MARS and RNAcmap3:The Master Database of All Possible RNA Sequences Integrated with RNAcmap for RNA Homology Search

sequences found in the huge,integrated database of protein sequences(Big Fantastic Database).In contrast,the existing nucleotide databases were not consolidated to facilitate wider and deeper homology search.Here,we built a comprehensive database by incorporating the non-coding RNA(ncRNA)sequences from RNAcentral,the transcriptome assembly and metagenome assembly from metagenomics RAST(MG-RAST),the genomic sequences from Genome Warehouse(GWH),and the genomic sequences from MGnify,in addition to the nucleotide(nt)database and its subsets in National Center of Biotechnology Information(NCBI).The resulting Master database of All possible RNA sequences(MARS)is 20-fold larger than NCBI’s nt database or 60-fold larger than RNAcentral.The new dataset along with a new split-search strategy allows a substantial improvement in homology search over existing state-of-the-art techniques.It also yields more accurate and more sensitive multiple sequence alignments(MSAs)than manually curated MSAs from Rfam for the majority of structured RNAs mapped to Rfam.The results indicate that MARS coupled with the fully automatic homology search tool RNAcmap will be useful for improved structural and functional inference of ncRNAs and RNA language models based on MSAs.MARS is accessible at https://ngdc.cncb.ac.cn/omix/release/OMIX003037,and RNAcmap3 is accessible at http://zhouyq-lab.szbl.ac.cn/download/.

Learning Sequential and Structural Dependencies Between Nucleotides for RNA N6-Methyladenosine Site Identification

N6-methyladenosine(m6A)is an important RNA methylation modification involved in regulating diverse biological processes across multiple species.Hence,the identification of m6A modification sites provides valuable insight into the biological mechanisms of complex diseases at the post-transcriptional level.Although a variety of identification algorithms have been proposed recently,most of them capture the features of m6A modification sites by focusing on the sequential dependencies of nucleotides at different positions in RNA sequences,while ignoring the structural dependencies of nucleotides in their threedimensional structures.To overcome this issue,we propose a cross-species end-to-end deep learning model,namely CR-NSSD,which conduct a cross-domain representation learning process integrating nucleotide structural and sequential dependencies for RNA m6A site identification.Specifically,CR-NSSD first obtains the pre-coded representations of RNA sequences by incorporating the position information into single-nucleotide states with chaos game representation theory.It then constructs a crossdomain reconstruction encoder to learn the sequential and structural dependencies between nucleotides.By minimizing the reconstruction and binary cross-entropy losses,CR-NSSD is trained to complete the task of m6A site identification.Extensive experiments have demonstrated the promising performance of CR-NSSD by comparing it with several state-of-the-art m6A identification algorithms.Moreover,the results of cross-species prediction indicate that the integration of sequential and structural dependencies allows CR-NSSD to capture general features of m6A modification sites among different species,thus improving the accuracy of cross-species identification.

Genetic variation of circHIBADH enhances prostate cancer risk through regulating HNRNPA1-related RNA splicing

The current study aimed to investigate associations of circRNAs and related genetic variants with the risk of prostate cancer(PCa)as well as to elucidate biological mechanisms underlying the associations.We first compared expression levels of circRNAs between 25 paired PCa and adjacent normal tissues to identify riskassociated circRNAs by using the MiOncoCirc database.We then used logistic regression models to evaluate associations between genetic variants in candidate circRNAs and PCa risk among 4662 prostate cancer patients and 3114 healthy controls,and identified circHIBADH rs11973492 T>C as a significant risk-associated variant(odds ratio=1.20,95%confidence interval:1.08-1.34,P=7.06×10^(-4))in a dominant genetic model,which altered the secondary structure of the corresponding RNA chain.In the in silico analysis,we found that circHIBADH sponged and silenced 21 RNA-binding proteins(RBPs)enriched in the RNA splicing pathway,among which HNRNPA1 was identified and validated as a hub RBP using an external RNA-sequencing data as well as the in-house(four tissue samples)and publicly available single-cell transcriptomes.Additionally,we demonstrated that HNRNPA1 influenced hallmarks including MYC target,DNA repair,and E2F target signaling pathways,thereby promoting carcinogenesis.In conclusion,genetic variants in circHIBADH may act as sponges and inhibitors of RNA splicing-associated RBPs including HNRNPA1,playing an oncogenic role in PCa.

