Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments

Benchmarking algorithms for spatially variable gene identification in spatial transcriptomics

MOTIVATION

The rapid development of spatial transcriptomics has underscored the importance of identifying spatially variable genes. As a fundamental task in spatial transcriptomic data analysis, spatially variable gene identification has been extensively studied. However, the lack of comprehensive benchmark makes it difficult to validate the effectiveness of various algorithms scattered across a large number of studies with real-world datasets.

RESULTS

In response, this article proposes a benchmark framework to evaluate algorithms for identifying spatially variable genes through the analysis of 30 synthesized and 74 real-world datasets, aiming to identify the best algorithms and their corresponding application scenarios. This framework can assist medical and life scientists in selecting suitable algorithms for their research, while also aid bioinformatics scientists in developing more powerful and efficient computational methods in spatial transcriptomic research.

AVAILABILITY AND IMPLEMENTATION

The source code of this benchmarking framework is available at both Github (https://github.com/XiDsLab/svg-benchmark) and Zenodo (https://doi.org/10.5281/zenodo.15031083). In addition, all real and synthetic datasets considered in this study are also publicly available at Zenodo (https://doi.org/10.5281/zenodo.7227771).

Systemic coordination of whole-body tissue remodeling during local regeneration in sea anemones

The complexity of regeneration extends beyond local wound responses, eliciting systemic processes across the entire organism. However, the functional relevance and coordination of distant molecular processes remain unclear. In the cnidarian Nematostella vectensis, we show that local regeneration triggers a systemic homeostatic response, leading to coordinated whole-body remodeling. Leveraging spatial transcriptomics, endogenous protein tagging, and live imaging, we comprehensively dissect this systemic response at the organismal scale. We identify proteolysis as a critical process driven by both local and systemic upregulation of metalloproteases. We show that metalloproteinase expression levels and activity scale with the extent of tissue loss. This proportional response drives long-range tissue and extracellular matrix movement. Our findings demonstrate the adaptive nature of the systematic response in regeneration, enabling the organism to maintain shape homeostasis while coping with a wide range of injuries.

SpaFun: Discovering Domain-specific Spatial Expression Patterns and New Disease-Relevant Genes using Functional Principal Component Analysis

SpaFun is a novel, non-model-based method developed to address limitations in existing spatially variable gene (SVG) detection techniques, particularly for large-scale spatially resolved transcriptomics (SRT) datasets. These limitations include computational inefficiency, limited statistical power with increasing data size, and the inability to capture spatial heterogeneity and co-expression patterns among genes. Built on functional principal component analysis (fPCA), SpaFun identifies domain-representative genes (DRGs) with significantly better computational efficiency and greater statistical power while accounting for spatial heterogeneity and co-expression patterns among genes. We applied SpaFun to three SRT datasets and demonstrated that SpaFun outperformed state-of-the-art algorithms for identifying representative genes for tumor regions (e.g., DESeq, edgeR, and limma), as well as recently developed novel algorithms designed for spatial omics to identify the representative genes (e.g., SPARK and CSIDE). This highlights SpaFun's ability to accurately identify genes most representative of each spatial domain (e.g., tumor, immune, or stroma regions). By uncovering novel disease-relevant genes overlooked by existing algorithms, SpaFun could provide insights into new molecular mechanisms and propose innovative therapeutic strategies to improve patient outcomes.

Categorization of 34 computational methods to detect spatially variable genes from spatially resolved transcriptomics data

In the analysis of spatially resolved transcriptomics data, detecting spatially variable genes (SVGs) is crucial. Numerous computational methods exist, but varying SVG definitions and methodologies lead to incomparable results. We review 34 state-of-the-art methods, classifying SVGs into three categories: overall, cell-type-specific, and spatial-domain-marker SVGs. Our review explains the intuitions underlying these methods, summarizes their applications, and categorizes the hypothesis tests they use in the trade-off between generality and specificity for SVG detection. We discuss challenges in SVG detection and propose future directions for improvement. Our review offers insights for method developers and users, advocating for category-specific benchmarking.

