Spatiotemporal mapping of gene expression landscapes and developmental trajectories during zebrafish embryogenesis

Spatial multi-omics analysis of metabolic heterogeneity in zebrafish exposed to microcystin-LR and its disinfection byproducts

Most studies on the biological effects of exogenous pollutants have focused on whole samples or cell populations, and lack spatial heterogeneity consideration due to technical limitations. Microcystin-LR (MC-LR) from cyanobacterial blooms threatens ecosystems and human health, while microcystin-LR disinfection by-products (MCLR-DBPs) in drinking water remain a concern for their toxin-like structure. This study introduces spatial multi-omics to investigate the disruptions caused by ingestion of MC-LR and MCLR-DBPs in zebrafish. The method integrates metabolomics, spatial metabolomics, and spatial transcriptomics to characterize the overall metabolic changes in whole zebrafish caused by MC-LR and MCLR-DBPs, then provides further insight into the variation of spatial distribution of metabolites and genes in MC-LR and MCLR-DBPs targeted organ. The results showed that MC-LR and MCLR-DBPs induced oxidative stress and metabolic imbalance, and disrupted the physiological homeostasis of zebrafish. Spatial multi-omics analysis further revealed that MC-LR and MCLR-DBPs exacerbate disruptions in energy and lipid metabolism, methylation processes, and immune pathways by modulating the expression of genes such as gatm, gnmt, cyp2p9, and tdo2b. In conclusion, this study developed a spatial multi-omics approach that not only enhances the understanding of the biological effects of MC-LR and MCLR-DBPs but also provides robust technical support for investigating other environmental pollutants.

Spatial transcriptomic imaging of an intact organism using volumetric DNA microscopy

Lymphatic, nervous and tumor tissues exhibit complex physiology arising from three-dimensional interactions within genetically unique microenvironments. Here we develop a technology capable of volumetrically imaging transcriptomes, genotypes and morphologies in a single measurement, without relying on prior knowledge of spatial organization or genetic sequences. Our method extends DNA microscopy into three dimensions at scales involving 107 molecules by forming a distributed intermolecular network of proximal unique DNA barcodes tagging complementary DNA molecules inside the specimen. After sequencing the DNA-encoded network, an image of molecular positions is inferred using geodesic spectral embeddings, a dimensionality reduction approach that we show to be especially suitable for this data-inverse problem. Applying whole-transcriptome volumetric DNA microscopy to intact zebrafish embryos, we demonstrate that three-dimensional image inference recapitulates zebrafish morphology and known gene expression patterns, capturing the spatial organization of gene sequences. Our extension of spatial genetic measurements to three dimensions, independent of prior templates, opens the door to detailed joint resolution of genomics and morphology in biological tissues.

Single-cell spatial transcriptomic atlas of the whole mouse brain

A comprehensive atlas of genes, cell types, and their spatial distribution across a whole mammalian brain is fundamental for understanding the function of the brain. Here, using single-nucleus RNA sequencing (snRNA-seq) and Stereo-seq techniques, we generated a mouse brain atlas with spatial information for 308 cell clusters at single-cell resolution, involving over 4 million cells, as well as for 29,655 genes. We have identified cell clusters exhibiting preference for cortical subregions and explored their associations with brain-related diseases. Additionally, we pinpointed 155 genes with distinct regional expression patterns within the brainstem and unveiled 513 long non-coding RNAs showing region-enriched expression in the adult brain. Parcellation of brain regions based on spatial transcriptomic information revealed fine structure for several brain areas. Furthermore, we have uncovered 411 transcription factor regulons showing distinct spatiotemporal dynamics during neurodevelopment. Thus, we have constructed a single-cell-resolution spatial transcriptomic atlas of the mouse brain with genome-wide coverage.

Advances in Spatial Omics Technologies

Rapidly developing spatial omics technologies provide us with new approaches to deeply understanding the diversity and functions of cell types within organisms. Unlike traditional approaches, spatial omics technologies enable researchers to dissect the complex relationships between tissue structure and function at the cellular or even subcellular level. The application of spatial omics technologies provides new perspectives on key biological processes such as nervous system development, organ development, and tumor microenvironment. This review focuses on the advancements and strategies of spatial omics technologies, summarizes their applications in biomedical research, and highlights the power of spatial omics technologies in advancing the understanding of life sciences related to development and disease.

From morphology to single-cell molecules: high-resolution 3D histology in biomedicine

High-resolution three-dimensional (3D) tissue analysis has emerged as a transformative innovation in the life sciences, providing detailed insights into the spatial organization and molecular composition of biological tissues. This review begins by tracing the historical milestones that have shaped the development of high-resolution 3D histology, highlighting key breakthroughs that have facilitated the advancement of current technologies. We then systematically categorize the various families of high-resolution 3D histology techniques, discussing their core principles, capabilities, and inherent limitations. These 3D histology techniques include microscopy imaging, tomographic approaches, single-cell and spatial omics, computational methods and 3D tissue reconstruction (e.g. 3D cultures and spheroids). Additionally, we explore a wide range of applications for single-cell 3D histology, demonstrating how single-cell and spatial technologies are being utilized in the fields such as oncology, cardiology, neuroscience, immunology, developmental biology and regenerative medicine. Despite the remarkable progress made in recent years, the field still faces significant challenges, including high barriers to entry, issues with data robustness, ambiguous best practices for experimental design, and a lack of standardization across methodologies. This review offers a thorough analysis of these challenges and presents recommendations to surmount them, with the overarching goal of nurturing ongoing innovation and broader integration of cellular 3D tissue analysis in both biology research and clinical practice.

Decoding the blueprints of embryo development with single-cell and spatial omics

Embryonic development is a complex and intricately regulated process that encompasses precise control over cell differentiation, morphogenesis, and the underlying gene expression changes. Recent years have witnessed a remarkable acceleration in the development of single-cell and spatial omic technologies, enabling high-throughput profiling of transcriptomic and other multi-omic information at the individual cell level. These innovations offer fresh and multifaceted perspectives for investigating the intricate cellular and molecular mechanisms that govern embryonic development. In this review, we provide an in-depth exploration of the latest technical advancements in single-cell and spatial multi-omic methodologies and compile a systematic catalog of their applications in the field of embryonic development. We deconstruct the research strategies employed by recent studies that leverage single-cell sequencing techniques and underscore the unique advantages of spatial transcriptomics. Furthermore, we delve into both the current applications, data analysis algorithms and the untapped potential of these technologies in advancing our understanding of embryonic development. With the continuous evolution of multi-omic technologies, we anticipate their widespread adoption and profound contributions to unraveling the intricate molecular foundations underpinning embryo development in the foreseeable future.

