DestVI identifies continuums of cell types in spatial transcriptomics data

DSCT: a novel deep-learning framework for rapid and accurate spatial transcriptomic cell typing

Unraveling complex cell-type-composition and gene-expression patterns at the cellular spatial resolution is crucial for understanding intricate cell functions in the brain. In this study, we developed Deep Neural Network-based Spatial Cell Typing (DSCT)-an innovative framework for spatial cell typing within spatial transcriptomic data sets. This approach utilizes a synergistic integration of an enhanced gene-selection strategy and a lightweight deep neural network for data training, offering a more rapid and accurate solution for the analysis of spatial transcriptomic data. Based on comprehensive analysis, DSCT achieved exceptional accuracy in cell-type identification across various brain regions, species and spatial transcriptomic platforms. It also performed well in mapping finer cell types, thereby showcasing its versatility and adaptability across diverse data sets. Strikingly, DSCT exhibited high efficiency and remarkable processing speed, with fewer computational resource demands. As such, this novel approach opens new avenues for exploring the spatial organization of cell types and gene-expression patterns, advancing our understanding of biological functions and pathologies within the nervous system.

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

KanCell is a deep learning model based on Kolmogorov-Arnold networks (KAN) designed to enhance cellular heterogeneity analysis by integrating single-cell RNA sequencing and spatial transcriptomics (ST) data. ST technologies provide insights into gene expression within tissue context, revealing cellular interactions and microenvironments. To fully leverage this potential, effective computational models are crucial. We evaluate KanCell on both simulated and real datasets from technologies such as STARmap, Slide-seq, Visium, and Spatial Transcriptomics. Our results demonstrate that KanCell outperforms existing methods across metrics like PCC, SSIM, COSSIM, RMSE, JSD, ARS, and ROC, with robust performance under varying cell numbers and background noise. Real-world applications on human lymph nodes, hearts, melanoma, breast cancer, dorsolateral prefrontal cortex, and mouse embryo brains confirmed its reliability. Compared with traditional approaches, KanCell effectively captures non-linear relationships and optimizes computational efficiency through KAN, providing an accurate and efficient tool for ST. By improving data accuracy and resolving cell type composition, KanCell reveals cellular heterogeneity, clarifies disease microenvironments, and identifies therapeutic targets, addressing complex biological challenges.

Primer on machine learning applications in brain immunology

Single-cell and spatial technologies have transformed our understanding of brain immunology, providing unprecedented insights into immune cell heterogeneity and spatial organisation within the central nervous system. These methods have uncovered complex cellular interactions, rare cell populations, and the dynamic immune landscape in neurological disorders. This review highlights recent advances in single-cell "omics" data analysis and discusses their applicability for brain immunology. Traditional statistical techniques, adapted for single-cell omics, have been crucial in categorizing cell types and identifying gene signatures, overcoming challenges posed by increasingly complex datasets. We explore how machine learning, particularly deep learning methods like autoencoders and graph neural networks, is addressing these challenges by enhancing dimensionality reduction, data integration, and feature extraction. Newly developed foundation models present exciting opportunities for uncovering gene expression programs and predicting genetic perturbations. Focusing on brain development, we demonstrate how single-cell analyses have resolved immune cell heterogeneity, identified temporal maturation trajectories, and uncovered potential therapeutic links to various pathologies, including brain malignancies and neurodegeneration. The integration of single-cell and spatial omics has elucidated the intricate cellular interplay within the developing brain. This mini-review is intended for wet lab biologists at all career stages, offering a concise overview of the evolving landscape of single-cell omics in the age of widely available artificial intelligence.

Spatial genomics: Mapping the landscape of fibrosis

Organ fibrosis causes major morbidity and mortality worldwide. Treatments for fibrosis are limited, with organ transplantation being the only cure. Here, we review how various state-of-the-art spatial genomics approaches are being deployed to interrogate fibrosis across multiple organs, providing exciting insights into fibrotic disease pathogenesis. These include the detailed topographical annotation of pathogenic cell populations and states, detection of transcriptomic perturbations in morphologically normal tissue, characterization of fibrotic and homeostatic niches and their cellular constituents, and in situ interrogation of ligand-receptor interactions within these microenvironments. Together, these powerful readouts enable detailed analysis of fibrosis evolution across time and space.

Novel insights into kidney disease: the scRNA-seq and spatial transcriptomics approaches: a literature review

Over the past decade, single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) have revolutionized biomedical research, particularly in understanding cellular heterogeneity in kidney diseases. This review summarizes the application and development of scRNA-seq combined with ST in the context of kidney disease. By dissecting cellular heterogeneity at an unprecedented resolution, these advanced techniques have identified novel cell subpopulations and their dynamic interactions within the renal microenvironment. The integration of scRNA-seq with ST has been instrumental in elucidating the cellular and molecular mechanisms underlying kidney development, homeostasis, and disease progression. This approach has not only identified key cellular players in renal pathophysiology but also revealed the spatial organization of cells within the kidney, which is crucial for understanding their functional specialization. This paper highlights the transformative impact of these techniques on renal research that have paved the way for targeted therapeutic interventions and personalized medicine in the management of kidney disease.

