Single-cell and spatial omics:exploring hypothalamic heterogeneity

Elucidating the complex dynamic cellular organization in the hypothalamus is critical for understanding its role in coordinating fundamental body functions. Over the past decade, single-cell and spatial omics technologies have significantly evolved, overcoming initial technical challenges in capturing and analyzing individual cells. These high-throughput omics technologies now offer a remarkable opportunity to comprehend the complex spatiotemporal patterns of transcriptional diversity and cell-type characteristics across the entire hypothalamus. Current single-cell and single-nucleus RNA sequencing methods comprehensively quantify gene expression by exploring distinct phenotypes across various subregions of the hypothalamus. However, single-cell/single-nucleus RNA sequencing requires isolating the cell/nuclei from the tissue, potentially resulting in the loss of spatial information concerning neuronal networks. Spatial transcriptomics methods, by bypassing the cell dissociation, can elucidate the intricate spatial organization of neural networks through their imaging and sequencing technologies. In this review, we highlight the applicative value of single-cell and spatial transcriptomics in exploring the complex molecular-genetic diversity of hypothalamic cell types, driven by recent high-throughput achievements.

Application of single-cell omics in inflammatory bowel disease

Over the past decade,the advent of single cell RNA-sequencing has revolutionized the approach in cellular transcriptomics research.The current technology offers an unbiased platform to understand how genotype correlates to phenotype.Single-cell omics applications in gastrointestinal(GI)research namely inflammatory bowel disease(IBD)has become popular in the last few years with multiple publications as single-cell omics techniques can be applied directly to the target organ,the GI tract at the tissue level.Through examination of mucosal tissue and peripheral blood in IBD,the recent boom in single cell research has identified a myriad of key immune players from enterocytes to tissue resident memory T cells,and explored functional heterogeneity within cellular subsets previously unreported.As we begin to unravel the complex mucosal immune system in states of health and disease like IBD,the power of exploration through single-cell omics can change our approach to translational research.As novel techniques evolve through multiplexing single-cell omics and spatial transcriptomics come to the forefront,we can begin to fully comprehend the disease IBD and better design targets of treatment.In addition,hopefully these techniques can ultimately begin to identify biomarkers of therapeutic response and answer clinically relevant questions in how to tailor individual therapy to patients through personalized medicine.

Progress in research on tumor microenvironment-based spatial omics technologies

Spatial omics technology integrates the concept of space into omics research and retains the spatial information of tissues or organs while obtaining molecular information.It is characterized by the ability to visualize changes in molecular information and yields intuitive and vivid visual results.Spatial omics technologies include spatial transcriptomics,spatial proteomics,spatial metabolomics,and other technologies,the most widely used of which are spatial transcriptomics and spatial proteomics.The tumor microenvironment refers to the surrounding microenvironment in which tumor cells exist,including the surrounding blood vessels,immune cells,fibroblasts,bone marrow-derived inflammatory cells,various signaling molecules,and extracellular matrix.A key issue in modern tumor biology is the application of spatial omics to the study of the tumor microenvironment,which can reveal problems that conventional research techniques cannot,potentially leading to the development of novel therapeutic agents for cancer.This paper summarizes the progress of research on spatial transcriptomics and spatial proteomics technologies for characterizing the tumor immune microenvironment.

Single-cell and spatial heterogeneity landscapes of mature epicardial cells

Tbx18,Wt1,and Tcf21 have been identified as epicardial markers during the early embryonic stage.However,the gene markers of mature epicardial cells remain unclear.Single-cell transcriptomic analysis was performed with the Seurat,Monocle,and CellphoneDB packages in R software with standard procedures.Spatial transcriptomics was performed on chilled Visium Tissue Optimization Slides(10x Genomics)and Visium Spatial Gene Expression Slides(10x Genomics).Spatial transcriptomics analysis was performed with Space Ranger software and R software.Immunofluorescence,whole-mount RNA in situ hybridization and X-gal staining were performed to validate the analysis results.Spatial transcriptomics analysis revealed distinct transcriptional profiles and functions between epicardial tissue and non-epicardial tissue.Several gene markers specific to postnatal epicardial tissue were identified,including Msln,C3,Efemp1,and Upk3b.Single-cell transcriptomic analysis revealed that cardiac cells from wildtype mouse hearts(from embryonic day 9.5 to postnatal day 9)could be categorized into six major cell types,which included epicardial cells.Throughout epicardial development,Wt1,Tbx18,and Upk3b were consistently expressed,whereas genes including Msln,C3,and Efemp1 exhibited increased expression during the mature stages of development.Pseudotime analysis further revealed two epicardial cell fates during maturation.Moreover,Upk3b,Msln,Efemp1,and C3 positive epicardial cells were enriched in extracellular matrix signaling.Our results suggested Upk3b,Efemp1,Msln,C3,and other genes were mature epicardium markers.Extracellular matrix signaling was found to play a critical role in the mature epicardium,thus suggesting potential therapeutic targets for heart regeneration in future clinical practice.