Advancements in the Developmental Zebrafish Model for Predictive Human Toxicology

The rapid assessment of chemical hazards to human health, with reduced reliance on mammalian testing, is essential in the 21st century. Early life stage zebrafish have emerged as a leading model in the field due to their amenability to high throughput developmental toxicity testing while retaining the benefits of using a whole vertebrate organism with high homology with humans. Zebrafish are particularly well suited for a variety of study areas that are more challenging in other vertebrate model systems including microbiome work, transgenerational studies, gene-environment interactions, molecular responses, and mechanisms of action. The high volume of data generated from zebrafish screening studies is highly valuable for QSAR modeling and dose modeling for use in predictive hazard assessment. Recent advancements and challenges in using early life stage zebrafish for predictive human toxicology are reviewed.

The novel role of foxi3 in zebrafish mandibular development

Recent studies have identified pathogenic variants in the FOXI3 gene associated with craniofacial microsomia pedigrees. In zebrafish, the foxi1 gene is considered a functional homolog of the mouse Foxi3. However, research on foxi3a and foxi3b, which display homologous genes in the naming of FOXI3 in zebrafish, has predominantly focused on their roles in epidermal ionocyte function. Our study reveals that disruption of foxi3a or foxi3b results in a reduced number of cranial neural crest cells (CNCCs) and hypoplastic mandibular cartilage in zebrafish. These findings introduce a new perspective on the functional homologs of FOXI3 and highlight an unrecognized role of foxi3 in zebrafish CNCC and mandibular development.

Learning Latent Trajectories in Developmental Time Series with Hidden-Markov Optimal Transport

Deriving the sequence of transitions between cell types, or differentiation events, that occur during organismal development is one of the fundamental challenges in developmental biology. Single-cell and spatial sequencing of samples from different developmental timepoints provide data to investigate differentiation but inferring a sequence of differentiation events requires: (1) finding trajectories, or ancestor:descendant relationships, between cells from consecutive timepoints; (2) coarse-graining these trajectories into a differentiation map , or collection of transitions between cell types , rather than individual cells. We introduce Hidden-Markov Optimal Transport ( HM - OT ), an algorithm that simultaneously groups cells into cell types and learns transitions between these cell types from developmental transcriptomics time series. HM - OT uses low-rank optimal transport to simultaneously align samples in a time series and learn a sequence of clusterings and a differentiation map with minimal total transport cost. We assume that the law governing cell-type trajectories is characterized by the joint law on consecutive time points, tantamount to a Markov assumption on these latent trajectories. HM - OT can learn these clusterings in a fully unsupervised manner or can generate the least-cost cell type differentiation map consistent with a given set of cell type labels. We validate the unsupervised clusters and cell type differentiation map output by HM - OT on a Stereo-seq dataset of zebrafish development, and we demonstrate the scalability of HM - OT to a massive Stereo-seq dataset of mouse embryonic development.

Code availability

Software is available at https://github.com/raphael-group/HM-OT.

STMiner: Gene-centric spatial transcriptomics for deciphering tumor tissues

Analyzing spatial transcriptomics data from tumor tissues poses several challenges beyond those of healthy samples, including unclear boundaries between different regions, uneven cell densities, and relatively higher cellular heterogeneity. Collectively, these bias the background against which spatially variable genes are identified, which can result in misidentification of spatial structures and hinder potential insight into complex pathologies. To overcome this problem, STMiner leverages 2D Gaussian mixture models and optimal transport theory to directly characterize the spatial distribution of genes rather than the capture locations of the cells expressing them (spots). By effectively mitigating the impacts of both background bias and data sparsity, STMiner reveals key gene sets and spatial structures overlooked by spot-based analytic tools, facilitating novel biological discoveries. The core concept of directly analyzing overall gene expression patterns also allows for a broader application beyond spatial transcriptomics, positioning STMiner for continuous expansion as spatial omics technologies evolve.

Integrative Multi-Omics Approaches for Identifying and Characterizing Biological Elements in Crop Traits: Current Progress and Future Prospects

The advancement of multi-omics tools has revolutionized the study of complex biological systems, providing comprehensive insights into the molecular mechanisms underlying critical traits across various organisms. By integrating data from genomics, transcriptomics, metabolomics, and other omics platforms, researchers can systematically identify and characterize biological elements that contribute to phenotypic traits. This review delves into recent progress in applying multi-omics approaches to elucidate the genetic, epigenetic, and metabolic networks associated with key traits in plants. We emphasize the potential of these integrative strategies to enhance crop improvement, optimize agricultural practices, and promote sustainable environmental management. Furthermore, we explore future prospects in the field, underscoring the importance of cutting-edge technological advancements and the need for interdisciplinary collaboration to address ongoing challenges. By bridging various omics platforms, this review aims to provide a holistic framework for advancing research in plant biology and agriculture.

Mapping cells through time and space with moscot

Single-cell genomic technologies enable the multimodal profiling of millions of cells across temporal and spatial dimensions. However, experimental limitations hinder the comprehensive measurement of cells under native temporal dynamics and in their native spatial tissue niche. Optimal transport has emerged as a powerful tool to address these constraints and has facilitated the recovery of the original cellular context1-4. Yet, most optimal transport applications are unable to incorporate multimodal information or scale to single-cell atlases. Here we introduce multi-omics single-cell optimal transport (moscot), a scalable framework for optimal transport in single-cell genomics that supports multimodality across all applications. We demonstrate the capability of moscot to efficiently reconstruct developmental trajectories of 1.7 million cells from mouse embryos across 20 time points. To illustrate the capability of moscot in space, we enrich spatial transcriptomic datasets by mapping multimodal information from single-cell profiles in a mouse liver sample and align multiple coronal sections of the mouse brain. We present moscot.spatiotemporal, an approach that leverages gene-expression data across both spatial and temporal dimensions to uncover the spatiotemporal dynamics of mouse embryogenesis. We also resolve endocrine-lineage relationships of delta and epsilon cells in a previously unpublished mouse, time-resolved pancreas development dataset using paired measurements of gene expression and chromatin accessibility. Our findings are confirmed through experimental validation of NEUROD2 as a regulator of epsilon progenitor cells in a model of human induced pluripotent stem cell islet cell differentiation. Moscot is available as open-source software, accompanied by extensive documentation.