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

Recent advances in spatially variable gene detection in spatial transcriptomics

With the emergence of advanced spatial transcriptomic technologies, there has been a surge in research papers dedicated to analyzing spatial transcriptomics data, resulting in significant contributions to our understanding of biology. The initial stage of downstream analysis of spatial transcriptomic data has centered on identifying spatially variable genes (SVGs) or genes expressed with specific spatial patterns across the tissue. SVG detection is an important task since many downstream analyses depend on these selected SVGs. Over the past few years, a plethora of new methods have been proposed for the detection of SVGs, accompanied by numerous innovative concepts and discussions. This article provides a selective review of methods and their practical implementations, offering valuable insights into the current literature in this field.

Detecting significant expression patterns in single-cell and spatial transcriptomics with a flexible computational approach

Gene expression data holds the potential to shed light on multiple biological processes at once. However, data analysis methods for single cell sequencing mostly focus on finding cell clusters or the principal progression line of the data. Data analysis for spatial transcriptomics mostly addresses clustering and finding spatially variable genes. Existing data analysis methods are effective in finding the main data features, but they might miss less pronounced, albeit significant, processes, possibly involving a subset of the samples. In this work we present SPIRAL: Significant Process InfeRence ALgorithm. SPIRAL is based on Gaussian statistics to detect all statistically significant biological processes in single cell, bulk and spatial transcriptomics data. The algorithm outputs a list of structures, each defined by a set of genes working simultaneously in a specific population of cells. SPIRAL is unique in its flexibility: the structures are constructed by selecting subsets of genes and cells based on statistically significant and consistent differential expression. Every gene and every cell may be part of one structure, more or none. SPIRAL also provides several visual representations of structures and pathway enrichment information. We validated the statistical soundness of SPIRAL on synthetic datasets and applied it to single cell, spatial and bulk RNA-sequencing datasets. SPIRAL is available at https://spiral.technion.ac.il/ .

Categorization of 33 computational methods to detect spatially variable genes from spatially resolved transcriptomics data

In the analysis of spatially resolved transcriptomics data, detecting spatially variable genes (SVGs) is crucial. Numerous computational methods exist, but varying SVG definitions and methodologies lead to incomparable results. We review 33 state-of-the-art methods, categorizing SVGs into three types: overall, cell-type-specific, and spatial-domain-marker SVGs. Our review explains the intuitions underlying these methods, summarizes their applications, and categorizes the hypothesis tests they use in the trade-off between generality and specificity for SVG detection. We discuss challenges in SVG detection and propose future directions for improvement. Our review offers insights for method developers and users, advocating for category-specific benchmarking.

Recent advances in differential expression analysis for single-cell RNA-seq and spatially resolved transcriptomic studies

Differential expression (DE) analysis is a necessary step in the analysis of single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) data. Unlike traditional bulk RNA-seq, DE analysis for scRNA-seq or SRT data has unique characteristics that may contribute to the difficulty of detecting DE genes. However, the plethora of DE tools that work with various assumptions makes it difficult to choose an appropriate one. Furthermore, a comprehensive review on detecting DE genes for scRNA-seq data or SRT data from multi-condition, multi-sample experimental designs is lacking. To bridge such a gap, here, we first focus on the challenges of DE detection, then highlight potential opportunities that facilitate further progress in scRNA-seq or SRT analysis, and finally provide insights and guidance in selecting appropriate DE tools or developing new computational DE methods.

DESpace: spatially variable gene detection via differential expression testing of spatial clusters

MOTIVATION

Spatially resolved transcriptomics (SRT) enables scientists to investigate spatial context of mRNA abundance, including identifying spatially variable genes (SVGs), i.e. genes whose expression varies across the tissue. Although several methods have been proposed for this task, native SVG tools cannot jointly model biological replicates, or identify the key areas of the tissue affected by spatial variability.

RESULTS

Here, we introduce DESpace, a framework, based on an original application of existing methods, to discover SVGs. In particular, our approach inputs all types of SRT data, summarizes spatial information via spatial clusters, and identifies spatially variable genes by performing differential gene expression testing between clusters. Furthermore, our framework can identify (and test) the main cluster of the tissue affected by spatial variability; this allows scientists to investigate spatial expression changes in specific areas of interest. Additionally, DESpace enables joint modeling of multiple samples (i.e. biological replicates); compared to inference based on individual samples, this approach increases statistical power, and targets SVGs with consistent spatial patterns across replicates. Overall, in our benchmarks, DESpace displays good true positive rates, controls for false positive and false discovery rates, and is computationally efficient.

AVAILABILITY AND IMPLEMENTATION

DESpace is freely distributed as a Bioconductor R package at https://bioconductor.org/packages/DESpace.