Identification and validation of a pyroptosis-related prognostic model for colorectal cancer based on bulk and single-cell RNA sequencing data

BACKGROUND Pyroptosis impacts the development of malignant tumors,yet its role in colorectal cancer(CRC)prognosis remains uncertain.AIM To assess the prognostic significance of pyroptosis-related genes and their association with CRC immune infiltration.METHODS Gene expression data were obtained from The Cancer Genome Atlas(TCGA)and single-cell RNA sequencing dataset GSE178341 from the Gene Expression Omnibus(GEO).Pyroptosis-related gene expression in cell clusters was analyzed,and enrichment analysis was conducted.A pyroptosis-related risk model was developed using the LASSO regression algorithm,with prediction accuracy assessed through K-M and receiver operating characteristic analyses.A nomo-gram predicting survival was created,and the correlation between the risk model and immune infiltration was analyzed using CIBERSORTx calculations.Finally,the differential expression of the 8 prognostic genes between CRC and normal samples was verified by analyzing TCGA-COADREAD data from the UCSC database.RESULTS An effective pyroptosis-related risk model was constructed using 8 genes-CHMP2B,SDHB,BST2,UBE2D2,GJA1,AIM2,PDCD6IP,and SEZ6L2(P<0.05).Seven of these genes exhibited differential expression between CRC and normal samples based on TCGA database analysis(P<0.05).Patients with higher risk scores demonstrated increased death risk and reduced overall survival(P<0.05).Significant differences in immune infiltration were observed between low-and high-risk groups,correlating with pyroptosis-related gene expression.CONCLUSION We developed a pyroptosis-related prognostic model for CRC,affirming its correlation with immune infiltration.This model may prove useful for CRC prognostic evaluation.

Single‑cell RNA sequencing opens a new era for cotton genomic research and gene functional analysis

Single-cell RNA sequencing(scRNA-seq)is one of the most advanced sequencing technologies for studying transcriptome landscape at the single-cell revolution.It provides numerous advantages over traditional RNA-seq.Since it was first used to profile single-cell transcriptome in plants in 2019,it has been extensively employed to perform different research in plants.Recently,scRNA-seq was also quickly adopted by the cotton research community to solve lots of scientific questions which have been never solved.In this comment,we highlighted the significant progress in employing scRNA-seq to cotton genetic and genomic study and its future potential applications.

Single-cell RNA sequencing analysis of the retina under acute high intraocular pressure

High intraocular pressure causes retinal ganglion cell injury in primary and secondary glaucoma diseases,yet the molecular landscape characteristics of retinal cells under high intraocular pressure remain unknown.Rat models of acute hypertension ocular pressure were established by injection of cross-linked hyaluronic acid hydrogel(Healaflow■).Single-cell RNA sequencing was then used to describe the cellular composition and molecular profile of the retina following high intraocular pressure.Our results identified a total of 12 cell types,namely retinal pigment epithelial cells,rod-photoreceptor cells,bipolar cells,Müller cells,microglia,cone-photoreceptor cells,retinal ganglion cells,endothelial cells,retinal progenitor cells,oligodendrocytes,pericytes,and fibroblasts.The single-cell RNA sequencing analysis of the retina under acute high intraocular pressure revealed obvious changes in the proportions of various retinal cells,with ganglion cells decreased by 23%.Hematoxylin and eosin staining and TUNEL staining confirmed the damage to retinal ganglion cells under high intraocular pressure.We extracted data from retinal ganglion cells and analyzed the retinal ganglion cell cluster with the most distinct expression.We found upregulation of the B3gat2 gene,which is associated with neuronal migration and adhesion,and downregulation of the Tsc22d gene,which participates in inhibition of inflammation.This study is the first to reveal molecular changes and intercellular interactions in the retina under high intraocular pressure.These data contribute to understanding of the molecular mechanism of retinal injury induced by high intraocular pressure and will benefit the development of novel therapies.