SIMVI disentangles intrinsic and spatial-induced cellular states in spatial omics data

Spatial omics technologies enable analysis of gene expression and interaction dynamics in relation to tissue structure and function. However, existing computational methods may not properly distinguish cellular intrinsic variability and intercellular interactions, and may thus fail to reliably capture spatial regulations. Here, we present Spatial Interaction Modeling using Variational Inference (SIMVI), an annotation-free deep learning framework that disentangles cell intrinsic and spatial-induced latent variables in spatial omics data with rigorous theoretical support. By this disentanglement, SIMVI enables estimation of spatial effects at a single-cell resolution, and empowers various downstream analyses. We demonstrate the superior performance of SIMVI across datasets from diverse platforms and tissues. SIMVI illuminates the cyclical spatial dynamics of germinal center B cells in human tonsil. Applying SIMVI to multiome melanoma data reveals potential tumor epigenetic reprogramming states. On our newly-collected cohort-level CosMx melanoma data, SIMVI uncovers space-and-outcome-dependent macrophage states and cellular communication machinery in tumor microenvironments.

Multi-task benchmarking of spatially resolved gene expression simulation models

BACKGROUND

Computational methods for spatially resolved transcriptomics (SRT) are often developed and assessed using simulated data. The effectiveness of these evaluations relies on the ability of simulation methods to accurately reflect experimental data. However, a systematic evaluation framework for spatial simulators is currently lacking.

RESULTS

Here, we present SpatialSimBench, a comprehensive evaluation framework that assesses 13 simulation methods using ten distinct STR datasets. We introduce simAdaptor, a tool that extends single-cell simulators by incorporating spatial variables, enabling them to simulate spatial data. SimAdaptor ensures SpatialSimBench is backwards compatible, facilitating direct comparisons between spatially aware simulators and existing non-spatial single-cell simulators through the adaption. Using SpatialSimBench, we demonstrate the feasibility of leveraging existing single-cell simulators for SRT data and highlight performance differences among methods. Additionally, we evaluate the simulation methods based on a total of 35 metrics across data property estimation, various downstream analyses, and scalability. In total, we generated 4550 results from 13 simulation methods, ten spatial datasets, and 35 metrics.

CONCLUSIONS

Our findings reveal that model estimation can be influenced by distribution assumptions and dataset characteristics. In summary, our evaluation framework provides guidelines for selecting appropriate methods for specific scenarios and informs future method development.

Infusing structural assumptions into dimensionality reduction for single-cell RNA sequencing data to identify small gene sets

Dimensionality reduction greatly facilitates the exploration of cellular heterogeneity in single-cell RNA sequencing data. While most of such approaches are data-driven, it can be useful to incorporate biologically plausible assumptions about the underlying structure or the experimental design. We propose the boosting autoencoder (BAE) approach, which combines the advantages of unsupervised deep learning for dimensionality reduction and boosting for formalizing assumptions. Specifically, our approach selects small sets of genes that explain latent dimensions. As illustrative applications, we explore the diversity of neural cell identities and temporal patterns of embryonic development.

Profiling immune cell tissue niches in the spatial -omics era

Immune responses require complex, spatially coordinated interactions between immune cells and their tissue environment. For decades, we have imaged tissue sections to visualize a limited number of immune-related macromolecules in situ, functioning as surrogates for cell types or processes of interest. However, this inevitably provides a limited snapshot of the tissue's immune landscape. Recent developments in high-throughput spatial -omics technologies, particularly spatial transcriptomics, and its application to human samples has facilitated a more comprehensive understanding of tissue immunity by mapping fine-grained immune cell states to their precise tissue location while providing contextual information about their immediate cellular and tissue environment. These data provide opportunities to investigate mechanisms underlying the spatial distribution of immune cells and its functional implications, including the identification of immune niches, although the criteria used to define this term have been inconsistent. Here, we review recent technological and analytic advances in multiparameter spatial profiling, focusing on how these methods have generated new insights in translational immunology. We propose a 3-step framework for the definition and characterization of immune niches, which is powerfully facilitated by new spatial profiling methodologies. Finally, we summarize current approaches to analyze adaptive immune repertoires and lymphocyte clonal expansion in a spatially resolved manner.

A Temporal and Spatial Atlas of Adaptive Immune Responses in the Lymph Node Following Viral Infection

The spatial organization of adaptive immune cells within lymph nodes is critical for understanding immune responses during infection and disease. Here, we introduce AIR-SPACE, an integrative approach that combines high-resolution spatial transcriptomics with paired, high-fidelity long-read sequencing of T and B cell receptors. This method enables the simultaneous analysis of cellular transcriptomes and adaptive immune receptor (AIR) repertoires within their native spatial context. We applied AIR-SPACE to mouse popliteal lymph nodes at five distinct time points after Vaccinia virus footpad infection and constructed a comprehensive map of the developing adaptive immune response. Our analysis revealed heterogeneous activation niches, characterized by Interferon-gamma (IFN-γ) production, during the early stages of infection. At later stages, we delineated sub-anatomical structures within the germinal center (GC) and observed evidence that antibody-producing plasma cells differentiate and exit the GC through the dark zone. Furthermore, by combining clonotype data with spatial lineage tracing, we demonstrate that B cell clones are shared among multiple GCs within the same lymph node, reinforcing the concept of a dynamic, interconnected network of GCs. Overall, our study demonstrates how AIR-SPACE can be used to gain insight into the spatial dynamics of infection responses within lymphoid organs.

