Advances in spatial transcriptomic data analysis

Shining the Light on Astrocytic Ensembles

While neurons have traditionally been considered the primary players in information processing, the role of astrocytes in this mechanism has largely been overlooked due to experimental constraints. In this review, we propose that astrocytic ensembles are active working groups that contribute significantly to animal conduct and suggest that studying the maps of these ensembles in conjunction with neurons is crucial for a more comprehensive understanding of behavior. We also discuss available methods for studying astrocytes and argue that these ensembles, complementarily with neurons, code and integrate complex behaviors, potentially specializing in concrete functions.

Deep learning in spatial transcriptomics: Learning from the next next-generation sequencing

Spatial transcriptomics (ST) technologies are rapidly becoming the extension of single-cell RNA sequencing (scRNAseq), holding the potential of profiling gene expression at a single-cell resolution while maintaining cellular compositions within a tissue. Having both expression profiles and tissue organization enables researchers to better understand cellular interactions and heterogeneity, providing insight into complex biological processes that would not be possible with traditional sequencing technologies. Data generated by ST technologies are inherently noisy, high-dimensional, sparse, and multi-modal (including histological images, count matrices, etc.), thus requiring specialized computational tools for accurate and robust analysis. However, many ST studies currently utilize traditional scRNAseq tools, which are inadequate for analyzing complex ST datasets. On the other hand, many of the existing ST-specific methods are built upon traditional statistical or machine learning frameworks, which have shown to be sub-optimal in many applications due to the scale, multi-modality, and limitations of spatially resolved data (such as spatial resolution, sensitivity, and gene coverage). Given these intricacies, researchers have developed deep learning (DL)-based models to alleviate ST-specific challenges. These methods include new state-of-the-art models in alignment, spatial reconstruction, and spatial clustering, among others. However, DL models for ST analysis are nascent and remain largely underexplored. In this review, we provide an overview of existing state-of-the-art tools for analyzing spatially resolved transcriptomics while delving deeper into the DL-based approaches. We discuss the new frontiers and the open questions in this field and highlight domains in which we anticipate transformational DL applications.

Exploring heterogeneity of tumor immune cells and adrenal cells in aldosterone-producing adenomas using single-cell RNA-seq and investigating differences by sex

The mechanism behind the higher incidence of aldosterone-producing adenoma (APA) in women compared to men is not yet understood. In this study, we utilized single-cell RNA sequencing (scRNA-seq) to investigate the immune cell infiltration and adrenal cell characteristics in APA. Our findings revealed a high presence of immune cells in the tumor microenvironment, with macrophages and T lymphocytes being the most prevalent. Comparison of infiltrating cells between males and females showed that female CD8+T cells had stronger cytotoxic and inflammation-related functions, while female myeloid cells had more enrichment in inflammatory pathways. Additionally, we found that female adrenal cells had greater upregulation of immune-related and antigen presentation pathways. Furthermore, our analysis revealed that zona glomerulosa (ZG) cells had a higher capability for aldosterone synthesis. These results provide a deeper understanding of the APA microenvironment in patients of different sexes and offer new insights into the onset of APA.

SODB facilitates comprehensive exploration of spatial omics data

Spatial omics technologies generate wealthy but highly complex datasets. Here we present Spatial Omics DataBase (SODB), a web-based platform providing both rich data resources and a suite of interactive data analytical modules. SODB currently maintains >2,400 experiments from >25 spatial omics technologies, which are freely accessible as a unified data format compatible with various computational packages. SODB also provides multiple interactive data analytical modules, especially a unique module, Spatial Omics View (SOView). We conduct comprehensive statistical analyses and illustrate the utility of both basic and advanced analytical modules using multiple spatial omics datasets. We demonstrate SOView utility with brain spatial transcriptomics data and recover known anatomical structures. We further delineate functional tissue domains with associated marker genes that were obscured when analyzed using previous methods. We finally show how SODB may efficiently facilitate computational method development. The SODB website is https://gene.ai.tencent.com/SpatialOmics/ . The command-line package is available at https://pysodb.readthedocs.io/en/latest/ .

Reconstruction of the tumor spatial microenvironment along the malignant-boundary-nonmalignant axis

Although advances in spatial transcriptomics (ST) enlarge to unveil spatial landscape of tissues, it remains challenging to delineate pathology-relevant and cellular localizations, and interactions exclusive to a spatial niche (e.g., tumor boundary). Here, we develop Cottrazm, integrating ST with hematoxylin and eosin histological image, and single-cell transcriptomics to delineate the tumor boundary connecting malignant and non-malignant cell spots in tumor tissues, deconvolute cell-type composition at spatial location, and reconstruct cell type-specific gene expression profiles at sub-spot level. We validate the performance of Cottrazm along the malignant-boundary-nonmalignant spatial axis. We identify specific macrophage and fibroblast subtypes localized around tumor boundary that interacted with tumor cells to generate a structural boundary, which limits T cell infiltration and promotes immune exclusion in tumor microenvironment. In this work, Cottrazm provides an integrated tool framework to dissect the tumor spatial microenvironment and facilitates the discovery of functional biological insights, thereby identifying therapeutic targets in oncologic ST datasets.

