A contrastive learning approach to integrate spatial transcriptomics and histological images

The rapid growth of spatially resolved transcriptomics technology provides new perspectives on spatial tissue architecture. Deep learning has been widely applied to derive useful representations for spatial transcriptome analysis. However, effectively integrating spatial multi-modal data remains challenging. Here, we present ConGcR, a contrastive learning-based model for integrating gene expression, spatial location, and tissue morphology for data representation and spatial tissue architecture identification. Graph convolution and ResNet were used as encoders for gene expression with spatial location and histological image inputs, respectively. We further enhanced ConGcR with a graph auto-encoder as ConGaR to better model spatially embedded representations. We validated our models using 16 human brains, four chicken hearts, eight breast tumors, and 30 human lung spatial transcriptomics samples. The results showed that our models generated more effective embeddings for obtaining tissue architectures closer to the ground truth than other methods. Overall, our models not only can improve tissue architecture identification's accuracy but also may provide valuable insights and effective data representation for other tasks in spatial transcriptome analyses.

Recent advances in spatially variable gene detection in spatial transcriptomics

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

Co-expression of immune checkpoints in glioblastoma revealed by single-nucleus RNA sequencing and spatial transcriptomics

Glioblastoma (GBM) is one of the most malignant tumors of the central nervous system. The pattern of immune checkpoint expression in GBM remains largely unknown. We performed snRNA-Seq and spatial transcriptomic (ST) analyses on untreated GBM samples. 8 major cell types were found in both tumor and adjacent normal tissues, with variations in infiltration grade. Neoplastic cells_6 was identified in malignant cells with high expression of invasion and proliferator-related genes, and analyzed its interactions with microglia, MDM cells and T cells. Significant alterations in ligand-receptor interactions were observed, particularly between Neoplastic cells_6 and microglia, and found prominent expression of VISTA/VSIG3, suggesting a potential mechanism for evading immune system attacks. High expression of TIM-3, VISTA, PSGL-1 and VSIG-3 with similar expression patterns in GBM, may have potential as therapeutic targets. The prognostic value of VISTA expression was cross-validated in 180 glioma patients, and it was observed that patients with high VISTA expression had a poorer prognosis. In addition, multimodal cross analysis integrated SnRNA-seq and ST, revealing complex intracellular communication and mapping the GBM tumor microenvironment. This study reveals novel molecular characteristics of GBM, co-expression of immune checkpoints, and potential therapeutic targets, contributing to improving the understanding and treatment of GBM.

Symbiotic symphony: Understanding host-microbiota dialogues in a spatial context

Modern precision sequencing techniques have established humans as a holobiont that live in symbiosis with the microbiome. Microbes play an active role throughout the life of a human ranging from metabolism and immunity to disease tolerance. Hence, it is of utmost significance to study the eukaryotic host in conjunction with the microbial antigens to obtain a complete picture of the host-microbiome crosstalk. Previous attempts at profiling host-microbiome interactions have been either superficial or been attempted to catalogue eukaryotic transcriptomic profile and microbial communities in isolation. Additionally, the nature of such immune-microbial interactions is not random but spatially organised. Hence, for a holistic clinical understanding of the interplay between hosts and microbiota, it's imperative to concurrently analyze both microbial and host genetic information, ensuring the preservation of their spatial integrity. Capturing these interactions as a snapshot in time at their site of action has the potential to transform our understanding of how microbes impact human health. In examining early-life microbial impacts, the limited presence of communities compels analysis within reduced biomass frameworks. However, with the advent of spatial transcriptomics we can address this challenge and expand our horizons of understanding these interactions in detail. In the long run, simultaneous spatial profiling of host-microbiome dialogues can have enormous clinical implications especially in gaining mechanistic insights into the disease prognosis of localised infections and inflammation. This review addresses the lacunae in host-microbiome research and highlights the importance of profiling them together to map their interactions while preserving their spatial context.

Unveiling the hidden role of disulfidptosis in kidney renal clear cell carcinoma: a prognostic signature for personalized treatment

The role of disulfidptosis in kidney renal clear cell carcinoma (KIRC) remains unknown. This study investigated disulfidptosis-related biomarkers for KIRC prognosis prediction and individualized treatment. KIRC patients were clustered by disulfidptosis profiles. Differential expression analysis, survival models, and machine learning were used to construct the disulfidptosis-related prognostic signature (DRPS). Characterizations of the tumor immune microenvironment, genetic drivers, drug sensitivity, and immunotherapy response were explored according to the DRPS risk stratification. Markers included in the signature were validated using single-cell, spatial transcriptomics, quantitative RT-qPCR, and immunohistochemistry. In the discovery cohort, we unveiled two clusters of KIRC patients that differed significantly in disulfidptosis regulator expressions and overall survival (OS). After multiple feature selection steps, a DRPS prognostic model with four features (CHAC1, COL7A1, FOXM1, SHOX2) was constructed and validated. Combined with clinical factors, the model demonstrated robust performance in the discovery and external validation cohorts (5-year AUC = 0.793 and 0.846, respectively). KIRC patients with high-risk scores are characterized by inferior OS, less tumor purity, and increased infiltrations of fibroblasts, M1 macrophages, and B cells. High-risk patients also have higher frequencies of BAP1 and AHNAK2 mutation. Besides, the correlation between the DRPS score and the chemotherapy-response signature indicated the potential effect of Gefitinib for high-risk patients. Among the signature genes, FOXM1 is highly expressed in cycling tumor cells and exhibits spatial aggregation, while others are expressed sparsely within tumor samples. The DRPS model enables improved clinical management and personalized KIRC therapy. The identified biomarkers and immune characteristics offer new mechanistic insight into disulfidptosis in KIRC.

Spatially resolved transcriptomic profiling of placental development in dairy cow

The placenta plays a crucial role in successful mammalian reproduction. Ruminant animals possess a semi-invasive placenta characterized by a highly vascularized structure formed by maternal endometrial caruncles and fetal placental cotyledons, essential for full-term fetal development. The cow placenta harbors at least two trophoblast cell populations: uninucleate (UNC) and binucleate (BNC) cells. However, the limited capacity to elucidate the transcriptomic dynamics of the placental natural environment has resulted in a poor understanding of both the molecular and cellular interactions between trophoblast cells and niches, and the molecular mechanisms governing trophoblast differentiation and functionalization. To fill this knowledge gap, we employed Stereo-seq to map spatial gene expression patterns at near single-cell resolution in the cow placenta at 90 and 130 days of gestation, attaining high-resolution, spatially resolved gene expression profiles. Based on clustering and cell marker gene expression analyses, key transcription factors, including YBX1 and NPAS2, were shown to regulate the heterogeneity of trophoblast cell subpopulations. Cell communication and trajectory analysis provided a framework for understanding cell-cell interactions and the differentiation of trophoblasts into BNCs in the placental microenvironment. Differential analysis of cell trajectories identified a set of genes involved in regulation of trophoblast differentiation. Additionally, spatial modules and co-variant genes that help shape specific tissue structures were identified. Together, these findings provide foundational insights into important biological pathways critical to the placental development and function in cows.

Single-cell and spatial transcriptomics reveal metastasis mechanism and microenvironment remodeling of lymph node in osteosarcoma

BACKGROUND

Osteosarcoma (OS) is the most common primary malignant bone tumor and is highly prone to metastasis. OS can metastasize to the lymph node (LN) through the lymphatics, and the metastasis of tumor cells reestablishes the immune landscape of the LN, which is conducive to the growth of tumor cells. However, the mechanism of LN metastasis of osteosarcoma and remodeling of the metastatic lymph node (MLN) microenvironment is not clear.

METHODS

Single-cell RNA sequencing of 18 samples from paracancerous, primary tumor, and lymph nodes was performed. Then, new signaling axes closely related to metastasis were identified using bioinformatics, in vitro experiments, and immunohistochemistry. The mechanism of remodeling of the LN microenvironment in tumor cells was investigated by integrating single-cell and spatial transcriptomics.

