Background Mammalian spermatogenesis is critical for the transmission of male genetic information,and singlecell sequencing technology can reveal its complex process.However,at present,there is no research on the dyna...Background Mammalian spermatogenesis is critical for the transmission of male genetic information,and singlecell sequencing technology can reveal its complex process.However,at present,there is no research on the dynamic transcription of bovine germ cell population.Results In this study,we used Stereo-seq to construct a spatial transcription map of bovine testicular tissue at two ages.Four germ cell groups and five somatic cell groups were determined,and functional enrichment characterized their different biological functions and the differences between calves and adult bulls.At the same time,we also defined the subpopulations of cells and marker genes,then,clarified the communications between germ cells.Conclusion Our study constructed a spatial transcription map of bovine testicular tissue for the first time,and systematically described the dynamic transcription changes during spermatogenesis.These data laid the foundation for the study of spermatogenesis in large mammals and elucidated the transcriptional dynamics underlying male germ cell development.展开更多
Microwave thermochemotherapy(MTC)has been applied to treat lip squamous cell carcinoma(LSCC),but a deeper understanding of its therapeutic mechanisms and molecular biology is needed.To address this,we used single-cell...Microwave thermochemotherapy(MTC)has been applied to treat lip squamous cell carcinoma(LSCC),but a deeper understanding of its therapeutic mechanisms and molecular biology is needed.To address this,we used single-cell transcriptomics(scRNA-seq)and spatial transcriptomics(ST)to highlight the pivotal role of tumor-associated neutrophils(TANs)among tumor-infiltrating immune cells and their therapeutic response to MTC.MNDA+TANs with anti-tumor activity(N1-phenotype)are found to be abundantly infiltrated by MTC with benefit of increased blood perfusion,and these TANs are characterized by enhanced cytotoxicity,ameliorated hypoxia,and upregulated IL1B,activating T&NK cells and fibroblasts via IL1B-IL1R.In this highly anti-tumor immunogenic and hypoxia-reversed microenvironment under MTC,fibroblasts accumulated in the tumor front(TF)can recruit N1-TANs via CXCL2-CXCR2 and clear N2-TANs(pro-tumor phenotype)via CXCL12-CXCR4,which results in the aggregation of N1-TANs and extracellular matrix(ECM)deposition.In addition,we construct an N1-TANs marker,MX2,which positively correlates with better prognosis in LSCC patients,and employ deep learning techniques to predict expression of MX2 from hematoxylin-eosin(H&E)-stained images so as to conveniently guide decision making in clinical practice.Collectively,our findings demonstrate that the N1-TANs/fibroblasts defense wall formed in response to MTC effectively combat LSCC.展开更多
Glial cells play crucial roles in regulating physiological and pathological functions,including sensation,the response to infection and acute injury,and chronic neurodegenerative disorders.Glial cells include astrocyt...Glial cells play crucial roles in regulating physiological and pathological functions,including sensation,the response to infection and acute injury,and chronic neurodegenerative disorders.Glial cells include astrocytes,microglia,and oligodendrocytes in the central nervous system,and satellite glial cells and Schwann cells in the peripheral nervous system.Despite the greater understanding of glial cell types and functional heterogeneity achieved through single-cell and single-nucleus RNA sequencing in animal models,few studies have investigated the transcriptomic profiles of glial cells in the human spinal cord.Here,we used high-throughput single-nucleus RNA sequencing and spatial transcriptomics to map the cellular and molecular heterogeneity of astrocytes,microglia,and oligodendrocytes in the human spinal cord.To explore the conservation and divergence across species,we compared these findings with those from mice.In the human spinal cord,astrocytes,microglia,and oligodendrocytes were each divided into six distinct transcriptomic subclusters.In the mouse spinal cord,astrocytes,microglia,and oligodendrocytes were divided into five,four,and five distinct transcriptomic subclusters,respectively.The comparative results revealed substantial heterogeneity in all glial cell types between humans and mice.Additionally,we detected sex differences in gene expression in human spinal cord glial cells.Specifically,in all astrocyte subtypes,the levels of NEAT1 and CHI3L1 were higher in males than in females,whereas the levels of CST3 were lower in males than in females.In all microglial subtypes,all differentially expressed genes were located on the sex chromosomes.In addition to sex-specific gene differences,the levels of MT-ND4,MT2A,MT-ATP6,MT-CO3,MT-ND2,MT-ND3,and MT-CO_(2) in all spinal cord oligodendrocyte subtypes were higher in females than in males.Collectively,the present dataset extensively characterizes glial cell heterogeneity and offers a valuable resource for exploring the cellular basis of spinal cordrelated illnesses,including chronic pain,amyotrophic lateral sclerosis,and multiple sclerosis.展开更多
While bulk RNA sequencing and single-cell RNA sequencing have shed light on cellular heterogeneity and potential molecular mechanisms in the musculoskeletal system in both physiological and various pathological states...While bulk RNA sequencing and single-cell RNA sequencing have shed light on cellular heterogeneity and potential molecular mechanisms in the musculoskeletal system in both physiological and various pathological states,the spatial localization of cells and molecules and intercellular interactions within the tissue context require further elucidation.Spatial transcriptomics has revolutionized biological research by simultaneously capturing gene expression profiles and in situ spatial information of tissues,gradually finding applications in musculoskeletal research.This review provides a summary of recent advances in spatial transcriptomics and its application to the musculoskeletal system.The classification and characteristics of data acquisition techniques in spatial transcriptomics are briefly outlined,with an emphasis on widely-adopted representative technologies and the latest technological breakthroughs,accompanied by a concise workflow for incorporating spatial transcriptomics into musculoskeletal system research.The role of spatial transcriptomics in revealing physiological mechanisms of the musculoskeletal system,particularly during developmental processes,is thoroughly summarized.Furthermore,recent discoveries and achievements of this emerging omics tool in addressing inflammatory,traumatic,degenerative,and tumorous diseases of the musculoskeletal system are compiled.Finally,challenges and potential future directions for spatial transcriptomics,both as a field and in its applications in the musculoskeletal system,are discussed.展开更多
KanCell is a deep learning model based on Kolmogorov-Arnold networks(KAN)designed to enhance cellular heterogeneity analysis by integrating single-cell RNA sequencing and spatial transcriptomics(ST)data.ST technologie...KanCell is a deep learning model based on Kolmogorov-Arnold networks(KAN)designed to enhance cellular heterogeneity analysis by integrating single-cell RNA sequencing and spatial transcriptomics(ST)data.ST technologies provide insights into gene expression within tissue context,revealing cellular interactions and microenvironments.To fully leverage this potential,effective computational models are crucial.We evaluate KanCell on both simulated and real datasets from technologies such as STARmap,Slide-seq,Visium,and Spatial Transcriptomics.Our results demonstrate that KanCell outperforms existing methods across metrics like PCC,SSIM,COSSIM,RMSE,JSD,ARS,and ROC,with robust performance under varying cell numbers and background noise.Real-world applications on human lymph nodes,hearts,melanoma,breast cancer,dorsolateral prefrontal cortex,and mouse embryo brains confirmed its reliability.Compared with traditional approaches,KanCell effectively captures non-linear relationships and optimizes computational efficiency through KAN,providing an accurate and efficient tool for ST.By improving data accuracy and resolving cell type composition,KanCell reveals cellular heterogeneity,clarifies disease microenvironments,and identifies therapeutic targets,addressing complex biological challenges.展开更多
