CellDART: cell type inference by domain adaptation of single-cell and spatial transcriptomic data

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

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

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

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

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.

Integration tools for scRNA-seq data and spatial transcriptomics sequencing data

Numerous methods have been developed to integrate spatial transcriptomics sequencing data with single-cell RNA sequencing (scRNA-seq) data. Continuous development and improvement of these methods offer multiple options for integrating and analyzing scRNA-seq and spatial transcriptomics data based on diverse research inquiries. However, each method has its own advantages, limitations and scope of application. Researchers need to select the most suitable method for their research purposes based on the actual situation. This review article presents a compilation of 19 integration methods sourced from a wide range of available approaches, serving as a comprehensive reference for researchers to select the suitable integration method for their specific research inquiries. By understanding the principles of these methods, we can identify their similarities and differences, comprehend their applicability and potential complementarity, and lay the foundation for future method development and understanding. This review article presents 19 methods that aim to integrate scRNA-seq data and spatial transcriptomics data. The methods are classified into two main groups and described accordingly. The article also emphasizes the incorporation of High Variance Genes in annotating various technologies, aiming to obtain biologically relevant information aligned with the intended purpose.

Novel insights into paclitaxel's role on tumor-associated macrophages in enhancing PD-1 blockade in breast cancer treatment

BACKGROUND

Triple-negative breast cancer (TNBC) poses unique challenges due to its complex nature and the need for more effective treatments. Recent studies showed encouraging outcomes from combining paclitaxel (PTX) with programmed cell death protein-1 (PD-1) blockade in treating TNBC, although the exact mechanisms behind the improved results are unclear.

METHODS

We employed an integrated approach, analyzing spatial transcriptomics and single-cell RNA sequencing data from TNBC patients to understand why the combination of PTX and PD-1 blockade showed better response in TNBC patients. We focused on toll-like receptor 4 (TLR4), a receptor of PTX, and its role in modulating the cross-presentation signaling pathways in tumor-associated macrophages (TAMs) within the tumor microenvironment. Leveraging insights obtained from patient-derived data, we conducted in vitro experiments using immunosuppressive bone marrow-derived macrophages (iBMDMs) to validate if PTX could augment the cross-presentation and phagocytosis activities. Subsequently, we extended our study to an in vivo murine model of TNBC to ascertain the effects of PTX on the cross-presentation capabilities of TAMs and its downstream impact on CD8+ T cell-mediated immune responses.

RESULTS

Data analysis from TNBC patients revealed that the activation of TLR4 and cross-presentation signaling pathways are crucial for the antitumor efficacy of PTX. In vitro studies showed that PTX treatment enhances the cross-presentation ability of iBMDMs. In vivo experiments demonstrated that PTX activates TLR4-dependent cross-presentation in TAMs, improving CD8+ T cell-mediated antitumor responses. The efficacy of PTX in promoting antitumor immunity was elicited when combined with PD-1 blockade, suggesting a complementary interaction.

CONCLUSIONS

This study reveals how PTX boosts the effectiveness of PD-1 inhibitors in treating TNBC. We found that PTX activates TLR4 signaling in TAMs. This activation enhances their ability to present antigens, thereby boosting CD8+ T cell antitumor responses. These findings not only shed light on PTX's immunomodulatory role in TNBC but also underscore the potential of targeting TAMs' antigen presentation capabilities in immunotherapy approaches.

Spatial omics techniques and data analysis for cancer immunotherapy applications

In-depth profiling of cancer cells/tissues is expanding our understanding of the genomic, epigenomic, transcriptomic, and proteomic landscape of cancer. However, the complexity of the cancer microenvironment, particularly its immune regulation, has made it difficult to exploit the potential of cancer immunotherapy. High-throughput spatial omics technologies and analysis pipelines have emerged as powerful tools for tackling this challenge. As a result, a potential revolution in cancer diagnosis, prognosis, and treatment is on the horizon. In this review, we discuss the technological advances in spatial profiling of cancer around and beyond the central dogma to harness the full benefits of immunotherapy. We also discuss the promise and challenges of spatial data analysis and interpretation and provide an outlook for the future.