RESULTS

From 18 single-cell sequencing samples, we obtained 117,964 cells. The pseudotime analysis revealed that osteoblast(OB) cells may follow a differentiation path from paracancerous tissue (PC) → primary tumor (PT) → MLN or from PC → PT, during the process of LN metastasis. Next, in combination of bioinformatics, in vitro and in vivo experiments, and immunohistochemistry, we determined that ETS2/IBSP, a new signal axis, might promote LN metastasis. Finally, single-cell and spatial dissection uncovered that OS cells could reshape the microenvironment of LN by interacting with various cell components, such as myeloid, cancer-associated fibroblasts (CAFs), and NK/T cells.

CONCLUSIONS

Collectively, our research revealed a new molecular mechanism of LN metastasis and clarified how OS cells influenced the LN microenvironment, which might provide new insight for blocking LN metastasis.

Single-cell and spatial transcriptomics analysis of human adrenal aging

OBJECTIVE

The human adrenal cortex comprises three functionally and structurally distinct layers that produce layer-specific steroid hormones. With aging, the human adrenal cortex undergoes functional and structural alteration or "adrenal aging", leading to the unbalanced production of steroid hormones. Given the marked species differences in adrenal biology, the underlying mechanisms of human adrenal aging have not been sufficiently studied. This study was designed to elucidate the mechanisms linking the functional and structural alterations of the human adrenal cortex.

METHODS

We conducted single-cell RNA sequencing and spatial transcriptomics analysis of the aged human adrenal cortex.

RESULTS

The data of this study suggest that the layer-specific alterations of multiple signaling pathways underlie the abnormal layered structure and layer-specific changes in steroidogenic cells. We also highlighted that macrophages mediate age-related adrenocortical cell inflammation and senescence.

CONCLUSIONS

This study is the first detailed analysis of the aged human adrenal cortex at single-cell resolution and helps to elucidate the mechanism of human adrenal aging, thereby leading to a better understanding of the pathophysiology of age-related disorders associated with adrenal aging.

Perturbed liver gene zonation in a mouse model of non-alcoholic steatohepatitis

Liver zonation characterizes the separation of metabolic pathways along the lobules and is required for optimal hepatic function. Wnt signaling is a master regulator of spatial liver zonation. A perivenous-periportal Wnt activity gradient orchestrates metabolic zonation by activating gene expression in perivenous hepatocytes, while suppressing gene expression in their periportal counterparts. However, the understanding as to the liver gene zonation and zonation regulators in diseases is limited. Non-alcoholic steatohepatitis (NASH) is a chronic liver disease characterized by fat accumulation, inflammation, and fibrosis. Here, we investigated the perturbation of liver gene zonation in a mouse NASH model by combining spatial transcriptomics, bulk RNAseq and in situ hybridization. Wnt-target genes represented a major subset of genes showing altered spatial expression in the NASH liver. The altered Wnt-target gene expression levels and zonation spatial patterns were in line with the up regulation of Wnt regulators and the augmentation of Wnt signaling. Particularly, we found that the Wnt activator Rspo3 expression was restricted to the perivenous zone in control liver but expanded to the periportal zone in NASH liver. AAV8-mediated RSPO3 overexpression in controls resulted in zonation changes, and further amplified the disturbed zonation of Wnt-target genes in NASH, similarly Rspo3 knockdown in Rspo3+/- mice resulted in zonation changes of Wnt-target genes in both chow and HFD mouse. Interestingly, there were no impacts on steatosis, inflammation, or fibrosis NASH pathology from RSPO3 overexpression nor Rspo3 knockdown. In summary, our study demonstrated the alteration of Wnt signaling in a mouse NASH model, leading to perturbed liver zonation.

Edge-relational window-attentional graph neural network for gene expression prediction in spatial transcriptomics analysis

Spatial transcriptomics (ST), containing gene expression with fine-grained (i.e., different windows) spatial location within tissue samples, has become vital in developing innovative treatments. Traditional ST technology, however, rely on costly specialized commercial equipment. Addressing this, our article aims to creates a cost-effective, virtual ST approach using standard tissue images for gene expression prediction, eliminating the need for expensive equipment. Conventional approaches in this field often overlook the long-distance spatial dependencies between different sample windows or need prior gene expression data. To overcome these limitations, we propose the Edge-Relational Window-Attentional Network (ErwaNet), enhancing gene prediction by capturing both local interactions and global structural information from tissue images, without prior gene expression data. ErwaNet innovatively constructs heterogeneous graphs to model local window interactions and incorporates an attention mechanism for global information analysis. This dual framework not only provides a cost-effective solution for gene expression predictions but also obviates the necessity of prior knowledge gene expression information, a significant advantage in the field of cancer research where it enables a more efficient and accessible analytical paradigm. ErwaNet stands out as a prior-free and easy-to-implement Graph Convolution Network (GCN) method for predicting gene expression from tissue images. Evaluation of the two public breast cancer datasets shows that ErwaNet, without additional information, outperforms the state-of-the-art (SOTA) methods. Code is available at https://github.com/biyecc/ErwaNet.

Integrating single-cell and spatial transcriptomics to elucidate the crosstalk between cancer-associated fibroblasts and cancer cells in hepatocellular carcinoma with spleen-deficiency syndrome

Background and aim

Most patients with hepatocellular carcinoma (HCC) in China have been diagnosed with spleen deficiency syndrome (SDS), which accelerates the progression of HCC by disrupting the tumor microenvironment homeostasis. This study aimed to investigate the intercellular crosstalk in HCC with SDS.

Experimental procedure

An HCC-SDS mouse model was established using orthotopic HCC transplantation based on reserpine-induced SDS. Single-cell data analysis and cancer cell prediction were conducted using Seurat and CopyKAT package, respectively. Intercellular interactions were explored using CellPhoneDB and CellChat and subsequently validated using co-culture assays, ELISA and histological staining. We performed pathway activity analysis using gene set variation analysis and the Seurat package. The extracellular matrix (ECM) remodeling was assessed using a gel contraction assay, atomic force microscopy, and Sirius red staining. The deconvolution of the spatial transcriptomics data using the "CARD" package based on single-cell data.

Results and conclusion

We successfully established the HCC-SDS mouse model. Twenty-nine clusters were identified. The interactions between cancer cells and cancer-associated fibroblasts (CAFs) were significantly enhanced via platelet-derived growth factor (PDGF) signaling in HCC-SDS. CAFs recruited in HCC-SDS lead to ECM remodeling and the activation of TGF-β signaling pathway. Deconvolution of the spatial transcriptome data revealed that CAFs physically surround cancer cells in HCC-SDS. This study reveals that the crosstalk of CAFs-cancer cells is crucial for the tumor-promoting effect of SDS. CAFs recruited by HCC via PDGFA may lead to ECM remodeling through activation of the TGF-β pathway, thereby forming a physical barrier to block immune cell infiltration under SDS.