Starfysh integrates spatial transcriptomic and histologic data to reveal heterogeneous tumor-immune hubs

Spatially resolved gene expression profiling provides insight into tissue organization and cell-cell crosstalk; however, sequencing-based spatial transcriptomics (ST) lacks single-cell resolution. Current ST analysis methods require single-cell RNA sequencing data as a reference for rigorous interpretation of cell states, mostly do not use associated histology images and are not capable of inferring shared neighborhoods across multiple tissues. Here we present Starfysh, a computational toolbox using a deep generative model that incorporates archetypal analysis and any known cell type markers to characterize known or new tissue-specific cell states without a single-cell reference. Starfysh improves the characterization of spatial dynamics in complex tissues using histology images and enables the comparison of niches as spatial hubs across tissues. Integrative analysis of primary estrogen receptor (ER)-positive breast cancer, triple-negative breast cancer (TNBC) and metaplastic breast cancer (MBC) tissues led to the identification of spatial hubs with patient- and disease-specific cell type compositions and revealed metabolic reprogramming shaping immunosuppressive hubs in aggressive MBC.

Mapping cells through time and space with moscot

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

Coordinated immune networks in leukemia bone marrow microenvironments distinguish response to cellular therapy

Understanding how intratumoral immune populations coordinate antitumor responses after therapy can guide treatment prioritization. We systematically analyzed an established immunotherapy, donor lymphocyte infusion (DLI), by assessing 348,905 single-cell transcriptomes from 74 longitudinal bone marrow samples of 25 patients with relapsed leukemia; a subset was evaluated by both protein- and transcriptome-based spatial analysis. In acute myeloid leukemia (AML) DLI responders, we identified clonally expanded ZNF683+ CD8+ cytotoxic T lymphocytes with in vitro specificity for patient-matched AML. These cells originated primarily from the DLI product and appeared to coordinate antitumor immune responses through interaction with diverse immune cell types within the marrow microenvironment. Nonresponders lacked this cross-talk and had cytotoxic T lymphocytes with elevated TIGIT expression. Our study identifies recipient bone marrow microenvironment differences as a determinant of an effective antileukemia response and opens opportunities to modulate cellular therapy.

Deep topic modeling of spatial transcriptomics in the rheumatoid arthritis synovium identifies distinct classes of ectopic lymphoid structures

Single-cell RNA sequencing studies have revealed the heterogeneity of cell states present in the rheumatoid arthritis (RA) synovium. However, it remains unclear how these cell types interact with one another in situ and how synovial microenvironments shape observed cell states. Here, we use spatial transcriptomics (ST) to define stable microenvironments across eight synovial tissue samples from six RA patients and characterize the cellular composition of ectopic lymphoid structures (ELS). To identify disease-relevant cellular communities, we developed DeepTopics, a scalable reference-free deconvolution method based on a Dirichlet variational autoencoder architecture. DeepTopics identified 22 topics across tissue samples that were defined by specific cell types, activation states, and/or biological processes. Some topics were defined by multiple colocalizing cell types, such as CD34+ fibroblasts and LYVE1+ macrophages, suggesting functional interactions. Within ELS, we discovered two divergent cellular patterns that were stable across ELS in each patient and typified by the presence or absence of a "germinal-center-like" topic. DeepTopics is a versatile and computationally efficient method for identifying disease-relevant microenvironments from ST data, and our results highlight divergent cellular architectures in histologically similar RA synovial samples that have implications for disease pathogenesis.

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.

NLSDeconv: an efficient cell-type deconvolution method for spatial transcriptomics data

SUMMARY

Spatial transcriptomics (ST) allows gene expression profiling within intact tissue samples but lacks single-cell resolution. This necessitates computational deconvolution methods to estimate the contributions of distinct cell types. This article introduces NLSDeconv, a novel cell-type deconvolution method based on non-negative least squares, along with an accompanying Python package. Benchmarking against 18 existing deconvolution methods on various ST datasets demonstrates NLSDeconv's competitive statistical performance and superior computational efficiency.

AVAILABILITY AND IMPLEMENTATION

NLSDeconv is freely available at https://github.com/tinachentc/NLSDeconv as a Python package.

Spall: accurate and robust unveiling cellular landscapes from spatially resolved transcriptomics data using a decomposition network

Recent developments in spatially resolved transcriptomics (SRT) enable the characterization of spatial structures for different tissues. Many decomposition methods have been proposed to depict the cellular distribution within tissues. However, existing computational methods struggle to balance spatial continuity in cell distribution with the preservation of cell-specific characteristics. To address this, we propose Spall, a novel decomposition network that integrates scRNA-seq data with SRT data to accurately infer cell type proportions. Spall introduced the GATv2 module, featuring a flexible dynamic attention mechanism to capture relationships between spots. This improves the identification of cellular distribution patterns in spatial analysis. Additionally, Spall incorporates skip connections to address the loss of cell-specific information, thereby enhancing the prediction capability for rare cell types. Experimental results show that Spall outperforms the state-of-the-art methods in reconstructing cell distribution patterns on multiple datasets. Notably, Spall reveals tumor heterogeneity in human pancreatic ductal adenocarcinoma samples and delineates complex tissue structures, such as the laminar organization of the mouse cerebral cortex and the mouse cerebellum. These findings highlight the ability of Spall to provide reliable low-dimensional embeddings for downstream analyses, offering new opportunities for deciphering tissue structures.