SpatialPrompt: spatially aware scalable and accurate tool for spot deconvolution and domain identification in spatial transcriptomics

Efficiently mapping of cell types in situ remains a major challenge in spatial transcriptomics. Most spot deconvolution tools ignore spatial coordinate information and perform extremely slow on large datasets. Here, we introduce SpatialPrompt, a spatially aware and scalable tool for spot deconvolution and domain identification. SpatialPrompt integrates gene expression, spatial location, and single-cell RNA sequencing (scRNA-seq) dataset as reference to accurately infer cell-type proportions of spatial spots. SpatialPrompt uses non-negative ridge regression and graph neural network to efficiently capture local microenvironment information. Our extensive benchmarking analysis on Visium, Slide-seq, and MERFISH datasets demonstrated superior performance of SpatialPrompt over 15 existing tools. On mouse hippocampus dataset, SpatialPrompt achieves spot deconvolution and domain identification within 2 minutes for 50,000 spots. Overall, domain identification using SpatialPrompt was 44 to 150 times faster than existing methods. We build a database housing 40 plus curated scRNA-seq datasets for seamless integration with SpatialPrompt for spot deconvolution.

Spatial transcriptomic brain imaging reveals the effects of immunomodulation therapy on specific regional brain cells in a mouse dementia model

Increasing evidence of brain-immune crosstalk raises expectations for the efficacy of novel immunotherapies in Alzheimer's disease (AD), but the lack of methods to examine brain tissues makes it difficult to evaluate therapeutics. Here, we investigated the changes in spatial transcriptomic signatures and brain cell types using the 10x Genomics Visium platform in immune-modulated AD models after various treatments. To proceed with an analysis suitable for barcode-based spatial transcriptomics, we first organized a workflow for segmentation of neuroanatomical regions, establishment of appropriate gene combinations, and comprehensive review of altered brain cell signatures. Ultimately, we investigated spatial transcriptomic changes following administration of immunomodulators, NK cell supplements and an anti-CD4 antibody, which ameliorated behavior impairment, and designated brain cells and regions showing probable associations with behavior changes. We provided the customized analytic pipeline into an application named STquantool. Thus, we anticipate that our approach can help researchers interpret the real action of drug candidates by simultaneously investigating the dynamics of all transcripts for the development of novel AD therapeutics.

PAGER-scFGA: unveiling cell functions and molecular mechanisms in cell trajectories through single-cell functional genomics analysis

Background: Understanding how cells and tissues respond to stress factors and perturbations during disease processes is crucial for developing effective prevention, diagnosis, and treatment strategies. Single-cell RNA sequencing (scRNA-seq) enables high-resolution identification of cells and exploration of cell heterogeneity, shedding light on cell differentiation/maturation and functional differences. Recent advancements in multimodal sequencing technologies have focused on improving access to cell-specific subgroups for functional genomics analysis. To facilitate the functional annotation of cell groups and characterization of molecular mechanisms underlying cell trajectories, we introduce the Pathways, Annotated Gene Lists, and Gene Signatures Electronic Repository for Single-Cell Functional Genomics Analysis (PAGER-scFGA). Results: We have developed PAGER-scFGA, which integrates cell functional annotations and gene-set enrichment analysis into popular single-cell analysis pipelines such as Scanpy. Using differentially expressed genes (DEGs) from pairwise cell clusters, PAGER-scFGA infers cell functions through the enrichment of potential cell-marker genesets. Moreover, PAGER-scFGA provides pathways, annotated gene lists, and gene signatures (PAGs) enriched in specific cell subsets with tissue compositions and continuous transitions along cell trajectories. Additionally, PAGER-scFGA enables the construction of a gene subcellular map based on DEGs and allows examination of the gene functional compartments (GFCs) underlying cell maturation/differentiation. In a real-world case study of mouse natural killer (mNK) cells, PAGER-scFGA revealed two major stages of natural killer (NK) cells and three trajectories from the precursor stage to NK T-like mature stage within blood, spleen, and bone marrow tissues. As the trajectories progress to later stages, the DEGs exhibit greater divergence and variability. However, the DEGs in different trajectories still interact within a network during NK cell maturation. Notably, PAGER-scFGA unveiled cell cytotoxicity, exocytosis, and the response to interleukin (IL) signaling pathways and associated network models during the progression from precursor NK cells to mature NK cells. Conclusion: PAGER-scFGA enables in-depth exploration of functional insights and presents a comprehensive knowledge map of gene networks and GFCs, which can be utilized for future studies and hypothesis generation. It is expected to become an indispensable tool for inferring cell functions and detecting molecular mechanisms within cell trajectories in single-cell studies. The web app (accessible at https://au-singlecell.streamlit.app/) is publicly available.