The p21+ perinecrotic hepatocytes produce the chemokine CXCL14 after a severe acetaminophen overdose promoting hepatocyte injury and delaying regeneration

Fifty percent of all acute liver failure (ALF) cases in the United States are due to acetaminophen (APAP) overdose. Assessment of canonical features of liver injury, such as plasma alanine aminotransferase activities are poor predictors of acute liver failure (ALF), suggesting the involvement of additional mechanisms independent of hepatocyte death. Previous work demonstrated a severe overdose of APAP results in impaired regeneration, the induction of senescence by p21, and increased mortality. We hypothesized that a discrete population of p21+ hepatocytes acquired a secretory phenotype that directly impedes liver recovery after a severe APAP overdose. Leveraging in-house human APAP explant liver and publicly available single-nuclei RNAseq data, we identified a subpopulation of p21+ hepatocytes enriched in a unique secretome of factors, such as CXCL14. Spatial transcriptomics in the mouse model of APAP overdose confirmed the presence of a p21+ hepatocyte population that directly surrounded the necrotic areas. In both male and female mice, we found a dose-dependent induction of p21 and persistent circulating levels of the p21-specific constituent, CXCL14, in the plasma after a severe APAP overdose. In parallel experiments, we targeted either the putative senescent hepatocytes with the senolytic drugs, dasatinib and quercetin, or CXCL14 with a neutralizing antibody. We found that targeting CXCL14 greatly enhanced liver recovery after APAP-induced liver injury, while targeting senescent hepatocytes had no effect. These data support the conclusion that the sustained induction of p21 in hepatocytes with persistent CXCL14 secretion are critical mechanistic events leading to ALF in mice and human patients.

Single-cell transcriptomics: background, technologies, applications, and challenges

Single-cell sequencing was developed as a high-throughput tool to elucidate unusual and transient cell states that are barely visible in the bulk. This technology reveals the evolutionary status of cells and differences between populations, helps to identify unique cell subtypes and states, reveals regulatory relationships between genes, targets and molecular mechanisms in disease processes, tumor heterogeneity, the state of the immune environment, etc. However, the high cost and technical limitations of single-cell sequencing initially prevented its widespread application, but with advances in research, numerous new single-cell sequencing techniques have been discovered, lowering the cost barrier. Many single-cell sequencing platforms and bioinformatics methods have recently become commercially available, allowing researchers to make fascinating observations. They are now increasingly being used in various industries. Several protocols have been discovered in this context and each technique has unique characteristics, capabilities and challenges. This review presents the latest advancements in single-cell transcriptomics technologies. This includes single-cell transcriptomics approaches, workflows and statistical approaches to data processing, as well as the potential advances, applications, opportunities and challenges of single-cell transcriptomics technology. You will also get an overview of the entry points for spatial transcriptomics and multi-omics.

[Identifying spatial domains from spatial transcriptome by graph attention network]

Due to the high dimensionality and complexity of the data, the analysis of spatial transcriptome data has been a challenging problem. Meanwhile, cluster analysis is the core issue of the analysis of spatial transcriptome data. In this article, a deep learning approach is proposed based on graph attention networks for clustering analysis of spatial transcriptome data. Our method first enhances the spatial transcriptome data, then uses graph attention networks to extract features from nodes, and finally uses the Leiden algorithm for clustering analysis. Compared with the traditional non-spatial and spatial clustering methods, our method has better performance in data analysis through the clustering evaluation index. The experimental results show that the proposed method can effectively cluster spatial transcriptome data and identify different spatial domains, which provides a new tool for studying spatial transcriptome data.

Integrating spatial transcriptomics and single-cell RNA-sequencing reveals the alterations in epithelial cells during nodular formation in benign prostatic hyperplasia

OBJECTIVE

Proliferative nodular formation represents a characteristic pathological feature of benign prostatic hyperplasia (BPH) and serves as the primary cause for prostate volume enlargement and consequent lower urinary tract symptoms (LUTS). Its specific mechanism is largely unknown, although several cellular processes have been reported to be involved in BPH initiation and development and highlighted the crucial role of epithelial cells in proliferative nodular formation. However, the technological limitations hinder the in vivo investigation of BPH patients.

METHODS

The robust cell type decomposition (RCTD) method was employed to integrate spatial transcriptomics and single cell RNA sequencing profiles, enabling the elucidation of epithelial cell alterations during nodular formation. Immunofluorescent and immunohistochemical staining was performed for verification.

RESULTS

The alterations of epithelial cells during the formation of nodules in BPH was observed, and a distinct subgroup of basal epithelial (BE) cells, referred to as BE5, was identified to play a crucial role in driving this progression through the hypoxia-induced epithelial-mesenchymal transition (EMT) signaling pathway. BE5 served as both the initiating cell during nodular formation and the transitional cell during the transformation from luminal epithelial (LE) to BE cells. A distinguishing characteristic of the BE5 cell subgroup in patients with BPH was its heightened hypoxia and upregulated expression of FOS. Histological verification results confirmed a significant association between c-Fos expression and key biological processes such as hypoxia and cell proliferation, as well as the close relationship between hypoxia and EMT in BPH tissues. Furthermore, a strong link between c-Fos expression and the progression of BPH was also been validated. Additionally, notable functional differences were observed in glandular and stromal nodules regarding BE5 cells, with BE5 in glandular nodules exhibiting enhanced capacities for EMT and cell proliferation characterized by club-like cell markers.

CONCLUSIONS

This study elucidated the comprehensive landscape of epithelial cells during in vivo nodular formation in patients, thereby offering novel insights into the initiation and progression of BPH.

Spatial transcriptomics reveals segregation of tumor cell states in glioblastoma and marked immunosuppression within the perinecrotic niche

Glioblastoma (GBM) remains an untreatable malignant tumor with poor patient outcomes, characterized by palisading necrosis and microvascular proliferation. While single-cell technology made it possible to characterize different lineage of glioma cells into neural progenitor-like (NPC-like), oligodendrocyte-progenitor-like (OPC-like), astrocyte-like (AC-like) and mesenchymal like (MES-like) states, it does not capture the spatial localization of these tumor cell states. Spatial transcriptomics empowers the study of the spatial organization of different cell types and tumor cell states and allows for the selection of regions of interest to investigate region-specific and cell-type-specific pathways. Here, we obtained paired 10x Chromium single-nuclei RNA-sequencing (snRNA-seq) and 10x Visium spatial transcriptomics data from three GBM patients to interrogate the GBM microenvironment. Integration of the snRNA-seq and spatial transcriptomics data reveals patterns of segregation of tumor cell states. For instance, OPC-like tumor and NPC-like tumor significantly segregate in two of the three samples. Our differentially expressed gene and pathway analyses uncovered significant pathways in functionally relevant niches. Specifically, perinecrotic regions were more immunosuppressive than the endogenous GBM microenvironment, and perivascular regions were more pro-inflammatory. Our gradient analysis suggests that OPC-like tumor cells tend to reside in areas closer to the tumor vasculature compared to tumor necrosis, which may reflect increased oxygen requirements for OPC-like cells. In summary, we characterized the localization of cell types and tumor cell states, the gene expression patterns, and pathways in different niches within the GBM microenvironment. Our results provide further evidence of the segregation of tumor cell states and highlight the immunosuppressive nature of the necrotic and perinecrotic niches in GBM.

A Single-cell and Spatial RNA-seq Database for Alzheimer's Disease (ssREAD)

Alzheimer's Disease (AD) pathology has been increasingly explored through single-cell and single-nucleus RNA-sequencing (scRNA-seq & snRNA-seq) and spatial transcriptomics (ST). However, the surge in data demands a comprehensive, user-friendly repository. Addressing this, we introduce a single-cell and spatial RNA-seq database for Alzheimer's disease (ssREAD). It offers a broader spectrum of AD-related datasets, an optimized analytical pipeline, and improved usability. The database encompasses 1,053 samples (277 integrated datasets) from 67 AD-related scRNA-seq & snRNA-seq studies, totaling 7,332,202 cells. Additionally, it archives 381 ST datasets from 18 human and mouse brain studies. Each dataset is annotated with details such as species, gender, brain region, disease/control status, age, and AD Braak stages. ssREAD also provides an analysis suite for cell clustering, identification of differentially expressed and spatially variable genes, cell-type-specific marker genes and regulons, and spot deconvolution for integrative analysis. ssREAD is freely available at https://bmblx.bmi.osumc.edu/ssread/.