STGAT: Graph attention networks for deconvolving spatial transcriptomics data

BACKGROUND AND OBJECTIVE

Spatially resolved gene expression profiles are crucial for understanding tissue structure and function. However, the lack of single-cell resolution in these profiles demands their integration with single-cell RNA sequencing data for accurate dataset deconvolution. We propose STGAT, an innovative deconvolution method that leverages graph attention networks to enhance spatial transcriptomic (ST) data analysis.

METHODS

STGAT generates pseudo-ST data that more comprehensively represents the cell-type composition within real-ST data by using three different sampling probabilities. A comprehensive combined graph is then constructed to capture the complex relationships both across pseudo- and real-ST data and within each dataset. Moreover, integrating a graph attention network further enables STGAT to dynamically assign the weights to the connections between spots, significantly enhancing the accuracy of cell-type composition predictions.

RESULTS

Extensive comparative experiments on simulated and real-world datasets, demonstrate the superior performance of STGAT for cell-type deconvolution. The method outperforms six established methods and is robust across various biological contexts.

CONCLUSION

STGAT exhibits more precise results in cell-type composition inference that are more consistent with the known knowledge, suggesting its potential utility in improving the resolution and accuracy of spatial transcriptomics data analysis.

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

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

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.

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.

scDOT: optimal transport for mapping senescent cells in spatial transcriptomics

The low resolution of spatial transcriptomics data necessitates additional information for optimal use. We developed scDOT, which combines spatial transcriptomics and single cell RNA sequencing to improve the ability to reconstruct single cell resolved spatial maps and identify senescent cells. scDOT integrates optimal transport and expression deconvolution to learn non-linear couplings between cells and spots and to infer cell placements. Application of scDOT to lung spatial transcriptomics data improves on prior methods and allows the identification of the spatial organization of senescent cells, their neighboring cells and novel genes involved in cell-cell interactions that may be driving senescence.

Recovering single-cell expression profiles from spatial transcriptomics with scResolve

Many popular spatial transcriptomics techniques lack single-cell resolution. Instead, these methods measure the collective gene expression for each location from a mixture of cells, potentially containing multiple cell types. Here, we developed scResolve, a method for recovering single-cell expression profiles from spatial transcriptomics measurements at multi-cellular resolution. scResolve accurately restores expression profiles of individual cells at their locations, which is unattainable with cell type deconvolution. Applications of scResolve on human breast cancer data and human lung disease data demonstrate that scResolve enables cell-type-specific differential gene expression analysis between different tissue contexts and accurate identification of rare cell populations. The spatially resolved cellular-level expression profiles obtained through scResolve facilitate more flexible and precise spatial analysis that complements raw multi-cellular level analysis.

Implementing distinct spatial proteogenomic technologies: opportunities, challenges, and key considerations

Our ability to understand the cellular complexity of tissues has been revolutionized in recent years with significant advances in proteogenomic technologies including those enabling spatial analyses. This has led to numerous consortium efforts, such as the human cell atlas initiative which aims to profile all cells in the human body in healthy and diseased contexts. The availability of such information will subsequently lead to the identification of novel biomarkers of disease and of course therapeutic avenues. However, before such an atlas of any given healthy or diseased tissue can be generated, several factors should be considered including which specific techniques are optimal for the biological question at hand. In this review, we aim to highlight some of the considerations we believe to be important in the experimental design and analysis process, with the goal of helping to navigate the rapidly changing landscape of technologies available.

SDePER: a hybrid machine learning and regression method for cell-type deconvolution of spatial barcoding-based transcriptomic data

Spatial barcoding-based transcriptomic (ST) data require deconvolution for cellular-level downstream analysis. Here we present SDePER, a hybrid machine learning and regression method to deconvolve ST data using reference single-cell RNA sequencing (scRNA-seq) data. SDePER tackles platform effects between ST and scRNA-seq data, ensuring a linear relationship between them while addressing sparsity and spatial correlations in cell types across capture spots. SDePER estimates cell-type proportions, enabling enhanced resolution tissue mapping by imputing cell-type compositions and gene expressions at unmeasured locations. Applications to simulated data and four real datasets showed SDePER's superior accuracy and robustness over existing methods.

SIMVI reveals intrinsic and spatial-induced states in spatial omics data

Spatial omics technologies enable the analysis of gene expression and interaction dynamics in relation to tissue structure and function. However, existing computational methods may not properly distinguish cellular intrinsic variability and intercellular interactions, and may thus fail to capture spatial regulations for further biological discoveries. Here, we present Spatial Interaction Modeling using Variational Inference (SIMVI), an annotation-free framework that disentangles cell intrinsic and spatial-induced latent variables for modeling gene expression in spatial omics data. We derive theoretical support for SIMVI in disentangling intrinsic and spatial-induced variations. By this disentanglement, SIMVI enables estimation of spatial effects (SE) at a single-cell resolution, and opens up various opportunities for novel downstream analyses. To demonstrate the potential of SIMVI, we applied SIMVI to spatial omics data from diverse platforms and tissues (MERFISH human cortex, Slide-seqv2 mouse hippocampus, Slide-tags human tonsil, spatial multiome human melanoma, cohort-level CosMx melanoma). In all tested datasets, SIMVI effectively disentangles variations and infers accurate spatial effects compared with alternative methods. Moreover, on these datasets, SIMVI uniquely uncovers complex spatial regulations and dynamics of biological significance. In the human tonsil data, SIMVI illuminates the cyclical spatial dynamics of germinal center B cells during maturation. Applying SIMVI to both RNA and ATAC modalities of the multiome melanoma data reveals potential tumor epigenetic reprogramming states. Application of SIMVI on our newly-collected cohort-level CosMx melanoma dataset uncovers space-and-outcome-dependent macrophage states and the underlying cellular communication machinery in the tumor microenvironments.

