Spatial transcriptomics elucidates localized immune responses in atherosclerotic coronary artery

A ruptured atherosclerotic plaque leads to exposure of deeper layers of the plaque to flowing blood and subsequently to thrombus formation. In contrast to the wealth of data on the occurrence of thrombi, little is known about the reasons why an atherosclerotic plaque is thrombogenic. One of the reasons is the relative inaccessibility of the atherosclerotic plaque. We have circumvented this problem by using 6-microns cryostat cross sections of human coronary arteries. These sections were mounted on coverslips that were exposed to flowing blood in a rectangular perfusion chamber. In normal-appearing arteries, platelet deposition was seen on the luminal side of the intima and on the adventitia. In atherosclerotic arteries, strongly increased platelet deposition was seen on the connective tissue of specific parts of the atherosclerotic plaque. The central lipid core of an advanced plaque was not reactive towards platelets. The results indicate that the atherosclerotic plaque by itself is more thrombogenic than the normal vessel wall. To study the cause of the increased thrombus formation on the atherosclerotic plaque, perfusion studies were combined with immunohistochemical studies. Immunohistochemical studies of adhesive proteins showed enrichment of collagen types I, III, V, and VI, vitronectin, fibronectin, fibrinogen/fibrin, and thrombospondin in the atherosclerotic plaque. Laminin and collagen type IV were not enriched. von Willebrand Factor (vWF) was not present in the plaque. The pattern of increased platelet deposition in serial cross sections corresponded best with areas in which collagen types I and III were enriched, but there were also areas in the plaque where both collagens were enriched but no increased reactivity was seen. Inhibition of platelet adhesion with a large range of antibodies or specific inhibitors showed that vWF from plasma and collagen types I and/or III in the plaque were involved. Fibronectin from plasma and fibronectin, fibrinogen, laminin, and thrombospondin in the vessel wall had no effect on platelet adhesion. We conclude that the increased thrombogenicity of atherosclerotic lesions is due to changes in quantity and nature of collagen types I and/or III.

The recent advances in spatial transcriptomics have brought unprecedented opportunities to understand the cellular heterogeneity in the spatial context. However, the current limitations of spatial technologies hamper the exploration of cellular localizations and interactions at single-cell level. Here, we present spatial transcriptomics deconvolution by topic modeling (STRIDE), a computational method to decompose cell types from spatial mixtures by leveraging topic profiles trained from single-cell transcriptomics. STRIDE accurately estimated the cell-type proportions and showed balanced specificity and sensitivity compared to existing methods. We demonstrated STRIDE’s utility by applying it to different spatial platforms and biological systems. Deconvolution by STRIDE not only mapped rare cell types to spatial locations but also improved the identification of spatially localized genes and domains. Moreover, topics discovered by STRIDE were associated with cell-type-specific functions and could be further used to integrate successive sections and reconstruct the three-dimensional architecture of tissues. Taken together, STRIDE is a versatile and extensible tool for integrated analysis of spatial and single-cell transcriptomics and is publicly available at https://github.com/wanglabtongji/STRIDE.