Single Cell Atlas: a single-cell multi-omics human cell encyclopedia

Single-cell sequencing datasets are key in biology and medicine for unraveling insights into heterogeneous cell populations with unprecedented resolution. Here, we construct a single-cell multi-omics map of human tissues through in-depth characterizations of datasets from five single-cell omics, spatial transcriptomics, and two bulk omics across 125 healthy adult and fetal tissues. We construct its complement web-based platform, the Single Cell Atlas (SCA, www.singlecellatlas.org ), to enable vast interactive data exploration of deep multi-omics signatures across human fetal and adult tissues. The atlas resources and database queries aspire to serve as a one-stop, comprehensive, and time-effective resource for various omics studies.

Spatial and single-cell explorations uncover prognostic significance and immunological functions of mitochondrial calcium uniporter in breast cancer

The mitochondrial calcium uniporter (MCU) is a transmembrane protein facilitating the entry of calcium ions into mitochondria from the cell cytosol. Maintaining calcium balance is crucial for enhancing cellular energy supply and regulating cell death. The interplay of calcium balance through MCU and the sodium-calcium exchanger is known, but its regulation in the breast cancer tumor microenvironment remains elusive. Further investigations are warranted to explore MCU's potential in BRCA clinical pathology, tumor immune microenvironment, and precision oncology. Our study, employing a multi-omics approach, identifies MCU as an independent diagnostic biomarker for breast cancer (BRCA), correlated with advanced clinical status and poor overall survival. Utilizing public datasets from GEO and TCGA, we discern differentially expressed genes in BRCA and examine their associations with immune gene expression, overall survival, tumor stage, gene mutation status, and infiltrating immune cells. Spatial transcriptomics is employed to investigate MCU gene expression in various regions of BRCA, while spatial transcriptomics and single-cell RNA-sequencing methods explore the correlation between MCUs and immune cells. Our findings are validated through the analysis of 59 BRCA patient samples, utilizing immunohistochemistry and bioinformatics to examine the relationship between MCU expression, clinicopathological features, and prognosis. The study uncovers the expression of key gene regulators in BRCA associated with genetic variations, deletions, and the tumor microenvironment. Mutations in these regulators positively correlate with different immune cells in six immune datasets, playing a pivotal role in immune cell infiltration in BRCA. Notably, high MCU performance is linked to CD8 + T cells infiltration in BRCA. Furthermore, pharmacogenomic analysis of BRCA cell lines indicates that MCU inactivation is associated with increased sensitivity to specific small molecule drugs. Our findings suggest that MCU alterations may be linked to BRCA progression, unveiling new diagnostic and prognostic implications for MCU in BRCA. The study underscores MCU's role in the tumor immune microenvironment and cell cycle progression, positioning it as a potential tool for BRCA precision medicine and drug screening.

Establishment of a cholangiocarcinoma risk evaluation model based on mucin expression levels

BACKGROUND

Cholangiocarcinoma (CCA) is a highly malignant cancer, characterized by frequent mucin overexpression. MUC1 has been identified as a critical oncogene in the progression of CCA. However, the comprehensive understanding of how the mucin family influences CCA progression and prognosis is still incomplete.

AIM

To investigate the functions of mucins on the progression of CCA and to establish a risk evaluation formula for stratifying CCA patients.

METHODS

Single-cell RNA sequencing data from 14 CCA samples were employed for elucidating the roles of mucins, complemented by bioinformatic analyses. Subsequent validations were conducted through spatial transcriptomics and immunohistochemistry. The construction of a risk evaluation model utilized the least absolute shrinkage and selection operator regression algorithm, which was further confirmed by independent cohorts and diverse data types.

RESULTS

CCA tumor cells with elevated levels of MUC1 and MUC4 showed activated nucleotide metabolic pathways and increased invasiveness. MUC5AC-high cells were found to promote CCA progression through WNT signaling. MUC5B-high cells exhibited robust cellular oxidation activities, leading to resistance against antitumoral treatments. MUC13-high cells were observed to secret chemokines, recruiting and transforming macrophages into the M2-polarized state, thereby suppressing antitumor immunity. MUC16-high cells were found to promote tumor progression through interleukin-1/nuclear factor kappa-light-chain-enhancer of activated B cells signaling upon interaction with neutrophils. Utilizing the expression levels of these mucins, a risk factor evaluation formula for CCA was developed and validated across multiple cohorts. CCA samples with higher risk factors exhibited stronger metastatic potential, chemotherapy resistance, and poorer prognosis.

CONCLUSION

Our study elucidates the functional mechanisms through which mucins contribute to CCA development, and provides tools for risk stratification in CCA.

Macrophage diversity in human cancers: New insight provided by single-cell resolution and spatial context

M1/M2 paradigm of macrophage plasticity has existed for decades. Now it becomes clear that this dichotomy doesn't adequately reflect the diversity of macrophage phenotypes in tumor microenvironment (TME). Tumor-associated macrophages (TAMs) are a major population of innate immune cells in the TME that promotes tumor cell proliferation, angiogenesis and lymphangiogenesis, invasion and metastatic niche formation, as well as response to anti-tumor therapy. However, the fundamental restriction in therapeutic TAM targeting is the limited knowledge about the specific TAM states in distinct human cancer types. Here we summarized the results of the most recent studies that use advanced technologies (e.g. single-cell RNA sequencing and spatial transcriptomics) allowing to decipher novel functional subsets of TAMs in numerous human cancers. The transcriptomic profiles of these TAM subsets and their clinical significance were described. We emphasized the characteristics of specific TAM subpopulations - TREM2+, SPP1+, MARCO+, FOLR2+, SIGLEC1+, APOC1+, C1QC+, and others, which have been most extensively characterized in several cancers, and are associated with cancer prognosis. Spatial transcriptomics technologies defined specific spatial interactions between TAMs and other cell types, especially fibroblasts, in tumors. Spatial transcriptomics methods were also applied to identify markers of immunotherapy response, which are expressed by macrophages or in the macrophage-abundant regions. We highlighted the perspectives for novel techniques that utilize spatial and single cell resolution in investigating new ligand-receptor interactions for effective immunotherapy based on TAM-targeting.

Spatial and single-cell colocalisation analysis reveals MDK-mediated immunosuppressive environment with regulatory T cells in colorectal carcinogenesis

BACKGROUND

Cell-cell interaction factors that facilitate the progression of adenoma to sporadic colorectal cancer (CRC) remain unclear, thereby hindering patient survival.

METHODS

We performed spatial transcriptomics on five early CRC cases, which included adenoma and carcinoma, and one advanced CRC. To elucidate cell-cell interactions within the tumour microenvironment (TME), we investigated the colocalisation network at single-cell resolution using a deep generative model for colocalisation analysis, combined with a single-cell transcriptome, and assessed the clinical significance in CRC patients.

FINDINGS

CRC cells colocalised with regulatory T cells (Tregs) at the adenoma-carcinoma interface. At early-stage carcinogenesis, cell-cell interaction inference between colocalised adenoma and cancer epithelial cells and Tregs based on the spatial distribution of single cells highlighted midkine (MDK) as a prominent signalling molecule sent from tumour epithelial cells to Tregs. Interaction between MDK-high CRC cells and SPP1+ macrophages and stromal cells proved to be the mechanism underlying immunosuppression in the TME. Additionally, we identified syndecan4 (SDC4) as a receptor for MDK associated with Treg colocalisation. Finally, clinical analysis using CRC datasets indicated that increased MDK/SDC4 levels correlated with poor overall survival in CRC patients.

INTERPRETATION

MDK is involved in the immune tolerance shown by Tregs to tumour growth. MDK-mediated formation of the TME could be a potential target for early diagnosis and treatment of CRC.

FUNDING

Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Science Research; OITA Cancer Research Foundation; AMED under Grant Number; Japan Science and Technology Agency (JST); Takeda Science Foundation; The Princess Takamatsu Cancer Research Fund.