Multi-omics in stress and health research: study designs that will drive the field forward

Despite decades of stress research, there still exist substantial gaps in our understanding of how social, environmental, and biological factors interact and combine with developmental stressor exposures, cognitive appraisals of stressors, and psychosocial coping processes to shape individuals' stress reactivity, health, and disease risk. Relatively new biological profiling approaches, called multi-omics, are helping address these issues by enabling researchers to quantify thousands of molecules from a single blood or tissue sample, thus providing a panoramic snapshot of the molecular processes occurring in an organism from a systems perspective. In this review, we summarize two types of research designs for which multi-omics approaches are best suited, and describe how these approaches can help advance our understanding of stress processes and the development, prevention, and treatment of stress-related pathologies. We first discuss incorporating multi-omics approaches into theory-rich, intensive longitudinal study designs to characterize, in high-resolution, the transition to stress-related multisystem dysfunction and disease throughout development. Next, we discuss how multi-omics approaches should be incorporated into intervention research to better understand the transition from stress-related dysfunction back to health, which can help inform novel precision medicine approaches to managing stress and fostering biopsychosocial resilience. Throughout, we provide concrete recommendations for types of studies that will help advance stress research, and translate multi-omics data into better health and health care.

Benchmarking computational methods to identify spatially variable genes and peaks

Spatially resolved transcriptomics offers unprecedented insight by enabling the profiling of gene expression within the intact spatial context of cells, effectively adding a new and essential dimension to data interpretation. To efficiently detect spatial structure of interest, an essential step in analyzing such data involves identifying spatially variable genes. Despite researchers having developed several computational methods to accomplish this task, the lack of a comprehensive benchmark evaluating their performance remains a considerable gap in the field. Here, we present a systematic evaluation of 14 methods using 60 simulated datasets generated by four different simulation strategies, 12 real-world transcriptomics, and three spatial ATAC-seq datasets. We find that spatialDE2 consistently outperforms the other benchmarked methods, and Moran's I achieves competitive performance in different experimental settings. Moreover, our results reveal that more specialized algorithms are needed to identify spatially variable peaks.

A SELECTIVE REVIEW OF RECENT DEVELOPMENTS IN SPATIALLY VARIABLE GENE DETECTION FOR SPATIAL TRANSCRIPTOMICS

With the emergence of advanced spatial transcriptomic technologies, there has been a surge in research papers dedicated to analyzing spatial transcriptomics data, resulting in significant contributions to our understanding of biology. The initial stage of downstream analysis of spatial transcriptomic data has centered on identifying spatially variable genes (SVGs) or genes expressed with specific spatial patterns across the tissue. SVG detection is an important task since many downstream analyses depend on these selected SVGs. Over the past few years, a plethora of new methods have been proposed for the detection of SVGs, accompanied by numerous innovative concepts and discussions. This article provides a selective review of methods and their practical implementations, offering valuable insights into the current literature in this field.

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.

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.

Principles and challenges of modeling temporal and spatial omics data

Studies with temporal or spatial resolution are crucial to understand the molecular dynamics and spatial dependencies underlying a biological process or system. With advances in high-throughput omic technologies, time- and space-resolved molecular measurements at scale are increasingly accessible, providing new opportunities to study the role of timing or structure in a wide range of biological questions. At the same time, analyses of the data being generated in the context of spatiotemporal studies entail new challenges that need to be considered, including the need to account for temporal and spatial dependencies and compare them across different scales, biological samples or conditions. In this Review, we provide an overview of common principles and challenges in the analysis of temporal and spatial omics data. We discuss statistical concepts to model temporal and spatial dependencies and highlight opportunities for adapting existing analysis methods to data with temporal and spatial dimensions.

Voyager: exploratory single-cell genomics data analysis with geospatial statistics

Exploratory spatial data analysis (ESDA) can be a powerful approach to understanding single-cell genomics datasets, but it is not yet part of standard data analysis workflows. In particular, geospatial analyses, which have been developed and refined for decades, have yet to be fully adapted and applied to spatial single-cell analysis. We introduce the Voyager platform, which systematically brings the geospatial ESDA tradition to (spatial) -omics, with local, bivariate, and multivariate spatial methods not yet commonly applied to spatial -omics, united by a uniform user interface. Using Voyager, we showcase biological insights that can be derived with its methods, such as biologically relevant negative spatial autocorrelation. Underlying Voyager is the SpatialFeatureExperiment data structure, which combines Simple Feature with SingleCellExperiment and AnnData to represent and operate on geometries bundled with gene expression data. Voyager has comprehensive tutorials demonstrating ESDA built on GitHub Actions to ensure reproducibility and scalability, using data from popular commercial technologies. Voyager is implemented in both R/Bioconductor and Python/PyPI, and features compatibility tests to ensure that both implementations return consistent results.