Novel cellular therapy has the potential to change cancer treatment. However, to design new therapies requires a better understanding of the tumor microenvironment (TME) and the cellular dynamics that occur within the tumor. This requires a high resolution and subcellular view of cancer cells, immune cells while identifying the neighborhood architecture that exists between them. Spatial transcriptomics and spatial proteomics show subcellular information and can be used to identify cellular cell types and find spatial deviation between cells. However, there are a lack of tools to investigate cellular behaviors on an individual cell basis while integrating pathological information, limiting the future of spatial biology. We introduce CellClique, a tool that assists in dissecting TMEs using spatial transcriptomics with the Xenium platform and Hematoxylin and Eosin (H&E) stains. CellClique integrates both generative and analytical capabilities to provide a comprehensive view of TMEs. On the generative end, CellClique uses generative AI to model both gene expressions and cell-neighborhood interactions for cell annotation, gene expression prediction, and gene imputation. On the analytical end, CellClique offers a comprehensive four-way spatial analysis scheme aimed at 1) understanding the effects of neighboring cells on a target cell, 2) dissecting cancer-normal cell boundaries, 3) observing immune invasion mechanisms, and 4) exploring cellular subtypes with the TME. We applied CellClique to study the cellular landscape in lung and colon cancers using multiple different neighborhood analyses. We generated proximal (10 microns), local (60 microns), and distal neighborhoods (120 microns) for each cell within each tissue and found distinct expression profiles of tumor-infiltrated lymphocytes, which are functionally different from lymphocytes excluded from the tumor. Additionally, we discovered expression variation patterns in cancer-normal boundary cell communities with various cell compositions. These compositions changed significantly as distance from the boundary changed. We also compared lymphocytes at different stages of life by analyzing expression differences among lymphocytes classified with over 80% confidence, those with a 50% probability of being lymphocytes, and those with below 20% probability. We further compared how these neighborhoods changed between lung cancer and colon cancer and showed cancer specific spatial deviations and expression. Together, these methods paint a complex picture of the TME but also identifies numerous avenues for future discovery and molecular investigation. These new approaches will guide future cellular therapy and the development of new treatments. The future integration of AI and machine learning will continue to make a tremendous impact on spatial transcriptomics and our approach to treating disease. Citation Format: Sachini Weerasekara, Natasha Darras, Nicolas Fernandez, Melinda Chen, Alina Ainbinder, Colles Price. CellClique: Dissecting tumor microenvironments at the single cell level using generative AI and spatial transcriptomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2418.

Cancer-associated fibroblasts (CAFs) are a diverse group of cells that significantly contribute to reshaping the tumor microenvironment (TME), and no research has systematically explored the molecular landscapes of senescence related CAFs (senes CAF) in NB. We utilized pan-cancer single cell and spatial transcriptomics analysis to identify the subpopulation of senes CAFs via senescence related genes, exploring its spatial distribution characteristics. Harnessing the maker genes with prognostic significance, we delineated the molecular landscapes of senes CAFs in bulk-seq data. We established the senes CAFs related signature (SCRS) by amalgamating 12 and 10 distinct machine learning (ML) algorithms to precisely diagnose stage 4 NB and to predict prognosis in NB. Based on risk scores calculated by prognostic SCRS, patients were categorized into high and low risk groups according to median risk score. We conducted comprehensive analysis between two risk groups, in terms of clinical applications, immune microenvironment, somatic mutations, immunotherapy, chemotherapy and single cell level. Ultimately, we explore the biological function of the hub gene JAK1 in pan-cancer multi-omics landscape. Through integrated analysis of pan-cancer spatial and single-cell transcriptomics data, we identified distinct functional subgroups of CAFs and characterized their spatial distribution patterns. With marker genes of senes CAF and leave-one-out cross-validation, we selected RF algorithm to establish diagnostic SCRS, and SuperPC algorithm to develop prognostic SCRS. SCRS demonstrated a stable predictive capability, outperforming the previously published NB signatures and clinic variables. We stratified NB patients into high and low risk group, which showed the low-risk group with a superior survival outcome, an abundant immune infiltration, a different mutation landscape, and an enhanced sensitivity to immunotherapy. Single cell analysis reveals biologically cellular variations underlying model genes of SCRS. Spatial transcriptomics delineated the molecular variant expressions of hub gene JAK1 in malignant cells across cancers, while immunohistochemistry validated the differential protein levels of JAK1 in NB. Based on multi-omics analysis and ML algorithms, we successfully developed the SCRS to enable accurate diagnosis and prognostic stratification in NB, which shed light on molecular landscapes of senes CAF and clinical utilization of SCRS.