Understanding Spondyloarthritis Pathogenesis: The Promise of Single-Cell Profiling

PURPOSE OF REVIEW

Single-cell profiling, either in suspension or within the tissue context, is a rapidly evolving field. The purpose of this review is to outline recent advancements and emerging trends with a specific focus on studies in spondyloarthritis.

RECENT FINDINGS

The introduction of sequencing-based approaches for the quantification of RNA, protein, or epigenetic modifications at single-cell resolution has provided a major boost to discovery-driven research. Fluorescent flow cytometry, mass cytometry, and image-based cytometry continue to evolve. Spatial transcriptomics and imaging mass cytometry have extended high-dimensional analysis to cells in tissues. Applications in spondyloarthritis include the indexing and functional characterization of cells, discovery of disease-associated cell states, and identification of signatures associated with therapeutic responses. Single-cell TCR-seq has provided evidence for clonal expansion of CD8+ T cells in spondyloarthritis. The use of single-cell profiling approaches in spondyloarthritis research is still in its early stages. Challenges include high cost and limited availability of diseased tissue samples. To harness the full potential of the rapidly expanding technical capabilities, large-scale collaborative efforts are imperative.

Applicability of Spatial Technology in Cancer Research

This review explores spatial mapping technologies in cancer research, highlighting their crucial role in understanding the complexities of the tumor microenvironment (TME). The TME, which is an intricate ecosystem of diverse cell types, has a significant impact on tumor dynamics and treatment outcomes. This review closely examines cutting-edge spatial mapping technologies, categorizing them into capture-, imaging-, and antibody-based approaches. Each technology was scrutinized for its advantages and disadvantages, factoring in aspects such as spatial profiling area, multiplexing capabilities, and resolution. Additionally, we draw attention to the nuanced choices researchers face, with capture-based methods lending themselves to hypothesis generation, and imaging/antibody-based methods that fit neatly into hypothesis testing. Looking ahead, we anticipate a scenario in which multi-omics data are seamlessly integrated, artificial intelligence enhances data analysis, and spatiotemporal profiling opens up new dimensions.

Multi-slice spatial transcriptome domain analysis with SpaDo

With the rapid advancements in spatial transcriptome sequencing, multiple tissue slices are now available, enabling the integration and interpretation of spatial cellular landscapes. Herein, we introduce SpaDo, a tool for multi-slice spatial domain analysis, including modules for multi-slice spatial domain detection, reference-based annotation, and multiple slice clustering at both single-cell and spot resolutions. We demonstrate SpaDo's effectiveness with over 40 multi-slice spatial transcriptome datasets from 7 sequencing platforms. Our findings highlight SpaDo's potential to reveal novel biological insights in multi-slice spatial transcriptomes.

Contrastive learning-based histopathological features infer molecular subtypes and clinical outcomes of breast cancer from unannotated whole slide images

The artificial intelligence-powered computational pathology has led to significant improvements in the speed and precision of tumor diagnosis, while also exhibiting substantial potential to infer genetic mutations and gene expression levels. However, current studies remain limited in predicting molecular subtypes and clinical outcomes in breast cancer. In this paper, we proposed a weakly supervised contrastive learning framework to address this challenge. Our framework first performed contrastive learning pretraining on a large number of unlabeled patches tiled from whole slide images (WSIs) to extract patch-level features. The gated attention mechanism was leveraged to aggregate patch-level features to produce slide feature that was then applied to various downstream tasks. To confirm the effectiveness of the proposed method, three public cohorts and one external independent cohort of breast cancer have been used to conducted evaluation experiments. The predictive powers of our model to infer gene expression, molecular subtypes, recurrence events and drug responses were validated across cohorts. In addition, the learned patch-level attention scores enabled us to generate heatmaps that were highly consistent with pathologist annotations and spatial transcriptomic data. These findings demonstrated that our model effectively established the high-order genotype-phenotype associations, thereby potentially extend the application of digital pathology in clinical practice.

Single-cell and spatial transcriptomics reveals that PTPRG activates the m6A methyltransferase VIRMA to block mitophagy-mediated neuronal death in Alzheimer's disease

Neuronal death is one of the key pathologies in Alzheimer's disease (AD). How neuronal death begins in AD is far from clear, so clarifying this process may help develop effective therapies. This study collected single-cell RNA sequencing data of 85 AD samples and 83 control samples, covering the prefrontal cortex, internal olfactory cortex, superior parietal lobe, superior frontal gyrus, caudal internal olfactory cortex, somatosensory cortex, hippocampus, superior frontal cortex and peripheral blood mononuclear cells. Additionally, spatial transcriptomic data of coronal sections from 6 AppNL-G-F AD mice and 6 control C57Bl/6 J mice were acquired. The main single-cell and spatial transcriptomics results were experimentally validated in wild type and 5 × FAD mice. We found that the microglia subpopulation Mic_PTPRG can communicate with specific types of neurons (especially excitatory ExNeu_PRKN_VIRMA and inhibitory InNeu_PRKN_VIRMA neuronal subpopulations) and cause them to express PTPRG during AD progression. Within neurons, PTPRG binds and upregulates the m6A methyltransferase VIRMA, thus inhibiting translation of PRKN mRNA to prevent the clearance of damaged mitochondria in neurons through suppressing mitophagy. As the disease progresses, the energy and nutrient metabolic pathways in neurons are reprogrammed, leading to their death. Consistently, we determined that PTPTRG can physically interact with VIRMA in mouse brains and PRKN is significantly upregulated in 5 × FAD mouse brain. Altogether, our findings demonstrate that PTPRG activates the m6A methyltransferase VIRMA to block mitophagy-mediated neuronal death in AD, which is a potential pathway, through which microglia and neuronal PTPRG modify neuronal connections in the brain during AD progression.

Identification of the key DNA damage response genes for predicting immunotherapy and chemotherapy efficacy in lung adenocarcinoma based on bulk, single-cell RNA sequencing, and spatial transcriptomics

BACKGROUND

Immune checkpoint inhibitors (ICI) plus chemotherapy is the preferred first-line treatment for advanced driver-negative lung adenocarcinoma (LUAD). The DNA damage response (DDR) is the main mechanism underlying chemotherapy resistance, and EGLN3 is a key DDR component.

METHOD

We conducted an analysis utilizing TCGA and GEO databases employing multiple labels-WGCNA, DEGs, and prognostic assessments. Using bulk RNA-seq and scRNA-seq data, we isolated EGLN3 as the single crucial DDR gene. Spatial transcriptome analysis revealed the spatial differential distribution of EGLN3. TIDE/IPS scores and pRRophetic/oncoPredict R packages were used to predict resistance to ICI and chemotherapy drugs, respectively.

RESULTS

EGLN3 was overexpressed in LUAD tissues (p < 0.001), with the high EGLN3 expression group exhibiting a poor prognosis (p = 0.00086, HR: 1.126 [1.039-1.22]). Spatial transcriptome analysis revealed EGLN3 overexpression in cancerous and hypoxic regions, positively correlating with DDR-related and TGF-β pathways. Drug response predictions indicated EGLN3's resistance to the common chemotherapy drugs, including cisplatin (p = 6.1e-14), docetaxel (p = 1.1e-07), and paclitaxel (p = 4.2e-07). Furthermore, on analyzing the resistance mechanism, we found that EGLN3 regulated DDR-related pathways and induced chemotherapy resistance. Additionally, EGLN3 influenced TGF-β signaling, Treg cells, and cancer-associated fibroblast cells, culminating in immunotherapy resistance. Moreover, validation using real-world data, such as GSE126044, GSE135222, and, IMvigor210, substantiated the response trends to immunotherapy and chemotherapy.

CONCLUSIONS

EGLN3 emerges as a potential biomarker predicting lower response to both immunotherapy and chemotherapy, suggesting its promise as a therapeutic target in advanced LUAD.