GTADC: A Graph-Based Method for Inferring Cell Spatial Distribution in Cancer Tissues

The heterogeneity of tumors poses a challenge for understanding cell interactions and constructing complex ecosystems within cancer tissues. Current research strategies integrate spatial transcriptomics (ST) and single-cell sequencing (scRNA-seq) data to thoroughly analyze this intricate system. However, traditional deep learning methods using scRNA-seq data tend to filter differentially expressed genes through statistical methods. In the context of cancer tissues, where cancer cells exhibit significant differences in gene expression compared to normal cells, this heterogeneity renders traditional analysis methods incapable of accurately capturing differences between cell types. Therefore, we propose a graph-based deep learning method, GTADC, which utilizes Silhouette scores to precisely capture genes with significant expression differences within each cell type, enhancing the accuracy of gene selection. Compared to traditional methods, GTADC not only considers the expression similarity of genes within their respective clusters but also comprehensively leverages information from the overall clustering structure. The introduction of graph structure effectively captures spatial relationships and topological structures between the two types of data, enabling GTADC to more accurately and comprehensively resolve the spatial composition of different cell types within tissues. This refinement allows GTADC to intricately reconstruct the cellular spatial composition, offering a precise solution for inferring cell spatial composition. This method allows for early detection of potential cancer cell regions within tissues, assessing their quantity and spatial information in cell populations. We aim to achieve a preliminary estimation of cancer occurrence and development, contributing to a deeper understanding of early-stage cancer and providing potential support for early cancer diagnosis.

Navigating the landscapes of spatial transcriptomics: How computational methods guide the way

Spatially resolved transcriptomics has been dramatically transforming biological and medical research in various fields. It enables transcriptome profiling at single-cell, multi-cellular, or sub-cellular resolution, while retaining the information of geometric localizations of cells in complex tissues. The coupling of cell spatial information and its molecular characteristics generates a novel multi-modal high-throughput data source, which poses new challenges for the development of analytical methods for data-mining. Spatial transcriptomic data are often highly complex, noisy, and biased, presenting a series of difficulties, many unresolved, for data analysis and generation of biological insights. In addition, to keep pace with the ever-evolving spatial transcriptomic experimental technologies, the existing analytical theories and tools need to be updated and reformed accordingly. In this review, we provide an overview and discussion of the current computational approaches for mining of spatial transcriptomics data. Future directions and perspectives of methodology design are proposed to stimulate further discussions and advances in new analytical models and algorithms. This article is categorized under: RNA Methods > RNA Analyses in Cells RNA Evolution and Genomics > Computational Analyses of RNA RNA Export and Localization > RNA Localization.

The role of microglia heterogeneity in synaptic plasticity and brain disorders: Will sequencing shed light on the discovery of new therapeutic targets?