Lung Cell Atlases in Health and Disease

The human lung cellular portfolio, traditionally characterized by cellular morphology and individual markers, is highly diverse, with over 40 cell types and a complex branching structure highly adapted for agile airflow and gas exchange. While constant during adulthood, lung cellular content changes in response to exposure, injury, and infection. Some changes are temporary, but others are persistent, leading to structural changes and progressive lung disease. The recent advance of single-cell profiling technologies allows an unprecedented level of detail and scale to cellular measurements, leading to the rise of comprehensive cell atlas styles of reporting. In this review, we chronical the rise of cell atlases and explore their contributions to human lung biology in health and disease.

ScRNA-seq and ST-seq in liver research

Spatial transcriptomics, which combine gene expression data with spatial information, has quickly expanded in recent years. With application of this method in liver research, our knowledge about liver development, regeneration, and diseases have been greatly improved. While this field is moving forward, a variety of problems still need to be addressed, including sensitivity, limited capacity to obtain exact single-cell information, data processing methods, as well as others. Methods like single-cell RNA sequencing (scRNA-seq) are usually used together with spatial transcriptome sequencing (ST-seq) to clarify cell-specific gene expression. In this review, we explore how advances of scRNA-seq and ST-seq, especially ST-seq, will pave the way to new opportunities to investigate fundamental questions in liver research. Finally, we will discuss the strengths, limitations, and future perspectives of ST-seq in liver research.

Screening cell-cell communication in spatial transcriptomics via collective optimal transport

Spatial transcriptomic technologies and spatially annotated single-cell RNA sequencing datasets provide unprecedented opportunities to dissect cell-cell communication (CCC). However, incorporation of the spatial information and complex biochemical processes required in the reconstruction of CCC remains a major challenge. Here, we present COMMOT (COMMunication analysis by Optimal Transport) to infer CCC in spatial transcriptomics, which accounts for the competition between different ligand and receptor species as well as spatial distances between cells. A collective optimal transport method is developed to handle complex molecular interactions and spatial constraints. Furthermore, we introduce downstream analysis tools to infer spatial signaling directionality and genes regulated by signaling using machine learning models. We apply COMMOT to simulation data and eight spatial datasets acquired with five different technologies to show its effectiveness and robustness in identifying spatial CCC in data with varying spatial resolutions and gene coverages. Finally, COMMOT identifies new CCCs during skin morphogenesis in a case study of human epidermal development.

Overview of gene expression techniques with an emphasis on vitamin D related studies

Each cell controls when and how its genes must be expressed for proper function. Every function in a cell is driven by signaling molecules through various regulatory cascades. Different cells in a multicellular organism may express very different sets of genes, even though they contain the same DNA. The set of genes expressed in a cell determines the set of proteins and functional RNAs it contains, giving it its unique properties. Malfunction in gene expression harms the cell and can lead to the development of various disease conditions. The use of rapid high-throughput gene expression profiling unravels the complexity of human disease at various levels. Peripheral blood mononuclear cells (PBMC) have been used frequently to understand gene expression homeostasis in various disease conditions. However, more studies are required to validate whether PBMC gene expression patterns accurately reflect the expression of other cells or tissues. Vitamin D, which is responsible for a multitude of health consequences, is also an immune modulatory hormone with major biological activities in the innate and adaptive immune systems. Vitamin D exerts its diverse biological effects in target tissues by regulating gene expression and its deficiency, is recognized as a public health problem worldwide. Understanding the genetic factors that affect vitamin D has the potential benefit that it will make it easier to identify individuals who require supplementation. Different technological advances in gene expression can be used to identify and assess the severity of disease and aid in the development of novel therapeutic interventions. This review focuses on different gene expression approaches and various clinical studies of vitamin D to investigate the role of gene expression in identifying the molecular signature of the disease.