Comprehensive phenotypic and genomic characterization of venous malformations

Venous malformations (VMs) are the most common vascular malformations. TEK and PIK3CA are the causal genes of VMs, and may be involved in the PI3K/AKT pathway. However, the downstream mechanisms underlying the TEK or PIK3CA mutations in VMs are not completely understood. This study aimed to identify a possible association between genetic mutations and clinicopathological features. A retrospective clinical, pathological, and genetic study of 114 patients with VMs was performed. TEK, PIK3CA, and combined TEK/PIK3CA mutations were identified in 49 (43%), 13 (11.4%), and 2 (1.75%) patients, respectively. TEK-mutant VMs more commonly occurred in younger patients than TEK and PIK3CA mutation-negative VMs (other-mutant VMs), and showed more frequent skin involvement and no lymphocytic aggregates. No significant differences were observed in sex, location of occurrence, malformed vessel size, vessel density, or thickness of the vascular smooth muscle among the VM genotypes. Immunohistochemical analysis revealed that the expression levels of phosphorylated AKT (p-AKT) were higher in the TEK-mutant VMs than those in PIK3CA-mutant and other-mutant VMs. The expression levels of p-mTOR and its downstream effectors were higher in all the VM genotypes than those in normal vessels. Spatial transcriptomics revealed that the genes involved in "blood vessel development", "positive regulation of cell migration", and "extracellular matrix organization" were up-regulated in a TEK-mutant VM. Significant genotype-phenotype correlations in clinical and pathological features were observed among the VM genotypes, indicating gene-specific effects. Detailed analysis of gene-specific effects in VMs may offer insights into the underlying molecular pathways and implications for targeted therapies.

Single-cell and spatial transcriptomics in endocrine research

Accumulating evidence suggests that cellular heterogeneity in organs and cell-cell and tissue-tissue interactions are crucial for maintaining physical homeostasis and disease progression. Endocrine organs also exhibit cellular heterogeneity and comprise multiple cell types. For instance, the pituitary gland comprises five types of pituitary hormone-producing cells as well as non-hormone-producing supporting cells, such as fibroblasts, endothelial cells, and folliculostellate cells. However, the functional roles of the interactions between hormone-producing and non-producing cells in the pituitary gland remain incompletely understood. Over the past decade, emerging technologies such as single-cell and spatial transcriptomics have provided excellent tools for studying cellular heterogeneity and their interactions; however, the application of these technologies in endocrine research remains limited. This review provides an overview of these technologies and discusses their strengths and limitations. Additionally, we also summarize the potential future applications of single-cell and spatial transcriptomics in the study of endocrine organs and their disorders.

Technical optimization of spatially resolved single-cell transcriptomic datasets to study clinical liver disease

Single cell and spatially resolved 'omic' techniques have enabled deep characterization of clinical pathologies that remain poorly understood, providing unprecedented insights into molecular mechanisms of disease. However, transcriptomic platforms are costly, limiting sample size, which increases the possibility of pre-analytical variables such as tissue processing and storage procedures impacting RNA quality and downstream analyses. Furthermore, spatial transcriptomics have not yet reached single cell resolution, leading to the development of multiple deconvolution methods to predict individual cell types within each transcriptome 'spot' on tissue sections. In this study, we performed spatial transcriptomics and single nucleus RNA sequencing (snRNAseq) on matched specimens from patients with either histologically normal or advanced fibrosis to establish important aspects of tissue handling, data processing, and downstream analyses of biobanked liver samples. We observed that tissue preservation technique impacts transcriptomic data, especially in fibrotic liver. Single cell mapping of the spatial transcriptome using paired snRNAseq data generated a spatially resolved, single cell dataset with 24 unique liver cell phenotypes. We determined that cell-cell interactions predicted using ligand-receptor analysis of snRNAseq data poorly correlated with cellular relationships identified using spatial transcriptomics. Our study provides a framework for generating spatially resolved, single cell datasets to study gene expression and cell-cell interactions in biobanked clinical samples with advanced liver disease.

vissE: a versatile tool to identify and visualise higher-order molecular phenotypes from functional enrichment analysis

Functional analysis of high throughput experiments using pathway analysis is now ubiquitous. Though powerful, these methods often produce thousands of redundant results owing to knowledgebase redundancies upstream. This scale of results hinders extensive exploration by biologists and can lead to investigator biases due to previous knowledge and expectations. To address this issue, we present vissE, a flexible network-based analysis and visualisation tool that organises information into semantic categories and provides various visualisation modules to characterise them with respect to the underlying data, thus providing a comprehensive view of the biological system. We demonstrate vissE's versatility by applying it to three different technologies: bulk, single-cell and spatial transcriptomics. Applying vissE to a factor analysis of a breast cancer spatial transcriptomic data, we identified stromal phenotypes that support tumour dissemination. Its adaptability allows vissE to enhance all existing gene-set enrichment and pathway analysis workflows, empowering biologists during molecular discovery.

Spatial and single-cell analyses uncover links between ALKBH1 and tumor-associated macrophages in gastric cancer

BACKGROUND

AlkB homolog 1, histone H2A dioxygenase (ALKBH1), a crucial enzyme involved in RNA demethylation in humans, plays a significant role in various cellular processes. While its role in tumor progression is well-established, its specific contribution to stomach adenocarcinoma (STAD) remains elusive. This study seeks to explore the clinical and pathological relevance of ALKBH1, its impact on the tumor immune microenvironment, and its potential for precision oncology in STAD.

METHODS

We adopted a comprehensive multi-omics approach to identify ALKBH1 as an potential diagnostic biomarker for STAD, demonstrating its association with advanced clinical stages and reduced overall survival rates. Our analysis involved the utilization of publicly available datasets from GEO and TCGA. We identified differentially expressed genes in STAD and scrutinized their relationships with immune gene expression, overall survival, tumor stage, gene mutation profiles, and infiltrating immune cells. Moreover, we employed spatial transcriptomics to investigate ALKBH1 expression across distinct regions of STAD. Additionally, we conducted spatial transcriptomic and single-cell RNA-sequencing analyses to elucidate the correlation between ALKBH1 expression and immune cell populations. Our findings were validated through immunohistochemistry and bioinformatics on 60 STAD patient samples.

RESULTS

Our study unveiled crucial gene regulators in STAD linked with genetic variations, deletions, and the tumor microenvironment. Mutations in these regulators demonstrated a positive association with distinct immune cell populations across six immune datasets, exerting a substantial influence on immune cell infiltration in STAD. Furthermore, we established a connection between elevated ALKBH1 expression and macrophage infiltration in STAD. Pharmacogenomic analysis of gastric cancer cell lines further indicated that ALKBH1 inactivation correlated with heightened sensitivity to specific small-molecule drugs.

CONCLUSION

In conclusion, our study highlights the potential role of ALKBH1 alterations in the advancement of STAD, shedding light on novel diagnostic and prognostic applications of ALKBH1 in this context. We underscore the significance of ALKBH1 within the tumor immune microenvironment, suggesting its utility as a precision medicine tool and for drug screening in the management of STAD.

Emerging tools for uncovering genetic and transcriptomic heterogeneities in bacteria

Bacterial communities display an astonishing degree of heterogeneities among their constituent cells across both the genomic and transcriptomic levels, giving rise to diverse social interactions and stress-adaptation strategies indispensable for proliferating in the natural environment (Ackermann in Nat Rev Microbiol 13:497-508, 2015). Our knowledge about bacterial heterogeneities and their physiological ramifications critically depends on our ability to unambiguously resolve the genetic and phenotypic states of the individual cells that make up the population. In this short review, I highlight several recently developed methods for studying bacterial heterogeneities, primarily focusing on single-cell techniques based on advanced sequencing and microscopy technologies. I will discuss the working principle of each technique as well as the types of problems each technique is best positioned to address. With significant improvements in resolution and throughput, these emerging tools together offer unprecedented and complementary views of various types of heterogeneities found within bacterial populations, paving the way for mechanistic dissections and systematic interventions in laboratory and clinical settings.