Spatial transcriptomics in health and disease

The ability to localize hundreds of macromolecules to discrete locations, structures and cell types in a tissue is a powerful approach to understand the cellular and spatial organization of an organ. Spatially resolved transcriptomic technologies enable mapping of transcripts at single-cell or near single-cell resolution in a multiplex manner. The rapid development of spatial transcriptomic technologies has accelerated the pace of discovery in several fields, including nephrology. Its application to preclinical models and human samples has provided spatial information about new cell types discovered by single-cell sequencing and new insights into the cell-cell interactions within neighbourhoods, and has improved our understanding of the changes that occur in response to injury. Integration of spatial transcriptomic technologies with other omics methods, such as proteomics and spatial epigenetics, will further facilitate the generation of comprehensive molecular atlases, and provide insights into the dynamic relationships of molecular components in homeostasis and disease. This Review provides an overview of current and emerging spatial transcriptomic methods, their applications and remaining challenges for the field.

Single-cell analysis reveals hypoxia-induced immunosuppressive microenvironment in intrahepatic cholangiocarcinoma

The role of hypoxia in the tumor microenvironment of intrahepatic cholangiocarcinoma (iCCA) remains unclear. Here, we generated a comprehensive atlas of the entire tumor microenvironment and delineated the multifaceted cell-cell interactions to decipher hypoxia-induced pro-tumor immune suppression. We discovered hypoxia is significantly associated with iCCA progression via the activation of HIF1A expression. Moreover, hypoxia-dependent PPARγ-mediated fatty acid oxidation in APOE+ TAMs promoted M2 macrophage polarization by activating the HIF1A-PPARG-CD36 axis. These polarized APOE+ TAMs recruited Treg cell infiltration via the CCL3-CCR5 pair to form an immunosuppressive microenvironment. APOE+ TAMs tended to co-localize spatially with Treg cells in the malignant tissue based on spatial transcriptome data and immunofluorescence analysis results. We identified tumor-reactive CXCL13+ CD8-PreTex with specific high expression of ENTPD1 and ITGAE, which acted as precursors of CD8-Tex and had higher cytotoxicity, lower exhaustion, and more vigorous proliferation. Consequently, CXCL13+ CD8-PreTex functioned as a positive regulator of antitumor immunity by expressing the pro-inflammatory cytokines IFNG and TNF, associated with a better survival outcome. Our study reveals the mechanisms involved in hypoxia-induced immunosuppression and suggests that targeting precursor-exhausted CXCL13+CD8+ T cells might provide a pratical immunotherapeutic approach.

Genome analysis through image processing with deep learning models

Genomic sequences are traditionally represented as strings of characters: A (adenine), C (cytosine), G (guanine), and T (thymine). However, an alternative approach involves depicting sequence-related information through image representations, such as Chaos Game Representation (CGR) and read pileup images. With rapid advancements in deep learning (DL) methods within computer vision and natural language processing, there is growing interest in applying image-based DL methods to genomic sequence analysis. These methods involve encoding genomic information as images or integrating spatial information from images into the analytical process. In this review, we summarize three typical applications that use image processing with DL models for genome analysis. We examine the utilization and advantages of these image-based approaches.

Cross-species single-cell spatial transcriptomic atlases of the cerebellar cortex

The molecular and cellular organization of the primate cerebellum remains poorly characterized. We obtained single-cell spatial transcriptomic atlases of macaque, marmoset, and mouse cerebella and identified primate-specific cell subtypes, including Purkinje cells and molecular-layer interneurons, that show different expression of the glutamate ionotropic receptor Delta type subunit 2 (GRID2) gene. Distinct gene expression profiles were found in anterior, posterior, and vestibular regions in all species, whereas region-selective gene expression was predominantly observed in the granular layer of primates and in the Purkinje layer of mice. Gene expression gradients in the cerebellar cortex matched well with functional connectivity gradients revealed with awake functional magnetic resonance imaging, with more lobule-specific differences between primates and mice than between two primate species. These comprehensive atlases and comparative analyses provide the basis for understanding cerebellar evolution and function.

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

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

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.

Unsupervised pattern identification in spatial gene expression atlas reveals mouse brain regions beyond established ontology

The rapid growth of large-scale spatial gene expression data demands efficient and reliable computational tools to extract major trends of gene expression in their native spatial context. Here, we used stability-driven unsupervised learning (i.e., staNMF) to identify principal patterns (PPs) of 3D gene expression profiles and understand spatial gene distribution and anatomical localization at the whole mouse brain level. Our subsequent spatial correlation analysis systematically compared the PPs to known anatomical regions and ontology from the Allen Mouse Brain Atlas using spatial neighborhoods. We demonstrate that our stable and spatially coherent PPs, whose linear combinations accurately approximate the spatial gene data, are highly correlated with combinations of expert-annotated brain regions. These PPs yield a brain ontology based purely on spatial gene expression. Our PP identification approach outperforms principal component analysis and typical clustering algorithms on the same task. Moreover, we show that the stable PPs reveal marked regional imbalance of brainwide genetic architecture, leading to region-specific marker genes and gene coexpression networks. Our findings highlight the advantages of stability-driven machine learning for plausible biological discovery from dense spatial gene expression data, streamlining tasks that are infeasible by conventional manual approaches.