A guidebook of spatial transcriptomic technologies, data resources and analysis approaches

Advances in transcriptomic technologies have deepened our understanding of the cellular gene expression programs of multicellular organisms and provided a theoretical basis for disease diagnosis and therapy. However, both bulk and single-cell RNA sequencing approaches lose the spatial context of cells within the tissue microenvironment, and the development of spatial transcriptomics has made overall bias-free access to both transcriptional information and spatial information possible. Here, we elaborate development of spatial transcriptomic technologies to help researchers select the best-suited technology for their goals and integrate the vast amounts of data to facilitate data accessibility and availability. Then, we marshal various computational approaches to analyze spatial transcriptomic data for various purposes and describe the spatial multimodal omics and its potential for application in tumor tissue. Finally, we provide a detailed discussion and outlook of the spatial transcriptomic technologies, data resources and analysis approaches to guide current and future research on spatial transcriptomics.

Spatial transcriptomics reveals a conserved segment polarity program that governs muscle patterning in Nematostella vectensis

During early animal evolution, the emergence of axially-polarized segments was central to the diversification of complex bilaterian body plans. Nevertheless, precisely how and when segment polarity pathways arose remains obscure. Here we demonstrate the molecular basis for segment polarization in developing larvae of the pre-bilaterian sea anemone Nematostella vectensis . Utilizing spatial transcriptomics, we first constructed a 3-D gene expression atlas of developing larval segments. Capitalizing on accurate in silico predictions, we identified Lbx and Uncx, conserved homeodomain-containing genes that occupy opposing subsegmental domains under the control of both BMP signaling and the Hox-Gbx cascade. Functionally, Lbx mutagenesis eliminated all molecular evidence of segment polarization at larval stage and caused an aberrant mirror-symmetric pattern of retractor muscles in primary polyps. These results demonstrate the molecular basis for segment polarity in a pre-bilaterian animal, suggesting that polarized metameric structures were present in the Cnidaria-Bilateria common ancestor over 600 million years ago.

Highlights

Nematostella endomesodermal tissue forms metameric segments and displays a transcriptomic profile similar to that observed in bilaterian mesoderm Construction of a comprehensive 3-D gene expression atlas enables systematic dissection of segmental identity in endomesoderm Lbx and Uncx , two conserved homeobox-containing genes, establish segment polarity in Nematostella The Cnidarian-Bilaterian common ancestor likely possessed the genetic toolkit to generate polarized metameric structures.

A Primer on Preprocessing, Visualization, Clustering, and Phenotyping of Barcode-Based Spatial Transcriptomics Data

Recent developments in spatially resolved transcriptomics (ST) have resulted in a large number of studies characterizing the architecture of tissues, the spatial distribution of cell types, and their interactions. Furthermore, ST promises to enable the discovery of more accurate drug targets while also providing a better understanding of the etiology and evolution of complex diseases. The analysis of ST brings similar challenges as seen in other gene expression assays such as scRNA-seq; however, there is the additional spatial information that warrants the development of suitable algorithms for the quality control, preprocessing, visualization, and other discovery-enabling approaches (e.g., clustering, cell phenotyping). In this chapter, we review some of the existing algorithms to perform these analytical tasks and highlight some of the unmet analytical challenges in the analysis of ST data. Given the diversity of available ST technologies, we focus this chapter on the analysis of barcode-based RNA quantitation techniques.

Single-cell and spatial transcriptomics: Advances in heart development and disease applications

Current transcriptomics technologies, including bulk RNA-seq, single-cell RNA sequencing (scRNA-seq), single-nucleus RNA-sequencing (snRNA-seq), and spatial transcriptomics (ST), provide novel insights into the spatial and temporal dynamics of gene expression during cardiac development and disease processes. Cardiac development is a highly sophisticated process involving the regulation of numerous key genes and signaling pathways at specific anatomical sites and developmental stages. Exploring the cell biological mechanisms involved in cardiogenesis also contributes to congenital heart disease research. Meanwhile, the severity of distinct heart diseases, such as coronary heart disease, valvular disease, cardiomyopathy, and heart failure, is associated with cellular transcriptional heterogeneity and phenotypic alteration. Integrating transcriptomic technologies in the clinical diagnosis and treatment of heart diseases will aid in advancing precision medicine. In this review, we summarize applications of scRNA-seq and ST in the cardiac field, including organogenesis and clinical diseases, and provide insights into the promise of single-cell and spatial transcriptomics in translational research and precision medicine.