Spatial analyses: Focusing on immune-stromal interactions to understand immunity in the tissue context

Immune cells perform their tasks in tissues, thus, they are highly dependent on their microenvironment. This means that the tissue context should be considered to fully understand their function. For a long time, it has been difficult to quantify these complex interrelationships in tissues and to spatially map the diversity of cell types involved. In recent years, several methods have become available that allow comprehensive profiling of immune cells and their microenvironment, at both the protein and transcriptional levels. We have used multiplex immunofluorescence histology in combination with machine-learning based cell segmentation and annotation to identify even rare immune cell populations, namely innate lymphoid cells, in various human tissues and found that they preferentially localize in fibrovascular niches. Those niches are located around blood vessels, enriched in stromal cells and extracellular matrix, and represent a location for innate lymphoid cells across various organs. By combining multiplexed histology and spatial transcriptomics on serial sections, we further identified those tissue areas as seed points for COVID-19 induced lung fibrosis and pin-pointed a pro-fibrotic macrophage population as driver of this process, leading to an expansion of the niches. At later disease stages, these areas were populated by lymphocytes, promoting the formation of tertiary lymphoid structures. Whether similar mechanisms apply to other diseases associated with fibrosis, such as autoimmune conditions, awaits further investigation.

Spatial transcriptomic interrogation of the tumour-stroma boundary in a 3D engineered model of ameloblastoma

Stromal cells are key components of the tumour microenvironment (TME) and their incorporation into 3D engineered tumour-stroma models is essential for tumour mimicry. By engineering tumouroids with distinct tumour and stromal compartments, it has been possible to identify how gene expression of tumour cells is altered and influenced by the presence of different stromal cells. Ameloblastoma is a benign epithelial tumour of the jawbone. In engineered, multi-compartment tumouroids spatial transcriptomics revealed an upregulation of oncogenes in the ameloblastoma transcriptome where osteoblasts were present in the stromal compartment (bone stroma). Where a gingival fibroblast stroma was engineered, the ameloblastoma tumour transcriptome revealed increased matrix remodelling genes. This study provides evidence to show the stromal-specific effect on tumour behaviour and illustrates the importance of engineering biologically relevant stroma for engineered tumour models. Our novel results show that an engineered fibroblast stroma causes the upregulation of matrix remodelling genes in ameloblastoma which directly correlates to measured invasion in the model. In contrast the presence of a bone stroma increases the expression of oncogenes by ameloblastoma cells.

Cellular heterogeneity in red and melanized focal muscle changes in farmed Atlantic salmon (Salmo salar) visualized by spatial transcriptomics

Spatial transcriptomics is a technique that provides insight into gene expression profiles in tissue sections while retaining structural information. We have employed this method to study the pathological conditions related to red and melanized focal changes in farmed Atlantic salmon (Salmo salar). Our findings support a model where similar molecular mechanisms are involved in both red and melanized filet discolorations and genes associated with several relevant pathways show distinct expression patterns in both sample types. Interestingly, there appears to be significant cellular heterogeneity in the foci investigated when looking at gene expression patterns. Some of the genes that show differential spatial expression are involved in cellular processes such as hypoxia and immune responses, providing new insight into the nature of muscle melanization in Atlantic salmon.

Spatial multimodal analysis revealed tertiary lymphoid structures as a risk stratification indicator in combined hepatocellular-cholangiocarcinoma

The microenvironment created by tertiary lymphoid structures (TLSs) can support and regulate immune responses, affecting the prognosis and immune treatment of patients. Nevertheless, the actual importance of TLSs for predicting the prognosis of combined hepatocellular-cholangiocarcinoma (cHCC-CCA) patients remains unclear. Herein, using spatial transcriptomic analysis, we revealed that a gene signature of TLSs specific to cHCC-CCA was associated with high-intensity immune infiltration. Then, a novel scoring system was developed to evaluate the distribution and frequency of TLSs in intra-tumoral and extra-tumoral regions (iTLS and eTLS scores) in 146 cHCC-CCA patients. iTLS score was positively associated with promising prognosis, likely due to the decreased frequency of suppressive immune cell like Tregs, and the ratio of CD163+ macrophages to macrophages in intra-tumoral TLSs via imaging mass cytometry, while improved prognosis is not necessarily indicated by a higher eTLS score. Overall, this study highlights the potential of TLSs as a prognostic factor and an indicator of immune therapy in cHCC-CCA.

Spatial Transcriptomics in a Case of Follicular Thyroid Carcinoma Reveals Clone-Specific Dysregulation of Genes Regulating Extracellular Matrix in the Invading Front

Follicular thyroid carcinoma (FTC) is recognized by its ability to invade the tumor capsule and blood vessels, although the exact molecular signals orchestrating this phenotype remain elusive. In this study, the spatial transcriptional landscape of an FTC is detailed with comparisons between the invasive front and histologically indolent central core tumor areas. The Visium spatial gene expression platform allowed us to interrogate and visualize the whole transcriptome in 2D across formalin-fixated paraffin-embedded (FFPE) tissue sections. Four different 6 × 6 mm areas of an FTC were scrutinized, including regions with capsular and vascular invasion, capsule-near area without invasion, and a central core area of the tumor. Following successful capturing and sequencing, several expressional clusters were identified with regional variation. Most notably, invasive tumor cell clusters were significantly over-expressing genes associated with pathways interacting with the extracellular matrix (ECM) remodeling and epithelial-to-mesenchymal transition (EMT). Subsets of these genes (POSTN and DPYSL3) were additionally validated using immunohistochemistry in an independent cohort of follicular thyroid tumors showing a clear gradient pattern from the core to the periphery of the tumor. Moreover, the reconstruction of the evolutionary tree identified the invasive clones as late events in follicular thyroid tumorigenesis. To our knowledge, this is one of the first 2D global transcriptional mappings of FTC using this platform to date. Invasive FTC clones develop in a stepwise fashion and display significant dysregulation of genes associated with the ECM and EMT - thus highlighting important molecular crosstalk for further investigations.

Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases

Spatial multi-omic studies have emerged as a promising approach to comprehensively analyze cells in tissues, enabling the joint analysis of multiple data modalities like transcriptome, epigenome, proteome, and metabolome in parallel or even the same tissue section. This review focuses on the recent advancements in spatial multi-omics technologies, including novel data modalities and computational approaches. We discuss the advancements in low-resolution and high-resolution spatial multi-omics methods which can resolve up to 10,000 of individual molecules at subcellular level. By applying and integrating these techniques, researchers have recently gained valuable insights into the molecular circuits and mechanisms which govern cell biology along the cardiovascular disease spectrum. We provide an overview of current data analysis approaches, with a focus on data integration of multi-omic datasets, highlighting strengths and weaknesses of various computational pipelines. These tools play a crucial role in analyzing and interpreting spatial multi-omics datasets, facilitating the discovery of new findings, and enhancing translational cardiovascular research. Despite nontrivial challenges, such as the need for standardization of experimental setups, data analysis, and improved computational tools, the application of spatial multi-omics holds tremendous potential in revolutionizing our understanding of human disease processes and the identification of novel biomarkers and therapeutic targets. Exciting opportunities lie ahead for the spatial multi-omics field and will likely contribute to the advancement of personalized medicine for cardiovascular diseases.

