Combining single-cell RNA-sequencing and bulk data to reveal immunity-related genes expression pattern in the systemic lupus erythematosus and target organ kidney

Background:Systemic lupus erythematosus(SLE)is a complex chronic autoimmune disease with no known cure.However,the regulatory mechanism of immunity-related genes is not fully understood in SLE.In order to explore new therapeutic targets,we used bioinformatical methods to analyze a series of data.Methods:After downloading and processing the data from Gene Expression Omnibus database,the differentially expressed genes of SLE were analyzed.CIBERSORT algorithm was used to analyze the immune infiltration of SLE.Based on single-cell RNA-sequencing data,the role of immune-related genes in SLE and its target organ(kidney)were analyzed.Key transcription factors affecting immune-related genes were identified.Cell-cell communication networks in SLE were analyzed.Results:In total,15 hub genes and 4 transcription factors were found in the bulk data.Monocytes and macrophages in GSE81622(SLE)showed more infiltration.There were four cell types were annotated in scRNA sequencing dataset(GSE135779),as follows T cells,monocyte,NK cells and B cells.Immunity-related genes were overexpressed in monocytes.Conclusion:The present study shows that immune-related genes affect SLE through monocytes and play an important role in target organ renal injury.

Single-cell RNA-sequencing combined with bulk RNA-sequencing analysis of peripheral blood reveals the characteristics and key immune cell genes of ulcerative colitis

BACKGROUND Ulcerative colitis(UC)is a complicated disease caused by the interaction between genetic and environmental factors that affects mucosal homeostasis and triggers an inappropriate immune response.Single-cell RNA sequencing(scRNA-seq)can be used to rapidly obtain the precise gene expression patterns of thousands of cells in the intestine,analyze the characteristics of cells with the same phenotype,and provide new insights into the growth and development of intestinal organs,the clonal evolution of cells,and immune cell changes.These findings can provide new ideas for the diagnosis and treatment of intestinal diseases.To identify clinical phenotypes and biomarkers that can predict the response of UC patients to specific therapeutic drugs and thus aid the diagnosis and treatment of UC.METHODS Using the Gene Expression Omnibus(GEO)database,we analyzed peripheral blood cell subtypes of patients with UC by scRNA-seq combined with bulk RNA sequencing(RNA-seq)to reveal the core genes of UC.We then combined weighted gene correlation network analysis(WGCNA)and least absolute shrinkage and selection operator(LASSO)analysis to reveal diagnostic markers of UC.RESULTS After processing the scRNA-seq data,we obtained data from approximately 24340 cells and identified 17 cell types.Through intercellular communication analysis,we selected monocyte marker genes as the candidate gene set for the prediction model.Construction of a WGCNA coexpression network identified RhoB,cathepsin D(CTSD)and zyxin(ZYX)as core genes.Immune infiltration analysis showed that these three core genes were strongly correlated with immune cells.Functional enrichment analysis showed that the differentially expressed genes were closely related to immune and inflammatory responses,which are associated with many challenges in the diagnosis and treatment of UC.CONCLUSION Through scRNA-seq analysis,LASSO diagnostic model building and WGCNA,we identified RhoB,CTSD and ZYX as core genes of UC that are closely related to monocyte infiltration that may serve as diagnostic markers and molecular targets for UC therapeutic intervention.

