retinal cells and retinal pigment epithelial cells (RPEs) in DR is still unclear, so this study aimed to generate a single-cell atlas and identify receptor‒ligand communication between retinal cells and RPEs. METHODS: A mouse single-cell RNA sequencing (scRNA-seq) dataset was retrieved from the GEO database (GSE178121) and was further analyzed with the R package Seurat. Cell cluster annotation was performed to further analyze cell‒cell communication. The differentially expressed genes (DEGs) in RPEs were explored through pathway enrichment analysis and the protein‒ protein interaction (PPI) network. Core genes in the PPI were verified by quantitative PCR in ARPE-19 cells. RESULTS: We observed an increased proportion of RPEs in STZ mice. Although some overall intercellular communication pathways did not differ significantly in the STZ and control groups, RPEs relayed significantly more signals in the STZ group. In addition, THBS1, ITGB1, COL9A3, ITGB8, VTN, TIMP2, and FBN1 were found to be the core DEGs of the PPI network in RPEs. qPCR results showed that the expression of ITGB1, COL9A3, ITGB8, VTN, TIMP2, and FBN1 was higher and consistent with scRNA-seq results in ARPE-19 cells under hyperglycemic conditions. CONCLUSION: Our study, for the first time, investigated how signals that RPEs relay to and from other cells underly the progression of DR based on scRNA-seq. These signaling pathways and hub genes may provide new insights into DR mechanisms and therapeutic targets.

Integrative Analysis of Long Isoform Sequencing and Functional Data Identifies Distinct Cortical Layer Neuronal Subtypes Derived from Human iPSCs.

Generation of human induced pluripotent stem cells (iPSCs) through reprogramming was a transformational change in the field of regenerative medicine that led to new possibilities for drug discovery and cell replacement therapy. Several protocols have been established to differentiate hiPSCs into neuronal lineages. However, low differentiation efficiency is one of the major drawbacks of these approaches. Here, we compared the efficiency of two methods of neuronal differentiation from iPSCs cultured in two different culture media, StemFlex Medium (SFM) and Essential 8 Medium (E8M). The results indicated that iPSCs cultured in E8M efficiently generated different types of neurons in a shorter time and without the growth of undifferentiated non-neuronal cells in the culture as compared to those generated from iPSCs in SFM. Furthermore, these neurons were validated as functional units immunocytochemically by confirming the expression of mature neuronal markers (i.e., NeuN, Beta tubulin, and Synapsin I), and whole-cell patch-clamp recordings. Long-read single-cell RNA sequencing confirms the presence of upper and deep layer cortical layer excitatory and inhibitory neuronal subtypes in addition to small populations of GABAergic neurons in day 30 neuronal cultures. Pathway analysis indicated that our protocol triggers the signaling transcriptional networks important for the process of neuronal differentiation in vivo.

Characterizing efficient feature selection for single-cell expression analysis.

Unsupervised feature selection is a critical step for efficient and accurate analysis of single-cell RNA-seq data. Previous benchmarks used two different criteria to compare feature selection methods: (i) proportion of ground-truth marker genes included in the selected features and (ii) accuracy of cell clustering using ground-truth cell types. Here, we systematically compare the performance of 11 feature selection methods for both criteria. We first demonstrate the discordance between these criteria and suggest using the latter. We then compare the distribution of selected genes in their means between feature selection methods. We show that lowly expressed genes exhibit seriously high coefficients of variation and are mostly excluded by high-performance methods. In particular, high-deviation- and high-expression-based methods outperform the widely used in Seurat package in clustering cells and data visualization. We further show they also enable a clear separation of the same cell type from different tissues as well as accurate estimation of cell trajectories.

Dual-specificity phosphatases 13 and 27 as key switches in muscle stem cell transition from proliferation to differentiation.

Muscle regeneration depends on muscle stem cell (MuSC) activity. Myogenic regulatory factors, including myoblast determination protein 1 (MyoD), regulate the fate transition of MuSCs. However, the direct target of MYOD in the process is not completely clear. Using previously established MyoD knock-in (MyoD-KI) mice, we revealed that MyoD targets dual-specificity phosphatase (Dusp) 13 and Dusp27. In Dusp13:Dusp27 double knock-out (DKO) mice, the ability for muscle regeneration after injury was reduced. Moreover, single-cell RNA sequencing of MyoD-high expressing MuSCs from MyoD-KI mice revealed that Dusp13 and Dusp27 are expressed only in specific populations within MyoD-high MuSCs, which also express Myogenin. Overexpressing Dusp13 in MuSCs causes premature muscle differentiation. Thus, we propose a model where DUSP13 and DUSP27 contribute to the fate transition of MuSCs from proliferation to differentiation during myogenesis.

Comprehensive investigation of malignant epithelial cell-related genes in clear cell renal cell carcinoma: development of a prognostic signature and exploration of tumor microenvironment interactions.

