Spatial Transcriptomics Technologies Research Articles

An Overview of Spatial Transcriptomics Methodologies in Traversing the Biological System

Transcriptomics covers the in-depth analysis of RNA molecules in cells or tissues and plays an essential role in understanding cellular functions and disease mechanisms. Advances in spatial transcriptomics (ST) in recent times have revolutionized the field by combining gene expression data with spatial information, enabling the analysis of RNA molecules within their tissue context. The evolution of spatial transcriptomics, particularly the integration of artificial intelligence (AI) in data analysis, and its diverse applications have been found to be superior methods in developmental research. Spatial transcriptomics technologies, along with single-cell RNA sequencing (scRNA-seq), offer unprecedented possibilities to unravel intricate cellular interactions within tissues. It emphasizes the importance of accurate cell localization for in-depth discoveries and developments via highthroughput spatial transcriptome profiling. The integration of artificial intelligence in spatial transcriptomics analysis is a key focus, showcasing its role in detecting spatially variable genes, clustering cell populations, communication analysis, and enhancing data interpretation. The evolution of AI methods tailored for spatial transcriptomics is highlighted, addressing the unique challenges posed by spatially resolved transcriptomic data. Applications of spatial transcriptomics integrated with other omics data, such as genomics, proteomics, and metabolomics, provide a detailed view of molecular processes within tissues and emerge in diverse applications. Integrating spatial transcriptomics with AI represents a transformative approach to understanding tissue architecture and cellular interactions. This innovative synergy not only enhances our understanding of gene expression patterns but also offers a holistic view of molecular processes within tissues, with profound implications for disease mechanisms and therapeutic development.

Benchmarking cell type annotation methods for 10x Xenium spatial transcriptomics data

BackgroundImaging-based spatial transcriptomics technologies allow us to explore spatial gene expression profiles at the cellular level. Cell type annotation of imaging-based spatial data is challenging due to the small gene panel, but it is a crucial step for downstream analyses. Many good reference-based cell type annotation tools have been developed for single-cell RNA sequencing and sequencing-based spatial transcriptomics data. However, the performance of the reference-based cell type annotation tools on imaging-based spatial transcriptomics data has not been well studied yet.ResultsWe compared performance of five reference-based methods (SingleR, Azimuth, RCTD, scPred and scmapCell) with the marker-gene-based manual annotation method on an imaging-based Xenium data of human breast cancer. A practical workflow has been demonstrated for preparing a high-quality single-cell RNA reference, evaluating the accuracy, and estimating the running time for reference-based cell type annotation tools.ConclusionsSingleR was the best performing reference-based cell type annotation tool for the Xenium platform, being fast, accurate and easy to use, with results closely matching those of manual annotation.

Integrating Tissue Microarray to GeoMx® Digital Spatial Profiler: Spatial Transcriptomics Assay with Bioinformatics Analysis.

Genome-wide or high-plex gene expression is important to understand the organism, tissue, and cellular mechanism. From microarrays to next-generation sequencing (RNA-Seq) at the bulk level and then to the single cell level, gene expression studies have undergone a long transition. The current bulk gene expression and pathway-centric approach toward disease and therapeutics is moving toward spatial transcriptomics, which makes it possible to profile gene expression from the cellular microenvironment without any loss of spatial information. Spatial transcriptomics allows us to understand cellular interactions, cell type abundance, and profile expression differences between the region of interest and its microenvironment. The technology is revolutionizing oncology, developmental biology, neuroscience, preclinical studies, and many therapeutic approaches, especially immunotherapy. Taking into consideration the diverse spatial transcriptomics technologies available, the current chapter aims to delineate the NGS assay protocol for Digital Spatial Profiler (DSP) and followbioinformatics analysis. While the workflow itself has been detailed elsewhere, in this chapter we are focusing on the integration of tissue microarrays, bioinformatics pipelines, and statistical approaches specific to GeoMx RNA assays as well as common errors that can occur while running a DSP RNA assay. The descriptions of these methods refer to the current version of the GeoMX DSP guidebook and GeoMx Data Analysis Manual, which can be downloaded from the documents section of NanoString website. These documents and user guides are continuously improved and updated; hence, it is important to regularly check the company's website for the most recent version.