KanCell: dissecting cellular heterogeneity in biological tissues through integrated single-cell and spatial transcriptomics

KanCell is a deep learning model based on Kolmogorov-Arnold networks (KAN) designed to enhance cellular heterogeneity analysis by integrating single-cell RNA sequencing (scRNA-seq) 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.

Spatial Transcriptomics: Biotechnologies, Computational Tools, and Neuroscience Applications

Spatial transcriptomics (ST) represents a revolutionary approach in molecular biology, providing unprecedented insights into the spatial organization of gene expression within tissues. This review aims to elucidate advancements in ST technologies, their computational tools, and their pivotal applications in neuroscience. It is begun with a historical overview, tracing the evolution from early image-based techniques to contemporary sequence-based methods. Subsequently, the computational methods essential for ST data analysis, including preprocessing, cell type annotation, spatial clustering, detection of spatially variable genes, cell-cell interaction analysis, and 3D multi-slices integration are discussed. The central focus of this review is the application of ST in neuroscience, where it has significantly contributed to understanding the brain's complexity. Through ST, researchers advance brain atlas projects, gain insights into brain development, and explore neuroimmune dysfunctions, particularly in brain tumors. Additionally, ST enhances understanding of neuronal vulnerability in neurodegenerative diseases like Alzheimer's and neuropsychiatric disorders such as schizophrenia. In conclusion, while ST has already profoundly impacted neuroscience, challenges remain issues such as enhancing sequencing technologies and developing robust computational tools. This review underscores the transformative potential of ST in neuroscience, paving the way for new therapeutic insights and advancements in brain research.

Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics

Single-cell transcriptomics has broadened our understanding of cellular diversity and gene expression dynamics in healthy and diseased tissues. Recently, spatial transcriptomics has emerged as a tool to contextualize single cells in multicellular neighbourhoods and to identify spatially recurrent phenotypes, or ecotypes. These technologies have generated vast datasets with targeted-transcriptome and whole-transcriptome profiles of hundreds to millions of cells. Such data have provided new insights into developmental hierarchies, cellular plasticity and diverse tissue microenvironments, and spurred a burst of innovation in computational methods for single-cell analysis. In this Review, we discuss recent advancements, ongoing challenges and prospects in identifying and characterizing cell states and multicellular neighbourhoods. We discuss recent progress in sample processing, data integration, identification of subtle cell states, trajectory modelling, deconvolution and spatial analysis. Furthermore, we discuss the increasing application of deep learning, including foundation models, in analysing single-cell and spatial transcriptomics data. Finally, we discuss recent applications of these tools in the fields of stem cell biology, immunology, and tumour biology, and the future of single-cell and spatial transcriptomics in biological research and its translation to the clinic.

Time-resolved single-cell transcriptomic sequencing

Cells experience continuous transformation under both physiological and pathological circumstances. Single-cell RNA sequencing (scRNA-seq) is competent in disclosing the disparities of cells; nevertheless, it poses challenges in linking the individual cell state at distinct time points. Although computational approaches based on scRNA-seq data have been put forward for trajectory analysis, the result is based on assumptions and fails to reflect the actual states. Consequently, it is necessary to incorporate a "time anchor" into the scRNA-seq library for the temporal documentation of the dynamic expression pattern. This review comprehensively overviews the time-resolved single-cell transcriptomic sequencing methodologies and applications. As scRNA-seq functions as the basis for profiling single-cell expression patterns, the review initially introduces various scRNA-seq approaches. Subsequently, the review focuses on the different experimental strategies for introducing a "time anchor" to scRNA-seq, highlighting their principles, strengths, weaknesses, and comparing their adaptation in various scenarios. Next, it provides a brief summary of applications in immunity response, cancer progression, and embryo development. Finally, the review concludes with a forward-looking perspective on future advancements in time-resolved single-cell transcriptomic sequencing.

Deconvolution of spatial transcriptomics data via graph contrastive learning and partial least square regression

Deciphering the cellular abundance in spatial transcriptomics (ST) is crucial for revealing the spatial architecture of cellular heterogeneity within tissues. However, some of the current spatial sequencing technologies are in low resolutions, leading to each spot having multiple heterogeneous cells. Additionally, current spatial deconvolution methods lack the ability to utilize multi-modality information such as gene expression and chromatin accessibility from single-cell multi-omics data. In this study, we introduce a graph Contrastive Learning and Partial Least Squares regression-based method, CLPLS, to deconvolute ST data. CLPLS is a flexible method that it can be extended to integrate ST data and single-cell multi-omics data, enabling the exploration of the spatially epigenomic heterogeneity. We applied CLPLS to both simulated and real datasets coming from different platforms. Benchmark analyses with other methods on these datasets show the superior performance of CLPLS in deconvoluting spots in single cell level.

Clustering-independent estimation of cell abundances in bulk tissues using single-cell RNA-seq data

Single-cell RNA sequencing has transformed the study of biological tissues by enabling transcriptomic characterizations of their constituent cell states. Computational methods for gene expression deconvolution use this information to infer the cell composition of related tissues profiled at the bulk level. However, current deconvolution methods are restricted to discrete cell types and have limited power to make inferences about continuous cellular processes such as cell differentiation or immune cell activation. We present ConDecon, a clustering-independent method for inferring the likelihood for each cell in a single-cell dataset to be present in a bulk tissue. ConDecon represents an improvement in phenotypic resolution and functionality with respect to regression-based methods. Using ConDecon, we discover the implication of neurodegenerative microglia inflammatory pathways in the mesenchymal transformation of pediatric ependymoma and characterize their spatial trajectories of activation. The generality of this approach enables the deconvolution of other data modalities, such as bulk ATAC-seq data.

The advance of single cell transcriptome to study kidney immune cells in diabetic kidney disease

Diabetic kidney disease (DKD) is a prevalent microvascular complication of diabetes mellitus and a primary cause of end-stage renal disease (ESRD). Increasing studies suggest that immune cells are involved in regulating renal inflammation, which contributes to the progression of DKD. Compared with conventional methods, single-cell sequencing technology is more developed technique that has advantages in resolving cellular heterogeneity, parallel multi-omics studies, and discovering new cell types. ScRNA-seq helps researchers to analyze specifically gene expressions, signaling pathways, intercellular communication as well as their regulations in various immune cells of kidney biopsy and urine samples. It is still challenging to investigate the function of each cell type in the pathophysiology of kidney due to its complex and heterogeneous structure and function. Here, we discuss the application of single-cell transcriptomics in the field of DKD and highlight several recent studies that explore the important role of immune cells including macrophage, T cells, B cells etc. in DKD through scRNA-seq analyses. Through combing the researches of scRNA-seq on immune cells in DKD, this review provides novel perspectives on the pathogenesis and immune therapeutic strategy for DKD.