Comprehensive transcriptional atlas of human adenomyosis deciphered by the integration of single-cell RNA-sequencing and spatial transcriptomics

Adenomyosis is a poorly understood gynecological disorder lacking effective treatments.Controversy persists regarding“invagination”and“metaplasia”theories.The endometrial-myometrial junction(EMJ)connects the endometrium and myometrium and is important for diagnosing and classifying adenomyosis,but its in-depth study is just beginning.Using single-cell RNA sequencing and spatial profiling,we mapped transcriptional alterations across eutopic endometrium,lesions,and EMJ.Within lesions,we identified unique epithelial(LGR5+)and invasive stromal(PKIB+)subpopulations,along with WFDC1+progenitor cells,supporting a complex interplay between“invagination”and“metaplasia”theories of pathogenesis.Further,we observed endothelial cell heterogeneity and abnormal angiogenic signaling involving vascular endothelial growth factor and angiopoietin pathways.Cell-cell communication differed markedly between ectopic and eutopic endometrium,with aberrant signaling in lesions involving pleiotrophin,TWEAK,and WNT cascades.This study reveals unique stem cell-like and invasive cell subpopulations within adenomyosis lesions identified,dysfunctional signaling,and EMJ abnormalities critical to developing precise diagnostic and therapeutic strategies.

HCCDB v2.0:Decompose Expression Variations by Single-cell RNA-seq and Spatial Transcriptomics in HCC

Large-scale transcriptomic data are crucial for understanding the molecular features of hepatocellular carcinoma(HCC).Integrated 15 transcriptomic datasets of HCC clinical samples,the first version of HCC database(HCCDB v1.0)was released in 2018.Through the meta-analysis of differentially expressed genes and prognosis-related genes across multiple datasets,it provides a systematic view of the altered biological processes and the inter-patient heterogeneities of HCC with high reproducibility and robustness.With four years having passed,the database now needs integration of recently published datasets.Furthermore,the latest single-cell and spatial transcriptomics have provided a great opportunity to decipher complex gene expression variations at the cellular level with spatial architecture.Here,we present HCCDB v2.0,an updated version that combines bulk,single-cell,and spatial transcriptomic data of HCC clinical samples.It dramatically expands the bulk sample size by adding 1656 new samples from 11 datasets to the existing 3917 samples,thereby enhancing the reliability of transcriptomic meta-analysis.A total of 182,832 cells and 69,352 spatial spots are added to the single-cell and spatial transcriptomics sections,respectively.A novel single-cell level and 2-dimension(sc-2D)metric is proposed as well to summarize cell type-specific and dysregulated gene expression patterns.Results are all graphically visualized in our online portal,allowing users to easily retrieve data through a user-friendly interface and navigate between different views.With extensive clinical phenotypes and transcriptomic data in the database,we show two applications for identifying prognosis-associated cells and tumor microenvironment.HCCDB v2.0 is available at http://lifeome.net/database/hccdb2.

Single-cell and spatial multi-omics in the plant sciences:Technical advances,applications,and perspectives

Plants contain a large number of cell types and exhibit complex regulatory mechanisms.Studies at the single-cell level have gradually become more common in plant science.Single-cell transcriptomics,spatial transcriptomics,and spatial metabolomics techniques have been combined to analyze plant development.These techniques have been used to study the transcriptomes and metabolomes of plant tissues at the single-cell level,enabling the systematic investigation of gene expression and metabolism in specific tissues and cell types during defined developmental stages.In this review,we present an overview of significant breakthroughs in spatial multi-omics in plants,and we discuss how these approaches may soon play essential roles in plant research.