A MEF2C transcription factor network regulates proliferation of glomerular endothelial cells in diabetic kidney disease.

The maintenance of a healthy epithelial-endothelial juxtaposition requires cross-talk within glomerular cellular niches. We sought to understand the spatially-anchored regulation and transition of endothelial and mesangial cells from health to injury in DKD. From 74 human kidney samples, an integrated multi-omics approach was leveraged to identify cellular niches, cell-cell communication, cell injury trajectories, and regulatory transcription factor (TF) networks in glomerular capillary endothelial (EC-GC) and mesangial cells. Data were culled from single nucleus RNA and ATAC sequencing and three orthogonal spatial transcriptomic technologies for correlation with histopathological and clinical trial data. We identified a cellular niche in diabetic glomeruli enriched in a proliferative endothelial cell subtype (prEC) and altered vascular smooth muscle cells (VSMCs). Cellular communication within this niche maintained pro-angiogenic signaling with loss of anti-angiogenic factors. We identified a TF network of MEF2C, MEF2A, and TRPS1 which regulated SEMA6A and PLXNA2, a receptor-ligand pair opposing angiogenesis. In silico knockout of the TF network accelerated the transition from healthy EC-GCs toward a degenerative (injury) endothelial phenotype, with concomitant disruption of EC-GC and prEC expression patterns. Glomeruli enriched in the prEC niche had histologic evidence of neovascularization. MEF2C activity was increased in diabetic glomeruli with nodular mesangial sclerosis. The gene regulatory network (GRN) of MEF2C was dysregulated in EC-GCs of patients with DKD, but sodium glucose transporter-2 inhibitor (SGLT2i) treatment reversed the MEF2C GRN effects of DKD. The MEF2C, MEF2A, and TRPS1 TF network carefully balances the fate of the EC-GC in DKD. When the TF network is "on" or over-expressed in DKD, EC-GCs may progress to a prEC state, while TF suppression leads to cell death. SGLT2i therapy may restore the balance of MEF2C activity.

Application of Spatial Transcriptomics in Digestive System Tumors.

In the field of digestive system tumor research, spatial transcriptomics technologies are used to delve into the spatial structure and the spatial heterogeneity of tumors and to analyze the tumor microenvironment (TME) and the inter-cellular interactions within it by revealing gene expression in tumors. These technologies are also instrumental in the diagnosis, prognosis, and treatment of digestive system tumors. This review provides a concise introduction to spatial transcriptomics and summarizes recent advances, application prospects, and technical challenges of these technologies in digestive system tumor research. This review also discusses the importance of combining spatial transcriptomics with single-cell RNA sequencing (scRNA-seq), artificial intelligence, and machine learning in digestive system cancer research.

Spatial differences in gene expression across the dorsal raphe nucleus in a model of early Alzheimer's disease.

Persons with Alzheimer's disease (AD) present with changes in mood, sleep, and arousal that may precede the clinical manifestation of cognitive decline. These early symptoms can be driven by changes in the serotonergic (5-HT) nuclei of the brainstem, particularly the dorsal raphe nucleus (DRN). It is unclear why all 5-HT neurons do not simultaneously develop AD pathology that progresses at the same rate. We sought to identify any underlying genetic components associated with susceptibility or resistance of 5-HT neurons to AD pathology. The Visium Spatial Gene Expression platform was used to identify transcriptomic changes across the DRN in a preclinical model of early AD, human tau-overexpressing mice (htau mice). We further used RNAscope and immunohistochemical assessment to validate findings of primary interest. We find that the DRN of htau mice differentially expresses AD-related genes, including those related to kinase binding, ion channel activity, ligand-receptor interactions, and regulation of serine/threonine kinases. We further find that computational sub-clustering of the DRN is consistent with previous circuitry-driven characterizations, allowing for spatial bounding of distinct subregions within the DRN. Of these, we find the dorsolateral DRN is preferentially impacted by 5-HT neuron loss and development of tau pathology, which coincides with increased expression of the long noncoding RNA Map2k3os. Map2k3os may serve regulatory roles relevant for tau phosphorylation and warrants further investigation to characterize its interactions. Overall, this report demonstrates the power of large-scale spatial transcriptomics technologies, while underscoring the need for convergent-data validation to overcome their limitations.