Applications of single-cell RNA sequencing in spermatogenesis and molecular evolution

Spermatogenic cell heterogeneity is determined by the complex process of spermatogenesis differentiation.However,effectively revealing the regulatory mechanisms underlying mammalian spermatogenic cell development and differentiation via traditional methods is difficult.Advances in technology have led to the emergence of many single-cell transcriptome sequencing protocols,which have partially addressed these challenges.In this review,we detail the principles of 10x Genomics technology and summarize the methods for downstream analysis of single-cell transcriptome sequencing data.Furthermore,we explore the role of single-cell transcriptome sequencing in revealing the heterogeneity of testicular ecological niche cells,delineating the establishment and disruption of testicular immune homeostasis during human spermatogenesis,investigating abnormal spermatogenesis in humans,and,ultimately,elucidating the molecular evolution of mammalian spermatogenesis.

Gene Expression Profile Identifies LncRNA AL355974.3 As a Potential Glioma Biomarker

Objective Glioma is a central nervous system tumor arising from glial cells.Despite significant advances in diagnosis and treatment,most patients with high-grade gliomas have a poor prognosis.Many studies have shown that long noncoding RNAs(lncRNAs)may play important roles in the development,progression and treatment of many tumors,including gliomas.Molecularly targeted therapy may be a new direction for the adjuvant treatment of glioma.Therefore,we hope that by studying differentially expressed lncRNAs(DElncRNAs)in glioma,we can discover lncRNAs that can serve as biomarkers for glioma and provide better therapeutic modalities for glioma patients.Methods First,the expression of lncRNAs in 5 normal brain(NB)tissues and 10 glioma tissues was examined by RNA sequencing(RNA-seq).Next,we performed Kaplan-Meier analysis of data from The Cancer Genome Atlas(TCGA)database to assess the prognostic value of these variables.Finally,functional analysis of the DElncRNAs was performed by means of Gene Ontology(GO)enrichment and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway analysis.Results RNA sequencing analysis revealed 85 upregulated miRNAs and 71 downregulated lncRNAs in low-grade glioma(LGG)and 50 upregulated lncRNAs and 70 downregulated lncRNAs in glioblastoma(GBM).Among them,AL355974.3 was the most upregulated lncRNA.LINC00632 was the most downregulated lncRNA.Second,LGG patients with higher AL355974.3 expression had worse overall survival according to Kaplan-Meier analysis of the TCGA database.Finally,bioinformatics analysis revealed that the target genes of these DElncRNAs were enriched in various biological processes and signaling pathways,such as cell metabolic and developmental processes.Conclusion Our findings provide evidence that AL355974.3 may be a new biomarker for glioma.

Expression changes of mi RNA-regulated genes associated with the formation of the leafy head in cabbage

The vegetative development of cabbage(Brassica oleracea var.capitata)passes through seedling,rosette,folding and heading stages.Leaves that form the rosette are large and mostly flat.In the following developmental stages,the plants produce leaves that curve inward to produce the leafy head.Many microRNAs and their target genes have been described participating in leaf development and leaf curvature.The aim of this study is to investigate the role of miRNA-regulated genes in the transition from the rosette to the heading stage.We compared the mi RNA and gene abundances between emerging rosette and heading leaves.To remove transcripts(miRNAs and genes)whose regulation was most likely associated with plant age rather than the change from rosette to heading stage,we utilized a non-heading collard green(B.oleracea var.acephala)morphotype as control.This resulted in 33 DEMs and 1998 DEGs with likely roles in the transition from rosette to heading stage in cabbage.Among these 1998 DEGs,we found enriched GO terms related to DNA-binding transcription factor activity,transcription regulator activity,iron ion binding,and photosynthesis.We predicted the target genes of these 33 DEMs and focused on the subset that was differentially expressed(1998DEGs)between rosette and heading stage leaves to construct mi RNA-target gene interaction networks.Our main finding is a role for miR396b-5p targeting two Arabidopsis thaliana orthologues of GROWTH REGULATING FACTORs 3(GRF3)and 4(GRF4)in pointed cabbage head formation.