Tumor heterogeneity: preclinical models, emerging technologies, and future applications

Heterogeneity describes the differences among cancer cells within and between tumors. It refers to cancer cells describing variations in morphology, transcriptional profiles, metabolism, and metastatic potential. More recently, the field has included the characterization of the tumor immune microenvironment and the depiction of the dynamics underlying the cellular interactions promoting the tumor ecosystem evolution. Heterogeneity has been found in most tumors representing one of the most challenging behaviors in cancer ecosystems. As one of the critical factors impairing the long-term efficacy of solid tumor therapy, heterogeneity leads to tumor resistance, more aggressive metastasizing, and recurrence. We review the role of the main models and the emerging single-cell and spatial genomic technologies in our understanding of tumor heterogeneity, its contribution to lethal cancer outcomes, and the physiological challenges to consider in designing cancer therapies. We highlight how tumor cells dynamically evolve because of the interactions within the tumor immune microenvironment and how to leverage this to unleash immune recognition through immunotherapy. A multidisciplinary approach grounded in novel bioinformatic and computational tools will allow reaching the integrated, multilayered knowledge of tumor heterogeneity required to implement personalized, more efficient therapies urgently required for cancer patients.

Statistical Analysis of Multiplex Immunofluorescence and Immunohistochemistry Imaging Data

Advances in multiplexed single-cell immunofluorescence (mIF) and multiplex immunohistochemistry (mIHC) imaging technologies have enabled the analysis of cell-to-cell spatial relationships that promise to revolutionize our understanding of tissue-based diseases and autoimmune disorders. Multiplex images are collected as multichannel TIFF files; then denoised, segmented to identify cells and nuclei, normalized across slides with protein markers to correct for batch effects, and phenotyped; and then tissue composition and spatial context at the cellular level are analyzed. This chapter discusses methods and software infrastructure for image processing and statistical analysis of mIF/mIHC data.

Spatial biology of cancer evolution

The natural history of cancers can be understood through the lens of evolution given that the driving forces of cancer development are mutation and selection of fitter clones. Cancer growth and progression are spatial processes that involve the breakdown of normal tissue organization, invasion and metastasis. For these reasons, spatial patterns are an integral part of histological tumour grading and staging as they measure the progression from normal to malignant disease. Furthermore, tumour cells are part of an ecosystem of tumour cells and their surrounding tumour microenvironment. A range of new spatial genomic, transcriptomic and proteomic technologies offers new avenues for the study of cancer evolution with great molecular and spatial detail. These methods enable precise characterizations of the tumour microenvironment, cellular interactions therein and micro-anatomical structures. In conjunction with spatial genomics, it emerges that tumours and microenvironments co-evolve, which helps explain observable patterns of heterogeneity and offers new routes for therapeutic interventions.

Detection of differentially expressed genes in spatial transcriptomics data by spatial analysis of spatial transcriptomics: A novel method based on spatial statistics

Background

Spatial transcriptomics (STs) simultaneously obtains the location and amount of gene expression within a tissue section. However, current methods like FindMarkers calculated the differentially expressed genes (DEGs) based on the classical statistics, which should abolish the spatial information.

Materials and methods

A new method named spatial analysis of spatial transcriptomics (saSpatial) was developed for both the location and the amount of gene expression. Then saSpatial was applied to detect DEGs in both inter- and intra-cross sections. DEGs detected by saSpatial were compared with those detected by FindMarkers.

Results

Spatial analysis of spatial transcriptomics was founded on the basis of spatial statistics. It was able to detect DEGs in different regions in the normal brain section. As for the brain with ischemic stroke, saSpatial revealed the DEGs for the ischemic core and penumbra. In addition, saSpatial characterized the genetic heterogeneity in the normal and ischemic cortex. Compared to FindMarkers, a larger number of valuable DEGs were found by saSpatial.

Conclusion

Spatial analysis of spatial transcriptomics was able to effectively detect DEGs in STs data. It was a simple and valuable tool that could help potential researchers to find more valuable genes in the future research.

Spatial statistics is a comprehensive tool for quantifying cell neighbor relationships and biological processes via tissue image analysis

Automated microscopy and computational image analysis has transformed cell biology, providing quantitative, spatially resolved information on cells and their constituent molecules from the sub-micron to the whole-organ scale. Here we explore the application of spatial statistics to the cellular relationships within tissue microscopy data and discuss how spatial statistics offers cytometry a powerful yet underused mathematical tool set for which the required data are readily captured using standard protocols and microscopy equipment. We also highlight the often-overlooked need to carefully consider the structural heterogeneity of tissues in terms of the applicability of different statistical measures and their accuracy and demonstrate how spatial analyses offer a great deal more than just basic quantification of biological variance. Ultimately, we highlight how statistical modeling can help reveal the hierarchical spatial processes that connect the properties of individual cells to the establishment of biological function.