Deep learning in integrating spatial transcriptomics with other modalities.

Spatial transcriptomics technologies have been extensively applied in biological research, enabling the study of transcriptome while preserving the spatial context of tissues. Paired with spatial transcriptomics data, platforms often provide histology and (or) chromatin images, which capture cellular morphology and chromatin organization. Additionally, single-cell RNA sequencing (scRNA-seq) data from matching tissues often accompany spatial data, offering a transcriptome-wide gene expression profile of individual cells. Integrating such additional data from other modalities can effectively enhance spatial transcriptomics data, and, conversely, spatial transcriptomics data can supplement scRNA-seq with spatial information. Moreover, the rapid development of spatial multi-omics technology has spurred the demand for the integration of spatial multi-omics data to present a more detailed molecular landscape within tissues. Numerous deep learning (DL) methods have been developed for integrating spatial transcriptomics with other modalities. However, a comprehensive review of DL approaches for integrating spatial transcriptomics data with other modalities remains absent. In this study, we systematically review the applications of DL in integrating spatial transcriptomics data with other modalities. We first delineate the DL techniques applied in this integration and the key tasks involved. Next, we detail these methods and categorize them based on integrated modality and key task. Furthermore, we summarize the integration strategies of these integration methods. Finally, we discuss the challenges and future directions in integrating spatial transcriptomics with other modalities, aiming to facilitate the development of robust computational methods that more comprehensively exploit multimodal information.

Supervised analysis of alternative polyadenylation from single-cell and spatial transcriptomics data with spvAPA.

Alternative polyadenylation (APA) is an important driver of transcriptome diversity that generates messenger RNA isoforms with distinct 3' ends. The rapid development of single-cell and spatial transcriptomic technologies opened up new opportunities for exploring APA data to discover hidden cell subpopulations invisible in conventional gene expression analysis. However, conventional gene-level analysis tools are not fully applicable to APA data, and commonly used unsupervised dimensionality reduction methods often disregard experimentally derived annotations such as cell type identities. Here, we proposed a supervised analytical framework termed spvAPA, specifically used for APA analysis from both single-cell and spatial transcriptomics data. First, an iterative imputation method based on weighted nearest neighbor was designed to recover missing APA signatures, by integrating both gene expression and APA modalities. Second, a supervised feature selection method based on sparse partial least squares discriminant analysis was devised to identify APA features distinguishing cell types or spatial morphologies. Additionally, spvAPA improves the visualization of high-dimensional data for discovering novel cell subtypes, which considers APA features and dual modalities of gene expression and APA. Evaluations across nine single-cell and spatial transcriptomics datasets demonstrate the effectiveness and applicability of spvAPA. spvAPA is available at https://github.com/BMILAB/spvAPA.

GraphPCA: a fast and interpretable dimension reduction algorithm for spatial transcriptomics data

The rapid advancement of spatial transcriptomics technologies has revolutionized our understanding of cell heterogeneity and intricate spatial structures within tissues and organs. However, the high dimensionality and noise in spatial transcriptomic data present significant challenges for downstream data analyses. Here, we develop GraphPCA, an interpretable and quasi-linear dimension reduction algorithm that leverages the strengths of graphical regularization and principal component analysis. Comprehensive evaluations on simulated and multi-resolution spatial transcriptomic datasets generated from various platforms demonstrate the capacity of GraphPCA to enhance downstream analysis tasks including spatial domain detection, denoising, and trajectory inference compared to other state-of-the-art methods.

Seq-Scope: repurposing Illumina sequencing flow cells for high-resolution spatial transcriptomics.