Microglia play a crucial role in interacting with neuronal synapses and modulating synaptic plasticity. This function is particularly significant during postnatal development, as microglia are responsible for removing excessive synapses to prevent neurodevelopmental deficits. Dysregulation of microglial synaptic function has been well-documented in various pathological conditions, notably Alzheimer's disease and multiple sclerosis. The recent application of RNA sequencing has provided a powerful and unbiased means to decipher spatial and temporal microglial heterogeneity. By identifying microglia with varying gene expression profiles, researchers have defined multiple subgroups of microglia associated with specific pathological states, including disease-associated microglia, interferon-responsive microglia, proliferating microglia, and inflamed microglia in multiple sclerosis, among others. However, the functional roles of these distinct subgroups remain inadequately characterized. This review aims to refine our current understanding of the potential roles of heterogeneous microglia in regulating synaptic plasticity and their implications for various brain disorders, drawing from recent sequencing research and functional studies. This knowledge may aid in the identification of pathogenetic biomarkers and potential factors contributing to pathogenesis, shedding new light on the discovery of novel drug targets. The field of sequencing-based data mining is evolving toward a multi-omics approach. With advances in viral tools for precise microglial regulation and the development of brain organoid models, we are poised to elucidate the functional roles of microglial subgroups detected through sequencing analysis, ultimately identifying valuable therapeutic targets.

Mapping cancer biology in space: applications and perspectives on spatial omics for oncology

Technologies to decipher cellular biology, such as bulk sequencing technologies and single-cell sequencing technologies, have greatly assisted novel findings in tumor biology. Recent findings in tumor biology suggest that tumors construct architectures that influence the underlying cancerous mechanisms. Increasing research has reported novel techniques to map the tissue in a spatial context or targeted sampling-based characterization and has introduced such technologies to solve oncology regarding tumor heterogeneity, tumor microenvironment, and spatially located biomarkers. In this study, we address spatial technologies that can delineate the omics profile in a spatial context, novel findings discovered via spatial technologies in oncology, and suggest perspectives regarding therapeutic approaches and further technological developments.

scEVOLVE: cell-type incremental annotation without forgetting for single-cell RNA-seq data

The evolution in single-cell RNA sequencing (scRNA-seq) technology has opened a new avenue for researchers to inspect cellular heterogeneity with single-cell precision. One crucial aspect of this technology is cell-type annotation, which is fundamental for any subsequent analysis in single-cell data mining. Recently, the scientific community has seen a surge in the development of automatic annotation methods aimed at this task. However, these methods generally operate at a steady-state total cell-type capacity, significantly restricting the cell annotation systems'capacity for continuous knowledge acquisition. Furthermore, creating a unified scRNA-seq annotation system remains challenged by the need to progressively expand its understanding of ever-increasing cell-type concepts derived from a continuous data stream. In response to these challenges, this paper presents a novel and challenging setting for annotation, namely cell-type incremental annotation. This concept is designed to perpetually enhance cell-type knowledge, gleaned from continuously incoming data. This task encounters difficulty with data stream samples that can only be observed once, leading to catastrophic forgetting. To address this problem, we introduce our breakthrough methodology termed scEVOLVE, an incremental annotation method. This innovative approach is built upon the methodology of contrastive sample replay combined with the fundamental principle of partition confidence maximization. Specifically, we initially retain and replay sections of the old data in each subsequent training phase, then establish a unique prototypical learning objective to mitigate the cell-type imbalance problem, as an alternative to using cross-entropy. To effectively emulate a model that trains concurrently with complete data, we introduce a cell-type decorrelation strategy that efficiently scatters feature representations of each cell type uniformly. We constructed the scEVOLVE framework with simplicity and ease of integration into most deep softmax-based single-cell annotation methods. Thorough experiments conducted on a range of meticulously constructed benchmarks consistently prove that our methodology can incrementally learn numerous cell types over an extended period, outperforming other strategies that fail quickly. As far as our knowledge extends, this is the first attempt to propose and formulate an end-to-end algorithm framework to address this new, practical task. Additionally, scEVOLVE, coded in Python using the Pytorch machine-learning library, is freely accessible at https://github.com/aimeeyaoyao/scEVOLVE.

