Spatial transcriptomics:recent developments and insights in respiratory research

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.

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.

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.

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.

Computational Strategies and Algorithms for Inferring Cellular Composition of Spatial Transcriptomics Data

Spatial transcriptomics technology has been an essential and powerful method for delineating tissue architecture at the molecular level.However,due to the limitations of the current spatial techniques,the cellular information cannot be directly measured but instead spatial spots typically varying from a diameter of 0.2 to 100µm are characterized.Therefore,it is vital to apply computational strategies for inferring the cellular composition within each spatial spot.The main objective of this review is to summarize the most recent progresses in estimating the exact cellular proportions for each spatial spot,and to prospect the future directions of this field.

Computational Approaches and Challenges in Spatial Transcriptomics

The development of spatial transcriptomics(ST)technologies has transformed genetic research from a single-cell data level to a two-dimensional spatial coordinate system and facilitated the study of the composition and function of various cell subsets in different environments and organs.The large-scale data generated by these ST technologies,which contain spatial gene expression information,have elicited the need for spatially resolved approaches to meet the requirements of computational and biological data interpretation.These requirements include dealing with the explosive growth of data to determine the cell-level and gene-level expression,correcting the inner batch effect and loss of expression to improve the data quality,conducting efficient interpretation and in-depth knowledge mining both at the single-cell and tissue-wide levels,and conducting multi-omics integration analysis to provide an extensible framework toward the in-depth understanding of biological processes.However,algorithms designed specifically for ST technologies to meet these requirements are still in their infancy.Here,we review computational approaches to these problems in light of corresponding issues and challenges,and present forward-looking insights into algorithm development.

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.

Spatially resolved transcriptomic profiling of placental development in dairy cow

The placenta plays a crucial role in successful mammalian reproduction.Ruminant animals possess a semi-invasive placenta characterized by a highly vascularized structure formed by maternal endometrial caruncles and fetal placental cotyledons,essential for full-term fetal development.The cow placenta harbors at least two trophoblast cell populations:uninucleate(UNC)and binucleate(BNC)cells.However,the limited capacity to elucidate the transcriptomic dynamics of the placental natural environment has resulted in a poor understanding of both the molecular and cellular interactions between trophoblast cells and niches,and the molecular mechanisms governing trophoblast differentiation and functionalization.To fill this knowledge gap,we employed Stereo-seq to map spatial gene expression patterns at near single-cell resolution in the cow placenta at 90 and 130 days of gestation,attaining high-resolution,spatially resolved gene expression profiles.Based on clustering and cell marker gene expression analyses,key transcription factors,including YBX1 and NPAS2,were shown to regulate the heterogeneity of trophoblast cell subpopulations.Cell communication and trajectory analysis provided a framework for understanding cell-cell interactions and the differentiation of trophoblasts into BNCs in the placental microenvironment.Differential analysis of cell trajectories identified a set of genes involved in regulation of trophoblast differentiation.Additionally,spatial modules and co-variant genes that help shape specific tissue structures were identified.Together,these findings provide foundational insights into important biological pathways critical to the placental development and function in cows.

Integration of Computational Analysis and Spatial Transcriptomics in Single-cell Studies

Recent advances of single-cell transcriptomics technologies and allied computational methodologies have revolutionized molecular cell biology.Meanwhile,pioneering explorations in spatial transcriptomics have opened up avenues to address fundamental biological questions in health and diseases.Here,we review the technical attributes of single-cell RNA sequencing and spatial transcriptomics,and the core concepts of computational data analysis.We further highlight the challenges in the application of data integration methodologies and the interpretation of the biological context of the findings.

Dissecting the brain with spatially resolved multi-omics

Recent studies have highlighted spatially resolved multi-omics technologies,including spatial genomics,transcriptomics,proteomics,and metabolomics,as powerful tools to decipher the spatial heterogeneity of the brain.Here,we focus on two major approaches in spatial transcriptomics(next-generation sequencing-based technologies and image-based technologies),and mass spectrometry imaging technologies used in spatial proteomics and spatial metabolomics.Furthermore,we discuss their applications in neuroscience,including building the brain atlas,uncovering gene expression patterns of neurons for special behaviors,deciphering the molecular basis of neuronal communication,and providing a more comprehensive explanation of the molecular mechanisms underlying central nervous system disorders.However,further efforts are still needed toward the integrative application of multi-omics technologies,including the real-time spatial multi-omics analysis in living cells,the detailed gene profile in a whole-brain view,and the combination of functional verification.

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.

TIST: Transcriptome and Histopathological Image Integrative Analysis for Spatial Transcriptomics

Sequencing-based spatial transcriptomics(ST)is an emerging technology to study in situ gene expression patterns at the whole-genome scale.Currently,ST data analysis is still complicated by high technical noises and low resolution.In addition to the transcriptomic data,matched histopathological images are usually generated for the same tissue sample along the ST experiment.The matched high-resolution histopathological images provide complementary cellular phenotypical information,providing an opportunity to mitigate the noises in ST data.We present a novel ST data analysis method called transcriptome and histopathological image integrative analysis for ST(TIST),which enables the identification of spatial clusters(SCs)and the enhancement of spatial gene expression patterns by integrative analysis of matched transcriptomic data and images.TIST devises a histopathological feature extraction method based on Markov random field(MRF)to learn the cellular features from histopathological images,and integrates them with the transcriptomic data and location information as a network,termed TIST-net.Based on TIST-net,SCs are identified by a random walk-based strategy,and gene expression patterns are enhanced by neighborhood smoothing.We benchmark TIST on both simulated datasets and 32 real samples against several state-of-the-art methods.Results show that TIST is robust to technical noises on multiple analysis tasks for sequencing-based ST data and can find interesting microstructures in different biological scenarios.TIST is available at http://lifeome.net/software/tist/and https://ngdc.cncb.ac.cn/biocode/tools/BT007317.