Spatial transcriptomics technologies aim to advance gene expression studies by profiling the entire transcriptome with intact spatial information from a single histological slide. However, the application of spatial transcriptomics is limited by low resolution, limited transcript coverage, complex procedures, poor scalability and high costs of initial setup and/or individual experiments. Seq-Scope repurposes the Illumina sequencing platform for high-resolution, high-content spatial transcriptome analysis, overcoming these limitations. It offers submicrometer resolution, high capture efficiency, rapid turnaround time and precise annotation of histopathology at a much lower cost than commercial alternatives. This protocol details the implementation of Seq-Scope with an Illumina NovaSeq 6000 sequencing flow cell, allowing the profiling of multiple tissue sections in an area of 7 mm × 7 mm or larger. We describe the preparation of a fresh-frozen tissue section for both histological imaging and sequencing library preparation and provide a streamlined computational pipeline with comprehensive instructions to integrate histological and transcriptomic data for high-resolution spatial analysis. This includes the use of conventional software tools for single-cell and spatial analysis, as well as our recently developed segmentation-free method for analyzing spatial data at submicrometer resolution. Aside from array production and sequencing, which can be done in batches, tissue processing, library preparation and running the computational pipeline can be completed within 3 days by researchers with experience in molecular biology, histology and basic Unix skills. Given its adaptability across various biological tissues, Seq-Scope establishes itself as an invaluable tool for researchers in molecular biology and histology.

Challenges and Opportunities in the Clinical Translation of High-Resolution Spatial Transcriptomics.

Pathology has always been fueled by technological advances. Histology powered the study of tissue architecture at single-cell resolution and remains a cornerstone of clinical pathology today. In the last decade, next-generation sequencing has become informative for the targeted treatment of many diseases, demonstrating the importance of genome-scale molecular information for personalized medicine. Today, revolutionary developments in spatial transcriptomics technologies digitalize gene expression at subcellular resolution in intact tissue sections, enabling the computational analysis of cell types, cellular phenotypes, and cell-cell communication in routinely collected and archival clinical samples. Here we review how such molecular microscopes work, highlight their potential to identify disease mechanisms and guide personalized therapies, and provide guidance for clinical study design. Finally, we discuss remaining challenges to the swift translation of high-resolution spatial transcriptomics technologies and how integration of multimodal readouts and deep learning approaches is bringing us closer to a holistic understanding of tissue biology and pathology.

Spatial pattern and differential expression analysis with spatial transcriptomic data.

The emergence of spatial transcriptomic technologies has opened new avenues for investigating gene activities while preserving the spatial context of tissues. Utilizing data generated by such technologies, the identification of spatially variable (SV) genes is an essential step in exploring tissue landscapes and biological processes. Particularly in typical experimental designs, such as case-control or longitudinal studies, identifying SV genes between groups is crucial for discovering significant biomarkers or developing targeted therapies for diseases. However, current methods available for analyzing spatial transcriptomic data are still in their infancy, and none of the existing methods are capable of identifying SV genes between groups. To overcome this challenge, we developed SPADE for spatial pattern and differential expression analysis to identify SV genes in spatial transcriptomic data. SPADE is based on a machine learning model of Gaussian process regression with a gene-specific Gaussian kernel, enabling the detection of SV genes both within and between groups. Through benchmarking against existing methods in extensive simulations and real data analyses, we demonstrated the preferred performance of SPADE in detecting SV genes within and between groups. The SPADE source code and documentation are publicly available at https://github.com/thecailab/SPADE.

STASCAN deciphers fine-resolution cell distribution maps in spatial transcriptomics by deep learning

Spatial transcriptomics technologies have been widely applied to decode cellular distribution by resolving gene expression profiles in tissue. However, sequencing techniques still limit the ability to create a fine-resolved spatial cell-type map. To this end, we develop a novel deep-learning-based approach, STASCAN, to predict the spatial cellular distribution of captured or uncharted areas where only histology images are available by cell feature learning integrating gene expression profiles and histology images. STASCAN is successfully applied across diverse datasets from different spatial transcriptomics technologies and displays significant advantages in deciphering higher-resolution cellular distribution and resolving enhanced organizational structures.