As a common malignant tumor,the heterogeneity of colorectal cancer plays an important role in tumor progression and treatment response.In recent years,the rapid development of single-cell transcriptomics and spatial t...As a common malignant tumor,the heterogeneity of colorectal cancer plays an important role in tumor progression and treatment response.In recent years,the rapid development of single-cell transcriptomics and spatial transcriptomics technologies has provided new perspectives for resolving the heterogeneity of colorectal cancer.These techniques can reveal the complexity of cellular composition and their interactions in the tumor microenvironment,and thus facilitate a deeper understanding of tumor biology.However,in practical applications,researchers still face technical challenges such as data processing and result interpretation.The aim of this paper is to explore how to use artificial intelligence(AI)technology to enhance the research efficiency of single-cell and spatial transcriptomics,analyze the current research progress and its limitations,and explore how combining AI approaches can provide new ideas for decoding the heterogeneity of colorectal cancer,and ultimately provide theoretical basis and practical guidance for the clinical precision treatment.展开更多
Diabetic kidney disease(DKD),a primary cause of end-stage renal disease,results from progressive tissue remodeling and loss of kidney function.While single-cell RNA sequencing has significantly accelerated our underst...Diabetic kidney disease(DKD),a primary cause of end-stage renal disease,results from progressive tissue remodeling and loss of kidney function.While single-cell RNA sequencing has significantly accelerated our understanding of cellular diversity and dynamics in DKD,its lack of spatial resolution limits insights into tissue-specific dysregulation and the microenvironment.Spatial transcriptomics(ST)is an innovative technology that combines gene expression with spatial localization,offering a powerful approach to dissect the molecular mechanisms of DKD.This mini-review introduces how ST has transformed DKD research by enabling spatially resolved analysis of cell interactions and identifying localized molecular alterations in glomeruli and tubules.ST has revealed dynamic intercellular communication within the renal microenvironment,lesion-specific gene expression patterns,and immune infiltration profiles.For example,SlideseqV2 has highlighted disease-specific cellular neighborhoods and associated signaling networks.Furthermore,ST has pinpointed key genes implicated in disease progression,such as fibrosis-related proteins and transcription factors in tubular damage.By integration of ST with computational tools such as machine learning and network-based analysis can help uncover gene regulatory mechanisms and potential therapeutic targets.However,challenges remain in limited spatial resolution,high data complexity,and computational demands.Addressing these limitations is essential for advancing precision medicine in DKD.展开更多
Spatial transcriptomics technology provides novel insights into the spatial organization of gene expression during embryonic development.In this study,we propose a method that integrates analysis across both temporal ...Spatial transcriptomics technology provides novel insights into the spatial organization of gene expression during embryonic development.In this study,we propose a method that integrates analysis across both temporal and spatial dimensions to investigate spatial transcriptomics data from mouse embryos at different developmental stages.We quantified the spatial expression pattern of each gene at various stages by calculating its Moran’s I.Furthermore,by employing time-series clustering to identify dynamic co-expression modules,we identified several developmentally stage-specific regulatory gene modules.A key finding was the presence of distinct,stage-specific gene network modules across different developmental periods:Early modules focused on morphogenesis,mid-stage on organ development,and late-stage on neural and tissue maturation.Functional enrichment analysis further confirmed the core biological functions of each module.The dynamic,spatially-resolved gene expression model constructed in this study not only provides new biological insights into the programmed spatiotemporal reorganization of gene regulatory networks during embryonic development but also presents an effective approach for analyzing complex spatiotemporal omics data.This work provides a new perspective for understanding developmental biology,regenerative medicine,and related fields.展开更多
BACKGROUND Hemorrhoids,a prevalent chronic condition globally,significantly impact patients'quality of life.While various surgical interventions,such as external stripping and internal ligation,procedure for prola...BACKGROUND Hemorrhoids,a prevalent chronic condition globally,significantly impact patients'quality of life.While various surgical interventions,such as external stripping and internal ligation,procedure for prolapse and hemorrhoids,and tissue selecting technique,are employed for treatment,they are often associated with postoperative complications,including unsatisfactory defecation,bleeding,and anal stenosis.In contrast,Xiaozhiling injection,a traditional Chinese medicine-based therapy,has emerged as a minimally invasive and effective alternative for internal hemorrhoids.This treatment offers distinct advantages,such as reduced dietary restrictions,broad applicability,and minimal induction of systemic inflammatory responses.Additionally,Xiaozhiling injection effectively eliminates hemorrhoid nuclei,prevents local tissue necrosis,preserves anal cushion integrity,and mitigates postoperative complications,including bleeding and prolapse.Despite its clinical efficacy,the molecular mechanisms underlying its therapeutic effects remain poorly understood,warranting further investigation.AIM To investigate the molecular mechanism underlying the therapeutic effect of Xiaozhiling injection in the treatment of internal hemorrhoids.METHODS An internal hemorrhoid model was established in rats,and the rats were randomly divided into a modeling group[control group(CK group)]and a treatment group.One week after injection,Stereo-seq and electron microscopy were used to study the changes in gene expression and subcellular structures in fibroblasts.RESULTS Single-cell sequencing revealed differences in the expression and transcript levels of the genes collagen 3 alpha 1,decorin,and actin alpha 2 in fibroblasts between the CK group and the treatment group.Spatial transcriptome analysis revealed that genes of the sphingosine kinase 1(Sphk1)/sphingosine-1-phosphate(S1P)pathway spatially overlapped with key genes of the transforming growth factor beta 1 pathway,namely,Sphk1,S1P receptor,and transforming growth factor beta 1,in the treatment group.The proportion of fibroblasts was lower in the treatment group than in the CK group,and Xiaozhiling treatment had a significant effect on the proportion of fibroblasts in hemorrhoidal tissue.Immunohistochemistry revealed a significant increase in the expression of a fibroblast marker.Electron microscopy showed that the endoplasmic reticulum of fibroblasts contained a large amount of glycogen,indicating cell activation.Fibroblast activation and the expression of key genes of the Sphk1-S1P pathway could be observed at the injection site,suggesting that after Xiaozhiling intervention,the Sphk1-S1P pathway could be activated to promote fibrosis.CONCLUSION Xiaozhiling injection exerts its therapeutic effects on internal hemorrhoids by promoting collagen synthesis and secretion in fibroblasts.After Xiaozhiling intervention,the Sphk1-S1P pathway can be activated to promote fibrosis.展开更多