Spatially resolved transcriptomics in immersive environments

Spatially resolved transcriptomics is an emerging class of high-throughput technologies that enable biologists to systematically investigate the expression of genes along with spatial information.Upon data acquisition,one major hurdle is the subsequent interpretation and visualization of the datasets acquired.To address this challenge,VR-Cardiomics is presented,which is a novel data visualization system with interactive functionalities designed to help biologists interpret spatially resolved transcriptomic datasets.By implementing the system in two separate immersive environments,fish tank virtual reality(FTVR)and head-mounted display virtual reality(HMD-VR),biologists can interact with the data in novel ways not previously possible,such as visually exploring the gene expression patterns of an organ,and comparing genes based on their 3D expression profiles.Further,a biologist-driven use-case is presented,in which immersive environments facilitate biologists to explore and compare the heart expression profiles of different genes.

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.

Promise of spatially resolved omics for tumor research

Tumors are spatially heterogeneous tissues that comprise numerous cell types with intricate structures.By interacting with the microenvironment,tumor cells undergo dynamic changes in gene expression and metabolism,resulting in spatiotemporal variations in their capacity for proliferation and metastasis.In recent years,the rapid development of histological techniques has enabled efficient and high-throughput biomolecule analysis.By preserving location information while obtaining a large number of gene and molecular data,spatially resolved metabolomics(SRM)and spatially resolved transcriptomics(SRT)approaches can offer new ideas and reliable tools for the in-depth study of tumors.This review provides a comprehensive introduction and summary of the fundamental principles and research methods used for SRM and SRT techniques,as well as a review of their applications in cancer-related fields.

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.

Hypoxic microenvironment induced spatial transcriptome changes in pancreatic cancer

Objective: Hypoxia is a significant feature of solid tumors, including pancreatic ductal adenocarcinoma(PDAC). It is associated with tumor invasion, metastasis, and drug resistance. However, the spatial distribution of hypoxia-related heterogeneity in PDAC remains unclear.Methods: Spatial transcriptomics(STs), a new technique, was used to investigate the ST features of engrafted human PDAC in the ischemic hind limbs of nude mice. Transcriptomes from ST spots in the hypoxic tumor and the control were clustered using differentially-expressed genes. These data were compared to determine the spatial organization of hypoxia-induced heterogeneity in PDAC. Clinical relevance was validated using the Tumor Cancer Genome Atlas and KM-plotter databases. The CMAP website was used to identify molecules that may serve as therapeutic targets for PDAC.Results: ST showed that the tumor cell subgroups decreased to 7 subgroups in the hypoxia group, compared to 9 subgroups in the control group. Different subgroups showed positional characteristics and different gene signatures. Subgroup 6 located at the invasive front showed a higher proliferative ability under hypoxia. Subgroup 6 had active functions including cell proliferation, invasion, and response to stress. Expressions of hypoxia-related genes, LDHA, TPI1, and ENO1, induced changes. CMAP analysis indicated that ADZ-6482, a PI3 K inhibitor, was targeted by the invasive subgroup in hypoxic tumors.Conclusions: This study is the first to describe hypoxic microenvironment-induced spatial transcriptome changes in PDAC, and to identify potential treatment targets for PDAC. These data will provide the basis for further investigations of the prognoses and treatments of hypoxic tumors.

The spatial transcriptomic landscape of human gingiva in health and periodontitis

The gingiva is a key oral barrier that protects oral tissues from various stimuli.A loss of gingival tissue homeostasis causes periodontitis,one of the most prevalent inflammatory diseases in humans.The human gingiva exists as a complex cell network comprising specialized structures.To understand the tissue-specific pathophysiology of the gingiva,we applied a recently developed spatial enhanced resolution omics-sequencing(Stereo-seq)technique to obtain a spatial transcriptome(ST)atlas of the gingiva in healthy individuals and periodontitis patients.By utilizing Stereo-seq,we identified the major cell types present in the gingiva,which included epithelial cells,fibroblasts,endothelial cells,and immune cells,as well as subgroups of epithelial cells and immune cells.We further observed that inflammation-related signalling pathways,such as the JAK-STAT and NF-κB signalling pathways,were significantly upregulated in the endothelial cells of the gingiva of periodontitis patients compared with those of healthy individuals.Additionally,we characterized the spatial distribution of periodontitis risk genes in the gingiva and found that the expression of IFI16 was significantly increased in endothelial cells of inflamed gingiva.In conclusion,our Stereo-seq findings may facilitate the development of innovative therapeutic strategies for periodontitis by mapping periodontitis-relevant genes and pathways and effector cells.

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.

Spatially resolved transcriptomics:advances and applications

Spatial transcriptomics,which is capable of both measuring all gene activity in a tissue sample and mapping where this activity occurs,is vastly improving our understanding of biological processes and disease.The field has expanded rapidly in recent years,and the development of several new technologies has resulted in spatially resolved transcriptomics(SRT)becoming highly multiplexed,high-resolution,and high-throughput.Here,we summarize and compare the major methods of SRT,including imagingbased methods,sequencing-based methods,and in situ sequencing methods.We also highlight some typical applications of SRT in neuroscience,cancer biology,developmental biology,and hematology.Finally,we discuss future possibilities for improving spatially resolved transcriptomic methods and the expected applications of such methods,especially in the adult bone marrow,anticipating that new developments will unlock the full potential of spatially resolved multi-omics in both biological research and the clinic.