Single-Nucleus RNA Sequencing Reveals Cardiac Macrophage Landscape in Hypoplastic Left Heart Syndrome

Background:Hypoplastic left heart syndrome(HLHS)is one of the most challenging congenital heart diseases in clinical treatment.In cardiac tissues,resident macrophages fulfill critical functions in maintaining a stable cardiac state and have strong regenerative capacity and organ specificity.However,the molecular mechanisms of macro-phages in HLHS remained unclear.Methods:Single-nucleus RNA sequencing(snRNA-seq)data of HLHS and healthy control(donors)samples obtained from the Gene Expression Omnibus(GEO)database were normalized and clustered using the Seurat package.The“FindMarkers”function was used to screen differentially expressed genes(DEGs)between the HLHS and donor groups and to analyze the functional enrichment of the set of genes of interest.Finally,cell-cell communication,pseudotime,and single-cell regulatory network inference and cluster-ing(SCENIC)analyses were used to study the mechanisms of macrophages in HLHS.Results:Based on the snRNA-seq data of HLHS and donors,we identified a total of 9 cell clusters,among which the proportion of macrophages was significantly less in the HLHS group than in the control group.Subdivision of macrophage subpopulations(Macrophages 1,2,and 3)showed that Macrophages 1 was mainly involved in nervous system development,angiogenesis,and apoptotic processes.In addition,analysis of communication between Macro-phages 1 and cardiomyocytes revealed that ligand-acceptor pairs such as GAS6/AXL,IL6,IGF1,THY1,and L1CAM were present only in the donor group.Finally,pesudotime and SCENIC analyses demonstrated that FOXO3 and ELF2 played a critical role for Macrophages 1 to maintain cardiac function in patients with HLHS.Conclusion:Our study improved the current understanding of the molecular mechanisms of macrophage devel-opment in HLHS,showing that manipulating the regulatory role of macrophages in the heart can be a novel treat-ment for HLHS.

Single-nuclei RNA sequencing uncovers heterogenous transcriptional signatures in Parkinson's disease associated with nuclear receptor-related factor 1 defect

Previous studies have found that deficiency in nuclear receptor-related factor 1(Nurr1),which participates in the development,differentiation,survival,and degeneration of dopaminergic neurons,is associated with Parkinson s disease,but the mechanism of action is perplexing.Here,we first asce rtained the repercussion of knocking down Nurr1 by pe rforming liquid chromatography coupled with tandem mass spectrometry.We found that 231 genes were highly expressed in dopaminergic neurons with Nurr1 deficiency,14 of which were linked to the Parkinson’s disease pathway based on Kyoto Encyclopedia of Genes and Genomes analysis.To better understand how Nurr1 deficiency autonomously invokes the decline of dopaminergic neurons and elicits Parkinson’s disease symptoms,we performed single-nuclei RNA sequencing in a Nurr1 LV-shRNA mouse model.The results revealed cellular heterogeneity in the substantia nigra and a number of activated genes,the preponderance of which encode components of the major histocompatibility Ⅱ complex.Cd74,H2-Ab1,H2-Aα,H2-Eb1,Lyz2,Mrc1,Slc6α3,Slc47α1,Ms4α4b,and Ptprc2 were the top 10 diffe rentially expressed genes.Immunofluorescence staining showed that,after Nurr1knockdown,the number of CD74-immunoreactive cells in mouse brain tissue was markedly increased.In addition,Cd74 expression was increased in a mouse model of Parkinson’s disease induced by treatment with 6-hydroxydopamine.Ta ken togethe r,our res ults suggest that Nurr1 deficiency results in an increase in Cd74 expression,thereby leading to the destruction of dopaminergic neuro ns.These findings provide a potential therapeutic target for the treatment of Parkinson’s disease.