An information theoretic approach to detecting spatially varying genes

A key step in spatial transcriptomics is identifying genes with spatially varying expression patterns. We adopt an information theoretic perspective to this problem by equating the degree of spatial coherence with the Jensen-Shannon divergence between pairs of nearby cells and pairs of distant cells. To avoid the notoriously difficult problem of estimating information theoretic divergences, we use modern approximation techniques to implement a computationally efficient algorithm designed to scale with in situ spatial transcriptomics technologies. In addition to being highly scalable, we show that our method, which we call maximization of spatial information (Maxspin), improves accuracy across several spatial transcriptomics platforms and a variety of simulations when compared with a variety of state-of-the-art methods. To further demonstrate the method, we generated in situ spatial transcriptomics data in a renal cell carcinoma sample using the CosMx Spatial Molecular Imager and used Maxspin to reveal novel spatial patterns of tumor cell gene expression.

SINFONIA: Scalable Identification of Spatially Variable Genes for Deciphering Spatial Domains

Recent advances in spatial transcriptomics have revolutionized the understanding of tissue organization. The identification of spatially variable genes (SVGs) is an essential step for downstream spatial domain characterization. Although several methods have been proposed for identifying SVGs, inadequate ability to decipher spatial domains, poor efficiency, and insufficient interoperability with existing standard analysis workflows still impede the applications of these methods. Here we propose SINFONIA, a scalable method for identifying spatially variable genes via ensemble strategies. Implemented in Python, SINFONIA can be seamlessly integrated into existing analysis workflows. Using 15 spatial transcriptomic datasets generated with different protocols and with different sizes, dimensions and qualities, we show the advantage of SINFONIA over three baseline methods and two variants via systematic evaluation of spatial clustering, domain resolution, latent representation, spatial visualization, and computational efficiency with 21 quantitative metrics. Additionally, SINFONIA is robust relative to the choice of the number of SVGs. We anticipate SINFONIA will facilitate the analysis of spatial transcriptomics.

Combined modelling of mRNA decay dynamics and single-molecule imaging in the Drosophila embryo uncovers a role for P-bodies in 5' to 3' degradation

Regulation of mRNA degradation is critical for a diverse array of cellular processes and developmental cell fate decisions. Many methods for determining mRNA half-lives rely on transcriptional inhibition or metabolic labelling. Here, we use a non-invasive method for estimating half-lives for hundreds of mRNAs in the early Drosophila embryo. This approach uses the intronic and exonic reads from a total RNA-seq time series and Gaussian process regression to model the dynamics of premature and mature mRNAs. We show how regulation of mRNA stability is used to establish a range of mature mRNA dynamics during embryogenesis, despite shared transcription profiles. Using single-molecule imaging, we provide evidence that, for the mRNAs tested, there is a correlation between short half-life and mRNA association with P-bodies. Moreover, we detect an enrichment of mRNA 3' ends in P-bodies in the early embryo, consistent with 5' to 3' degradation occurring in P-bodies for at least a subset of mRNAs. We discuss our findings in relation to recently published data suggesting that the primary function of P-bodies in other biological contexts is mRNA storage.

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.

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.

Emerging artificial intelligence applications in Spatial Transcriptomics analysis

Spatial transcriptomics (ST) has advanced significantly in the last few years. Such advancement comes with the urgent need for novel computational methods to handle the unique challenges of ST data analysis. Many artificial intelligence (AI) methods have been developed to utilize various machine learning and deep learning techniques for computational ST analysis. This review provides a comprehensive and up-to-date survey of current AI methods for ST analysis.

Modeling zero inflation is not necessary for spatial transcriptomics

BACKGROUND

Spatial transcriptomics are a set of new technologies that profile gene expression on tissues with spatial localization information. With technological advances, recent spatial transcriptomics data are often in the form of sparse counts with an excessive amount of zero values.