Clear cell renal cell carcinoma (ccRCC) is a prevalent malignancy with complex heterogeneity within epithelial cells, which plays a crucial role in tumor progression and immune regulation. Yet, the clinical importance of the malignant epithelial cell-related genes (MECRGs) in ccRCC remains insufficiently understood. This research aims to undertake a comprehensive investigation into the functions and clinical relevance of malignant epithelial cell-related genes in ccRCC, providing valuable understanding of the molecular mechanisms and offering potential targets for treatment strategies. Using data from single-cell sequencing, we successfully identified 219 MECRGs and established a prognostic model MECRGS (MECRGs' signature) by synergistically analyzing 101 machine-learning models using 10 different algorithms. Remarkably, the MECRGS demonstrated superior predictive performance compared to traditional clinical features and 92 previously published signatures across six cohorts, showcasing its independence and accuracy. Upon stratifying patients into high- and low-MECRGS subgroups using the specified cut-off threshold, we noted that patients with elevated MECRGS scores displayed characteristics of an immune suppressive tumor microenvironment (TME) and showed worse outcomes after immunotherapy. Additionally, we discovered a distinct ccRCC tumor cell subtype characterized by the high expressions of PLOD2 (procollagen-lysine,2-oxoglutarate 5-dioxygenase 2) and SAA1 (Serum Amyloid A1), which we further validated in the Renji tissue microarray (TMA) cohort. Lastly, 'Cellchat' revealed potential crosstalk patterns between these cells and other cell types, indicating their potential role in recruiting CD163 + macrophages and regulatory T cells (Tregs), thereby establishing an immunosuppressive TME. PLOD2 + SAA1 + cancer cells with intricate crosstalk patterns indeed show promise for potential therapeutic interventions.

Requirement of Pdgfrα+ cells for calvarial bone repair.

Platelet-derived growth factor receptor α (PDGFRα) is often considered as a general marker of mesenchymal cells and fibroblasts, but also shows expression in a portion of osteoprogenitor cells. Within the skeleton, Pdgfrα+ mesenchymal cells have been identified in bone marrow and periosteum of long bones, where they play a crucial role in participating in fracture repair. A similar examination of Pdgfrα+ cells in calvarial bone healing has not been examined. Here, we utilize Pdgfrα-CreERTM;mT/mG reporter animals to examine the contribution of Pdgfrα+ mesenchymal cells to calvarial bone repair through histology and single-cell RNA sequencing (scRNA-Seq). Results showed that Pdgfrα+ mesenchymal cells are present in several cell clusters by scRNA-Seq, and by histology a dramatic increase in Pdgfrα+ cells populated the defect site at early timepoints to give rise to healed bone tissue overtime. Notably, diphtheria toxin-mediated ablation of Pdgfrα reporter+ cells resulted in significantly impaired calvarial bone healing. Our findings suggest that Pdgfrα-expressing cells within the calvarial niche play a critical role in the process of calvarial bone repair.

Lipid-associated macrophages reshape BAT cell identity in obesity.

Obesity and type 2 diabetes cause a loss in brown adipose tissue (BAT) activity, but the molecular mechanisms that drive BAT cell remodeling remain largely unexplored. Using a multilayered approach, we comprehensively mapped a reorganization in BAT cells. We uncovered a subset of macrophages as lipid-associated macrophages (LAMs), which were massively increased in genetic and dietary model of BAT expansion. LAMs participate in this scenario by capturing extracellular vesicles carrying damaged lipids and mitochondria released from metabolically stressed brown adipocytes. CD36 scavenger receptor drove LAM phenotype, and CD36-deficient LAMs were able to increase brown fat genes in adipocytes. LAMs released transforming growth factor ß1 (TGF-ß1), which promoted the loss of brown adipocyte identity through aldehyde dehydrogenase 1 family member A1 (Aldh1a1) induction. These findings unfold cell dynamic changes in BAT during obesity and identify LAMs as key responders to tissue metabolic stress and drivers of loss of brown adipocyte identity.

Decoding cellular plasticity and niche regulation of limbal stem cells during corneal wound healing.

BACKGROUND: Dysfunction or deficiency of corneal epithelium results in vision impairment or blindness in severe cases. The rapid and effective regeneration of corneal epithelial cells relies on the limbal stem cells (LSCs). However, the molecular and functional responses of LSCs and their niche cells to injury remain elusive. METHODS: Single-cell RNA sequencing was performed on corneal tissues from normal mice and corneal epithelium defect models. Bioinformatics analysis was performed to confirm the distinct characteristics and cell fates of LSCs. Knockdown of Creb5 and OSM treatment experiment were performed to determine their roles of in corneal epithelial wound healing. RESULTS: Our data defined the molecular signatures of LSCs and reconstructed the pseudotime trajectory of corneal epithelial cells. Gene network analyses characterized transcriptional landmarks that potentially regulate LSC dynamics, and identified a transcription factor Creb5, that was expressed in LSCs and significantly upregulated after injury. Loss-of-function experiments revealed that silencing Creb5 delayed the corneal epithelial healing and LSC mobilization. Through cell-cell communication analysis, we identified 609 candidate regeneration-associated ligand-receptor interaction pairs between LSCs and distinct niche cells, and discovered a unique subset of Arg1+ macrophages infiltrated after injury, which were present as the source of Oncostatin M (OSM), an IL-6 family cytokine, that were demonstrated to effectively accelerate the corneal epithelial wound healing. CONCLUSIONS: This research provides a valuable single-cell resource and reference for the discovery of mechanisms and potential clinical interventions aimed at ocular surface reconstruction.