Spatiotemporal characterization of glial cell activation in an Alzheimer's disease model by spatially resolved transcriptomics

The molecular changes that occur with the progression of Alzheimer's disease (AD) are well known, but an understanding of the spatiotemporal heterogeneity of changes in the brain is lacking. Here, we investigated the spatially resolved transcriptome in a 5XFAD AD model at different ages to understand regional changes at the molecular level. Spatially resolved transcriptomic data were obtained from 5XFAD AD models and age-matched control mice. Differentially expressed genes were identified using spots clustered by anatomical structures. Gene signatures of activation of microglia and astrocytes were calculated and mapped on the spatially resolved transcriptomic data. We identified early alterations in the white matter (WM) of the AD model before the definite accumulation of amyloid plaques in the gray matter (GM). Changes in the early stage of the disease involved primarily glial cell activation in the WM, whereas the changes in the later stage of pathology were prominent in the GM. We confirmed that disease-associated microglia (DAM) and astrocyte (DAA) signatures also showed initial changes in WM and that activation spreads to GM. Trajectory inference using microglial gene sets revealed the subdivision of DAMs with different spatial patterns. Taken together, these results help to understand the spatiotemporal changes associated with reactive glial cells as a major pathophysiological characteristic of AD. The heterogeneous spatial molecular changes apply to identifying diagnostic and therapeutic targets caused by amyloid accumulation in AD.

Spatial transcriptomics deconvolution at single-cell resolution using Redeconve

Computational deconvolution with single-cell RNA sequencing data as reference is pivotal to interpreting spatial transcriptomics data, but the current methods are limited to cell-type resolution. Here we present Redeconve, an algorithm to deconvolute spatial transcriptomics data at single-cell resolution, enabling interpretation of spatial transcriptomics data with thousands of nuanced cell states. We benchmark Redeconve with the state-of-the-art algorithms on diverse spatial transcriptomics platforms and datasets and demonstrate the superiority of Redeconve in terms of accuracy, resolution, robustness, and speed. Application to a human pancreatic cancer dataset reveals cancer-clone-specific T cell infiltration, and application to lymph node samples identifies differential cytotoxic T cells between IgA+ and IgG+ spots, providing novel insights into tumor immunology and the regulatory mechanisms underlying antibody class switch.

Explainable machine learning for profiling the immunological synapse and functional characterization of therapeutic antibodies

Therapeutic antibodies are widely used to treat severe diseases. Most of them alter immune cells and act within the immunological synapse; an essential cell-to-cell interaction to direct the humoral immune response. Although many antibody designs are generated and evaluated, a high-throughput tool for systematic antibody characterization and prediction of function is lacking. Here, we introduce the first comprehensive open-source framework, scifAI (single-cell imaging flow cytometry AI), for preprocessing, feature engineering, and explainable, predictive machine learning on imaging flow cytometry (IFC) data. Additionally, we generate the largest publicly available IFC dataset of the human immunological synapse containing over 2.8 million images. Using scifAI, we analyze class frequency and morphological changes under different immune stimulation. T cell cytokine production across multiple donors and therapeutic antibodies is quantitatively predicted in vitro, linking morphological features with function and demonstrating the potential to significantly impact antibody design. scifAI is universally applicable to IFC data. Given its modular architecture, it is straightforward to incorporate into existing workflows and analysis pipelines, e.g., for rapid antibody screening and functional characterization.