Application of single-cell RNA sequencing in head and neck squamous cell carcinoma

Single-cell RNA sequencing has been broadly applied to head and neck squamous cell carcinoma(HNSCC) for characterizing the heterogeneity and genomic mutations of HNSCC benefiting from the advantage of single-cell resolution. We summarized most of the current studies and aimed to explore their research methods and ideas, as well as how to transform them into clinical applications. Through single-cell RNA sequencing, we found the differences in tumor cells’ expression programs and differentiation tracks. The studies of immune microenvironment allowed us to distinguish immune cell subpopulations, the extensive expression of immune checkpoints, and the complex crosstalk network between immune cells and non-immune cells. For cancerassociated fibroblasts(CAFs), single-cell RNA sequencing had made an irreplaceable contribution to the exploration of their differentiation status, specific CAFs markers, and the interaction with tumor cells and immune cells. In addition, we demonstrated in detail how single-cell RNA sequencing explored the HNSCC epithelial-tomesenchymal transition(EMT) model and the mechanism of drug resistance, as well as its clinical value.

Recent developments in application of single-cell RNA sequencing in the tumour immune microenvironment and cancer therapy

The advent of single-cell RNA sequencing(scRNA-seq)has provided insight into the tumour immune microenvironment(TIME).This review focuses on the application of scRNA-seq in investigation of the TIME.Over time,scRNA-seq methods have evolved,and components of the TIME have been deciphered with high resolution.In this review,we first introduced the principle of scRNA-seq and compared different sequencing approaches.Novel cell types in the TIME,a continuous transitional state,and mutual intercommunication among TIME components present potential targets for prognosis prediction and treatment in cancer.Thus,we concluded novel cell clusters of cancerassociated fibroblasts(CAFs),T cells,tumour-associated macrophages(TAMs)and dendritic cells(DCs)discovered after the application of scRNA-seq in TIME.We also proposed the development of TAMs and exhausted T cells,as well as the possible targets to interrupt the process.In addition,the therapeutic interventions based on cellular interactions in TIME were also summarized.For decades,quantification of the TIME components has been adopted in clinical practice to predict patient survival and response to therapy and is expected to play an important role in the precise treatment of cancer.Summarizing the current findings,we believe that advances in technology and wide application of single-cell analysis can lead to the discovery of novel perspectives on cancer therapy,which can subsequently be implemented in the clinic.Finally,we propose some future directions in the field of TIME studies that can be aided by scRNA-seq technology.

Single-cell RNA sequencing in cornea research:Insights into limbal stem cells and their niche regulation

The corneal epithelium is composed of stratified squamous epithelial cells on the outer surface of the eye,which acts as a protective barrier and is critical for clear and stable vision.Its continuous renewal or wound healing depends on the proliferation and differentiation of limbal stem cells(LSCs),a cell population that resides at the limbus in a highly regulated niche.Dysfunction of LSCs or their niche can cause limbal stem cell deficiency,a disease that is manifested by failed epithelial wound healing or even blindness.Nevertheless,compared to stem cells in other tissues,little is known about the LSCs and their niche.With the advent of single-cell RNA sequencing,our understanding of LSC characteristics and their microenvironment has grown considerably.In this review,we summarized the current findings from single-cell studies in the field of cornea research and focused on important advancements driven by this technology,including the heterogeneity of the LSC population,novel LSC markers and regulation of the LSC niche,which will provide a reference for clinical issues such as corneal epithelial wound healing,ocular surface reconstruction and interventions for related diseases.

Global Annotation of Small RNA and MicroRNA Mature Sequences from Developing Ovules of Gossypium hirsutum L.

The involvement of small RNAs in cotton fiber development is under explored.The objective of this work was to directly clone,annotate,and analyze small RNAs of developing ovules to reveal

Single-cell RNA sequencing reveals the dynamics of hepatic non-parenchymal cells in autoprotection against acetaminophen-induced hepatotoxicity