Opportunities and Challenges in Advancing Plant Research with Single-cell Omics

Plants possess diverse cell types and intricate regulatory mechanisms to adapt to the ever-changing environment of nature.Various strategies have been employed to study cell types and their developmental progressions,including single-cell sequencing methods which provide high-dimensional catalogs to address biological concerns.In recent years,single-cell sequencing technologies in transcriptomics,epigenomics,proteomics,metabolomics,and spatial transcriptomics have been increasingly used in plant science to reveal intricate biological relationships at the single-cell level.However,the application of single-cell technologies to plants is more limited due to the challenges posed by cell structure.This review outlines the advancements in single-cell omics technologies,their implications in plant systems,future research applications,and the challenges of single-cell omics in plant systems.

Advances and applications in spatial transcriptomics

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.

Laser capture microdissection for biomedical research: towards high-throughput, multi-omics, and single-cell resolution

Spatial omics technologies have become powerful methods to provide valuable insights into cells and tissues within a complex context,significantly enhancing our understanding of the intricate and multifaceted biological system.With an increasing focus on spatial heterogeneity,there is a growing need for unbiased,spatially resolved omics technologies.Laser capture microdissection(LCM)is a cutting-edge method for acquiring spatial information that can quickly collect regions of interest(ROIs)from heterogeneous tissues,with resolutions ranging from single cells to cell populations.Thus,LCM has been widely used for studying the cellular and molecular mechanisms of diseases.This review focuses on the differences among four types of commonly used LCM technologies and their applications in omics and disease research.Key attributes of application cases are also highlighted,such as throughput and spatial resolution.In addition,we comprehensively discuss the existing challenges and the great potential of LCM in biomedical research,disease diagnosis,and targeted therapy from the perspective of high-throughput,multi-omics,and single-cell resolution.

Osteoimmunology research in rheumatoid arthritis:From single-cell omics approach

Cellular immune responses as well as generalized and periarticular bone loss are the key pathogenic features of rheumatoid arthritis(RA).Under the pathological conditions of RA,dysregulated inflammation and immune processes tightly interact with skeletal system,resulting in pathological bone damage via inhibition of bone formation or induction of bone resorption.Singlecell omics technologies are revolutionary tools in the field of modern biological research.They enable the display of the state and function of cells in various environments from a single-cell resolution,thus making it conducive to identify the dysregulated molecular mechanisms of bone destruction in RA as well as the discovery of potential therapeutic targets and biomarkers.Here,we summarize the latest findings of single-cell omics technologies in osteoimmunology research in RA.These results suggest that single-cell omics have made significant contributions to transcriptomics and dynamics of specific cells involved in bone remodeling,providing a new direction for our understanding of cellular heterogeneity in the study of osteoimmunology in RA.

Decoding Human Biology and Disease Using Single-cell Omics Technologies

Over the past decade,advances in single-cell omics(SCO)technologies have enabled the investigation of cellular heterogeneity at an unprecedented resolution and scale,opening a new avenue for understanding human biology and disease.In this review,we summarize the developments of sequencing-based SCO technologies and computational methods,and focus on considerable insights acquired from SCO sequencing studies to understand normal and diseased properties,with a particular emphasis on cancer research.We also discuss the technological improvements of SCO and its possible contribution to fundamental research of the human,as well as its great potential in clinical diagnoses and personalized therapies of human disease.

Establishment of a cholangiocarcinoma risk evaluation model based on mucin expression levels

BACKGROUND Cholangiocarcinoma(CCA)is a highly malignant cancer,characterized by frequent mucin overexpression.MUC1 has been identified as a critical oncogene in the progression of CCA.However,the comprehensive understanding of how the mucin family influences CCA progression and prognosis is still incomplete.AIM To investigate the functions of mucins on the progression of CCA and to establish a risk evaluation formula for stratifying CCA patients.METHODS Single-cell RNA sequencing data from 14 CCA samples were employed for elucidating the roles of mucins,complemented by bioinformatic analyses.Subse-quent validations were conducted through spatial transcriptomics and immuno-histochemistry.The construction of a risk evaluation model utilized the least absolute shrinkage and selection operator regression algorithm,which was further confirmed by independent cohorts and diverse data types.RESULTS CCA tumor cells with elevated levels of MUC1 and MUC4 showed activated nucleotide metabolic pathways and increased invasiveness.MUC5AC-high cells were found to promote CCA progression through WNT signaling.MUC5B-high cells exhibited robust cellular oxidation activities,leading to resistance against antitumoral treatments.MUC13-high cells were observed to secret chemokines,recruiting and transforming macrophages into the M2-polarized state,thereby suppressing antitumor immunity.MUC16-high cells were found to promote tumor progression through interleukin-1/nuclear factor kappa-light-chain-enhancer of activated B cells signaling upon interaction with neutrophils.Utilizing the expression levels of these mucins,a risk factor evaluation formula for CCA was developed and validated across multiple cohorts.CCA samples with higher risk factors exhibited stronger metastatic potential,chemotherapy resistance,and poorer prognosis.CONCLUSION Our study elucidates the functional mechanisms through which mucins contribute to CCA development,and provides tools for risk stratification in CCA.