Spatial transcriptomics is a cutting-edge technology that analyzes gene expression at the cellular level within tissues while integrating spatial location information. This concept, which combines high-plex RNA sequencing with spatial data, emerged in the early 2010s. Spatial transcriptomics has rapidly expanded with the development of technologies such as in situ hybridization, in situ sequencing, in situ spatial barcoding, and microdissection-based methods. Each technique offers advanced mapping resolution and precise spatial assessments at the single-cell level. Over the past decade, the use of spatial transcriptomics on clinical samples has enabled researchers to identify gene expressions in specific diseased foci, significantly enhancing our understanding of cellular interactions and disease processes. In the field of rheumatology, the complex and elusive pathophysiology of diseases such as rheumatoid arthritis, systemic lupus erythematosus, and Sjögren’s syndrome remains a challenge for personalized treatment. Spatial transcriptomics provides insights into how different cell populations interact within disease foci, such as the synovial tissue, kidneys, and salivary glands. This review summarizes the development of spatial transcriptomics and current insights into the pathophysiology of autoimmune rheumatic diseases, focusing on immune cell distribution and cellular interactions within tissues. We also explore the potential of spatial transcriptomics from a clinical perspective and discuss the possibilities for translating this technology to the bedside.

The Role of Immune Cells in Breast Tissue and Immunotherapy for the Treatment of Breast Cancer

Background: Effective cancer immunotherapy is significantly affected by intratumoral heterogeneity, not only in different cell types and functions but also in the spatial pattern of immune cells within the tumor tissue. While most single-cell RNA sequencing technologies could enable the exploration of gene expression heterogeneity at the single-cell level, they cannot reveal spatial information within the tumor for us to map the whole transcriptome with morphological context. Spatial transcriptomics is an in situ capturing technique, which profiles gene expression at the RNA level, whilst preserving the spatial information of histological tissue sections. Thus we try to develop our spatial transcriptomics assay through a serial of validation experiments and to explore the mechanism of immune therapy against tumors. Material and Method: Firstly, the tumor was frozen and sections were cryosectioned at 10 μm thickness. Sections processing including H&E staining, tissue optimization, permeabilization, reverse transcription, and cDNA library preparation was carried out following the user guide of Visium Spatial Gene Expression Reagent Kits (10X Genomics). And the final cDNA libraries were sequenced using the Illumina Novaseq instrument. Using gene expression matrix and aligned H&E image processed by Cell Ranger and Loupe Browser with default parameters, we performed data QC, normalization, dimensionality reduction, and clustering by Seurat and cell components identification in tumor microenvironment based on a multistep approach. Differential gene expression and spatial heterogeneity were analyzed in each cluster. Results: Here we established a reliable system for spatial transcriptomics assay to elucidate the mechanism of anti-PD1 treatment in MC38 syngeneic colorectal tumor. Using this system, we constructed the gene expression spatial patterns of tumor cells and immune cells, including periphery and center of tumor cell, immune-cell enrichment area, and tumor cell surrounding the immune cell. Furthermore, we explored the cell-cell interactions in which the tumor cells directly interact with adjacent non-tumor cells, for example, macrophages. Using this system, we tried to investigate the mechanism of anti-PD-1 therapy against the tumors by comparing the spatial transcriptome profiles after the treatment with anti-PD-1 or isotype control in colorectal cancer models. Interestingly, changes in both gene-level and spatial levels were observed, and it will help reveal how the immune therapy may shake up the complex ecosystem. Summary: We established a reliable system for spatial transcriptomics analysis and explored the mechanism of anti-PD-1 treatment against the colorectal cancer by using this system. Citation Format: Panpan Wang, Huacheng He, Qiyao Zhang, Jingjing Wang, Dan Lu, Fei Duan, Qiangqiang Fan, Qingyang Gu, Qunsheng Ji. Establishment of spatial transcriptomics assay to study the mechanism of immune therapy against tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6128.

Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): Dutch Heart Foundation Atherosclerosis is characterized by the accumulation of lipids and immune cells in the arterial wall and it is the main underlying pathology causing cardiovascular diseases (CVD). Cytokines are involved in all stages of atherosclerosis and most cytokines contribute to disease progression. For instance, IL-6 cytokine family members, such as IL-6, oncostatin-M and cardiotrophin-1, are closely related to CVDs. However, the role of leukemia inhibitory factor (LIF), a member of the IL-6 cytokine family, and its receptor (LIFR) in atherosclerosis remains unknown. Therefore, the aim of this study is to assess the contribution of LIFR signaling to atherosclerosis development by systemically inhibiting LIFR in an atherosclerotic mouse model. First, the expression of LIF and LIFR was examined using single-cell RNA sequencing data of human carotid artery plaques. Mast cells highly and exclusively express LIF, whereas LIFR was specifically expressed by activated endothelial cells. To validate LIFR signaling in vivo, female western-type diet fed Ldlr-/- mice were treated with LIFR inhibitor EC359 (5 mg/kg s.c., n=15) or control solvent (n=15) three times per week for eight weeks. During the experiment, weights of the mice did not differ between groups, whereas total serum cholesterol levels were significantly reduced in EC359 treated mice (p=0.03). In particular, EC359 treatment reduced VLDL levels. Additionally, EC359 treatment significantly reduced Sr1, Abca1 and Lrp1 expression in the liver, suggesting an altered lipid metabolism in EC359 treated mice. Furthermore, LIFR inhibition affected innate immunity, both systemically and locally in the plaque. Neutrophil numbers were significantly increased in the circulation (ctrl 18.7±1.7%; EC359 28.0±2.0%; p=0.002) and aortic plaques (ctrl 3.9±0.8%; EC359 9.9±1.9%; p=0.003) of mice treated with EC359 compared to control mice. Moreover, LIFR inhibition affected circulating monocytes resulting in less inflammatory monocytes. The adaptive immune cells, such as T and B cells, remained largely unaffected. Collectively, the reduction of serum total cholesterol and increase in neutrophil numbers in EC359 treated mice resulted in a trend towards reduced atherosclerotic stenosis grade compared to control mice (39.8±1.4% versus 34.9±1.9%; p=0.054), but it did not affect plaque collagen and monocyte/macrophage content in the aortic root. Conclusively, LIFR signaling inhibition reduces atherosclerotic stenosis grade, lowers plasma total cholesterol levels and affects innate immunity.

Unveiling the spatiotemporal strategies of plants in response to biotic and abiotic stresses:A comprehensive review.

Understanding the mechanisms underlying key agricultural traits remains a central challenge in crop research, but recent advances in technologies are providing powerful tools to address this issue. Among these, single-cell and spatial transcriptomics have revealed tissue heterogeneity and spatial organization, offering unique insights into cellular gene expression dynamics and the coordinated activity of multiple cell types. These approaches help uncover how specific cell types contribute to agricultural traits and refine candidate loci lists through integration with trait-associated loci. Additionally, single-cell and spatial transcriptomics have the potential to serve as cell-level readout platforms integrating cellular perturbations, enabling high-throughput discovery of causal relationships between genotype and gene expression at the cellular level in plants. Successful implementation will accelerate the identification of key genetic variants for crop improvement. Here we review lessons learned from application of single-cell screening in mammalian cells, highlight major technical and biological barriers to its use in plants, and outline potential strategies to overcome these challenges. Together, the widespread application and integration of single-cell and spatial transcriptomics with other technologies enable not only the descriptive cataloging of cell states but also the causal interrogation of sequence functions and regulatory networks at cell-type resolution, ultimately advancing gene function studies and accelerating crop improvement.

It has been indicated that the content and structure of the elastin and collagen of the arterial wall can subject to a significant alteration due to the atherosclerosis. Consequently, a high tissue stiffness, stress, and even damage/rupture are triggered in the arterial wall. Although many studies so far have been conducted to quantify the mechanical properties of the coronary arteries, none of them consider the role of collagen damage of the healthy and atherosclerotic human coronary arterial walls. Recently, a fiber family-based constitutive equation was proposed to capture the anisotropic mechanical response of the healthy and atherosclerotic human coronary arteries via both the histostructural and uniaxial data. In this study, experimental mechanical measurements along with histological data of the healthy and atherosclerotic arterial walls were employed to determine the constitutive damage parameters and remodeling of the collagen fibers. To do this, the preconditioned arterial tissues were excised from human cadavers within 5-h postmortem, and the mean angle of their collagen fibers was precisely determined. Thereafter, a group of quasistatic axial and circumferential loadings were applied to the arterial walls, and the constrained nonlinear minimization method was employed to identify the arterial parameters according to the axial and circumferential extension data. The remodeling of the collagen fibers during the tensile test was also predicted via Artificial Neural Networks algorithm. Regardless of loading direction, the results presented a noteworthy load-bearing capability and stiffness of the atherosclerotic arteries compared to the healthy ones (P<0.005). Theoretical fiber angles were found to be consistent with the experimental histological data with less than 2 and 5° difference for the healthy and atherosclerotic arterial walls, respectively. The pseudoelastic damage model data were also compared with that of the experimental data, and interestingly, the arterial mechanical behavior for both the primary loading (up to the elastic region) and the discontinuous softening (up to the ultimate stress) was well addressed. The proposed model predicted well the mechanical response of the arterial tissue considering the damage of collagen fibers for both the healthy and atherosclerotic arterial walls.