Single-cell profiling of the pig cecum at various developmental stages

The gastrointestinal tract is essential for food digestion,nutrient absorption,waste elimination,and microbial defense.Single-cell transcriptome profiling of the intestinal tract has greatly enriched our understanding of cellular diversity,functional heterogeneity,and their importance in intestinal tract development and disease.Although such profiling has been extensively conducted in humans and mice,the single-cell gene expression landscape of the pig cecum remains unexplored.Here,single-cell RNA sequencing was performed on 45572 cells obtained from seven cecal samples in pigs at four different developmental stages(days(D)30,42,150,and 730).Analysis revealed 12 major cell types and 38 subtypes,as well as their distinctive genes,transcription factors,and regulons,many of which were conserved in humans.An increase in the relative proportions of CD8^(+)T and Granzyme A(low expression)natural killer T cells(GZMA^(low)NKT)cells and a decrease in the relative proportions of epithelial stem cells,Tregs,RHEX^(+)T cells,and plasmacytoid dendritic cells(pDCs)were noted across the developmental stages.Moreover,the post-weaning period exhibited an up-regulation in mitochondrial genes,COX2 and ND2,as well as genes involved in immune activation in multiple cell types.Cell-cell crosstalk analysis indicated that IBP6^(+)fibroblasts were the main signal senders at D30,whereas IBP6^(−)fibroblasts assumed this role at the other stages.NKT cells established interactions with epithelial cells and IBP6^(+)fibroblasts in the D730 cecum through mediation of GZMA-F2RL1/F2RL2 pairs.This study provides valuable insights into cellular heterogeneity and function in the pig cecum at different development stages.

Single-cell RNA-Seq reveals transcriptional regulatory networks directing the development of mouse maxillary prominence

During vertebrate embryonic development,neural crest-derived ectomesenchyme within the maxillary prominences undergoes precisely coordinated proliferation and differentiation to give rise to diverse craniofacial structures,such as tooth and palate.However,the transcriptional regulatory networks underpinning such an intricate process have not been fully elucidated.Here,we perform single-cell RNA-Seq to comprehensively characterize the transcriptional dynamics during mouse maxillary development from embryonic day(E)10.5eE14.5.Our single-cell transcriptome atlas of~28,000 cells uncovers mesenchymal cell populations representing distinct differentiating states and reveals their developmental trajectory,suggesting that the segregation of dental from the palatal mesenchyme occurs at E11.5.Moreover,we identify a series of key transcription factors(TFs)associated with mesenchymal fate transitions and deduce the gene regulatory networks directed by these TFs.Collectively,our study provides important resources and insights for achieving a systems-level understanding of craniofacial morphogenesis and abnormality.

Single-cell RNA-seq of Lotus japonicus provide insights into identification and function of root cell types of legume

The roots of legume plant play a crucial role in nitrogen fixation. However, the transcriptomes of different cell types of legume root and their functions remain largely unknown. Here, we performed single-cell RNA sequencing and profiled more than 22,000 single cells from root tips of Lotus japonicus, a model species of legume.We identified seven clusters corresponding to seven major cell types, which were validated by in situ hybridization. Further analysis revealed regulatory programs including phytohormone and nodulation associated with specific cell types, and revealed conserved and diverged features for the cell types. Our results represent the first single-cell resolution transcriptome for legume root tips and a valuable resource for studying the developmental and physiological functions of various cell types in legumes.

Distortion-free PCA on sample space for highly variable gene detection from single-cell RNA-seq data

Single-cell RNA-seq (scRNA-seq) allows the analysis of gene expression in each cell, which enables the detection of highly variable genes (HVG) that contribute to cell-to-cell variation within a homogeneous cell population. HVG detection is necessary for clustering analysis to improve the clustering result. scRNA-seq includes some genes that are expressed with a certain probability in all cells which make the cells indistinguishable. These genes are referred to as background noise. To remove the background noise and select the informative genes for clustering analysis, in this paper, we propose an effective HVG detection method based on principal component analysis (PCA). The proposed method utilizes PCA to evaluate the genes (features) on the sample space. The distortion-free principal components are selected to calculate the distance from the origin to gene as the weight of each gene. The genes that have the greatest distances to the origin are selected for clustering analysis. Experimental results on both synthetic and gene expression datasets show that the proposed method not only removes the background noise to select the informative genes for clustering analysis, but also outperforms the existing HVG detection methods.