Spatial transcriptomics is an organizational study done on tissue sections that preserves the spatial information of the sample.Spatial transcriptomics aims to combine spatial information with gene expression data to ...Spatial transcriptomics is an organizational study done on tissue sections that preserves the spatial information of the sample.Spatial transcriptomics aims to combine spatial information with gene expression data to quantify the mRNA expression of a large number of genes in the spatial context of tissues and cells.As a paradigm shift in biological research,spatial transcriptomics can provide both spatial location information and transcriptome-level cellular gene expression data,elucidating the interactions between cells and the microenvironment.From the understanding of the entire functional life cycle of RNA to the characterization of molecular mechanisms to the mapping of gene expression in various tissue regions,by choosing the appropriate spatial transcriptome technology,researchers can achieve a deeper exploration of biological developmental processes,disease pathogenesis,etc.In recent years,the field of spatial transcriptomics has ushered in several challenges along with its rapid development,such as the dependence on sample types,the resolution of visualized genes,the difficulty of commercialization,and the ability to obtain detailed single-cell information.In this paper,we summarize and review the four major categories of spatial transcriptome technologies and compare and analyze the technical advantages and major challenges of multiple research strategies to assist current experimental design and research analysis.Finally,the importance of spatial transcriptomics in the integration of multi-omics analysis and disease modeling as well as the future development prospects are summarized and outlined.展开更多
Tumor research is a fundamental focus of medical science,yet the intrinsic heterogeneity and complexity of tumors present challenges in understanding their biological mechanisms of initiation,progression,and metastasi...Tumor research is a fundamental focus of medical science,yet the intrinsic heterogeneity and complexity of tumors present challenges in understanding their biological mechanisms of initiation,progression,and metastasis.Recent advancements in single-cell transcriptomic sequencing have revolutionized the way researchers explore tumor biology by providing unprecedented resolution.However,a key limitation of single-cell sequencing is the loss of spatial information during single-cell preparation.Spatial transcriptomics(ST)emerges as a cutting-edge technology in tumor research that preserves the spatial information of RNA transcripts,thereby facilitating a deeper understanding of the tumor heterogeneity,the intricate interplay between tumor cells and the tumor microenvironment.This review systematically introduces ST technologies and summarizes their latest applications in tumor research.Furthermore,we provide a thorough overview of the bioinformatics analysis workflow for ST data and offer an online tutorial(https://github.com/Siyua nHuan g1/ST_Analy sis_Handb ook).Lastly,we discuss the potential future directions of ST.We believe that ST will become a powerful tool in unraveling tumor biology and offer new insights for effective treatment and precision medicine in oncology.展开更多
The respiratory system's complex cellular heterogeneity presents unique challenges to researchers in this field.Although bulk RNA sequencing and single-cell RNA sequencing(scRNA-seq)have provided insights into cel...The respiratory system's complex cellular heterogeneity presents unique challenges to researchers in this field.Although bulk RNA sequencing and single-cell RNA sequencing(scRNA-seq)have provided insights into cell types and heterogeneity in the respiratory system,the relevant specific spatial localization and cellular interactions have not been clearly elucidated.Spatial transcriptomics(ST)has filled this gap and has been widely used in respiratory studies.This review focuses on the latest iterative technology of ST in recent years,summarizing how ST can be applied to the physiological and pathological processes of the respiratory system,with emphasis on the lungs.Finally,the current challenges and potential development directions are proposed,including high-throughput full-length transcriptome,integration of multi-omics,temporal and spatial omics,bioinformatics analysis,etc.These viewpoints are expected to advance the study of systematic mechanisms,including respiratory studies.展开更多
Spatial transcriptomics(ST)has become a key technology for interrogating gene expression within spatial context,providing spatially resolved insights into tissue architecture and microenvironmental organization.Rapid ...Spatial transcriptomics(ST)has become a key technology for interrogating gene expression within spatial context,providing spatially resolved insights into tissue architecture and microenvironmental organization.Rapid advances in experimental platforms and analytical methods,however,have resulted in challenges for technology selection,methodological comparison,and data interpretation.In this review,we present a systematic summary of 594 ST analysis tools spanning 77 ST technologies(as of September 2025).We outline the complete analytical workflow and discuss major analytical tasks,including data preprocessing,denoising and imputation,spatial pattern and domain identification,cellular composition,trajectory analysis,cell-cell communication,and spatial multi-omics integration.For each task,we summarize representative methodological principles and emphasize platform-dependent considerations arising from differences in spatial resolution and detection efficiency.We further highlight how analytical applications of ST data have enabled biomedical discoveries by revealing spatial heterogeneity,tissue organization,and context-dependent cellular interactions.Furthermore,we develop SpatialToolDB(https://www.spatialtooldb.yelab.site/),a systematically curated,categorized,and continuously updated platform that integrates the ST technologies,analytical methods,and related databases covered in this review,facilitating informed tool selection and method comparison.We also discuss development trends and future directions of spatial-omics technologies and analytical tools,including advances in spatial technologies,AI-driven computation,benchmarking and standardization,and improved experimental validation for mechanistic and predictive spatial biology.Together,this review and SpatialToolDB provide a data-driven foundation for selecting ST platforms and analytical strategies tailored to diverse biological and translational research applications.展开更多
Recent advancements in spatial transcriptomics(ST)technologies offer unprecedented opportunities to unveil the spatial heterogeneity of gene expression and cell states within tissues.Despite these capabilities of the ...Recent advancements in spatial transcriptomics(ST)technologies offer unprecedented opportunities to unveil the spatial heterogeneity of gene expression and cell states within tissues.Despite these capabilities of the ST data,accurately dissecting spatiotemporal structures(e.g.,spatial domains,temporal trajectories,and functional interactions)remains challenging.Here,we introduce a computational framework,PearlST(partial differential equation[PDE]-enhanced adversarial graph autoencoder of ST),for accurate inference of spatiotemporal structures from the ST data using PDE-enhanced adversarial graph autoencoder.PearlST employs contrastive learning to extract histological image features,integrates a PDE-based diffusion model to enhance characterization of spatial features at domain boundaries,and learns the latent low-dimensional embeddings via Wasserstein adversarial regularized graph autoencoders.Comparative analyses across multiple ST datasets with varying resolutions demonstrate that PearlST outperforms existing methods in spatial clustering,trajectory inference,and pseudotime analysis.Furthermore,PearlST elucidates functional regulations of the latent features by linking intercellular ligand-receptor interactions to most contributing genes of the low-dimensional embeddings,as illustrated in a human breast cancer dataset.Overall,PearlST proves to be a powerful tool for extracting interpretable latent features and dissecting intricate spatiotemporal structures in ST data across various biological contexts.展开更多