SnPATHO-seq, a versatile FFPE single-nucleus RNA sequencing method to unlock pathology archives

Formalin-fixed paraffin-embedded (FFPE) samples are valuable but underutilized in single-cell omics research due to their low RNA quality. In this study, leveraging a recent advance in single-cell genomic technology, we introduce snPATHO-seq, a versatile method to derive high-quality single-nucleus transcriptomic data from FFPE samples. We benchmarked the performance of the snPATHO-seq workflow against existing 10x 3’ and Flex assays designed for frozen or fresh samples and highlighted the consistency in snRNA-seq data produced by all workflows. The snPATHO-seq workflow also demonstrated high robustness when tested across a wide range of healthy and diseased FFPE tissue samples. When combined with FFPE spatial transcriptomic technologies such as FFPE Visium, the snPATHO-seq provides a multi-modal sampling approach for FFPE samples, allowing more comprehensive transcriptomic characterization.

Dual decoding of cell types and gene expression in spatial transcriptomics with PANDA.

Sequencing-based spatial transcriptomics technologies have revolutionized our understanding of complex biological systems by enabling transcriptome profiling while preserving spatial context. However, spot-level expression measurements often amalgamate signals from diverse cells, obscuring potential heterogeneity. Existing methods aim to deconvolute spatial transcriptomics data into cell type proportions for each spot using single-cell RNA sequencing references but overlook cell-type-specific gene expression, essential for uncovering intra-type heterogeneity. We present PANDA (ProbAbilistic-based decoNvolution with spot-aDaptive cell type signAtures), a novel method that concurrently deciphers spot-level gene expression into both cell type proportions and cell-type-specific gene expression. PANDA integrates archetypal analysis to capture within-cell-type heterogeneity and dynamically learns cell type signatures for each spot during deconvolution. Simulations demonstrate PANDA's superior performance. Applied to real spatial transcriptomics data from diverse tissues, including tumor, brain, and developing heart, PANDA reconstructs spatial structures and reveals subtle transcriptional variations within specific cell types, offering a comprehensive understanding of tissue dynamics.

Dual spatial host-bacterial gene expression in Mycobacterium abscessus respiratory infections

Co-localization of spatial transcriptome information of host and pathogen can revolutionize our understanding of microbial pathogenesis. Here, we aimed to demonstrate that customized bacterial probes can be successfully used to identify host-pathogen interactions in formalin-fixed-paraffin-embedded (FFPE) tissues by probe-based spatial transcriptomics technology. We analyzed the spatial gene expression of bacterial transcripts with the host transcriptomic profile in murine lung tissue chronically infected with Mycobacterium abscessus embedded in agar beads. Customized mycobacterial probes were designed for the constitutively expressed rpoB gene (an RNA polymerase β subunit) and the virulence factor precursor lsr2, modulated by oxidative stress. We found a correlation between the rpoB expression, bacterial abundance in the airways, and an increased expression of lsr2 virulence factor in lung tissue with high oxidative stress. Overall, we demonstrate the potential of dual bacterial and host gene expression assay in FFPE tissues, paving the way for the simultaneous detection of host and bacterial transcriptomes in pathological tissues.

A multi-modality and multi-granularity collaborative learning framework for identifying spatial domains and spatially variable genes.

Recent advances in spatial transcriptomics technologies have provided multi-modality data integrating gene expression, spatial context, and histological images. Accurately identifying spatial domains and spatially variable genes is crucial for understanding tissue structures and biological functions. However, effectively combining multi-modality data to identify spatial domains and determining SVGs closely related to these spatial domains remains a challenge. In this study, we propose spatial transcriptomics multi-modality and multi-granularity collaborative learning (spaMMCL). For detecting spatial domains, spaMMCL mitigates the adverse effects of modality bias by masking portions of gene expression data, integrates gene and image features using a shared graph convolutional network, and employs graph self-supervised learning to deal with noise from feature fusion. Simultaneously, based on the identified spatial domains, spaMMCL integrates various strategies to detect potential SVGs at different granularities, enhancing their reliability and biological significance. Experimental results demonstrate that spaMMCL substantially improves the identification of spatial domains and SVGs. The code and data of spaMMCL are available on Github: Https://github.com/liangxiao-cs/spaMMCL.