Gaining a better understanding of autoprotection against drug-induced liver injury(DILI)may provide new strategies for its prevention and therapy.However,little is known about the underlying mechanisms of this phenomenon.We used single-cell RNA sequencing to characterize the dynamics and functions of hepatic non-parenchymal cells(NPCs)in autoprotection against DILI,using acetaminophen(APAP)as a model drug.Autoprotection was modeled through pretreatment with a mildly hepatotoxic dose of APAP in mice,followed by a higher dose in a secondary challenge.NPC subsets and dynamic changes were identified in the APAP(hepatotoxicity-sensitive)and APAP-resistant(hepatotoxicity-resistant)groups.A chemokine(C-C motif)ligand 2^(+)endothelial cell subset almost disappeared in the APAP-resistant group,and an R-spondin 3^(+)endothelial cell subset promoted hepatocyte proliferation and played an important role in APAP autoprotection.Moreover,the dendritic cell subset DC-3 may protect the liver from APAP hepatotoxicity by inducing low reactivity and suppressing the autoimmune response and occurrence of inflammation.DC-3 cells also promoted angiogenesis through crosstalk with endothelial cells via vascular endothelial growth factor-associated ligand-receptor pairs and facilitated liver tissue repair in the APAP-resistant group.In addition,the natural killer cell subsets NK-3 and NK-4 and the Sca-1^(-)CD62L^(+)natural killer T cell subset may promote autoprotection through interferon-γ-dependent pathways.Furthermore,macrophage and neutrophil subpopulations with anti-inflammatory phenotypes promoted tolerance to APAP hepatotoxicity.Overall,this study reveals the dynamics of NPCs in the resistance to APAP hepatotoxicity and provides novel insights into the mechanism of autoprotection against DILI at a high resolution.

Circulating Exosomal Lnc RNAs as Novel Diagnostic Predictors of Severity and Sites of White Matter Hyperintensities

Objective Exosomal long noncoding RNAs(lnc RNAs) are the key to diagnosing and treating various diseases. This study aimed to investigate the diagnostic value of plasma exosomal lnc RNAs in white matter hyperintensities(WMH).Methods We used high-throughput sequencing to determine the differential expression(DE) profiles of lnc RNAs in plasma exosomes from WMH patients and controls. The sequencing results were verified in a validation cohort using q RT-PCR. The diagnostic potential of candidate exosomal lnc RNAs was proven by binary logistic analysis and receiver operating characteristic(ROC) curves. The diagnostic value of DE exo-lnc RNAs was determined by the area under the curve(AUC). The WMH group was then divided into subgroups according to the Fazekas scale and white matter lesion site, and the correlation of DE exo-lnc RNAs in the subgroup was evaluated.Results In our results, four DE exo-lnc RNAs were identified, and ROC curve analysis revealed that exolnc_011797 and exo-lnc_004326 exhibited diagnostic efficacy for WMH. Furthermore, WMH subgroup analysis showed exo-lnc_011797 expression was significantly increased in Fazekas 3 patients and was significantly elevated in patients with paraventricular matter hyperintensities.Conclusion Plasma exosomal lnc RNAs have potential diagnostic value in WMH. Moreover, exolnc_011797 is considered to be a predictor of the severity and location of WMH.

OsBLS6.2:A rice bacterial leaf streak resistance gene identified by GWAS and RNA-seq

Bacterial leaf streak(BLS),caused by Xanthomonas oryzae pv.oryzicola(Xoc),is a bacterial disease affecting rice production in Asia and Africa,whose severity is expected to increase with climate change.Identification of new quantitative-trait loci(QTL)or resistance genes for BLS resistance is essential for developing resistant rice.A genome-wide association study to identify QTL associated with BLS resistance was conducted using phenotypic and genotypic data from 429 rice accessions.Of 47 QTL identified,45 were novel and two co-localized with previously reported QTL or genes conferring BLS resistance.qBLS6.2 on chromosome 6 explained the greatest phenotypic variation.Combined analysis of differential expression and annotations of predicted genes near qBLS6.2 based on haplotype and disease phenotype identified OsBLS6.2(LOC_Os06g02960)as a candidate gene for qBLS6.2.OsBLS6.2 knockout plants showed higher resistance to Xoc than wild-type plants.Many other candidate genes for resistance to Xoc were identified.