Benchmarking cell-type clustering methods for spatially resolved transcriptomics data

Spatially resolved transcriptomics technologies enable the measurement of transcriptome information while retaining the spatial context at the regional, cellular or sub-cellular level. While previous computational methods have relied on gene expression information alone for clustering single-cell populations, more recent methods have begun to leverage spatial location and histology information to improve cell clustering and cell-type identification. In this study, using seven semi-synthetic datasets with real spatial locations, simulated gene expression and histology images as well as ground truth cell-type labels, we evaluate 15 clustering methods based on clustering accuracy, robustness to data variation and input parameters, computational efficiency, and software usability. Our analysis demonstrates that even though incorporating the additional spatial and histology information leads to increased accuracy in some datasets, it does not consistently improve clustering compared with using only gene expression data. Our results indicate that for the clustering of spatial transcriptomics data, there are still opportunities to enhance the overall accuracy and robustness by improving information extraction and feature selection from spatial and histology data.

Human Brain Organoids-on-Chip: Advances, Challenges, and Perspectives for Preclinical Applications

There is an urgent need for predictive in vitro models to improve disease modeling and drug target identification and validation, especially for neurological disorders. Cerebral organoids, as alternative methods to in vivo studies, appear now as powerful tools to decipher complex biological processes thanks to their ability to recapitulate many features of the human brain. Combining these innovative models with microfluidic technologies, referred to as brain organoids-on-chips, allows us to model the microenvironment of several neuronal cell types in 3D. Thus, this platform opens new avenues to create a relevant in vitro approach for preclinical applications in neuroscience. The transfer to the pharmaceutical industry in drug discovery stages and the adoption of this approach by the scientific community requires the proposition of innovative microphysiological systems allowing the generation of reproducible cerebral organoids of high quality in terms of structural and functional maturation, and compatibility with automation processes and high-throughput screening. In this review, we will focus on the promising advantages of cerebral organoids for disease modeling and how their combination with microfluidic systems can enhance the reproducibility and quality of these in vitro models. Then, we will finish by explaining why brain organoids-on-chips could be considered promising platforms for pharmacological applications.

Elucidating tumor heterogeneity from spatially resolved transcriptomics data by multi-view graph collaborative learning

Spatially resolved transcriptomics (SRT) technology enables us to gain novel insights into tissue architecture and cell development, especially in tumors. However, lacking computational exploitation of biological contexts and multi-view features severely hinders the elucidation of tissue heterogeneity. Here, we propose stMVC, a multi-view graph collaborative-learning model that integrates histology, gene expression, spatial location, and biological contexts in analyzing SRT data by attention. Specifically, stMVC adopting semi-supervised graph attention autoencoder separately learns view-specific representations of histological-similarity-graph or spatial-location-graph, and then simultaneously integrates two-view graphs for robust representations through attention under semi-supervision of biological contexts. stMVC outperforms other tools in detecting tissue structure, inferring trajectory relationships, and denoising on benchmark slices of human cortex. Particularly, stMVC identifies disease-related cell-states and their transition cell-states in breast cancer study, which are further validated by the functional and survival analysis of independent clinical data. Those results demonstrate clinical and prognostic applications from SRT data.

CCPLS reveals cell-type-specific spatial dependence of transcriptomes in single cells

MOTIVATION

Cell-cell communications regulate internal cellular states, e.g. gene expression and cell functions, and play pivotal roles in normal development and disease states. Furthermore, single-cell RNA sequencing methods have revealed cell-to-cell expression variability of highly variable genes (HVGs), which is also crucial. Nevertheless, the regulation of cell-to-cell expression variability of HVGs via cell-cell communications is still largely unexplored. The recent advent of spatial transcriptome methods has linked gene expression profiles to the spatial context of single cells, which has provided opportunities to reveal those regulations. The existing computational methods extract genes with expression levels influenced by neighboring cell types. However, limitations remain in the quantitativeness and interpretability: they neither focus on HVGs nor consider the effects of multiple neighboring cell types.

RESULTS

Here, we propose CCPLS (Cell-Cell communications analysis by Partial Least Square regression modeling), which is a statistical framework for identifying cell-cell communications as the effects of multiple neighboring cell types on cell-to-cell expression variability of HVGs, based on the spatial transcriptome data. For each cell type, CCPLS performs PLS regression modeling and reports coefficients as the quantitative index of the cell-cell communications. Evaluation using simulated data showed our method accurately estimated the effects of multiple neighboring cell types on HVGs. Furthermore, applications to the two real datasets demonstrate that CCPLS can extract biologically interpretable insights from the inferred cell-cell communications.