GTAD: a graph-based approach for cell spatial composition inference from integrated scRNA-seq and ST-seq data

With the emergence of spatial transcriptome sequencing (ST-seq), research now heavily relies on the joint analysis of ST-seq and single-cell RNA sequencing (scRNA-seq) data to precisely identify cell spatial composition in tissues. However, common methods for combining these datasets often merge data from multiple cells to generate pseudo-ST data, overlooking topological relationships and failing to represent spatial arrangements accurately. We introduce GTAD, a method utilizing the Graph Attention Network for deconvolution of integrated scRNA-seq and ST-seq data. GTAD effectively captures cell spatial relationships and topological structures within tissues using a graph-based approach, enhancing cell-type identification and our understanding of complex tissue cellular landscapes. By integrating scRNA-seq and ST data into a unified graph structure, GTAD outperforms traditional 'pseudo-ST' methods, providing robust and information-rich results. GTAD performs exceptionally well with synthesized spatial data and accurately identifies cell spatial composition in tissues like the mouse cerebral cortex, cerebellum, developing human heart and pancreatic ductal carcinoma. GTAD holds the potential to enhance our understanding of tissue microenvironments and cellular diversity in complex bio-logical systems. The source code is available at https://github.com/zzhjs/GTAD.

Application of single-cell sequencing to the research of tumor microenvironment

Single-cell sequencing is a technique for detecting and analyzing genomes, transcriptomes, and epigenomes at the single-cell level, which can detect cellular heterogeneity lost in conventional sequencing hybrid samples, and it has revolutionized our understanding of the genetic heterogeneity and complexity of tumor progression. Moreover, the tumor microenvironment (TME) plays a crucial role in the formation, development and response to treatment of tumors. The application of single-cell sequencing has ushered in a new age for the TME analysis, revealing not only the blueprint of the pan-cancer immune microenvironment, but also the heterogeneity and differentiation routes of immune cells, as well as predicting tumor prognosis. Thus, the combination of single-cell sequencing and the TME analysis provides a unique opportunity to unravel the molecular mechanisms underlying tumor development and progression. In this review, we summarize the recent advances in single-cell sequencing and the TME analysis, highlighting their potential applications in cancer research and clinical translation.

Spatial Transcriptomic Cell-type Deconvolution Using Graph Neural Networks

Spatially resolved transcriptomics performs high-throughput measurement of transcriptomes while preserving spatial information about the cellular organizations. However, many spatially resolved transcriptomic technologies can only distinguish spots consisting of a mixture of cells instead of working at single-cell resolution. Here, we present STdGCN, a graph neural network model designed for cell type deconvolution of spatial transcriptomic (ST) data that can leverage abundant single-cell RNA sequencing (scRNA-seq) data as reference. STdGCN is the first model incorporating the expression profiles from single cell data as well as the spatial localization information from the ST data for cell type deconvolution. Extensive benchmarking experiments on multiple ST datasets showed that STdGCN outperformed 14 published state-of-the-art models. Applied to a human breast cancer Visium dataset, STdGCN discerned spatial distributions between stroma, lymphocytes and cancer cells for tumor microenvironment dissection. In a human heart ST dataset, STdGCN detected the changes of potential endothelial-cardiomyocyte communications during tissue development.

Spatial Transcriptomics: Technical Aspects of Recent Developments and Their Applications in Neuroscience and Cancer Research

Spatial transcriptomics is a newly emerging field that enables high-throughput investigation of the spatial localization of transcripts and related analyses in various applications for biological systems. By transitioning from conventional biological studies to "in situ" biology, spatial transcriptomics can provide transcriptome-scale spatial information. Currently, the ability to simultaneously characterize gene expression profiles of cells and relevant cellular environment is a paradigm shift for biological studies. In this review, recent progress in spatial transcriptomics and its applications in neuroscience and cancer studies are highlighted. Technical aspects of existing technologies and future directions of new developments (as of March 2023), computational analysis of spatial transcriptome data, application notes in neuroscience and cancer studies, and discussions regarding future directions of spatial multi-omics and their expanding roles in biomedical applications are emphasized.