Exploring the Pathogenesis of Autoimmune Liver Diseases from the Heterogeneity of Target Cells.

The incidence of autoimmune liver diseases (ALDs) and research on their pathogenesis are increasing annually. However, except for autoimmune hepatitis, which responds well to immunosuppression, primary biliary cholangitis and primary sclerosing cholangitis are insensitive to immunosuppressive therapy. Besides the known effects of the environment, genetics, and immunity on ALDs, the heterogeneity of target cells provides new insights into their pathogenesis. This review started by exploring the heterogeneity in the development, structures, and functions of hepatocytes and epithelial cells of the small and large bile ducts. For example, cytokeratin (CK) 8 and CK18 are primarily expressed in hepatocytes, while CK7 and CK19 are primarily expressed in intrahepatic cholangiocytes. Additionally, emerging technologies of single-cell RNA sequencing and spatial transcriptomic are being applied to study ALDs. This review offered a new perspective on understanding the pathogenic mechanisms and potential treatment strategies for ALDs.

Deciphering the spatiotemporal transcriptional landscape of intestinal diseases (Review).

The intestines are the largest barrier organ in the human body. The intestinal barrier plays a crucial role in maintaining the balance of the intestinal environment and protecting the intestines from harmful bacterial invasion. Single­cell RNA sequencing technology allows the detection of the different cell types in the intestine in two dimensions and the exploration of cell types that have not been fully characterized. The intestinal mucosa is highly complex in structure, and its proper functioning is linked to multiple structures in the proximal­distal intestinal and luminal­mucosal axes. Spatial localization is at the core of the efforts to explore the interactions between the complex structures. Spatial transcriptomics (ST) is a method that allows for comprehensive tissue analysis and the acquisition of spatially separated genetic information from individual cells, while preserving their spatial location and interactions. This approach also prevents the loss of fragile cells during tissue disaggregation. The emergence of ST technology allows us to spatially dissect enzymatic processes and interactions between multiple cells, genes, proteins and signals in the intestine. This includes the exchange of oxygen and nutrients in the intestine, different gradients of microbial populations and the role of extracellular matrix proteins. This regionally precise approach to tissue studies is gaining more acceptance and is increasingly applied in the investigation of disease mechanisms related to the gastrointestinal tract. Therefore, this review summarized the application of ST in gastrointestinal diseases.

Human post-implantation blastocyst-like characteristics of Muse cells isolated from human umbilical cord.

Muse cells, identified as cells positive for the pluripotent surface marker SSEA-3, are pluripotent-like endogenous stem cells located in the bone marrow (BM), peripheral blood, and organ connective tissues. The detailed characteristics of SSEA-3(+) cells in extraembryonic tissue, however, are unknown. Here, we demonstrated that similar to human-adult tissue-Muse cells collected from the BM, adipose tissue, and dermis as SSEA-3(+), human-umbilical cord (UC)-SSEA-3(+) cells express pluripotency markers, differentiate into triploblastic-lineage cells at a single cell level, migrate to damaged tissue, and exhibit low telomerase activity and non-tumorigenicity. Notably, ~ 20% of human-UC-SSEA-3(+) cells were negative for X-inactive specific transcript (XIST), a naïve pluripotent stem cell characteristic, whereas all human adult tissue-Muse cells are XIST-positive. Single-cell RNA sequencing revealed that the gene expression profile of human-UC-SSEA-3(+) cells was more similar to that of human post-implantation blastocysts than human-adult tissue-Muse cells. The DNA methylation level showed the same trend, and notably, the methylation levels in genes particularly related to differentiation were lower in human-UC-SSEA-3(+) cells than in human-adult tissue-Muse cells. Furthermore, human-UC-SSEA-3(+) cells newly express markers specific to extraembryonic-, germline-, and hematopoietic-lineages after differentiation induction in vitro whereas human-adult tissue-Muse cells respond only partially to the induction. Among various stem/progenitor cells in living bodies, those that exhibit properties similar to post-implantation blastocysts in a naïve state have not yet been found in humans. Easily accessible human-UC-SSEA-3(+) cells may be a valuable tool for studying early-stage human development and human reproductive medicine.