Single-cell transcriptomic atlas of goat ovarian aging

Background The ovaries are one of the first organs that undergo degenerative changes earlier in the aging process,and ovarian aging is shown by a decrease in the number and quality of oocytes.However,little is known about the molecular mechanisms of female age-related fertility decline in different types of ovarian cells during aging,especially in goats.Therefore,the aim of this study was to reveal the mechanisms driving ovarian aging in goats at single-cell resolution.Results For the first time,we surveyed the single-cell transcriptomic landscape of over 27,000 ovarian cells from newborn,young and aging goats,and identified nine ovarian cell types with distinct gene-expression signatures.Functional enrichment analysis showed that ovarian cell types were involved in their own unique biological processes,such as Wnt beta-catenin signalling was enriched in germ cells,whereas ovarian steroidogenesis was enriched in granulosa cells(GCs).Further analysis showed that ovarian aging was linked to GCs-specific changes in the antioxidant system,oxidative phosphorylation,and apoptosis.Subsequently,we identified a series of dynamic genes,such as AMH,CRABP2,THBS1 and TIMP1,which determined the fate of GCs.Additionally,FOXO1,SOX4,and HIF1A were identified as significant regulons that instructed the differentiation of GCs in a distinct manner during ovarian aging.Conclusions This study revealed a comprehensive aging-associated transcriptomic atlas characterizing the cell typespecific mechanisms during ovarian aging at the single-cell level and offers new diagnostic biomarkers and potential therapeutic targets for age-related goat ovarian diseases.

Food nutrition and toxicology targeting on specific organs in the era of single-cell sequencing

Due to the complex natures of dietary food components,it is difficult to elucidate how the compounds affect host health.Dietary food often selectively presents its mechanism of action on different cell types,and participates in the modulation of targeted cells and their microenvironments within organs.However,the limitations of traditional in vitro assays or in vivo animal experiments cannot comprehensively examine cellular heterogeneity and the tissue-biased influences.Single-cell RNA sequencing(sc RNA-seq)has emerged as an indispensable methodology to decompose tissues into different cell types for the demonstration of transcriptional profiles of individual cells.Sc RNA-seq applications has been summarized on three typical organs(brain,liver,kidney),and two representative immune-and tumor related health problems.The everincreasing role of sc RNA-seq in dietary food research with further improvement can provide sub-cellular information and the coupling between other cellular modalities.In this review,we propose utilizing sc RNAseq to more effectively capture the subtle and complex effects of food chemicals,and how they may lead to health problems at single-cell resolution.This novel technique will be valuable to elucidate the underlying mechanism of both the health benefits of food nutrients and the detrimental consequences food toxicants at the cellular level.

Single-cell sequencing technology in diabetic wound healing:New insights into the progenitors-based repair strategies

Diabetes mellitus(DM),an increasingly prevalent chronic metabolic disease,is characterised by prolonged hyperglycaemia,which leads to long-term health consequences.Although much effort has been put into understanding the pathogenesis of diabetic wounds,the underlying mechanisms remain unclear.The advent of single-cell RNA sequencing(scRNAseq)has revolutionised biological research by enabling the identification of novel cell types,the discovery of cellular markers,the analysis of gene expression patterns and the prediction of develop-mental trajectories.This powerful tool allows for an in-depth exploration of pathogenesis at the cellular and molecular levels.In this editorial,we focus on progenitor-based repair strategies for diabetic wound healing as revealed by scRNAseq and highlight the biological behaviour of various healing-related cells and the alteration of signalling pathways in the process of diabetic wound healing.ScRNAseq could not only deepen our understanding of the complex biology of diabetic wounds but also identify and validate new targets for inter-vention,offering hope for improved patient outcomes in the management of this challenging complication of DM.