Spatial transcriptomics technique detects RNA sequences(St?hl et al., 2016) and quantifies their expression in the positional context(Ke et al., 2013), which can provide important information such as cell heterogeneit...Spatial transcriptomics technique detects RNA sequences(St?hl et al., 2016) and quantifies their expression in the positional context(Ke et al., 2013), which can provide important information such as cell heterogeneity, cell developmental trajectory, differential gene expression between tissue regions or sections.展开更多
Current integration methods for single-cell RNA sequencing(scRNA-seq)data and spatial transcriptomics(ST)data are typically designed for specific tasks,such as deconvolution of cell types or spatial distribution predi...Current integration methods for single-cell RNA sequencing(scRNA-seq)data and spatial transcriptomics(ST)data are typically designed for specific tasks,such as deconvolution of cell types or spatial distribution prediction of RNA transcripts.These methods usually only offer a partial analysis of ST data,neglecting the complex relationship between spatial expression patterns underlying cell-type specificity and intercellular cross-talk.Here,we present eMCI,an explainable multimodal correlation integration model based on deep neural network framework.eMCI leverages the fusion of scRNA-seq and ST data using different spot–cell correlations to integrate multiple synthetic analysis tasks of ST data at cellular level.First,eMCI can achieve better or comparable accuracy in cell-type classification and deconvolution according to wide evaluations and comparisons with state-of-the-art methods on both simulated and real ST datasets.Second,eMCI can identify key components across spatial domains responsible for different cell types and elucidate the spatial expression patterns underlying cell-type specificity and intercellular communication,by employing an attribution algorithm to dissect the visual input.Especially,eMCI has been applied to 3 cross-species datasets,including zebrafish melanomas,soybean nodule maturation,and human embryonic lung,which accurately and efficiently estimate per-spot cell composition and infer proximal and distal cellular interactions within the spatial and temporal context.In summary,eMCI serves as an integrative analytical framework to better resolve the spatial transcriptome based on existing single-cell datasets and elucidate proximal and distal intercellular signal transduction mechanisms over spatial domains without requirement of biological prior reference.This approach is expected to facilitate the discovery of spatial expression patterns of potential biomolecules with cell type and cell–cell communication specificity.展开更多
Alzheimer’s disease(AD)is the most common form of dementia.In addition to the lack of effective treatments,there are limitations in diagnostic capabilities.The complexity of AD itself,together with a variety of other...Alzheimer’s disease(AD)is the most common form of dementia.In addition to the lack of effective treatments,there are limitations in diagnostic capabilities.The complexity of AD itself,together with a variety of other diseases often observed in a patient’s history in addition to their AD diagnosis,make deciphering the molecular mechanisms that underlie AD,even more important.Large datasets of single-cell RNA sequencing,single-nucleus RNA-sequencing(snRNA-seq),and spatial transcriptomics(ST)have become essential in guiding and supporting new investigations into the cellular and regional susceptibility of AD.However,with unique technology,software,and larger databases emerging;a lack of integration of these data can contribute to ineffective use of valuable knowledge.Importantly,there was no specialized database that concentrates on ST in AD that offers comprehensive differential analyses under various conditions,such as sex-specific,region-specific,and comparisons between AD and control groups until the new Single-cell and Spatial RNA-seq databasE for Alzheimer’s Disease(ssREAD)database(Wang et al.,2024)was introduced to meet the scientific community’s growing demand for comprehensive,integrated,and accessible data analysis.展开更多
The mechanical microenvironment profoundly influences cell behavior,tissue organization,and disease progression.Recent advances in biomechanical imaging and single-cell multi-omics technologies have enabled the explor...The mechanical microenvironment profoundly influences cell behavior,tissue organization,and disease progression.Recent advances in biomechanical imaging and single-cell multi-omics technologies have enabled the exploration of tissue heterogeneity from both physical and molecular perspectives.This review summarizes key biomechanical imaging methods and evaluates their capabilities and limitations.We further discuss computational strategies for predicting mechanical properties and highlight integrative approaches that link these predictions with single-cell/spatial omics data.These integrative strategies will pave the way for mechanobiologyinformed precision medicine.展开更多
Spatial transcriptomics is undergoing rapid advancements and iterations.It is a beneficial tool to significantly enhance our understanding of tissue organization and relationships between cells.Recent technological ad...Spatial transcriptomics is undergoing rapid advancements and iterations.It is a beneficial tool to significantly enhance our understanding of tissue organization and relationships between cells.Recent technological advancements have achieved subcellular resolution,providing much denser spot placement for downstream analysis.A key challenge for this following analysis is accurate cell segmentation and the assignment of spots to individual cells.The primary objective of this study was to evaluate the effectiveness of a new cell segmentation approach based on subcellular level spatial transcriptomic data by confirming nuclei positions and using Voronoi diagrams,compared to direct clustering with cellbin data.Our findings demonstrate that the Voronoi method not only outperforms traditional methods in providing clearer boundaries and better separation of cell types,but also excels in preserving the most transcripts,addressing the issue of low capture efficiency.This integrative methodology presents a substantial advancement in spatial transcriptomics,offering improved cell type classification and spatial pattern recognition.展开更多
基金supported by Biological Breeding-Major Projects to Yun Ma(Grant No.2023ZD0404803)Key R&D Program of Ningxia Hui Autonomous Region to Lingkai Zhang(2023BBF01007)and(2023BCF01006)。
文摘Background Mammalian spermatogenesis is critical for the transmission of male genetic information,and singlecell sequencing technology can reveal its complex process.However,at present,there is no research on the dynamic transcription of bovine germ cell population.Results In this study,we used Stereo-seq to construct a spatial transcription map of bovine testicular tissue at two ages.Four germ cell groups and five somatic cell groups were determined,and functional enrichment characterized their different biological functions and the differences between calves and adult bulls.At the same time,we also defined the subpopulations of cells and marker genes,then,clarified the communications between germ cells.Conclusion Our study constructed a spatial transcription map of bovine testicular tissue for the first time,and systematically described the dynamic transcription changes during spermatogenesis.These data laid the foundation for the study of spermatogenesis in large mammals and elucidated the transcriptional dynamics underlying male germ cell development.