RESULTS

We perform a comprehensive analysis on 20 spatial transcriptomics datasets collected from 11 distinct technologies to characterize the distributional properties of the expression count data and understand the statistical nature of the zero values. Across datasets, we show that a substantial fraction of genes displays overdispersion and/or zero inflation that cannot be accounted for by a Poisson model, with genes displaying overdispersion substantially overlapped with genes displaying zero inflation. In addition, we find that either the Poisson or the negative binomial model is sufficient for modeling the majority of genes across most spatial transcriptomics technologies. We further show major sources of overdispersion and zero inflation in spatial transcriptomics including gene expression heterogeneity across tissue locations and spatial distribution of cell types. In particular, when we focus on a relatively homogeneous set of tissue locations or control for cell type compositions, the number of detected overdispersed and/or zero-inflated genes is substantially reduced, and a simple Poisson model is often sufficient to fit the gene expression data there.

CONCLUSIONS

Our study provides the first comprehensive evidence that excessive zeros in spatial transcriptomics are not due to zero inflation, supporting the use of count models without a zero inflation component for modeling spatial transcriptomics.

Museum of spatial transcriptomics

The function of many biological systems, such as embryos, liver lobules, intestinal villi, and tumors, depends on the spatial organization of their cells. In the past decade, high-throughput technologies have been developed to quantify gene expression in space, and computational methods have been developed that leverage spatial gene expression data to identify genes with spatial patterns and to delineate neighborhoods within tissues. To comprehensively document spatial gene expression technologies and data-analysis methods, we present a curated review of literature on spatial transcriptomics dating back to 1987, along with a thorough analysis of trends in the field, such as usage of experimental techniques, species, tissues studied, and computational approaches used. Our Review places current methods in a historical context, and we derive insights about the field that can guide current research strategies. A companion supplement offers a more detailed look at the technologies and methods analyzed: https://pachterlab.github.io/LP_2021/ .

Computational elucidation of spatial gene expression variation from spatially resolved transcriptomics data

Recent advances in spatially resolved transcriptomics (SRT) have revolutionized biological and medical research and enabled unprecedented insight into the functional organization and cell communication of tissues and organs in situ. Identifying and elucidating gene spatial expression variation (SE analysis) is fundamental to elucidate the SRT landscape. There is an urgent need for public repositories and computational techniques of SRT data in SE analysis alongside technological breakthroughs and large-scale data generation. Increasing efforts to use in silico techniques in SE analysis have been made. However, these attempts are widely scattered among a large number of studies that are not easily accessible or comprehensible by both medical and life scientists. This study provides a survey and a summary of public resources on SE analysis in SRT studies. An updated systematic overview of state-of-the-art computational approaches and tools currently available in SE analysis are presented herein, emphasizing recent advances. Finally, the present study explores the future perspectives and challenges of in silico techniques in SE analysis. This study guides medical and life scientists to look for dedicated resources and more competent tools for characterizing spatial patterns of gene expression.

Spatial components of molecular tissue biology

Methods for profiling RNA and protein expression in a spatially resolved manner are rapidly evolving, making it possible to comprehensively characterize cells and tissues in health and disease. To maximize the biological insights obtained using these techniques, it is critical to both clearly articulate the key biological questions in spatial analysis of tissues and develop the requisite computational tools to address them. Developers of analytical tools need to decide on the intrinsic molecular features of each cell that need to be considered, and how cell shape and morphological features are incorporated into the analysis. Also, optimal ways to compare different tissue samples at various length scales are still being sought. Grouping these biological problems and related computational algorithms into classes across length scales, thus characterizing common issues that need to be addressed, will facilitate further progress in spatial transcriptomics and proteomics.

Mapping Gene Expression in the Spatial Dimension

The main function and biological processes of tissues are determined by the combination of gene expression and spatial organization of their cells. RNA sequencing technologies have primarily interrogated gene expression without preserving the native spatial context of cells. However, the emergence of various spatially-resolved transcriptome analysis methods now makes it possible to map the gene expression to specific coordinates within tissues, enabling transcriptional heterogeneity between different regions, and for the localization of specific transcripts and novel spatial markers to be revealed. Hence, spatially-resolved transcriptome analysis technologies have broad utility in research into human disease and developmental biology. Here, recent advances in spatially-resolved transcriptome analysis methods are summarized, including experimental technologies and computational methods. Strengths, challenges, and potential applications of those methods are highlighted, and perspectives in this field are provided.