An overview of multiomics:a powerful tool applied in cancer molecular subtyping for cancer therapy

During the process of carcinogenesis and tumor progression,various molecular alternations occur in different omics levels.In recent years,multiomics approaches including genomics,epigenetics,transcriptomics,proteomics,metabolomics,single-cell omics,and spatial omics have been applied in mapping diverse omics profiles of cancers.The development of high-throughput technologies such as sequencing and mass spectrometry has revealed different omics levels of tumor cells or tissues separately.While focusing on a single omics level results in a lack of accuracy,joining multiple omics approaches together undoubtedly benefits accurate molecular subtyping and precision medicine for cancer patients.With the deepening of tumor research in recent years,taking pathological classification as the only criterion of diagnosis and predicting prognosis and treatment response is found to be not accurate enough.Therefore,identifying precise molecular subtypes by exploring the molecular alternations during tumor occurrence and development is of vital importance.The review provides an overview of the advanced technologies and recent progress in multiomics applied in cancer molecular subtyping and detailedly explains the application of multiomics in identifying cancer driver genes and metastasis-related genes,exploring tumor microenvironment,and selecting liquid biopsy biomarkers and potential therapeutic targets.

Spatially resolved transcriptomics: a comprehensive review of their technological advances, applications, and challenges

The ability to explore life kingdoms is largely driven by innovations and breakthroughs in technology,from the invention of the microscope 350 years ago to the recent emergence of single-cell sequencing,by which the scientific community has been able to visualize life at an unprecedented resolution.Most recently,the Spatially Resolved Transcriptomics(SRT)technologies have filled the gap in probing the spatial or even three-dimensional organization of the molecular foundation behind the molecular mysteries of life,including the origin of different cellular populations developed from totipotent cells and human diseases.In this review,we introduce recent progresses and challenges on SRT from the perspectives of technologies and bioinformatic tools,as well as the representative SRT applications.With the currently fast-moving progress of the SRT technologies and promising results from early adopted research projects,we can foresee the bright future of such new tools in understanding life at the most profound analytical level.

Dynamic heterogeneity of colorectal cancer during progression revealed clinical risk-associated cell types and regulations in single-cell resolution and spatial context

Background:Tumor heterogeneity is contributed by tumor cells and the microenvironment.Dynamics of tumor heterogeneity during colorectal cancer(CRC)progression have not been elucidated.Methods:Eight single-cell RNA sequencing(scRNA-seq)data sets of CRC were included.Milo was utilized to reveal the differential abundance of cell clusters during progression.The differentiation trajectory was imputed by using the Palantir algorithm and metabolic states were assessed by using scMetabolism.Three spatial transcription sequencing(ST-seq)data sets of CRC were used to validate cell-type abundances and colocalization.Cancer-associated regulatory hubs were defined as communication networks affecting tumor biological behaviors.Finally,quantitative reverse transcription polymerase chain reaction and immunohistochemistry staining were performed for validation.Results:TM4SF1t,SOX4t,and MKI67t tumor cells;CXCL12t cancer-associated fibroblasts;CD4t resident memory T cells;Treg;IgAt plasma cells;and several myeloid subsets were enriched in stage IV CRC,most of which were associated with overall survival of patients.Trajectory analysis indicated that tumor cells from patients with advanced-stage CRC were less differentiated,when metabolic heterogeneity showed a highest metabolic signature in terminal states of stromal cells,T cells,and myeloid cells.Moreover,ST-seq validated cell-type abundance in a spatial context and also revealed the correlation of immune infiltration between tertiary lymphoid structures and tumors followed by validation in our cohort.Importantly,analysis of cancer-associated regulatory hubs revealed a cascade of activated pathways including leukocyte apoptotic process,MAPK pathway,myeloid leukocyte differentiation,and angiogenesis during CRC progression.Conclusions:Tumor heterogeneity was dynamic during progression,with the enrichment of immunosuppressive Treg,myeloid cells,and fibrotic cells.The differential state of tumor cells was associated with cancer staging.Assessment of cancer-associated regulatory hubs suggested impaired antitumor immunity and increased metastatic ability during CRC progression.