STAN, a computational framework for inferring spatially informed transcription factor activity

Transcription factors (TFs) drive significant cellular changes in response to environmental cues and intercellular signaling. Neighboring cells influence TF activity and, consequently, cellular fate and function. Spatial transcriptomics (ST) captures mRNA expression patterns across tissue samples, enabling characterization of the local microenvironment. However, these datasets have not been fully leveraged to systematically estimate TF activity governing cell identity. Here, we present STAN ( S patially informed T ranscription factor A ctivity N etwork), a linear mixed-effects computational method that predicts spot-specific, spatially informed TF activities by integrating curated TF-target gene priors, mRNA expression, spatial coordinates, and morphological features from corresponding imaging data. We tested STAN using lymph node, breast cancer, and glioblastoma ST datasets to demonstrate its applicability by identifying TFs associated with specific cell types, spatial domains, pathological regions, and ligand‒receptor pairs. STAN augments the utility of STs to reveal the intricate interplay between TFs and spatial organization across a spectrum of cellular contexts.

Joint inference of discrete cell types and continuous type-specific variability in single-cell datasets with MMIDAS

Reproducible definition and identification of cell types is essential to enable investigations into their biological function and to understand their relevance in the context of development, disease and evolution. Current approaches model variability in data as continuous latent factors, followed by clustering as a separate step, or immediately apply clustering on the data. We show that such approaches can suffer from qualitative mistakes in identifying cell types robustly, particularly when the number of such cell types is in the hundreds or even thousands. Here we propose an unsupervised method, Mixture Model Inference with Discrete-coupled AutoencoderS (MMIDAS), which combines a generalized mixture model with a multi-armed deep neural network to jointly infer the discrete type and continuous type-specific variability. Using four recent datasets of brain cells spanning different technologies, species and conditions, we demonstrate that MMIDAS can identify reproducible cell types and infer cell type-dependent continuous variability in both unimodal and multimodal datasets.

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.

Data enhancement in the age of spatial biology

Unveiling the intricate interplay of cells in their native environment lies at the heart of understanding fundamental biological processes and unraveling disease mechanisms, particularly in complex diseases like cancer. Spatial transcriptomics (ST) offers a revolutionary lens into the spatial organization of gene expression within tissues, empowering researchers to study both cell heterogeneity and microenvironments in health and disease. However, current ST technologies often face limitations in either resolution or the number of genes profiled simultaneously. Integrating ST data with complementary sources, such as single-cell transcriptomics and detailed tissue staining images, presents a powerful solution to overcome these limitations. This review delves into the computational approaches driving the integration of spatial transcriptomics with other data types. By illuminating the key challenges and outlining the current algorithmic solutions, we aim to highlight the immense potential of these methods to revolutionize our understanding of cancer biology.

Joint inference of discrete cell types and continuous type-specific variability in single-cell datasets with MMIDAS

Reproducible definition and identification of cell types is essential to enable investigations into their biological function, and understanding their relevance in the context of development, disease and evolution. Current approaches model variability in data as continuous latent factors, followed by clustering as a separate step, or immediately apply clustering on the data. We show that such approaches can suffer from qualitative mistakes in identifying cell types robustly, particularly when the number of such cell types is in the hundreds or even thousands. Here, we propose an unsupervised method, MMIDAS, which combines a generalized mixture model with a multi-armed deep neural network, to jointly infer the discrete type and continuous type-specific variability. Using four recent datasets of brain cells spanning different technologies, species, and conditions, we demonstrate that MMIDAS can identify reproducible cell types and infer cell type-dependent continuous variability in both uni-modal and multi-modal datasets.

Simulating multiple variability in spatially resolved transcriptomics with scCube

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

Machine learning integrative approaches to advance computational immunology

The study of immunology, traditionally reliant on proteomics to evaluate individual immune cells, has been revolutionized by single-cell RNA sequencing. Computational immunologists play a crucial role in analysing these datasets, moving beyond traditional protein marker identification to encompass a more detailed view of cellular phenotypes and their functional roles. Recent technological advancements allow the simultaneous measurements of multiple cellular components-transcriptome, proteome, chromatin, epigenetic modifications and metabolites-within single cells, including in spatial contexts within tissues. This has led to the generation of complex multiscale datasets that can include multimodal measurements from the same cells or a mix of paired and unpaired modalities. Modern machine learning (ML) techniques allow for the integration of multiple "omics" data without the need for extensive independent modelling of each modality. This review focuses on recent advancements in ML integrative approaches applied to immunological studies. We highlight the importance of these methods in creating a unified representation of multiscale data collections, particularly for single-cell and spatial profiling technologies. Finally, we discuss the challenges of these holistic approaches and how they will be instrumental in the development of a common coordinate framework for multiscale studies, thereby accelerating research and enabling discoveries in the computational immunology field.