Action of circulating and infiltrating B cells in the immune microenvironment of colorectal cancer by single-cell sequencing analysis

BACKGROUND The complexity of the immune microenvironment has an impact on the treatment of colorectal cancer(CRC),one of the most prevalent malignancies worldwide.In this study,multi-omics and single-cell sequencing techniques were used to investigate the mechanism of action of circulating and infiltrating B cells in CRC.By revealing the heterogeneity and functional differences of B cells in cancer immunity,we aim to deepen our understanding of immune regulation and provide a scientific basis for the development of more effective cancer treatment strategies.AIM To explore the role of circulating and infiltrating B cell subsets in the immune microenvironment of CRC,explore the potential driving mechanism of B cell development,analyze the interaction between B cells and other immune cells in the immune microenvironment and the functions of communication molecules,and search for possible regulatory pathways to promote the anti-tumor effects of B cells.METHODS A total of 69 paracancer(normal),tumor and peripheral blood samples were collected from 23 patients with CRC from The Cancer Genome Atlas database(https://portal.gdc.cancer.gov/).After the immune cells were sorted by multicolor flow cytometry,the single cell transcriptome and B cell receptor group library were sequenced using the 10X Genomics platform,and the data were analyzed using bioinformatics tools such as Seurat.The differences in the number and function of B cell infiltration between tumor and normal tissue,the interaction between B cell subsets and T cells and myeloid cell subsets,and the transcription factor regulatory network of B cell subsets were explored and analyzed.RESULTS Compared with normal tissue,the infiltrating number of CD20+B cell subsets in tumor tissue increased significantly.Among them,germinal center B cells(GCB)played the most prominent role,with positive clone expansion and heavy chain mutation level increasing,and the trend of differentiation into memory B cells increased.However,the number of plasma cells in the tumor microenvironment decreased significantly,and the plasma cells secreting IgA antibodies decreased most obviously.In addition,compared with the immune microenvironment of normal tissues,GCB cells in tumor tissues became more closely connected with other immune cells such as T cells,and communication molecules that positively regulate immune function were significantly enriched.CONCLUSION The role of GCB in CRC tumor microenvironment is greatly enhanced,and its affinity to tumor antigen is enhanced by its significantly increased heavy chain mutation level.Meanwhile,GCB has enhanced its association with immune cells in the microenvironment,which plays a positive anti-tumor effect.

The early life immune dynamics and cellular drivers at single-cell resolution in lamb forestomachs and abomasum

Background Four-chambered stomach including the forestomachs(rumen,reticulum,and omasum)and abomasum allows ruminants convert plant fiber into high-quality animal products.The early development of this four-chambered stomach is crucial for the health and well-being of young ruminants,especially the immune development.However,the dynamics of immune development are poorly understood.Results We investigated the early gene expression patterns across the four-chambered stomach in Hu sheep,at 5,10,15,and 25 days of age.We found that forestomachs share similar gene expression patterns,all four stomachs underwent widespread activation of both innate and adaptive immune responses from d 5 to 25,whereas the metabolic function were significantly downregulated with age.We constructed a cell landscape of the four-chambered stomach using single-cell sequencing.Integrating transcriptomic and single-cell transcriptomic analyses revealed that the immune-associated module hub genes were highly expressed in T cells,monocytes and macrophages,as well as the defense-associated module hub genes were highly expressed in endothelial cells in the four-stomach tissues.Moreover,the non-immune cells such as epithelial cells play key roles in immune maturation.Cell communication analysis predicted that in addition to immune cells,non-immune cells recruit immune cells through macrophage migration inhibitory factor signaling in the forestomachs.Conclusions Our results demonstrate that the immune and defense responses of four stomachs are quickly developing with age in lamb's early life.We also identified the gene expression patterns and functional cells associated with immune development.Additionally,we identified some key receptors and signaling involved in immune regulation.These results help to understand the early life immune development at single-cell resolution,which has implications to develop nutritional manipulation and health management strategies based on specific targets including key receptors and signaling pathways.

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.

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.