基金supported by National Natural Science Foundation of China grants(Nos.82173326 and 82473058)Key Research and Development Project of Sichuan Province(Nos.2024YFFK0374 and 2024YFFK0198)Interdisciplinary Innovation Project of West China College of Stomatology,Sichuan University(RD-03-202004).
文摘Microwave thermochemotherapy(MTC)has been applied to treat lip squamous cell carcinoma(LSCC),but a deeper understanding of its therapeutic mechanisms and molecular biology is needed.To address this,we used single-cell transcriptomics(scRNA-seq)and spatial transcriptomics(ST)to highlight the pivotal role of tumor-associated neutrophils(TANs)among tumor-infiltrating immune cells and their therapeutic response to MTC.MNDA+TANs with anti-tumor activity(N1-phenotype)are found to be abundantly infiltrated by MTC with benefit of increased blood perfusion,and these TANs are characterized by enhanced cytotoxicity,ameliorated hypoxia,and upregulated IL1B,activating T&NK cells and fibroblasts via IL1B-IL1R.In this highly anti-tumor immunogenic and hypoxia-reversed microenvironment under MTC,fibroblasts accumulated in the tumor front(TF)can recruit N1-TANs via CXCL2-CXCR2 and clear N2-TANs(pro-tumor phenotype)via CXCL12-CXCR4,which results in the aggregation of N1-TANs and extracellular matrix(ECM)deposition.In addition,we construct an N1-TANs marker,MX2,which positively correlates with better prognosis in LSCC patients,and employ deep learning techniques to predict expression of MX2 from hematoxylin-eosin(H&E)-stained images so as to conveniently guide decision making in clinical practice.Collectively,our findings demonstrate that the N1-TANs/fibroblasts defense wall formed in response to MTC effectively combat LSCC.
基金supported by the National Natural Science Foundation of China,No.82301403(to DZ)。
文摘Glial cells play crucial roles in regulating physiological and pathological functions,including sensation,the response to infection and acute injury,and chronic neurodegenerative disorders.Glial cells include astrocytes,microglia,and oligodendrocytes in the central nervous system,and satellite glial cells and Schwann cells in the peripheral nervous system.Despite the greater understanding of glial cell types and functional heterogeneity achieved through single-cell and single-nucleus RNA sequencing in animal models,few studies have investigated the transcriptomic profiles of glial cells in the human spinal cord.Here,we used high-throughput single-nucleus RNA sequencing and spatial transcriptomics to map the cellular and molecular heterogeneity of astrocytes,microglia,and oligodendrocytes in the human spinal cord.To explore the conservation and divergence across species,we compared these findings with those from mice.In the human spinal cord,astrocytes,microglia,and oligodendrocytes were each divided into six distinct transcriptomic subclusters.In the mouse spinal cord,astrocytes,microglia,and oligodendrocytes were divided into five,four,and five distinct transcriptomic subclusters,respectively.The comparative results revealed substantial heterogeneity in all glial cell types between humans and mice.Additionally,we detected sex differences in gene expression in human spinal cord glial cells.Specifically,in all astrocyte subtypes,the levels of NEAT1 and CHI3L1 were higher in males than in females,whereas the levels of CST3 were lower in males than in females.In all microglial subtypes,all differentially expressed genes were located on the sex chromosomes.In addition to sex-specific gene differences,the levels of MT-ND4,MT2A,MT-ATP6,MT-CO3,MT-ND2,MT-ND3,and MT-CO_(2) in all spinal cord oligodendrocyte subtypes were higher in females than in males.Collectively,the present dataset extensively characterizes glial cell heterogeneity and offers a valuable resource for exploring the cellular basis of spinal cordrelated illnesses,including chronic pain,amyotrophic lateral sclerosis,and multiple sclerosis.
基金supported by The National Natural Science Youth Foundation of China(Grant No.82102584).
文摘While bulk RNA sequencing and single-cell RNA sequencing have shed light on cellular heterogeneity and potential molecular mechanisms in the musculoskeletal system in both physiological and various pathological states,the spatial localization of cells and molecules and intercellular interactions within the tissue context require further elucidation.Spatial transcriptomics has revolutionized biological research by simultaneously capturing gene expression profiles and in situ spatial information of tissues,gradually finding applications in musculoskeletal research.This review provides a summary of recent advances in spatial transcriptomics and its application to the musculoskeletal system.The classification and characteristics of data acquisition techniques in spatial transcriptomics are briefly outlined,with an emphasis on widely-adopted representative technologies and the latest technological breakthroughs,accompanied by a concise workflow for incorporating spatial transcriptomics into musculoskeletal system research.The role of spatial transcriptomics in revealing physiological mechanisms of the musculoskeletal system,particularly during developmental processes,is thoroughly summarized.Furthermore,recent discoveries and achievements of this emerging omics tool in addressing inflammatory,traumatic,degenerative,and tumorous diseases of the musculoskeletal system are compiled.Finally,challenges and potential future directions for spatial transcriptomics,both as a field and in its applications in the musculoskeletal system,are discussed.
基金supported by the National Natural Science Foundation of China(52361145714,21673252)the Beijing Municipal Education Commission(2019821001)+1 种基金Climbing Program Foundation from Beijing Institute of Petrochemical Technology(BIPTAAl-2021007)the ZhiYuan Fund key Project from Beijing Institute of Petrochemical Technology(2024003).
文摘KanCell is a deep learning model based on Kolmogorov-Arnold networks(KAN)designed to enhance cellular heterogeneity analysis by integrating single-cell RNA sequencing and spatial transcriptomics(ST)data.ST technologies provide insights into gene expression within tissue context,revealing cellular interactions and microenvironments.To fully leverage this potential,effective computational models are crucial.We evaluate KanCell on both simulated and real datasets from technologies such as STARmap,Slide-seq,Visium,and Spatial Transcriptomics.Our results demonstrate that KanCell outperforms existing methods across metrics like PCC,SSIM,COSSIM,RMSE,JSD,ARS,and ROC,with robust performance under varying cell numbers and background noise.Real-world applications on human lymph nodes,hearts,melanoma,breast cancer,dorsolateral prefrontal cortex,and mouse embryo brains confirmed its reliability.Compared with traditional approaches,KanCell effectively captures non-linear relationships and optimizes computational efficiency through KAN,providing an accurate and efficient tool for ST.By improving data accuracy and resolving cell type composition,KanCell reveals cellular heterogeneity,clarifies disease microenvironments,and identifies therapeutic targets,addressing complex biological challenges.