Lamination-based organoid spatially resolved transcriptomics technique for primary lung and liver organoid characterization

Spatial-transcriptomics technologies have demonstrated exceptional performance in characterizing brain and visceral organ tissues, as well as brain and retinal organoids. However, it has not yet been proven whether spatial transcriptomics can effectively characterize primary tissue-derived organoids, as the standardized tissue sectioning or slicing methods are not applicable for such organoids. Herein, we present a technique, lamination-based organoid spatially resolved transcriptomics (LOSRT), for organoid-spatially resolved transcriptomics based on organoid lamination. Primary mouse lung and liver-derived organoids were used in this study. The organoids were formulated using the droplet-engineering method and laminated using a homemade device with weight compression. This technique preserved most cells in individual organoids while maintaining delicate epithelium structures in laminated domains that can be recognized through visual segmentation. The mouse lung and liver organoids were resolved comprising various cell types, including alveolar cells, damage-associated transient progenitor cells, basal cells, macrophages, endothelial cells, fibroblasts, hepatocytes, and hepatic stellate cells. The distribution and count of cells were confirmed using immunohistology and identified with spatial transcriptomic features. This study reports an automated and integrated spatial transcriptomics method for primary organoids. It has the potential to standardize and rapidly characterize primary tissue-derived organoids.

Dissecting the dynamic cellular transcriptional atlas of adult teleost testis development throughout the annual reproductive cycle

Teleost testis development during the annual cycle involves dramatic changes in cellular compositions and molecular events. In this study, the testicular cells derived from adult black rockfish at distinct stages - regressed, regenerating and differentiating - were meticulously dissected via single-cell transcriptome sequencing. A continuous developmental trajectory of spermatogenic cells, from spermatogonia to spermatids, was delineated, elucidating the molecular events involved in spermatogenesis. Subsequently, the dynamic regulation of gene expression associated with spermatogonia proliferation and differentiation was observed across spermatogonia subgroups and developmental stages. A bioenergetic transition from glycolysis to mitochondrial respiration of spermatogonia during the annual developmental cycle was demonstrated, and a deeper level of heterogeneity and molecular characteristics was revealed by re-clustering analysis. Additionally, the developmental trajectory of Sertoli cells was delineated, alongside the divergence of Leydig cells and macrophages. Moreover, the interaction network between testicular micro-environment somatic cells and spermatogenic cells was established. Overall, our study provides detailed information on both germ and somatic cells within teleost testes during the annual reproductive cycle, which lays the foundation for spermatogenesis regulation and germplasm preservation of endangered species.

Leveraging zebrafish to investigate pancreatic development, regeneration, and diabetes

The zebrafish has become an outstanding model for studying organ development and tissue regeneration, which is prominently leveraged for studies of pancreatic development, insulin-producing β-cells, and diabetes. Although studied for more than two decades, many aspects remain elusive and it has only recently been possible to investigate these due to technical advances in transcriptomics, chemical-genetics, genome editing, drug screening, and in vivo imaging. Here, we review recent findings on zebrafish pancreas development, β-cell regeneration, and how zebrafish can be used to provide novel insights into gene functions, disease mechanisms, and therapeutic targets in diabetes, inspiring further use of zebrafish for the development of novel therapies for diabetes.

Clustering-independent estimation of cell abundances in bulk tissues using single-cell RNA-seq data

Single-cell RNA-sequencing has transformed the study of biological tissues by enabling transcriptomic characterizations of their constituent cell states. Computational methods for gene expression deconvolution use this information to infer the cell composition of related tissues profiled at the bulk level. However, current deconvolution methods are restricted to discrete cell types and have limited power to make inferences about continuous cellular processes like cell differentiation or immune cell activation. We present ConDecon, a clustering-independent method for inferring the likelihood for each cell in a single-cell dataset to be present in a bulk tissue. ConDecon represents an improvement in phenotypic resolution and functionality with respect to regression-based methods. Using ConDecon, we discover the implication of neurodegenerative microglia inflammatory pathways in the mesenchymal transformation of pediatric ependymoma and characterize their spatial trajectories of activation. The generality of this approach enables the deconvolution of other data modalities such as bulk ATAC-seq data.

Deconvolving organogenesis in space and time via spatial transcriptomics in thick tissues

Organ development is guided by a space-time landscape that constraints cell behavior. This landscape is challenging to characterize for the hair follicle - the most abundant mini organ - due to its complex microscopic structure and asynchronous development. We developed 3DEEP, a tissue clearing and spatial transcriptomic strategy for characterizing tissue blocks up to 400 µm in thickness. We captured 371 hair follicles at different stages of organogenesis in 1 mm3 of skin of a 12-hour-old mouse with 6 million transcripts from 81 genes. From this single time point, we deconvoluted follicles by age based on whole-organ molecular pseudotimes to animate a stop-motion 3D atlas of follicle development along its trajectory. We defined molecular stages for hair follicle organogenesis and characterized the order of emergence for its structures, differential signaling dynamics at its top and bottom, morphogen shifts preceding and accompanying structural changes, and series of structural changes leading to the formation of its canal and opening. We further found that hair follicle stem cells and their niche are established and stratified early in organogenesis, before the formation of the hair bulb. Overall, this work demonstrates the power of increased depth of spatial transcriptomics to provide a four-dimensional analysis of organogenesis.

Spatiotemporal transcriptomic landscape of rice embryonic cells during seed germination

Characterizing cellular features during seed germination is crucial for understanding the complex biological functions of different embryonic cells in regulating seed vigor and seedling establishment. We performed spatially enhanced resolution omics sequencing (Stereo-seq) and single-cell RNA sequencing (scRNA-seq) to capture spatially resolved single-cell transcriptomes of germinating rice embryos. An automated cell-segmentation model, employing deep learning, was developed to accommodate the analysis requirements. The spatial transcriptomes of 6, 24, 36, and 48 h after imbibition unveiled both known and previously unreported embryo cell types, including two unreported scutellum cell types, corroborated by in situ hybridization and functional exploration of marker genes. Temporal transcriptomic profiling delineated gene expression dynamics in distinct embryonic cell types during seed germination, highlighting key genes involved in nutrient metabolism, biosynthesis, and signaling of phytohormones, reprogrammed in a cell-type-specific manner. Our study provides a detailed spatiotemporal transcriptome of rice embryo and presents a previously undescribed methodology for exploring the roles of different embryonic cells in seed germination.