A Survey on Methods for Predicting Polyadenylation Sites from DNA Sequences,Bulk RNA-seq,and Single-cell RNA-seq

Alternative polyadenylation(APA)plays important roles in modulating mRNA stability,translation,and subcellular localization,and contributes extensively to shaping eukaryotic transcriptome complexity and proteome diversity.Identification of poly(A)sites(pAs)on a genomewide scale is a critical step toward understanding the underlying mechanism of APA-mediated gene regulation.A number of established computational tools have been proposed to predict pAs from diverse genomic data.Here we provided an exhaustive overview of computational approaches for predicting pAs from DNA sequences,bulk RNA sequencing(RNA-seq)data,and single-cell RNA sequencing(scRNA-seq)data.Particularly,we examined several representative tools using bulk RNA-seq and scRNA-seq data from peripheral blood mononuclear cells and put forward operable suggestions on how to assess the reliability of pAs predicted by different tools.We also proposed practical guidelines on choosing appropriate methods applicable to diverse scenarios.Moreover,we discussed in depth the challenges in improving the performance of pA prediction and benchmarking different methods.Additionally,we highlighted outstanding challenges and opportunities using new machine learning and integrative multi-omics techniques,and provided our perspective on how computational methodologies might evolve in the future for non-30 untranslated region,tissuespecific,cross-species,and single-cell pA prediction.

Single-cell RNA-seq Revealed that Altered Tumor Infiltrating Lymphocytes in Cirrhotic Liver Indicate Development of Hepatocellular Carcinoma

Background and Aims:Cirrhosis is the precursor lesion for most hepatocellular carcinoma(HCC)cases.However,no biomarker effectively predicted HCC initiation before diagnosis by imaging.We aimed to investigate the hallmarks of immune microenvironments in healthy,cirrhotic livers and HCC tumor tissues and to identify immune biomarkers of cirrhosis-HCC transition.Methods:Expression matrices of singlecell RNA sequencing studies were downloaded and integrated with Seurat package vignettes.Clustering was performed to analyze the immune cell compositions of different sample types.Results:The cirrhotic liver and HCC tumors had distinct immune microenvironments,but the immune landscape of cirrhotic livers was not markedly modified compared with healthy livers.Two subsets of B cells and three subsets of T cells were identified in the samples.Among the T cells,naïve T cells were more prominent in the cirrhotic and healthy liver samples than in the HCC samples.In contrast,the neutrophil count was lower in cirrhotic livers.Two macrophage clusters were identified,one that actively interacted with T cells and B cells and was enriched in cirrhotic blood compared with HCC blood samples.Conclusions:Decreased naïve T cell infiltration and increased neutrophil infiltration in the liver may indicate the development of HCC in cirrhotic patients.Alterations in blood-resident immune cells may also be a sign of HCC development in cirrhotic patients.The dynamics of the immune cell subsets may serve as novel biomarkers to predict the transition from cirrhosis to HCC.

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.

Single-cell RNA-seq data analysis on the receptor ACE2 expression reveals the potential risk of different human organs vulnerable to 2019-nCoV infection

It has been known that,the novel coronavirus,2019-nCoV,which is considered similar to SARS-CoV,invades human cells via the receptor angiotensin converting enzyme II(ACE2).Moreover,lung cells that have ACE2 expression may be the main target cells during 2019-nCoV infection.However,some patients also exhibit non-respiratory symptoms,such as kidney failure,implying that 2019-nCoV could also invade other organs.To construct a risk map of different human organs,we analyzed the single-cell RNA sequencing(scRNA-seq)datasets derived from major human physiological systems,including the respiratory,cardiovascular,digestive,and urinary systems.Through scRNA-seq data analyses,we identified the organs at risk,such as lung,heart,esophagus,kidney,bladder,and ileum,and located specific cell types(i.e.,type II alveolar cells(AT2),myocardial cells,proximal tubule cells of the kidney,ileum and esophagus epithelial cells,and bladder urothelial cells),which are vulnerable to 2019-nCoV infection.Based on the findings,we constructed a risk map indicating the vulnerability of different organs to 2019-nCoV infection.This study may provide potential clues for further investigation of the pathogenesis and route of 2019-nCoV infection.