Neuroimmunologists seek to understand the interactions between the central nervous system (CNS) and the immune system, both under homeostatic conditions and in diseases. Unanswered questions include those relating to the diversity and specificity of the meningeal T cell repertoire; the routes taken by immune cells that patrol the meninges under healthy conditions and invade the parenchyma during pathology; the opposing effects (beneficial or detrimental) of these cells on CNS function; the role of immune cells after CNS injury; and the evolutionary link between the two systems, resulting in their tight interaction and interdependence. This Review summarizes the current standing of and challenging questions related to interactions between adaptive immunity and the CNS and considers the possible directions in which these aspects of neuroimmunology will be heading over the next decade.

Atherosclerosis, manifesting itself as acute coronary syndrome, stroke, and peripheral arterial diseases, is a chronic progressive inflammatory disease characterized by the accumulation of lipid and fibrous elements in arterial walls, which is driven by responses of both innate and adaptive immunity. As a possible trigger, several studies have suggested that various bacteria and viruses are associated with atherosclerotic diseases. The local vascular immune response against these infectious agents is performed mainly via the pattern recognition receptors. It is known that alleles of the wild-type TLR4 Asp299Gly and Thr399Ile polymorphisms are associated with the progression of atherosclerosis, while carriers of the 299Gly and 399Thr minor alleles which diminish the TLR4-mediated immune response are characterized by a lower level of circulating inflammatory markers and by smaller carotid intima-media thickness. However, minor allele carriers have an elevated risk of myocardial infarction, while wild-type allele possesses a protective effect. It seems to be that population features play the major role in the determination of 299Gly-mediated myocardial infarction risk modification. Concerning various types of cardiovascular events, all positive or negative statistically significant associations were found only for myocardial infarction, whereas two studies in which the role of Asp299Gly polymorphism in stroke development was investigated did not find any association. Regarding other polymorphisms, the number of performed studies is much lower, and the results are inconclusive.

Spatial transcriptomics (ST) technologies have revolutionized our ability to map gene expression patterns within native tissue context, providing unprecedented insights into tissue architecture and cellular heterogeneity. However, accurately deconvolving cell-type compositions from ST spots remains challenging due to the sparse and averaged nature of ST data, which is essential for accurately depicting tissue architecture. While numerous computational methods have been developed for cell-type deconvolution and spatial distribution reconstruction, most fail to capture tissue complexity at the single-cell level, thereby limiting their applicability in practical scenarios. To this end, we propose a novel cycle-consistent generative adversarial network named STCGAN for cellular deconvolution in spatial transcriptomic. STCGAN first employs a cycle-consistent generative adversarial network (CGAN) to pre-train on ST data, ensuring that both the mapping from ST data to latent space and its reverse mapping are consistent, capturing complex spatial gene expression patterns and learning robust latent representations. Based on the learned representation, STCGAN then optimizes a trainable cell-to-spot mapping matrix to integrate scRNA-seq data with ST data, accurately estimating cellular composition within each capture spot and effectively reconstructing the spatial distribution of cells across the tissue. To further enhance deconvolution accuracy, we incorporate spatial-aware regularization that ensures accurate cellular distribution reconstruction within the spatial context. Benchmarking against seven state-of-the-art methods on five simulated and real datasets from various tissues, STCGAN consistently delivers superior cell-type deconvolution performance. The code of STCGAN can be downloaded from https://github.com/cs-wangbo/STCGAN and all the mentioned datasets are available on Zenodo at https://zenodo.org/doi/10.5281/zenodo.10799113.

Single-cell technologies: From research to application