Spatiotemporal omics for biology and medicine

The completion of the Human Genome Project has provided a foundational blueprint for understanding human life. Nonetheless, understanding the intricate mechanisms through which our genetic blueprint is involved in disease or orchestrates development across temporal and spatial dimensions remains a profound scientific challenge. Recent breakthroughs in cellular omics technologies have paved new pathways for understanding the regulation of genomic elements and the relationship between gene expression, cellular functions, and cell fate determination. The advent of spatial omics technologies, encompassing both imaging and sequencing-based methodologies, has enabled a comprehensive understanding of biological processes from a cellular ecosystem perspective. This review offers an updated overview of how spatial omics has advanced our understanding of the translation of genetic information into cellular heterogeneity and tissue structural organization and their dynamic changes over time. It emphasizes the discovery of various biological phenomena, related to organ functionality, embryogenesis, species evolution, and the pathogenesis of diseases.

StaVia: spatially and temporally aware cartography with higher-order random walks for cell atlases

Single-cell atlases pose daunting computational challenges pertaining to the integration of spatial and temporal information and the visualization of trajectories across large atlases. We introduce StaVia, a computational framework that synergizes multi-faceted single-cell data with higher-order random walks that leverage the memory of cells' past states, fused with a cartographic Atlas View that offers intuitive graph visualization. This spatially aware cartography captures relationships between cell populations based on their spatial location as well as their gene expression and developmental stage. We demonstrate this using zebrafish gastrulation data, underscoring its potential to dissect complex biological landscapes in both spatial and temporal contexts.

The single-cell atlas of the epididymis in mice reveals the changes in epididymis function before and after sexual maturity

Introduction: The epididymis is important for sperm transport, maturation, and storage. Methods: The head and tail of the epididymis of 5-week-old and 10-week-old C57 BL/6J male mice were used for single-cell sequencing. Results: 10 cell types including main, basal, and narrow/clear cells are identified. Next, we performed cell subgroup analysis, functional enrichment analysis, and differentiation potential prediction on principal cells, clear cells, and basal cells. Our study indicates that the principal cells are significantly involved in sperm maturation, as well as in antiviral and anti-tumor immune responses. Clear cells are likely to play a crucial role in safeguarding sperm and maintaining epididymal pH levels. Basal cells are implicated in the regulation of inflammatory and stress responses. The composition and functions of the various cell types within the epididymis undergo significant changes before and after sexual maturity. Furthermore, pseudo-temporal analysis elucidates the protective and supportive roles of epididymal cells in sperm maturation during sexual maturation. Discussion: This study offers a theoretical framework and forecasts for the investigation of epididymal sperm maturation and epididymal immunity.

STdGCN: spatial transcriptomic cell-type deconvolution using graph convolutional networks

Spatially resolved transcriptomics integrates high-throughput transcriptome measurements with preserved spatial cellular organization information. However, many technologies cannot reach single-cell resolution. We present STdGCN, a graph model leveraging single-cell RNA sequencing (scRNA-seq) as reference for cell-type deconvolution in spatial transcriptomic (ST) data. STdGCN incorporates expression profiles from scRNA-seq and spatial localization from ST data for deconvolution. Extensive benchmarking on multiple datasets demonstrates that STdGCN outperforms 17 state-of-the-art models. In a human breast cancer Visium dataset, STdGCN delineates stroma, lymphocytes, and cancer cells, aiding tumor microenvironment analysis. In human heart ST data, STdGCN identifies changes in endothelial-cardiomyocyte communications during tissue development.

CellRank 2: unified fate mapping in multiview single-cell data

Single-cell RNA sequencing allows us to model cellular state dynamics and fate decisions using expression similarity or RNA velocity to reconstruct state-change trajectories; however, trajectory inference does not incorporate valuable time point information or utilize additional modalities, whereas methods that address these different data views cannot be combined or do not scale. Here we present CellRank 2, a versatile and scalable framework to study cellular fate using multiview single-cell data of up to millions of cells in a unified fashion. CellRank 2 consistently recovers terminal states and fate probabilities across data modalities in human hematopoiesis and endodermal development. Our framework also allows combining transitions within and across experimental time points, a feature we use to recover genes promoting medullary thymic epithelial cell formation during pharyngeal endoderm development. Moreover, we enable estimating cell-specific transcription and degradation rates from metabolic-labeling data, which we apply to an intestinal organoid system to delineate differentiation trajectories and pinpoint regulatory strategies.

Simulating multiple variability in spatially resolved transcriptomics with scCube

A pressing challenge in spatially resolved transcriptomics (SRT) is to benchmark the computational methods. A widely-used approach involves utilizing simulated data. However, biases exist in terms of the currently available simulated SRT data, which seriously affects the accuracy of method evaluation and validation. Herein, we present scCube ( https://github.com/ZJUFanLab/scCube ), a Python package for independent, reproducible, and technology-diverse simulation of SRT data. scCube not only enables the preservation of spatial expression patterns of genes in reference-based simulations, but also generates simulated data with different spatial variability (covering the spatial pattern type, the resolution, the spot arrangement, the targeted gene type, and the tissue slice dimension, etc.) in reference-free simulations. We comprehensively benchmark scCube with existing single-cell or SRT simulators, and demonstrate the utility of scCube in benchmarking spot deconvolution, gene imputation, and resolution enhancement methods in detail through three applications.

Status of single-cell RNA sequencing for reproductive toxicology in zebrafish and the transcriptomic trade-off

The utilization of transcriptomic studies identifying profiles of gene expression, especially in toxicogenomics, has catapulted next-generation sequencing to the forefront of reproductive toxicology. An innovative yet underutilized RNA sequencing technique emerging into this field is single-cell RNA sequencing (scRNA-seq), which provides sequencing at the individual cellular level of gonad tissue. ScRNA-seq provides a novel and unique perspective for identifying distinct cellular profiles, including identification of rare cell subtypes. The specificity of scRNA-seq is a powerful tool for reproductive toxicity research, especially for translational animal models including zebrafish. Studies to date not only have focused on 'tissue atlassing' or characterizing what cell types make up different tissues but have also begun to include toxicant exposure as a factor that this review aims to explore. Future scRNA-seq studies will contribute to understanding exposure-induced outcomes; however, the trade-offs with traditional methods need to be considered.

Spatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine

Aging and regeneration represent complex biological phenomena that have long captivated the scientific community. To fully comprehend these processes, it is essential to investigate molecular dynamics through a lens that encompasses both spatial and temporal dimensions. Conventional omics methodologies, such as genomics and transcriptomics, have been instrumental in identifying critical molecular facets of aging and regeneration. However, these methods are somewhat limited, constrained by their spatial resolution and their lack of capacity to dynamically represent tissue alterations. The advent of emerging spatiotemporal multi-omics approaches, encompassing transcriptomics, proteomics, metabolomics, and epigenomics, furnishes comprehensive insights into these intricate molecular dynamics. These sophisticated techniques facilitate accurate delineation of molecular patterns across an array of cells, tissues, and organs, thereby offering an in-depth understanding of the fundamental mechanisms at play. This review meticulously examines the significance of spatiotemporal multi-omics in the realms of aging and regeneration research. It underscores how these methodologies augment our comprehension of molecular dynamics, cellular interactions, and signaling pathways. Initially, the review delineates the foundational principles underpinning these methods, followed by an evaluation of their recent applications within the field. The review ultimately concludes by addressing the prevailing challenges and projecting future advancements in the field. Indubitably, spatiotemporal multi-omics are instrumental in deciphering the complexities inherent in aging and regeneration, thus charting a course toward potential therapeutic innovations.

Accurately deciphering spatial domains for spatially resolved transcriptomics with stCluster

Spatial transcriptomics provides valuable insights into gene expression within the native tissue context, effectively merging molecular data with spatial information to uncover intricate cellular relationships and tissue organizations. In this context, deciphering cellular spatial domains becomes essential for revealing complex cellular dynamics and tissue structures. However, current methods encounter challenges in seamlessly integrating gene expression data with spatial information, resulting in less informative representations of spots and suboptimal accuracy in spatial domain identification. We introduce stCluster, a novel method that integrates graph contrastive learning with multi-task learning to refine informative representations for spatial transcriptomic data, consequently improving spatial domain identification. stCluster first leverages graph contrastive learning technology to obtain discriminative representations capable of recognizing spatially coherent patterns. Through jointly optimizing multiple tasks, stCluster further fine-tunes the representations to be able to capture complex relationships between gene expression and spatial organization. Benchmarked against six state-of-the-art methods, the experimental results reveal its proficiency in accurately identifying complex spatial domains across various datasets and platforms, spanning tissue, organ, and embryo levels. Moreover, stCluster can effectively denoise the spatial gene expression patterns and enhance the spatial trajectory inference. The source code of stCluster is freely available at https://github.com/hannshu/stCluster.

Sex-specific developmental gene expression atlas unveils dimorphic gene networks in C. elegans

Sex-specific traits and behaviors emerge during development by the acquisition of unique properties in the nervous system of each sex. However, the genetic events responsible for introducing these sex-specific features remain poorly understood. In this study, we create a comprehensive gene expression atlas of pure populations of hermaphrodites and males of the nematode Caenorhabditis elegans across development. We discover numerous differentially expressed genes, including neuronal gene families like transcription factors, neuropeptides, and G protein-coupled receptors. We identify INS-39, an insulin-like peptide, as a prominent male-biased gene expressed specifically in ciliated sensory neurons. We show that INS-39 serves as an early-stage male marker, facilitating the effective isolation of males in high-throughput experiments. Through complex and sex-specific regulation, ins-39 plays pleiotropic sexually dimorphic roles in various behaviors, while also playing a shared, dimorphic role in early life stress. This study offers a comparative sexual and developmental gene expression database for C. elegans. Furthermore, it highlights conserved genes that may underlie the sexually dimorphic manifestation of different human diseases.

A spatial transcriptome map of the developing maize ear

A comprehensive understanding of inflorescence development is crucial for crop genetic improvement, as inflorescence meristems give rise to reproductive organs and determine grain yield. However, dissecting inflorescence development at the cellular level has been challenging owing to a lack of specific marker genes to distinguish among cell types, particularly in different types of meristems that are vital for organ formation. In this study, we used spatial enhanced resolution omics-sequencing (Stereo-seq) to construct a precise spatial transcriptome map of the developing maize ear primordium, identifying 12 cell types, including 4 newly defined cell types found mainly in the inflorescence meristem. By extracting the meristem components for detailed clustering, we identified three subtypes of meristem and validated two MADS-box genes that were specifically expressed at the apex of determinate meristems and involved in stem cell determinacy. Furthermore, by integrating single-cell RNA transcriptomes, we identified a series of spatially specific networks and hub genes that may provide new insights into the formation of different tissues. In summary, this study provides a valuable resource for research on cereal inflorescence development, offering new clues for yield improvement.

The chromatin accessibility dynamics during cell fate specifications in zebrafish early embryogenesis

Chromatin accessibility plays a critical role in the regulation of cell fate decisions. Although gene expression changes have been extensively profiled at the single-cell level during early embryogenesis, the dynamics of chromatin accessibility at cis-regulatory elements remain poorly studied. Here, we used a plate-based single-cell ATAC-seq method to profile the chromatin accessibility dynamics of over 10 000 nuclei from zebrafish embryos. We investigated several important time points immediately after zygotic genome activation (ZGA), covering key developmental stages up to dome. The results revealed key chromatin signatures in the first cell fate specifications when cells start to differentiate into enveloping layer (EVL) and yolk syncytial layer (YSL) cells. Finally, we uncovered many potential cell-type specific enhancers and transcription factor motifs that are important for the cell fate specifications.

METTL3-dependent m6A modification of PSEN1 mRNA regulates craniofacial development through the Wnt/β-catenin signaling pathway

Craniofacial malformations, often associated with syndromes, are prevalent birth defects. Emerging evidence underscores the importance of m6A modifications in various bioprocesses such as stem cell differentiation, tissue development, and tumorigenesis. Here, in vivo, experiments with zebrafish models revealed that mettl3-knockdown embryos at 144 h postfertilization exhibited aberrant craniofacial features, including altered mouth opening, jaw dimensions, ethmoid plate, tooth formation and hypoactive behavior. Similarly, low METTL3 expression inhibited the proliferation and migration of BMSCs, HEPM cells, and DPSCs. Loss of METTL3 led to reduced mRNA m6A methylation and PSEN1 expression, impacting craniofacial phenotypes. Co-injection of mettl3 or psen1 mRNA rescued the level of Sox10 fusion protein, promoted voluntary movement, and mitigated abnormal craniofacial phenotypes induced by mettl3 knockdown in zebrafish. Mechanistically, YTHDF1 enhanced the mRNA stability of m6A-modified PSEN1, while decreased METTL3-mediated m6A methylation hindered β-catenin binding to PSEN1, suppressing Wnt/β-catenin signaling. Pharmacological activation of the Wnt/β-catenin pathway partially alleviated the phenotypes of mettl3 morphant and reversed the decreases in cell proliferation and migration induced by METTL3 silencing. This study elucidates the pivotal role of METTL3 in craniofacial development via the METTL3/YTHDF1/PSEN1/β-catenin signaling axis.