Effects of vitrification and cryostorage duration on single-cell RNA-Seq profiling of vitrified-thawed human metaphase Ⅱ oocytes

Oocyte cryopreservation is widely used for clinical and social reasons.Previous studies have demonstrated that conventional slow-freezing cryopreservation procedures,but not storage time,can alter the gene expression profiles of frozen oocytes.Whether vitrification procedures and the related frozen storage durations have any effects on the transcriptomes of human metaphase Ⅱ oocytes remain unknown.Four women(30–32 years old)who had undergone IVF treatment were recruited for this study.RNA-Seq profiles of 3 fresh oocytes and 13 surviving vitrified-thawed oocytes(3,3,4,and 3 oocytes were cryostored for 1,2,3,and 12 months)were analyzed at a single-cell resolution.A total of 1987 genes were differentially expressed in the 13 vitrifiedthawed oocytes.However,no differentially expressed genes were found between any two groups among the 1-,2-,3-,and 12-month storage groups.Further analysis revealed that the aberrant genes in the vitrified oocytes were closely related to oogenesis and development.Our findings indicated that the effects of vitrification on the transcriptomes of mature human oocytes are induced by the procedure itself,suggesting that long-term cryostorage of human oocytes is safe.

Single-cell analysis of cellular heterogeneity and interactions in the ischemia-reperfusion injured mouse intestine

Nine major cell populations among 46,716 cells were identified in mouse intestinal ischemia‒reperfusion(II/R)injury by single-cell RNA sequencing.For enterocyte cells,11 subclusters were found,in which enterocyte cluster 1(EC1),enterocyte cluster 3(EC3),and enterocyte cluster 8(EC8)were newly discovered cells in ischemia 45 min/reperfusion 720 min(I 45 min/R 720 min)group.EC1 and EC3 played roles in digestion and absorption,and EC8 played a role in cell junctions.For TA cells,after ischemia 45 min/reperfusion 90 min(I 45 min/R 90 min),many TA cells at the stage of proliferation were identified.For Paneth cells,Paneth cluster 3 was observed in the resting state of normal jejunum.After I 45 min/R 90 min,three new subsets were found,in which Paneth cluster 1 had good antigen presentation activity.The main functions of goblet cells were to synthesize and secrete mucus,and a novel subcluster(goblet cluster 5)with highly proliferative ability was discovered in I 45 min/R 90 min group.As a major part of immune system,the changes in T cells with important roles were clarified.Notably,enterocyte cells secreted Guca2b to interact with Gucy2c receptor on the membranes of stem cells,TA cells,Paneth cells,and goblet cells to elicit intercellular communication.One marker known as glutathione S-transferase mu 3(GSTM3)affected intestinal mucosal barrier function by adjusting mitogen-activated protein kinases(MAPK)signaling during II/R injury.The data on the heterogeneity of intestinal cells,cellular communication and the mechanism of GSTM3 provide a cellular basis for treating II/R injury.

Imputing single-cell RNA-seq data by considering cell heterogeneity and prior expression of dropouts

Single-cell RNA sequencing(scRNA-seq)provides a powerful tool to determine expression patterns of thousands of individual cells.However,the analysis of scRNA-seq data remains a computational challenge due to the high technical noise such as the presence of dropout events that lead to a large proportion of zeros for expressed genes.Taking into account the cell heterogeneity and the relationship between dropout rate and expected expression level,we present a cell sub-population based bounded low-rank(PBLR)method to impute the dropouts of scRNA-seq data.Through application to both simulated and real scRNA-seq datasets,PBLR is shown to be effective in recovering dropout events,and it can dramaimprove the low・dimensional representation and the recovery of gene-gene relationships masked by dropout events compared to several state-of-the-art methods・Moreover,PBLR also detects accurate and robust cell sub-populations automatically,shedding light on its flexibility and generality for scRNA-seq data analysis.