VSPACE: exploring virtual spatial representation of articular chondrocytes at the single-cell level.

vSPACE is a web-based application presenting a spatial representation of scRNAseq data obtained from human articular cartilage by emulating the concept of spatial transcriptomics technology, but virtually. This virtual 2D plot presentation of human articular cartage cells generates several zonal distribution patterns, for one or multiple genes at a time, revealing patterns that scientists can appreciate as imputed spatial distribution patterns along the zonal axis. vSPACE is implemented in Python Dash as a web-based toolbox designed for data visualization of zonal gene expression patterns in articular cartilage chondrocytes. This tool is freely accessible at: https://vspace.cse.uconn.edu/The source code and extra materials for this service can be downloaded from: https://github.com/zhacheny/vSPACE.

ELLA: Modeling Subcellular Spatial Variation of Gene Expression within Cells in High-Resolution Spatial Transcriptomics.

Spatial transcriptomic technologies are becoming increasingly high-resolution, enabling precise measurement of gene expression at the subcellular level. Here, we introduce a computational method called subcellular expression localization analysis (ELLA), for modeling the subcellular localization of mRNAs and detecting genes that display spatial variation within cells in high-resolution spatial transcriptomics. ELLA creates a unified cellular coordinate system to anchor diverse cell shapes and morphologies, utilizes a nonhomogeneous Poisson process to model spatial count data, leverages an expression gradient function to characterize subcellular expression patterns, and produces effective control of type I error and high statistical power. We illustrate the benefits of ELLA through comprehensive simulations and applications to four spatial transcriptomics datasets from distinct technologies, where ELLA not only identifies genes with distinct subcellular localization patterns but also associates these patterns with unique mRNA characteristics. Specifically, ELLA shows that genes enriched in the nucleus exhibit an abundance of long noncoding RNAs or protein-coding mRNAs, often characterized by longer gene lengths. Conversely, genes containing signal recognition peptides, encoding ribosomal proteins, or involved in membrane related activities tend to enrich in the cytoplasm or near the cellular membrane. Furthermore, ELLA reveals dynamic subcellular localization patterns during the cell cycle, with certain genes showing decreased nuclear enrichment in the G1 phase while others maintain their enrichment patterns throughout the cell cycle. Overall, ELLA represents a calibrated, powerful, robust, scalable, and versatile tool for modeling subcellular spatial expression variation across diverse high-resolution spatial transcriptomic platforms.

INSPIRE: interpretable, flexible and spatially-aware integration of multiple spatial transcriptomics datasets from diverse sources.

Recent advances in spatial transcriptomics technologies have led to a growing number of diverse datasets, offering unprecedented opportunities to explore tissue organizations and functions within spatial contexts. However, it remains a significant challenge to effectively integrate and interpret these data, often originating from different samples, technologies, and developmental stages. In this paper, we present INSPIRE, a deep learning method for integrative analyses of multiple spatial transcriptomics datasets to address this challenge. With designs of graph neural networks and an adversarial learning mechanism, INSPIRE enables spatially informed and adaptable integration of data from varying sources. By incorporating non-negative matrix factorization, INSPIRE uncovers interpretable spatial factors with corresponding gene programs, revealing tissue architectures, cell type distributions and biological processes. We demonstrate the capabilities of INSPIRE by applying it to human cortex slices from different samples, mouse brain slices with complementary views, mouse hippocampus and embryo slices generated through different technologies, and spatiotemporal organogenesis atlases containing half a million spatial spots. INSPIRE shows superior performance in identifying detailed biological signals, effectively borrowing information across distinct profiling technologies, and elucidating dynamical changes during embryonic development. Furthermore, we utilize INSPIRE to build 3D models of tissues and whole organisms from multiple slices, demonstrating its power and versatility.