Deficiency of Acetyltransferase nat10 in Zebrafish Causes Developmental Defects in the Visual Function

Purpose

N4-acetylcytidine (ac4C) is a post-transcriptional RNA modification catalyzed by N-acetyltransferase 10 (NAT10), a critical factor known to influence mRNA stability. However, the role of ac4C in visual development remains unexplored.

Methods

Analysis of public datasets and immunohistochemical staining were conducted to assess the expression pattern of nat10 in zebrafish. We used CRISPR/Cas9 and RNAi technologies to knockout (KO) and knockdown (KD) nat10, the zebrafish ortholog of human NAT10, and evaluated its effects on early development. To assess the impact of nat10 knockdown on visual function, we performed comprehensive histological evaluations and behavioral analyses. Transcriptome profiling and real-time (RT)-PCR were utilized to detect alterations in gene expression resulting from the nat10 knockdown. Dot-blot and RNA immunoprecipitation (RIP)-PCR analyses were conducted to verify changes in ac4C levels in both total RNA and opsin mRNA specifically. Additionally, we used the actinomycin D assay to examine the stability of opsin mRNA following the nat10 KD.

Results

Our study found that the zebrafish NAT10 protein shares similar structural properties with its human counterpart. We observed that the nat10 gene was prominently expressed in the visual system during early zebrafish development. A deficiency of nat10 in zebrafish embryos resulted in increased mortality and developmental abnormalities. Behavioral and histological assessments indicated significant vision impairment in nat10 KD zebrafish. Transcriptomic analysis and RT-PCR identified substantial downregulation of retinal transcripts related to phototransduction, light response, photoreceptors, and visual perception in the nat10 KD group. Dot-blot and RIP-PCR analyses confirmed a pronounced reduction in ac4C levels in both total RNA and specifically in opsin messenger RNA (mRNA). Additionally, by evaluating mRNA decay in zebrafish treated with actinomycin D, we observed a significant decrease in the stability of opsin mRNA in the nat10 KD group.

Conclusions

The ac4C-mediated mRNA modification plays an essential role in maintaining visual development and retinal function. The loss of NAT10-mediated ac4C modification results in significant disruptions to these processes, underlining the importance of this RNA modification in ocular development.

Introducing single cell stereo-sequencing technology to transform the plant transcriptome landscape

Single cell RNA-sequencing (scRNA-seq) advancements have helped detect transcriptional heterogeneities in biological samples. However, scRNA-seq cannot currently provide high-resolution spatial transcriptome information or identify subcellular organs in biological samples. These limitations have led to the development of spatially enhanced-resolution omics-sequencing (Stereo-seq), which combines spatial information with single cell transcriptomics to address the challenges of scRNA-seq alone. In this review, we discuss the advantages of Stereo-seq technology. We anticipate that the application of such an integrated approach in plant research will advance our understanding of biological process in the plant transcriptomics era. We conclude with an outlook of how such integration will enhance crop improvement.

SCAN: Spatiotemporal Cloud Atlas for Neural cells

The nervous system is one of the most complicated and enigmatic systems within the animal kingdom. Recently, the emergence and development of spatial transcriptomics (ST) and single-cell RNA sequencing (scRNA-seq) technologies have provided an unprecedented ability to systematically decipher the cellular heterogeneity and spatial locations of the nervous system from multiple unbiased aspects. However, efficiently integrating, presenting and analyzing massive multiomic data remains a huge challenge. Here, we manually collected and comprehensively analyzed high-quality scRNA-seq and ST data from the nervous system, covering 10 679 684 cells. In addition, multi-omic datasets from more than 900 species were included for extensive data mining from an evolutionary perspective. Furthermore, over 100 neurological diseases (e.g. Alzheimer's disease, Parkinson's disease, Down syndrome) were systematically analyzed for high-throughput screening of putative biomarkers. Differential expression patterns across developmental time points, cell types and ST spots were discerned and subsequently subjected to extensive interpretation. To provide researchers with efficient data exploration, we created a new database with interactive interfaces and integrated functions called the Spatiotemporal Cloud Atlas for Neural cells (SCAN), freely accessible at http://47.98.139.124:8799 or http://scanatlas.net. SCAN will benefit the neuroscience research community to better exploit the spatiotemporal atlas of the neural system and promote the development of diagnostic strategies for various neurological disorders.

STOmicsDB: a comprehensive database for spatial transcriptomics data sharing, analysis and visualization

Recent technological developments in spatial transcriptomics allow researchers to measure gene expression of cells and their spatial locations at the single-cell level, generating detailed biological insight into biological processes. A comprehensive database could facilitate the sharing of spatial transcriptomic data and streamline the data acquisition process for researchers. Here, we present the Spatial TranscriptOmics DataBase (STOmicsDB), a database that serves as a one-stop hub for spatial transcriptomics. STOmicsDB integrates 218 manually curated datasets representing 17 species. We annotated cell types, identified spatial regions and genes, and performed cell-cell interaction analysis for these datasets. STOmicsDB features a user-friendly interface for the rapid visualization of millions of cells. To further facilitate the reusability and interoperability of spatial transcriptomic data, we developed standards for spatial transcriptomic data archiving and constructed a spatial transcriptomic data archiving system. Additionally, we offer a distinctive capability of customizing dedicated sub-databases in STOmicsDB for researchers, assisting them in visualizing their spatial transcriptomic analyses. We believe that STOmicsDB could contribute to research insights in the spatial transcriptomics field, including data archiving, sharing, visualization and analysis. STOmicsDB is freely accessible at https://db.cngb.org/stomics/.

amer1 Regulates Zebrafish Craniofacial Development by Interacting with the Wnt/β-Catenin Pathway

Microtia-atresia is a rare type of congenital craniofacial malformation causing severe damage to the appearance and hearing ability of affected individuals. The genetic factors associated with microtia-atresia have not yet been determined. The AMER1 gene has been identified as potentially pathogenic for microtia-atresia in two twin families. An amer1 mosaic knockdown zebrafish model was constructed using CRISPR/Cas9. The phenotype and the development process of cranial neural crest cells of the knockdown zebrafish were examined. Components of the Wnt/β-catenin pathway were examined by qPCR, Western blotting, and immunofluorescence assay. IWR-1-endo, a reversible inhibitor of the Wnt/β-catenin pathway, was applied to rescue the abnormal phenotype. The present study showed that the development of mandibular cartilage in zebrafish was severely compromised by amer1 knockdown using CRISPR/Cas9. Specifically, amer1 knockdown was found to affect the proliferation and apoptosis of cranial neural crest cells, as well as their differentiation to chondrocytes. Mechanistically, amer1 exerted an antagonistic effect on the Wnt/β-catenin pathway. The application of IWR-1-endo could partially rescue the abnormal phenotype. We demonstrated that amer1 was essential for the craniofacial development of zebrafish by interacting with the Wnt/β-catenin pathway. These findings provide important insight into the role of amer1 in zebrafish mandibular development and the pathology of microtia-atresia caused by AMER1 gene mutations in humans.