Single cell RNA sequencing reveals mesenchymal heterogeneity and critical functions of Cd271 in tooth development

BACKGROUND Accumulating evidence suggests that the maxillary process,to which cranial crest cells migrate,is essential to tooth development.Emerging studies indicate that Cd271 plays an essential role in odontogenesis.However,the underlying mechanisms have yet to be elucidated.AIM To establish the functionally heterogeneous population in the maxillary process,elucidate the effects of Cd271 deficiency on gene expression differences.METHODS p75NTR knockout(Cd271-/-)mice(from American Jackson laboratory)were used to collect the maxillofacial process tissue of p75NTR knockout mice,and the wildtype maxillofacial process of the same pregnant mouse wild was used as control.After single cell suspension,the cDNA was prepared by loading the single cell suspension into the 10x Genomics Chromium system to be sequenced by NovaSeq6000 sequencing system.Finally,the sequencing data in Fastq format were obtained.The FastQC software is used to evaluate the quality of data and CellRanger analyzed the data.The gene expression matrix is read by R software,and Seurat is used to control and standardize the data,reduce the dimension and cluster.We search for marker genes for subgroup annotation by consulting literature and database;explore the effect of p75NTR knockout on mesenchymal stem cells(MSCs)gene expression and cell proportion by cell subgrouping,differential gene analysis,enrichment analysis and protein-protein interaction network analysis;understand the interaction between MSCs cells and the differentiation trajectory and gene change characteristics of p75NTR knockout MSCs by cell communication analysis and pseudo-time analysis.Last we verified the findings single cell sequencing in vitro.RESULTS We identified 21 cell clusters,and we re-clustered these into three subclusters.Importantly,we revealed the cell–cell communication networks between clusters.We clarified that Cd271 was significantly associated with the regulation of mineralization.CONCLUSION This study provides comprehensive mechanistic insights into the maxillary-process-derived MSCs and demonstrates that Cd271 is significantly associated with the odontogenesis in mesenchymal populations.

Integrated analysis of single-cell and bulk RNA-seq establishes a novel signature for prediction in gastric cancer

BACKGROUND Single-cell sequencing technology provides the capability to analyze changes in specific cell types during the progression of disease.However,previous single-cell sequencing studies on gastric cancer(GC)have largely focused on immune cells and stromal cells,and further elucidation is required regarding the alterations that occur in gastric epithelial cells during the development of GC.AIM To create a GC prediction model based on single-cell and bulk RNA sequencing(bulk RNA-seq)data.METHODS In this study,we conducted a comprehensive analysis by integrating three singlecell RNA sequencing(scRNA-seq)datasets and ten bulk RNA-seq datasets.Our analysis mainly focused on determining cell proportions and identifying differentially expressed genes(DEGs).Specifically,we performed differential expression analysis among epithelial cells in GC tissues and normal gastric tissues(NAGs)and utilized both single-cell and bulk RNA-seq data to establish a prediction model for GC.We further validated the accuracy of the GC prediction model in bulk RNA-seq data.We also used Kaplan–Meier plots to verify the correlation between genes in the prediction model and the prognosis of GC.RESULTS By analyzing scRNA-seq data from a total of 70707 cells from GC tissue,NAG,and chronic gastric tissue,10 cell types were identified,and DEGs in GC and normal epithelial cells were screened.After determining the DEGs in GC and normal gastric samples identified by bulk RNA-seq data,a GC predictive classifier was constructed using the Least absolute shrinkage and selection operator(LASSO)and random forest methods.The LASSO classifier showed good performance in both validation and model verification using The Cancer Genome Atlas and Genotype-Tissue Expression(GTEx)datasets[area under the curve(AUC)_min=0.988,AUC_1se=0.994],and the random forest model also achieved good results with the validation set(AUC=0.92).Genes TIMP1,PLOD3,CKS2,TYMP,TNFRSF10B,CPNE1,GDF15,BCAP31,and CLDN7 were identified to have high importance values in multiple GC predictive models,and KM-PLOTTER analysis showed their relevance to GC prognosis,suggesting their potential for use in GC diagnosis and treatment.CONCLUSION A predictive classifier was established based on the analysis of RNA-seq data,and the genes in it are expected to serve as auxiliary markers in the clinical diagnosis of GC.