基金Supported by the Shandong Province Medical and Health Science and Technology Development Plan Project,No.202203030713Yantai Science and Technology Program,No.2024YD005,No.2024YD007 and No.2024YD010and Science and Technology Program of Yantai Affiliated Hospital of Binzhou Medical University,No.YTFY2022KYQD06。
文摘As a common malignant tumor,the heterogeneity of colorectal cancer plays an important role in tumor progression and treatment response.In recent years,the rapid development of single-cell transcriptomics and spatial transcriptomics technologies has provided new perspectives for resolving the heterogeneity of colorectal cancer.These techniques can reveal the complexity of cellular composition and their interactions in the tumor microenvironment,and thus facilitate a deeper understanding of tumor biology.However,in practical applications,researchers still face technical challenges such as data processing and result interpretation.The aim of this paper is to explore how to use artificial intelligence(AI)technology to enhance the research efficiency of single-cell and spatial transcriptomics,analyze the current research progress and its limitations,and explore how combining AI approaches can provide new ideas for decoding the heterogeneity of colorectal cancer,and ultimately provide theoretical basis and practical guidance for the clinical precision treatment.
基金Supported by Science and Technology Research Program of Chongqing Municipal Education Commission,No.KJQN202100538Talent Innovation Project in Life Sciences of Chongqing Normal University,No.CSSK2023-04.
文摘Diabetic kidney disease(DKD),a primary cause of end-stage renal disease,results from progressive tissue remodeling and loss of kidney function.While single-cell RNA sequencing has significantly accelerated our understanding of cellular diversity and dynamics in DKD,its lack of spatial resolution limits insights into tissue-specific dysregulation and the microenvironment.Spatial transcriptomics(ST)is an innovative technology that combines gene expression with spatial localization,offering a powerful approach to dissect the molecular mechanisms of DKD.This mini-review introduces how ST has transformed DKD research by enabling spatially resolved analysis of cell interactions and identifying localized molecular alterations in glomeruli and tubules.ST has revealed dynamic intercellular communication within the renal microenvironment,lesion-specific gene expression patterns,and immune infiltration profiles.For example,SlideseqV2 has highlighted disease-specific cellular neighborhoods and associated signaling networks.Furthermore,ST has pinpointed key genes implicated in disease progression,such as fibrosis-related proteins and transcription factors in tubular damage.By integration of ST with computational tools such as machine learning and network-based analysis can help uncover gene regulatory mechanisms and potential therapeutic targets.However,challenges remain in limited spatial resolution,high data complexity,and computational demands.Addressing these limitations is essential for advancing precision medicine in DKD.
基金supported by the National Natural Science Foundation of China(Grant Nos.12090052,U24A2014,and 12325405).
文摘Spatial transcriptomics technology provides novel insights into the spatial organization of gene expression during embryonic development.In this study,we propose a method that integrates analysis across both temporal and spatial dimensions to investigate spatial transcriptomics data from mouse embryos at different developmental stages.We quantified the spatial expression pattern of each gene at various stages by calculating its Moran’s I.Furthermore,by employing time-series clustering to identify dynamic co-expression modules,we identified several developmentally stage-specific regulatory gene modules.A key finding was the presence of distinct,stage-specific gene network modules across different developmental periods:Early modules focused on morphogenesis,mid-stage on organ development,and late-stage on neural and tissue maturation.Functional enrichment analysis further confirmed the core biological functions of each module.The dynamic,spatially-resolved gene expression model constructed in this study not only provides new biological insights into the programmed spatiotemporal reorganization of gene regulatory networks during embryonic development but also presents an effective approach for analyzing complex spatiotemporal omics data.This work provides a new perspective for understanding developmental biology,regenerative medicine,and related fields.
基金Supported by the National Natural Science Foundation of China,No.81774118the Foreign Cooperation Project of Science and Technology Department of Fujian Province,No.2023I0021the Medical Innovation Project of Fujian Province,No.2024CXB013.
文摘BACKGROUND Hemorrhoids,a prevalent chronic condition globally,significantly impact patients'quality of life.While various surgical interventions,such as external stripping and internal ligation,procedure for prolapse and hemorrhoids,and tissue selecting technique,are employed for treatment,they are often associated with postoperative complications,including unsatisfactory defecation,bleeding,and anal stenosis.In contrast,Xiaozhiling injection,a traditional Chinese medicine-based therapy,has emerged as a minimally invasive and effective alternative for internal hemorrhoids.This treatment offers distinct advantages,such as reduced dietary restrictions,broad applicability,and minimal induction of systemic inflammatory responses.Additionally,Xiaozhiling injection effectively eliminates hemorrhoid nuclei,prevents local tissue necrosis,preserves anal cushion integrity,and mitigates postoperative complications,including bleeding and prolapse.Despite its clinical efficacy,the molecular mechanisms underlying its therapeutic effects remain poorly understood,warranting further investigation.AIM To investigate the molecular mechanism underlying the therapeutic effect of Xiaozhiling injection in the treatment of internal hemorrhoids.METHODS An internal hemorrhoid model was established in rats,and the rats were randomly divided into a modeling group[control group(CK group)]and a treatment group.One week after injection,Stereo-seq and electron microscopy were used to study the changes in gene expression and subcellular structures in fibroblasts.RESULTS Single-cell sequencing revealed differences in the expression and transcript levels of the genes collagen 3 alpha 1,decorin,and actin alpha 2 in fibroblasts between the CK group and the treatment group.Spatial transcriptome analysis revealed that genes of the sphingosine kinase 1(Sphk1)/sphingosine-1-phosphate(S1P)pathway spatially overlapped with key genes of the transforming growth factor beta 1 pathway,namely,Sphk1,S1P receptor,and transforming growth factor beta 1,in the treatment group.The proportion of fibroblasts was lower in the treatment group than in the CK group,and Xiaozhiling treatment had a significant effect on the proportion of fibroblasts in hemorrhoidal tissue.Immunohistochemistry revealed a significant increase in the expression of a fibroblast marker.Electron microscopy showed that the endoplasmic reticulum of fibroblasts contained a large amount of glycogen,indicating cell activation.Fibroblast activation and the expression of key genes of the Sphk1-S1P pathway could be observed at the injection site,suggesting that after Xiaozhiling intervention,the Sphk1-S1P pathway could be activated to promote fibrosis.CONCLUSION Xiaozhiling injection exerts its therapeutic effects on internal hemorrhoids by promoting collagen synthesis and secretion in fibroblasts.After Xiaozhiling intervention,the Sphk1-S1P pathway can be activated to promote fibrosis.