AVAILABILITY AND IMPLEMENTATION

The R package is available at https://github.com/bioinfo-tsukuba/CCPLS. The data are available at https://github.com/bioinfo-tsukuba/CCPLS_paper.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Computational solutions for spatial transcriptomics

Transcriptome level expression data connected to the spatial organization of the cells and molecules would allow a comprehensive understanding of how gene expression is connected to the structure and function in the biological systems. The spatial transcriptomics platforms may soon provide such information. However, the current platforms still lack spatial resolution, capture only a fraction of the transcriptome heterogeneity, or lack the throughput for large scale studies. The strengths and weaknesses in current ST platforms and computational solutions need to be taken into account when planning spatial transcriptomics studies. The basis of the computational ST analysis is the solutions developed for single-cell RNA-sequencing data, with advancements taking into account the spatial connectedness of the transcriptomes. The scRNA-seq tools are modified for spatial transcriptomics or new solutions like deep learning-based joint analysis of expression, spatial, and image data are developed to extract biological information in the spatially resolved transcriptomes. The computational ST analysis can reveal remarkable biological insights into spatial patterns of gene expression, cell signaling, and cell type variations in connection with cell type-specific signaling and organization in complex tissues. This review covers the topics that help choosing the platform and computational solutions for spatial transcriptomics research. We focus on the currently available ST methods and platforms and their strengths and limitations. Of the computational solutions, we provide an overview of the analysis steps and tools used in the ST data analysis. The compatibility with the data types and the tools provided by the current ST analysis frameworks are summarized.

The role of single-cell genomics in human genetics

Single-cell sequencing is a powerful approach that can detect genetic alterations and their phenotypic consequences in the context of human development, with cellular resolution. Humans start out as single-cell zygotes and undergo fission and differentiation to develop into multicellular organisms. Before fertilisation and during development, the cellular genome acquires hundreds of mutations that propagate down the cell lineage. Whether germline or somatic in nature, some of these mutations may have significant genotypic impact and lead to diseased cellular phenotypes, either systemically or confined to a tissue. Single-cell sequencing enables the detection and monitoring of the genotype and the consequent molecular phenotypes at a cellular resolution. It offers powerful tools to compare the cellular lineage between 'normal' and 'diseased' conditions and to establish genotype-phenotype relationships. By preserving cellular heterogeneity, single-cell sequencing, unlike bulk-sequencing, allows the detection of even small, diseased subpopulations of cells within an otherwise normal tissue. Indeed, the characterisation of biopsies with cellular resolution can provide a mechanistic view of the disease. While single-cell approaches are currently used mainly in basic research, it can be expected that applications of these technologies in the clinic may aid the detection, diagnosis and eventually the treatment of rare genetic diseases as well as cancer. This review article provides an overview of the single-cell sequencing technologies in the context of human genetics, with an aim to empower clinicians to understand and interpret the single-cell sequencing data and analyses. We discuss the state-of-the-art experimental and analytical workflows and highlight current challenges/limitations. Notably, we focus on two prospective applications of the technology in human genetics, namely the annotation of the non-coding genome using single-cell functional genomics and the use of single-cell sequencing data for in silico variant prioritisation.

Accurate inference of genome-wide spatial expression with iSpatial

Spatially resolved transcriptomic analyses can reveal molecular insights underlying tissue structure and context-dependent cell-cell or cell-environment interaction. Because of the current technical limitation, obtaining genome-wide spatial transcriptome at single-cell resolution is challenging. Here, we developed a new algorithm named iSpatial to derive the spatial pattern of the entire transcriptome by integrating spatial transcriptomic and single-cell RNA-seq datasets. Compared to other existing methods, iSpatial has higher accuracy in predicting gene expression and spatial distribution. Furthermore, it reduces false-positive and false-negative signals in the original datasets. By testing iSpatial with multiple spatial transcriptomic datasets, we demonstrate its wide applicability to datasets from different tissues and by different techniques. Thus, we provide a computational approach to reveal spatial organization of the entire transcriptome at single-cell resolution. With numerous high-quality datasets available in the public domain, iSpatial provides a unique way to understand the structure and function of complex tissues and disease processes.

Cancer vaccines: Building a bridge over troubled waters

Cancer vaccines aim to direct the immune system to eradicate cancer cells. Here we review the essential immunologic concepts underpinning natural immunity and highlight the multiple unique challenges faced by vaccines targeting cancer. Recent technological advances in mass spectrometry, neoantigen prediction, genetically and pharmacologically engineered mouse models, and single-cell omics have revealed new biology, which can help to bridge this divide. We particularly focus on translationally relevant aspects, such as antigen selection and delivery and the monitoring of human post-vaccination responses, and encourage more aggressive exploration of novel approaches.

webSCST: an interactive web application for single-cell RNA-sequencing data and spatial transcriptomic data integration

SUMMARY

Integrative analysis of single-cell RNA-sequencing (scRNA-seq) data with spatial data for the same species and organ would provide each cell sample with a predictive spatial location, which would facilitate biological study. However, publicly available spatial sequencing datasets for specific species and organs are rare and are often displayed in different formats.In this study we introduce a new web-based scRNA-seq analysis tool, webSCST, that integrates well-organized spatial transcriptome sequencing datasets categorized by species and organs, provides a user-friendly interface for raw single-cell processing with popular integration methods, and allows users to submit their raw scRNA-seq data once to obtain predicted spatial locations for each cell type.