EAGS: efficient and adaptive Gaussian smoothing applied to high-resolved spatial transcriptomics

BACKGROUND

The emergence of high-resolved spatial transcriptomics (ST) has facilitated the research of novel methods to investigate biological development, organism growth, and other complex biological processes. However, high-resolved and whole transcriptomics ST datasets require customized imputation methods to improve the signal-to-noise ratio and the data quality.

FINDINGS

We propose an efficient and adaptive Gaussian smoothing (EAGS) imputation method for high-resolved ST. The adaptive 2-factor smoothing of EAGS creates patterns based on the spatial and expression information of the cells, creates adaptive weights for the smoothing of cells in the same pattern, and then utilizes the weights to restore the gene expression profiles. We assessed the performance and efficiency of EAGS using simulated and high-resolved ST datasets of mouse brain and olfactory bulb.

CONCLUSIONS

Compared with other competitive methods, EAGS shows higher clustering accuracy, better biological interpretations, and significantly reduced computational consumption.

Cellular and molecular mechanisms of fibrosis and resolution in bleomycin-induced pulmonary fibrosis mouse model revealed by spatial transcriptome analysis

The bleomycin-induced pulmonary fibrosis mouse model is commonly used in idiopathic pulmonary fibrosis research, but its cellular and molecular changes and efficiency as a model at the molecular level are not fully understood. In this study, we used spatial transcriptome technology to investigate the cellular and molecular changes in the lungs of bleomycin-induced pulmonary fibrosis mouse models. Our analyses revealed cell dynamics during fibrosis in epithelial cells, mesenchymal cells, immunocytes, and erythrocytes with their spatial distribution available. We confirmed the differentiation of the alveolar type II (AT2) cell type expressing Krt8, and we inferred their trajectories from both the AT2 cells and club cells. In addition to the fibrosis process, we also noticed evidence of self-resolving, especially to identify possible self-resolving related genes, including Prkca. Our findings provide insights into the cellular and molecular mechanisms underlying fibrosis resolution and represent the first spatiotemporal transcriptome dataset of the bleomycin-induced fibrosis mouse model.

Spatial transcriptomics in development and disease

The proper functioning of diverse biological systems depends on the spatial organization of their cells, a critical factor for biological processes like shaping intricate tissue functions and precisely determining cell fate. Nonetheless, conventional bulk or single-cell RNA sequencing methods were incapable of simultaneously capturing both gene expression profiles and the spatial locations of cells. Hence, a multitude of spatially resolved technologies have emerged, offering a novel dimension for investigating regional gene expression, spatial domains, and interactions between cells. Spatial transcriptomics (ST) is a method that maps gene expression in tissue while preserving spatial information. It can reveal cellular heterogeneity, spatial organization and functional interactions in complex biological systems. ST can also complement and integrate with other omics methods to provide a more comprehensive and holistic view of biological systems at multiple levels of resolution. Since the advent of ST, new methods offering higher throughput and resolution have become available, holding significant potential to expedite fresh insights into comprehending biological complexity. Consequently, a rapid increase in associated research has occurred, using these technologies to unravel the spatial complexity during developmental processes or disease conditions. In this review, we summarize the recent advancement of ST in historical, technical, and application contexts. We compare different types of ST methods based on their principles and workflows, and present the bioinformatics tools for analyzing and integrating ST data with other modalities. We also highlight the applications of ST in various domains of biomedical research, especially development and diseases. Finally, we discuss the current limitations and challenges in the field, and propose the future directions of ST.

Latent feature extraction with a prior-based self-attention framework for spatial transcriptomics

Rapid advances in spatial transcriptomics (ST) have revolutionized the interrogation of spatial heterogeneity and increase the demand for comprehensive methods to effectively characterize spatial domains. As a prerequisite for ST data analysis, spatial domain characterization is a crucial step for downstream analyses and biological implications. Here we propose a prior-based self-attention framework for spatial transcriptomics (PAST), a variational graph convolutional autoencoder for ST, which effectively integrates prior information via a Bayesian neural network, captures spatial patterns via a self-attention mechanism, and enables scalable application via a ripple walk sampler strategy. Through comprehensive experiments on data sets generated by different technologies, we show that PAST can effectively characterize spatial domains and facilitate various downstream analyses, including ST visualization, spatial trajectory inference and pseudotime analysis. Also, we highlight the advantages of PAST for multislice joint embedding and automatic annotation of spatial domains in newly sequenced ST data. Compared with existing methods, PAST is the first ST method that integrates reference data to analyze ST data. We anticipate that PAST will open up new avenues for researchers to decipher ST data with customized reference data, which expands the applicability of ST technology.

Harnessing computational spatial omics to explore the spatial biology intricacies

Spatially resolved transcriptomics (SRT) has unlocked new dimensions in our understanding of intricate tissue architectures. However, this rapidly expanding field produces a wealth of diverse and voluminous data, necessitating the evolution of sophisticated computational strategies to unravel inherent patterns. Two distinct methodologies, gene spatial pattern recognition (GSPR) and tissue spatial pattern recognition (TSPR), have emerged as vital tools in this process. GSPR methodologies are designed to identify and classify genes exhibiting noteworthy spatial patterns, while TSPR strategies aim to understand intercellular interactions and recognize tissue domains with molecular and spatial coherence. In this review, we provide a comprehensive exploration of SRT, highlighting crucial data modalities and resources that are instrumental for the development of methods and biological insights. We address the complexities and challenges posed by the use of heterogeneous data in developing GSPR and TSPR methodologies and propose an optimal workflow for both. We delve into the latest advancements in GSPR and TSPR, examining their interrelationships. Lastly, we peer into the future, envisaging the potential directions and perspectives in this dynamic field.

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.