基金supported by the National Natural Science Foundation of China(Grant No.22275071)
文摘Spatial transcriptomics is an organizational study done on tissue sections that preserves the spatial information of the sample.Spatial transcriptomics aims to combine spatial information with gene expression data to quantify the mRNA expression of a large number of genes in the spatial context of tissues and cells.As a paradigm shift in biological research,spatial transcriptomics can provide both spatial location information and transcriptome-level cellular gene expression data,elucidating the interactions between cells and the microenvironment.From the understanding of the entire functional life cycle of RNA to the characterization of molecular mechanisms to the mapping of gene expression in various tissue regions,by choosing the appropriate spatial transcriptome technology,researchers can achieve a deeper exploration of biological developmental processes,disease pathogenesis,etc.In recent years,the field of spatial transcriptomics has ushered in several challenges along with its rapid development,such as the dependence on sample types,the resolution of visualized genes,the difficulty of commercialization,and the ability to obtain detailed single-cell information.In this paper,we summarize and review the four major categories of spatial transcriptome technologies and compare and analyze the technical advantages and major challenges of multiple research strategies to assist current experimental design and research analysis.Finally,the importance of spatial transcriptomics in the integration of multi-omics analysis and disease modeling as well as the future development prospects are summarized and outlined.
基金supported by the National Key R&D Program of China[2020YFE0204200 to R.X.]the National Natural Science Foundation of China[12371286,11971039 to R.X.,12201219 to J.M.]+1 种基金Sino-Russian Mathematics Center,Foundation of Qinglonghu laboratory,Shanghai Sailing Program(No.21YF1410600 to J.M.)Shanghai Key Program of Computational Biology(No.23JS1400500,23JS1400800 to J.M.).
文摘Tumor research is a fundamental focus of medical science,yet the intrinsic heterogeneity and complexity of tumors present challenges in understanding their biological mechanisms of initiation,progression,and metastasis.Recent advancements in single-cell transcriptomic sequencing have revolutionized the way researchers explore tumor biology by providing unprecedented resolution.However,a key limitation of single-cell sequencing is the loss of spatial information during single-cell preparation.Spatial transcriptomics(ST)emerges as a cutting-edge technology in tumor research that preserves the spatial information of RNA transcripts,thereby facilitating a deeper understanding of the tumor heterogeneity,the intricate interplay between tumor cells and the tumor microenvironment.This review systematically introduces ST technologies and summarizes their latest applications in tumor research.Furthermore,we provide a thorough overview of the bioinformatics analysis workflow for ST data and offer an online tutorial(https://github.com/Siyua nHuan g1/ST_Analy sis_Handb ook).Lastly,we discuss the potential future directions of ST.We believe that ST will become a powerful tool in unraveling tumor biology and offer new insights for effective treatment and precision medicine in oncology.
基金supported by the National Natural Science Foundation of China(82271629)the Central Funds Guiding the Local Science and Technology Development of Shenzhen(2021Szvup024)+1 种基金the Jiangsu Provincial Key Research and Development Program(BE2021664)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX23_0312)。
文摘The respiratory system's complex cellular heterogeneity presents unique challenges to researchers in this field.Although bulk RNA sequencing and single-cell RNA sequencing(scRNA-seq)have provided insights into cell types and heterogeneity in the respiratory system,the relevant specific spatial localization and cellular interactions have not been clearly elucidated.Spatial transcriptomics(ST)has filled this gap and has been widely used in respiratory studies.This review focuses on the latest iterative technology of ST in recent years,summarizing how ST can be applied to the physiological and pathological processes of the respiratory system,with emphasis on the lungs.Finally,the current challenges and potential development directions are proposed,including high-throughput full-length transcriptome,integration of multi-omics,temporal and spatial omics,bioinformatics analysis,etc.These viewpoints are expected to advance the study of systematic mechanisms,including respiratory studies.
基金supported by the National Key Research and Development Program of China(grant no.2025YFC3410200)the National Natural Science Foundation of China(grant nos.62433016,62572391,62402382,and 62102319).
文摘Spatial transcriptomics(ST)preserves spatial context in gene expression analysis yet faces limitations like low resolution and RNA capture inefficiency.To address these,we present stSCI,a computational method integrating single-cell(SC)and ST data into a unified,batch-corrected embedding space.
基金supported by the National Key Research and Development Program(2024YFC3407700)the National Natural Science Foundation of China(82073145 and 3247040729)+2 种基金Shanghai Jiao Tong University 2030 Initiative(WH510363003/018)Fundamental and Interdisciplinary Disciplines Breakthrough Plan of the Ministry of Education of China(JYB2025XDXM604 and JYB2025XDXM611)the Research Funds of Centre for Leading Medicine and Advanced Technologies of IHM(2023IHM01032).
文摘Spatial transcriptomics(ST)has become a key technology for interrogating gene expression within spatial context,providing spatially resolved insights into tissue architecture and microenvironmental organization.Rapid advances in experimental platforms and analytical methods,however,have resulted in challenges for technology selection,methodological comparison,and data interpretation.In this review,we present a systematic summary of 594 ST analysis tools spanning 77 ST technologies(as of September 2025).We outline the complete analytical workflow and discuss major analytical tasks,including data preprocessing,denoising and imputation,spatial pattern and domain identification,cellular composition,trajectory analysis,cell-cell communication,and spatial multi-omics integration.For each task,we summarize representative methodological principles and emphasize platform-dependent considerations arising from differences in spatial resolution and detection efficiency.We further highlight how analytical applications of ST data have enabled biomedical discoveries by revealing spatial heterogeneity,tissue organization,and context-dependent cellular interactions.Furthermore,we develop SpatialToolDB(https://www.spatialtooldb.yelab.site/),a systematically curated,categorized,and continuously updated platform that integrates the ST technologies,analytical methods,and related databases covered in this review,facilitating informed tool selection and method comparison.We also discuss development trends and future directions of spatial-omics technologies and analytical tools,including advances in spatial technologies,AI-driven computation,benchmarking and standardization,and improved experimental validation for mechanistic and predictive spatial biology.Together,this review and SpatialToolDB provide a data-driven foundation for selecting ST platforms and analytical strategies tailored to diverse biological and translational research applications.