The promise and challenge of spatial omics in dissecting tumour microenvironment and the role of AI

Growing evidence supports the critical role of tumour microenvironment (TME) in tumour progression, metastases, and treatment response. However, the in-situ interplay among various TME components, particularly between immune and tumour cells, are largely unknown, hindering our understanding of how tumour progresses and responds to treatment. While mainstream single-cell omics techniques allow deep, single-cell phenotyping, they lack crucial spatial information for in-situ cell-cell interaction analysis. On the other hand, tissue-based approaches such as hematoxylin and eosin and chromogenic immunohistochemistry staining can preserve the spatial information of TME components but are limited by their low-content staining. High-content spatial profiling technologies, termed spatial omics, have greatly advanced in the past decades to overcome these limitations. These technologies continue to emerge to include more molecular features (RNAs and/or proteins) and to enhance spatial resolution, opening new opportunities for discovering novel biological knowledge, biomarkers, and therapeutic targets. These advancements also spur the need for novel computational methods to mine useful TME insights from the increasing data complexity confounded by high molecular features and spatial resolution. In this review, we present state-of-the-art spatial omics technologies, their applications, major strengths, and limitations as well as the role of artificial intelligence (AI) in TME studies.

Machine Learning for Uncovering Biological Insights in Spatial Transcriptomics Data

Development and homeostasis in multicellular systems both require exquisite control over spatial molecular pattern formation and maintenance. Advances in spatially-resolved and high-throughput molecular imaging methods such as multiplexed immunofluorescence and spatial transcriptomics (ST) provide exciting new opportunities to augment our fundamental understanding of these processes in health and disease. The large and complex datasets resulting from these techniques, particularly ST, have led to rapid development of innovative machine learning (ML) tools primarily based on deep learning techniques. These ML tools are now increasingly featured in integrated experimental and computational workflows to disentangle signals from noise in complex biological systems. However, it can be difficult to understand and balance the different implicit assumptions and methodologies of a rapidly expanding toolbox of analytical tools in ST. To address this, we summarize major ST analysis goals that ML can help address and current analysis trends. We also describe four major data science concepts and related heuristics that can help guide practitioners in their choices of the right tools for the right biological questions.

spSeudoMap: cell type mapping of spatial transcriptomics using unmatched single-cell RNA-seq data

Since many single-cell RNA-seq (scRNA-seq) data are obtained after cell sorting, such as when investigating immune cells, tracking cellular landscape by integrating single-cell data with spatial transcriptomic data is limited due to cell type and cell composition mismatch between the two datasets. We developed a method, spSeudoMap, which utilizes sorted scRNA-seq data to create virtual cell mixtures that closely mimic the gene expression of spatial data and trains a domain adaptation model for predicting spatial cell compositions. The method was applied in brain and breast cancer tissues and accurately predicted the topography of cell subpopulations. spSeudoMap may help clarify the roles of a few, but crucial cell types.

Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST

Spatial transcriptomics technologies generate gene expression profiles with spatial context, requiring spatially informed analysis tools for three key tasks, spatial clustering, multisample integration, and cell-type deconvolution. We present GraphST, a graph self-supervised contrastive learning method that fully exploits spatial transcriptomics data to outperform existing methods. It combines graph neural networks with self-supervised contrastive learning to learn informative and discriminative spot representations by minimizing the embedding distance between spatially adjacent spots and vice versa. We demonstrated GraphST on multiple tissue types and technology platforms. GraphST achieved 10% higher clustering accuracy and better delineated fine-grained tissue structures in brain and embryo tissues. GraphST is also the only method that can jointly analyze multiple tissue slices in vertical or horizontal integration while correcting batch effects. Lastly, GraphST demonstrated superior cell-type deconvolution to capture spatial niches like lymph node germinal centers and exhausted tumor infiltrating T cells in breast tumor tissue.