Multiscale topology classifies cells in subcellular spatial transcriptomics

Spatial transcriptomics measures in situ gene expression at millions of locations within a tissue1, hitherto with some trade-off between transcriptome depth, spatial resolution and sample size2. Although integration of image-based segmentation has enabled impactful work in this context, it is limited by imaging quality and tissue heterogeneity. By contrast, recent array-based technologies offer the ability to measure the entire transcriptome at subcellular resolution across large samples3-6. Presently, there exist no approaches for cell type identification that directly leverage this information to annotate individual cells. Here we propose a multiscale approach to automatically classify cell types at this subcellular level, using both transcriptomic information and spatial context. We showcase this on both targeted and whole-transcriptome spatial platforms, improving cell classification and morphology for human kidney tissue and pinpointing individual sparsely distributed renal mouse immune cells without reliance on image data. By integrating these predictions into a topological pipeline based on multiparameter persistent homology7-9, we identify cell spatial relationships characteristic of a mouse model of lupus nephritis, which we validate experimentally by immunofluorescence. The proposed framework readily generalizes to new platforms, providing a comprehensive pipeline bridging different levels of biological organization from genes through to tissues.

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.

Single-Cell Sequencing: High-Resolution Analysis of Cellular Heterogeneity in Autoimmune Diseases

Autoimmune diseases (AIDs) are complex in etiology and diverse in classification but clinically show similar symptoms such as joint pain and skin problems. As a result, the diagnosis is challenging, and usually, only broad treatments can be available. Consequently, the clinical responses in patients with different types of AIDs are unsatisfactory. Therefore, it is necessary to conduct more research to figure out the pathogenesis and therapeutic targets of AIDs. This requires research technologies with strong extraction and prediction capabilities. Single-cell sequencing technology analyses the genomic, epigenomic, or transcriptomic information at the single-cell level. It can define different cell types and states in greater detail, further revealing the molecular mechanisms that drive disease progression. These advantages enable cell biology research to achieve an unprecedented resolution and scale, bringing a whole new vision to life science research. In recent years, single-cell technology especially single-cell RNA sequencing (scRNA-seq) has been widely used in various disease research. In this paper, we present the innovations and applications of single-cell sequencing in the medical field and focus on the application contributing to the differential diagnosis and precise treatment of AIDs. Despite some limitations, single-cell sequencing has a wide range of applications in AIDs. We finally present a prospect for the development of single-cell sequencing. These ideas may provide some inspiration for subsequent research.

CellsFromSpace: a fast, accurate, and reference-free tool to deconvolve and annotate spatially distributed omics data

Motivation

Spatial transcriptomics enables the analysis of cell crosstalk in healthy and diseased organs by capturing the transcriptomic profiles of millions of cells within their spatial contexts. However, spatial transcriptomics approaches also raise new computational challenges for the multidimensional data analysis associated with spatial coordinates.

Results

In this context, we introduce a novel analytical framework called CellsFromSpace based on independent component analysis (ICA), which allows users to analyze various commercially available technologies without relying on a single-cell reference dataset. The ICA approach deployed in CellsFromSpace decomposes spatial transcriptomics data into interpretable components associated with distinct cell types or activities. ICA also enables noise or artifact reduction and subset analysis of cell types of interest through component selection. We demonstrate the flexibility and performance of CellsFromSpace using real-world samples to demonstrate ICA's ability to successfully identify spatially distributed cells as well as rare diffuse cells, and quantitatively deconvolute datasets from the Visium, Slide-seq, MERSCOPE, and CosMX technologies. Comparative analysis with a current alternative reference-free deconvolution tool also highlights CellsFromSpace's speed, scalability and accuracy in processing complex, even multisample datasets. CellsFromSpace also offers a user-friendly graphical interface enabling non-bioinformaticians to annotate and interpret components based on spatial distribution and contributor genes, and perform full downstream analysis.

Availability and implementation

CellsFromSpace (CFS) is distributed as an R package available from github at https://github.com/gustaveroussy/CFS along with tutorials, examples, and detailed documentation.

Spotless, a reproducible pipeline for benchmarking cell type deconvolution in spatial transcriptomics

Spatial transcriptomics (ST) technologies allow the profiling of the transcriptome of cells while keeping their spatial context. Since most commercial untargeted ST technologies do not yet operate at single-cell resolution, computational methods such as deconvolution are often used to infer the cell type composition of each sequenced spot. We benchmarked 11 deconvolution methods using 63 silver standards, 3 gold standards, and 2 case studies on liver and melanoma tissues. We developed a simulation engine called synthspot to generate silver standards from single-cell RNA-sequencing data, while gold standards are generated by pooling single cells from targeted ST data. We evaluated methods based on their performance, stability across different reference datasets, and scalability. We found that cell2location and RCTD are the top-performing methods, but surprisingly, a simple regression model outperforms almost half of the dedicated spatial deconvolution methods. Furthermore, we observe that the performance of all methods significantly decreased in datasets with highly abundant or rare cell types. Our results are reproducible in a Nextflow pipeline, which also allows users to generate synthetic data, run deconvolution methods and optionally benchmark them on their dataset (https://github.com/saeyslab/spotless-benchmark).