基金supported by grants from the National Key R&D Program of China(2021YFF1200903)the National Natural Science Foundation of China(62273364,11931019,11871070,and 62362062)+2 种基金the Guangdong Basic and Applied Basic Research Foundation(2020B1515020047)Fundamental Research Funds for the Central Universities,Sun Yat-sen University(231lgbj025)the open fund of Information Materials and Intelligent Sensing Laboratory of Anhui Province(grant no.IMIS202105).
文摘Recent advancements in spatial transcriptomics(ST)technologies offer unprecedented opportunities to unveil the spatial heterogeneity of gene expression and cell states within tissues.Despite these capabilities of the ST data,accurately dissecting spatiotemporal structures(e.g.,spatial domains,temporal trajectories,and functional interactions)remains challenging.Here,we introduce a computational framework,PearlST(partial differential equation[PDE]-enhanced adversarial graph autoencoder of ST),for accurate inference of spatiotemporal structures from the ST data using PDE-enhanced adversarial graph autoencoder.PearlST employs contrastive learning to extract histological image features,integrates a PDE-based diffusion model to enhance characterization of spatial features at domain boundaries,and learns the latent low-dimensional embeddings via Wasserstein adversarial regularized graph autoencoders.Comparative analyses across multiple ST datasets with varying resolutions demonstrate that PearlST outperforms existing methods in spatial clustering,trajectory inference,and pseudotime analysis.Furthermore,PearlST elucidates functional regulations of the latent features by linking intercellular ligand-receptor interactions to most contributing genes of the low-dimensional embeddings,as illustrated in a human breast cancer dataset.Overall,PearlST proves to be a powerful tool for extracting interpretable latent features and dissecting intricate spatiotemporal structures in ST data across various biological contexts.
文摘Spatial transcriptomics technique detects RNA sequences(St?hl et al., 2016) and quantifies their expression in the positional context(Ke et al., 2013), which can provide important information such as cell heterogeneity, cell developmental trajectory, differential gene expression between tissue regions or sections.
基金supported by the National Key R&D Program of China(Nos.2023YFF1204700 and 2022YFF1202100)the National Natural Science Foundation of China(Nos.12371485,62201150,T2341022,62172164,12322119,and 12271180)the Natural Science Foundation of Guangdong Province of China(Nos.2022A1515110759,2023A1515110558,and 2024A1515011797).
文摘Current integration methods for single-cell RNA sequencing(scRNA-seq)data and spatial transcriptomics(ST)data are typically designed for specific tasks,such as deconvolution of cell types or spatial distribution prediction of RNA transcripts.These methods usually only offer a partial analysis of ST data,neglecting the complex relationship between spatial expression patterns underlying cell-type specificity and intercellular cross-talk.Here,we present eMCI,an explainable multimodal correlation integration model based on deep neural network framework.eMCI leverages the fusion of scRNA-seq and ST data using different spot–cell correlations to integrate multiple synthetic analysis tasks of ST data at cellular level.First,eMCI can achieve better or comparable accuracy in cell-type classification and deconvolution according to wide evaluations and comparisons with state-of-the-art methods on both simulated and real ST datasets.Second,eMCI can identify key components across spatial domains responsible for different cell types and elucidate the spatial expression patterns underlying cell-type specificity and intercellular communication,by employing an attribution algorithm to dissect the visual input.Especially,eMCI has been applied to 3 cross-species datasets,including zebrafish melanomas,soybean nodule maturation,and human embryonic lung,which accurately and efficiently estimate per-spot cell composition and infer proximal and distal cellular interactions within the spatial and temporal context.In summary,eMCI serves as an integrative analytical framework to better resolve the spatial transcriptome based on existing single-cell datasets and elucidate proximal and distal intercellular signal transduction mechanisms over spatial domains without requirement of biological prior reference.This approach is expected to facilitate the discovery of spatial expression patterns of potential biomolecules with cell type and cell–cell communication specificity.
文摘Alzheimer’s disease(AD)is the most common form of dementia.In addition to the lack of effective treatments,there are limitations in diagnostic capabilities.The complexity of AD itself,together with a variety of other diseases often observed in a patient’s history in addition to their AD diagnosis,make deciphering the molecular mechanisms that underlie AD,even more important.Large datasets of single-cell RNA sequencing,single-nucleus RNA-sequencing(snRNA-seq),and spatial transcriptomics(ST)have become essential in guiding and supporting new investigations into the cellular and regional susceptibility of AD.However,with unique technology,software,and larger databases emerging;a lack of integration of these data can contribute to ineffective use of valuable knowledge.Importantly,there was no specialized database that concentrates on ST in AD that offers comprehensive differential analyses under various conditions,such as sex-specific,region-specific,and comparisons between AD and control groups until the new Single-cell and Spatial RNA-seq databasE for Alzheimer’s Disease(ssREAD)database(Wang et al.,2024)was introduced to meet the scientific community’s growing demand for comprehensive,integrated,and accessible data analysis.
基金supported by the Beijing Natural Science Foundation(Grant No.5232014)the National Natural Science Foundation of China(Grant No.82373116 and No.32571524)the Key Research and Development Program of Ningxia Hui Autonomous Region(Grant No.2022BEG02055).
文摘The mechanical microenvironment profoundly influences cell behavior,tissue organization,and disease progression.Recent advances in biomechanical imaging and single-cell multi-omics technologies have enabled the exploration of tissue heterogeneity from both physical and molecular perspectives.This review summarizes key biomechanical imaging methods and evaluates their capabilities and limitations.We further discuss computational strategies for predicting mechanical properties and highlight integrative approaches that link these predictions with single-cell/spatial omics data.These integrative strategies will pave the way for mechanobiologyinformed precision medicine.
文摘Spatial transcriptomics is undergoing rapid advancements and iterations.It is a beneficial tool to significantly enhance our understanding of tissue organization and relationships between cells.Recent technological advancements have achieved subcellular resolution,providing much denser spot placement for downstream analysis.A key challenge for this following analysis is accurate cell segmentation and the assignment of spots to individual cells.The primary objective of this study was to evaluate the effectiveness of a new cell segmentation approach based on subcellular level spatial transcriptomic data by confirming nuclei positions and using Voronoi diagrams,compared to direct clustering with cellbin data.Our findings demonstrate that the Voronoi method not only outperforms traditional methods in providing clearer boundaries and better separation of cell types,but also excels in preserving the most transcripts,addressing the issue of low capture efficiency.This integrative methodology presents a substantial advancement in spatial transcriptomics,offering improved cell type classification and spatial pattern recognition.