AVAILABILITY AND IMPLEMENTATION

webSCST implemented in shiny with all major browsers supported is available at http://www.webscst.com. webSCST is also freely available as an R package at https://github.com/swsoyee/webSCST.

Analyzing Spatial Transcriptomics Data Using Giotto

Spatial transcriptomic technologies have been developed rapidly in recent years. The addition of spatial context to expression data holds the potential to revolutionize many fields in biology. However, the lack of computational tools remains a bottleneck that is preventing the broader utilization of these technologies. Recently, we have developed Giotto as a comprehensive, generally applicable, and user-friendly toolbox for spatial transcriptomic data analysis and visualization. Giotto implements a rich set of algorithms to enable robust spatial data analysis. To help users get familiar with the Giotto environment and apply it effectively in analyzing new datasets, we will describe the detailed protocols for applying Giotto without any advanced programming skills. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Getting Giotto set up for use Basic Protocol 2: Pre-processing Basic Protocol 3: Clustering and cell-type identification Basic Protocol 4: Cell-type enrichment and deconvolution analyses Basic Protocol 5: Spatial structure analysis tools Basic Protocol 6: Spatial domain detection by using a hidden Markov random field model Support Protocol 1: Spatial proximity-associated cell-cell interactions Support Protocol 2: Assembly of a registered 3D Giotto object from 2D slices.

Unravelling Prostate Cancer Heterogeneity Using Spatial Approaches to Lipidomics and Transcriptomics

Simple Summary

Prostate cancer is a heterogenous disease in terms of disease aggressiveness and therapy response, leading to dilemmas in treatment decisions. This heterogeneity reflects the multifocal nature of prostate cancer and its diversity in cellular and molecular composition, necessitating spatial molecular approaches. Here in view of the emerging importance of rewired lipid metabolism as a source of biomarkers and therapeutic targets for prostate cancer, we highlight recent advancements in technologies that enable the spatial mapping of lipids and related metabolic pathways associated with prostate cancer development and progression. We also evaluate their potential for future implementation in treatment decision-making in the clinical management of prostate cancer.

Abstract

Due to advances in the detection and management of prostate cancer over the past 20 years, most cases of localised disease are now potentially curable by surgery or radiotherapy, or amenable to active surveillance without treatment. However, this has given rise to a new dilemma for disease management; the inability to distinguish indolent from lethal, aggressive forms of prostate cancer, leading to substantial overtreatment of some patients and delayed intervention for others. Driving this uncertainty is the critical deficit of novel targets for systemic therapy and of validated biomarkers that can inform treatment decision-making and to select and monitor therapy. In part, this lack of progress reflects the inherent challenge of undertaking target and biomarker discovery in clinical prostate tumours, which are cellularly heterogeneous and multifocal, necessitating the use of spatial analytical approaches. In this review, the principles of mass spectrometry-based lipid imaging and complementary gene-based spatial omics technologies, their application to prostate cancer and recent advancements in these technologies are considered. We put in perspective studies that describe spatially-resolved lipid maps and metabolic genes that are associated with prostate tumours compared to benign tissue and increased risk of disease progression, with the aim of evaluating the future implementation of spatial lipidomics and complementary transcriptomics for prognostication, target identification and treatment decision-making for prostate cancer.

Hippo signaling in cardiac fibroblasts during development, tissue repair, and fibrosis

The evolutionarily conserved Hippo signaling pathway plays key roles in regulating the balance between cell proliferation and apoptosis, cell differentiation, organ size control, tissue repair, and regeneration. Recently, the Hippo pathway has been shown to regulate heart fibrosis, defined as excess extracellular matrix (ECM) deposition and increased tissue stiffness. Cardiac fibroblasts (CFs) are the primary cell type that produces, degrades, and remodels the ECM during homeostasis, aging, inflammation, and tissue repair and regeneration. Here, we review the available evidence from the current literature regarding how the Hippo pathway regulates the formation and function of CFs during heart development and tissue repair.

Statistical and machine learning methods for spatially resolved transcriptomics data analysis

The recent advancement in spatial transcriptomics technology has enabled multiplexed profiling of cellular transcriptomes and spatial locations. As the capacity and efficiency of the experimental technologies continue to improve, there is an emerging need for the development of analytical approaches. Furthermore, with the continuous evolution of sequencing protocols, the underlying assumptions of current analytical methods need to be re-evaluated and adjusted to harness the increasing data complexity. To motivate and aid future model development, we herein review the recent development of statistical and machine learning methods in spatial transcriptomics, summarize useful resources, and highlight the challenges and opportunities ahead.