Benchmarking mapping algorithms for cell-type annotating in mouse brain by integrating single-nucleus RNA-seq and Stereo-seq data

Limited gene capture efficiency and spot size of spatial transcriptome (ST) data pose significant challenges in cell-type characterization. The heterogeneity and complexity of cell composition in the mammalian brain make it more challenging to accurately annotate ST data from brain. Many algorithms attempt to characterize subtypes of neuron by integrating ST data with single-nucleus RNA sequencing (snRNA-seq) or single-cell RNA sequencing. However, assessing the accuracy of these algorithms on Stereo-seq ST data remains unresolved. Here, we benchmarked 9 mapping algorithms using 10 ST datasets from four mouse brain regions in two different resolutions and 24 pseudo-ST datasets from snRNA-seq. Both actual ST data and pseudo-ST data were mapped using snRNA-seq datasets from the corresponding brain regions as reference data. After comparing the performance across different areas and resolutions of the mouse brain, we have reached the conclusion that both robust cell-type decomposition and SpatialDWLS demonstrated superior robustness and accuracy in cell-type annotation. Testing with publicly available snRNA-seq data from another sequencing platform in the cortex region further validated our conclusions. Altogether, we developed a workflow for assessing suitability of mapping algorithm that fits for ST datasets, which can improve the efficiency and accuracy of spatial data annotation.

MUSTANG: Multi-sample spatial transcriptomics data analysis with cross-sample transcriptional similarity guidance

Spatially resolved transcriptomics has revolutionized genome-scale transcriptomic profiling by providing high-resolution characterization of transcriptional patterns. Here, we present our spatial transcriptomics analysis framework, MUSTANG (MUlti-sample Spatial Transcriptomics data ANalysis with cross-sample transcriptional similarity Guidance), which is capable of performing multi-sample spatial transcriptomics spot cellular deconvolution by allowing both cross-sample expression-based similarity information sharing as well as spatial correlation in gene expression patterns within samples. Experiments on a semi-synthetic spatial transcriptomics dataset and three real-world spatial transcriptomics datasets demonstrate the effectiveness of MUSTANG in revealing biological insights inherent in the cellular characterization of tissue samples under study.

Integrative spatial analysis reveals a multi-layered organization of glioblastoma

Glioma contains malignant cells in diverse states. Here, we combine spatial transcriptomics, spatial proteomics, and computational approaches to define glioma cellular states and uncover their organization. We find three prominent modes of organization. First, gliomas are composed of small local environments, each typically enriched with one major cellular state. Second, specific pairs of states preferentially reside in proximity across multiple scales. This pairing of states is consistent across tumors. Third, these pairwise interactions collectively define a global architecture composed of five layers. Hypoxia appears to drive the layers, as it is associated with a long-range organization that includes all cancer cell states. Accordingly, tumor regions distant from any hypoxic/necrotic foci and tumors that lack hypoxia such as low-grade IDH-mutant glioma are less organized. In summary, we provide a conceptual framework for the organization of cellular states in glioma, highlighting hypoxia as a long-range tissue organizer.

Integrative multi-omics profiling in human decedents receiving pig heart xenografts

In a previous study, heart xenografts from 10-gene-edited pigs transplanted into two human decedents did not show evidence of acute-onset cellular- or antibody-mediated rejection. Here, to better understand the detailed molecular landscape following xenotransplantation, we carried out bulk and single-cell transcriptomics, lipidomics, proteomics and metabolomics on blood samples obtained from the transplanted decedents every 6 h, as well as histological and transcriptomic tissue profiling. We observed substantial early immune responses in peripheral blood mononuclear cells and xenograft tissue obtained from decedent 1 (male), associated with downstream T cell and natural killer cell activity. Longitudinal analyses indicated the presence of ischemia reperfusion injury, exacerbated by inadequate immunosuppression of T cells, consistent with previous findings of perioperative cardiac xenograft dysfunction in pig-to-nonhuman primate studies. Moreover, at 42 h after transplantation, substantial alterations in cellular metabolism and liver-damage pathways occurred, correlating with profound organ-wide physiological dysfunction. By contrast, relatively minor changes in RNA, protein, lipid and metabolism profiles were observed in decedent 2 (female) as compared to decedent 1. Overall, these multi-omics analyses delineate distinct responses to cardiac xenotransplantation in the two human decedents and reveal new insights into early molecular and immune responses after xenotransplantation. These findings may aid in the development of targeted therapeutic approaches to limit ischemia reperfusion injury-related phenotypes and improve outcomes.

Emerging Roles of Spatial Transcriptomics in Liver Research

Spatial transcriptomics, leveraging sequencing- and imaging-based techniques, has emerged as a groundbreaking technology for mapping gene expression within the complex architectures of tissues. This approach provides an in-depth understanding of cellular and molecular dynamics across various states of healthy and diseased livers. Through the integration of sophisticated bioinformatics strategies, it enables detailed exploration of cellular heterogeneity, transitions in cell states, and intricate cell-cell interactions with remarkable precision. In liver research, spatial transcriptomics has been particularly revelatory, identifying distinct zonated functions of hepatocytes that are crucial for understanding the metabolic and detoxification processes of the liver. Moreover, this technology has unveiled new insights into the pathogenesis of liver diseases, such as the role of lipid-associated macrophages in steatosis and endothelial cell signals in liver regeneration and repair. In the domain of liver cancer, spatial transcriptomics has proven instrumental in delineating intratumor heterogeneity, identifying supportive microenvironmental niches and revealing the complex interplay between tumor cells and the immune system as well as susceptibility to immune checkpoint inhibitors. In conclusion, spatial transcriptomics represents a significant advance in hepatology, promising to enhance our understanding and treatment of liver diseases.