Spatial RNA sequencing methods show high resolution of single cell in cancer metastasis and the formation of tumor microenvironment

Cancer metastasis often leads to death and therapeutic resistance. This process involves the participation of a variety of cell components, especially cellular and intercellular communications in the tumor microenvironment (TME). Using genetic sequencing technology to comprehensively characterize the tumor and TME is therefore key to understanding metastasis and therapeutic resistance. The use of spatial transcriptome sequencing enables the localization of gene expressions and cell activities in tissue sections. By examining the localization change as well as gene expression of these cells, it is possible to characterize the progress of tumor metastasis and TME formation. With improvements of this technology, spatial transcriptome sequencing technology has been extended from local regions to whole tissues, and from single sequencing technology to multimodal analysis combined with a variety of datasets. This has enabled the detection of every single cell in tissue slides, with high resolution, to provide more accurate predictive information for tumor treatments. In this review, we summarize the results of recent studies dealing with new multimodal methods and spatial transcriptome sequencing methods in tumors to illustrate recent developments in the imaging resolution of micro-tissues.

Spatial transcriptomics demonstrates the role of CD4 T cells in effector CD8 T cell differentiation during chronic viral infection

CD4 T cell help is critical to sustain effector CD8 T cell responses during chronic infection, notably via T follicular helper (Tfh)-derived interleukin-21 (IL-21). Conversely, CD4 depletion results in severe CD8 T cell dysfunction and lifelong viremia despite CD4 T cell reemergence following transient depletion. These observations suggest that repopulating CD4 subsets are functionally or numerically insufficient to orchestrate a robust CD8 response. We utilize spatial transcriptomics and single-cell RNA sequencing (scRNA-seq) to investigate CD4 T cell heterogeneity under CD4-replete and -deplete conditions and explore cellular interactions during chronic infection. Although IL-21-producing Tfh cells repopulate following transient CD4 depletion, they are outnumbered by immunomodulatory CD4 T cells. Moreover, the splenic architecture appears perturbed, with decreases in white pulp regions, coinciding with germinal center losses. These disruptions in splenic architecture are associated with diminished Tfh and progenitor CD8 T cell colocalization, providing a potential mechanism for impaired progenitor-to-effector CD8 T cell differentiation during "un-helped" conditions.

Spatial Transcriptomics-Based Identification of Molecular Markers for Nanomedicine Distribution in Tumor Tissue

The intratumoral accumulation of nanomedicine has been considered a passive process, referred to as the enhanced permeability and retention effect. Recent studies have suggested that the tumor uptake of nanomedicines follows an energy-dependent pathway rather than being a passive process. Herein, to explore the factor candidates that are associated with nanomedicine tumor uptake, a molecular marker identification platform is developed by integrating microscopic fluorescence images of a nanomedicine distribution with spatial transcriptomics information. When this approach is applied to PEGylated liposomes, molecular markers related to hypoxia, glycolysis, and apoptosis can be identified as being related to the intratumoral distribution of the nanomedicine. It is expected that the method can be applied to explain the distribution of a wide range of nanomedicines and that the data obtained from this analysis can enhance the precise utilization of nanomedicines.

Decoding brain memory formation by single-cell RNA sequencing

To understand how distinct memories are formed and stored in the brain is an important and fundamental question in neuroscience and computational biology. A population of neurons, termed engram cells, represents the physiological manifestation of a specific memory trace and is characterized by dynamic changes in gene expression, which in turn alters the synaptic connectivity and excitability of these cells. Recent applications of single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq) are promising approaches for delineating the dynamic expression profiles in these subsets of neurons, and thus understanding memory-specific genes, their combinatorial patterns and regulatory networks. The aim of this article is to review and discuss the experimental and computational procedures of sc/snRNA-seq, new studies of molecular mechanisms of memory aided by sc/snRNA-seq in human brain diseases and related mouse models, and computational challenges in understanding the regulatory mechanisms underlying long-term memory formation.

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.