Special Issue on “Single-cell Omics Analysis”

The cell has been primarily studied as a part of its bulk population for decades until recent breakthroughs in single-cell omics technologies. The study of the seemingly isogenic cellular populations often buries diverse cellular characteristics. Even in cells with the same cellular history,heterogeneity inherently arises due to the stochastic fluctuation of gene expression during transcription and translation or noises in signaling pathways. These hidden cell-to-cell variations can be paramount in the diagnosis and treatment of disease. For instance,the heterogeneity in tumor cells is crucial in understanding tumor initiation,progression,metastasis,and therapeutic response. A very small subpopulation of cells that may confer the most resistance in a preclinical drug test could be responsible for tumor relapse in patients after treatment. Thus,as medicine becomes more and more personalized,there is a greater desire to more accurately represent and understand single cells and the distinct subpopulations.

Special Issue on “Single-cell Omics Analysis”

We are very pleased to announce a special issue, to be published in the spring of 2020, on "Single-cell Omics Analysis" in the journal Genomics, Proteomics & Bioinformatics(GPB). The cell has been primarily studied as a part of its bulk population for decades until recent

中药作用靶点及分子机制的转录组学研究思路与方法

中医药是我国的传统和特色医学,一直以来为人们的健康保驾护航,但随着现代医学的发展,它逐渐面临着如何实现客观化、精确化和靶向化的难题。转录组学是连接基因与表型的桥梁,决定遗传物质表达与否,受外源和内源因素的共同调控,能反映内部基因组与外部物理特征的整体网络动态联系,该特点与中医整体观理论相合,符合中药多靶点、多层次整体网络治疗的特性。因此,越来越多的学者从转录组学层面研究中药作用靶点和分子机制,以探索中医药的精准医疗。相关研究已如雨后春笋,相应检测技术也从传统的基因芯片、RNA测序,向单分子测序、单细胞测序及空间转录组发展,但转录组学作为一个相对年轻的领域,在中医药的应用仍需不断探索和完善,才能成为挖掘中医药宝藏的有力武器。有鉴于此,本文系统梳理了当前转录组学在中药研究中的现状、转录组学研究常用的检测技术,以及基于基因芯片、RNA测序、单细胞测序、空间转录组和RNA干扰技术等的转录组学研究思路与方法,并指出现有研究可能存在的问题,以期为中药在转录组层面的靶点和机制研究提供方法学基础。

High-resolution anatomical and spatial transcriptome analyses reveal two types of meristematic cell pools within the secondary vascular tissue of poplar stem

The secondary vascular tissue emanating from meristems is central to understanding how vascular plants such as forest trees evolve,grow,and regulate secondary radial growth.However,the overall molecular characterization of meristem origins and developmental trajectories from primary to secondary vascular tissues in woody tree stems is technically challenging.In this study,we combined high-resolution anatomic analysis with a spatial transcriptome(ST)technique to define features of meristematic cells in a developmental gradient from primary to secondary vascular tissues in poplar stems.The tissue-specific gene expression of meristems and derived vascular tissue types were accordingly mapped to specific anatomical domains.Pseudotime analyses were used to track the origins and changes of meristems throughout the development from primary to secondary vascular tissues.Surprisingly,two types of meristematic-like cell pools within secondary vascular tissues were inferred based on high-resolution microscopy combined with ST,and the results were confirmed by in situ hybridization of,transgenic trees,and single-cell sequencing.The rectangle shape procambium-like(PCL)cells develop from procambium meristematic cells and are located within the phloem domain to produce phloem cells,whereas fusiform shape cambium zone(CZ)meristematic cells develop from fusiform metacambium meristematic cells and are located inside the CZ to produce xylem cells.The gene expression atlas and transcriptional networks spanning the primary transition to secondary vascular tissues generated in this work provide new resources for studying the regulation of meristem activities and the evolution of vascular plants.A web server(https://pgx.zju.edu.cn/stRNAPal/)was also established to facilitate the use of ST RNA-seq data.