Recent advances in spatially resolved transcriptomics: challenges and opportunities

Single-cell RNA sequencing (scRNA-seq) has greatly advanced our understanding of cellular heterogeneity by profiling individual cell transcriptomes. However, cell dissociation from the tissue structure causes a loss of spatial information, which hinders the identification of intercellular communication networks and global transcriptional patterns present in the tissue architecture. To overcome this limitation, novel transcriptomic platforms that preserve spatial information have been actively developed. Significant achievements in imaging technologies have enabled in situ targeted transcriptomic profiling in single cells at singlemolecule resolution. In addition, technologies based on mRNA capture followed by sequencing have made possible profiling of the genome-wide transcriptome at the 55-100 μm resolution. Unfortunately, neither imaging-based technology nor capturebased method elucidates a complete picture of the spatial transcriptome in a tissue. Therefore, addressing specific biological questions requires balancing experimental throughput and spatial resolution, mandating the efforts to develop computational algorithms that are pivotal to circumvent technology-specific limitations. In this review, we focus on the current state-of-the-art spatially resolved transcriptomic technologies, describe their applications in a variety of biological domains, and explore recent discoveries demonstrating their enormous potential in biomedical research. We further highlight novel integrative computational methodologies with other data modalities that provide a framework to derive biological insight into heterogeneous and complex tissue organization. [BMB Reports 2022; 55(3): 113-124].

Analysis and Visualization of Spatial Transcriptomic Data

Human and animal tissues consist of heterogeneous cell types that organize and interact in highly structured manners. Bulk and single-cell sequencing technologies remove cells from their original microenvironments, resulting in a loss of spatial information. Spatial transcriptomics is a recent technological innovation that measures transcriptomic information while preserving spatial information. Spatial transcriptomic data can be generated in several ways. RNA molecules are measured by in situ sequencing, in situ hybridization, or spatial barcoding to recover original spatial coordinates. The inclusion of spatial information expands the range of possibilities for analysis and visualization, and spurred the development of numerous novel methods. In this review, we summarize the core concepts of spatial genomics technology and provide a comprehensive review of current analysis and visualization methods for spatial transcriptomics.

Principles of Spatial Transcriptomics Analysis: A Practical Walk-Through in Kidney Tissue

Spatial transcriptomic technologies capture genome-wide readouts across biological tissue space. Moreover, recent advances in this technology, including Slide-seqV2, have achieved spatial transcriptomic data collection at a near-single cell resolution. To-date, a repertoire of computational tools has been developed to discern cell type classes given the transcriptomic profiles of tissue coordinates. Upon applying these tools, we can explore the spatial patterns of distinct cell types and characterize how genes are spatially expressed within different cell type contexts. The kidney is one organ whose function relies upon spatially defined structures consisting of distinct cellular makeup. Thus, the application of Slide-seqV2 to kidney tissue has enabled us to elucidate spatially characteristic cellular and genetic profiles at a scale that remains largely unexplored. Here, we review spatial transcriptomic technologies, as well as computational approaches for cell type mapping and spatial cell type and transcriptomic characterizations. We take kidney tissue as an example to demonstrate how the technologies are applied, while considering the nuances of this architecturally complex tissue.

Transcriptional and epigenetic regulation in thymic epithelial cells

The thymus is required for the development of both adaptive and innate-like T cell subsets. There is keen interest in manipulating thymic function for therapeutic purposes in circumstances of autoimmunity, immunodeficiency, and for purposes of immunotherapy. Within the thymus, thymic epithelial cells play essential roles in directing T cell development. Several transcription factors are known to be essential for thymic epithelial cell development and function, and a few transcription factors have been studied in considerable detail. However, the role of many other transcription factors is less well understood. Further, it is likely that roles exist for other transcription factors not yet known to be important in thymic epithelial cells. Recent progress in understanding of thymic epithelial cell heterogeneity has provided some new insight into transcriptional requirements in subtypes of thymic epithelial cells. However, it is unknown whether progenitors of thymic epithelial cells exist in the adult thymus, and consequently, developmental relationships linking putative precursors with differentiated cell types are poorly understood. While we do not presently possess a clear understanding of stage-specific requirements for transcription factors in thymic epithelial cells, new single-cell transcriptomic and epigenomic technologies should enable rapid progress in this field. Here, we review our current knowledge of transcription factors involved in the development, maintenance, and function of thymic epithelial cells, and the mechanisms by which they act.