Mapping the single cell spatial immune landscapes of the melanoma microenvironment

Melanoma is a highly immunogenic malignancy with an elevated mutational burden, diffuse lymphocytic infiltration, and one of the highest response rates to immune checkpoint inhibitors (ICIs). However, over half of all late-stage patients treated with ICIs will either not respond or develop progressive disease. Spatial imaging technologies are being increasingly used to study the melanoma tumor microenvironment (TME). The goal of such studies is to understand the complex interplay between the stroma, melanoma cells, and immune cell-types as well as their association with treatment response. Investigators seeking a better understanding of the role of cell location within the TME and the importance of spatial expression of biomarkers are increasingly turning to highly multiplexed imaging approaches to more accurately measure immune infiltration as well as to quantify receptor-ligand interactions (such as PD-1 and PD-L1) and cell-cell contacts. CyTOF-IMC (Cytometry by Time of Flight - Imaging Mass Cytometry) has enabled high-dimensional profiling of melanomas, allowing researchers to identify complex cellular subpopulations and immune cell interactions with unprecedented resolution. Other spatial imaging technologies, such as multiplexed immunofluorescence and spatial transcriptomics, have revealed distinct patterns of immune cell infiltration, highlighting the importance of spatial relationships, and their impact in modulating immunotherapy responses. Overall, spatial imaging technologies are just beginning to transform our understanding of melanoma biology, providing new avenues for biomarker discovery and therapeutic development. These technologies hold great promise for advancing personalized medicine to improve patient outcomes in melanoma and other solid malignancies.

Unsupervised spatially embedded deep representation of spatial transcriptomics

Optimal integration of transcriptomics data and associated spatial information is essential towards fully exploiting spatial transcriptomics to dissect tissue heterogeneity and map out inter-cellular communications. We present SEDR, which uses a deep autoencoder coupled with a masked self-supervised learning mechanism to construct a low-dimensional latent representation of gene expression, which is then simultaneously embedded with the corresponding spatial information through a variational graph autoencoder. SEDR achieved higher clustering performance on manually annotated 10 × Visium datasets and better scalability on high-resolution spatial transcriptomics datasets than existing methods. Additionally, we show SEDR's ability to impute and denoise gene expression (URL: https://github.com/JinmiaoChenLab/SEDR/ ).

Profibrotic Subsets of SPP1+ Macrophages and POSTN+ Fibroblasts Contribute to Fibrotic Scarring in Acne Keloidalis

Acne keloidalis is a primary scarring alopecia characterized by longstanding inflammation in the scalp causing keloid-like scar formation and hair loss. Histologically, acne keloidalis is characterized by mixed leukocytic infiltrates in the acute stage followed by a granulomatous reaction and extensive fibrosis in the later stages. To further explore its pathogenesis, bulk RNA sequencing, single-cell RNA sequencing, and spatial transcriptomics were applied to occipital scalp biopsy specimens of lesional and adjacent no-lesional skin in patients with clinically active disease. Unbiased clustering revealed 19 distinct cell populations, including 2 notable populations: POSTN+ fibroblasts with enriched extracellular matrix signatures and SPP1+ myeloid cells with an M2 macrophage phenotype. Cell communication analyses indicated that fibroblasts and myeloid cells communicated by SPP1 signaling networks in lesional skin. A reverse transcriptomics in silico approach identified corticosteroids as possessing the capability to reverse the gene expression signatures of SPP1+ myeloid cells and POSTN+ fibroblasts. Intralesional corticosteroid injection greatly reduced SPP1 and POSTN gene expression as well as acne keloidalis disease activity. Spatial transcriptomics and immunofluorescence staining verified microanatomic specificity of SPP1+ myeloid cells and POSTN+ fibroblasts with disease activity. In summary, the communication between POSTN+ fibroblasts and SPP1+ myeloid cells by SPP1 axis may contribute to the pathogenesis of acne keloidalis.

Cell-type brain-region specific changes in prefrontal cortex of a mouse model of alcohol dependence

The prefrontal cortex is a crucial regulator of alcohol drinking, and dependence, and other behavioral phenotypes associated with AUD. Comprehensive identification of cell-type specific transcriptomic changes in alcohol dependence will improve our understanding of mechanisms underlying the excessive alcohol use associated with alcohol dependence and will refine targets for therapeutic development. We performed single nucleus RNA sequencing (snRNA-seq) and Visium spatial gene expression profiling on the medial prefrontal cortex (mPFC) obtained from C57BL/6 J mice exposed to the two-bottle choice-chronic intermittent ethanol (CIE) vapor exposure (2BC-CIE, defined as dependent group) paradigm which models phenotypes of alcohol dependence including escalation of alcohol drinking. Gene co-expression network analysis and differential expression analysis identified highly dysregulated co-expression networks in multiple cell types. Dysregulated modules and their hub genes suggest novel understudied targets for studying molecular mechanisms contributing to the alcohol dependence state. A subtype of inhibitory neurons was the most alcohol-sensitive cell type and contained a downregulated gene co-expression module; the hub gene for this module is Cpa6, a gene previously identified by GWAS to be associated with excessive alcohol consumption. We identified an astrocytic Gpc5 module significantly upregulated in the alcohol-dependent group. To our knowledge, there are no studies linking Cpa6 and Gpc5 to the alcohol-dependent phenotype. We also identified neuroinflammation related gene expression changes in multiple cell types, specifically enriched in microglia, further implicating neuroinflammation in the escalation of alcohol drinking. Here, we present a comprehensive atlas of cell-type specific alcohol dependence mediated gene expression changes in the mPFC and identify novel cell type-specific targets implicated in alcohol dependence.

Challenges and Opportunities for the Clinical Translation of Spatial Transcriptomics Technologies

Background

The first spatially resolved transcriptomics platforms, GeoMx (Nanostring) and Visium (10x Genomics) were launched in 2019 and were recognized as the method of the year by Nature Methods in 2020. The subsequent refinement and expansion of these and other technologies to increase -plex, work with formalin-fixed paraffin-embedded tissue, and analyze protein in addition to gene expression have only added to their significance and impact on the biomedical sciences. In this perspective, we focus on two platforms for spatial transcriptomics, GeoMx and Visium, and how these platforms have been used to provide novel insight into kidney disease. The choice of platform will depend largely on experimental questions and design. The application of these technologies to clinically sourced biopsies presents the opportunity to identify specific tissue biomarkers that help define disease etiology and more precisely target therapeutic interventions in the future.

Summary

In this review, we provide a description of the existing and emerging technologies that can be used to capture spatially resolved gene and protein expression data from tissue. These technologies have provided new insight into the spatial heterogeneity of diseases, how reactions to disease are distributed within a tissue, which cells are affected, and molecular pathways that predict disease and response to therapy.

Key Message

The upcoming years will see intense use of spatial transcriptomics technologies to better define the pathophysiology of kidney diseases and develop novel diagnostic tests to guide personalized treatments for patients.

Targeting stroma and tumor, silencing galectin 1 treats orthotopic mouse hepatocellular carcinoma

This study examines inhibiting galectin 1 (Gal1) as a treatment option for hepatocellular carcinoma (HCC). Gal1 has immunosuppressive and cancer-promoting roles. Our data showed that Gal1 was highly expressed in human and mouse HCC. The levels of Gal1 positively correlated with the stages of human HCC and negatively with survival. The roles of Gal1 in HCC were studied using overexpression (OE) or silencing using Igals1 siRNA delivered by AAV9. Prior to HCC initiation induced by RAS and AKT mutations, lgals1-OE and silencing had opposite impacts on tumor load. The treatment effect of lgals1 siRNA was further demonstrated by intersecting HCC at different time points when the tumor load had already reached 9% or even 42% of the body weight. Comparing spatial transcriptomic profiles of Gal1 silenced and OE HCC, inhibiting matrix formation and recognition of foreign antigen in CD45+ cell-enriched areas located at tumor-margin likely contributed to the anti-HCC effects of Gal1 silencing. Within the tumors, silencing Gal1 inhibited translational initiation, elongation, and termination. Furthermore, Gal1 silencing increased immune cells as well as expanded cytotoxic T cells within the tumor, and the anti-HCC effect of lgals1 siRNA was CD8-dependent. Overall, Gal1 silencing has a promising potential for HCC treatment.

Transformer with convolution and graph-node co-embedding: An accurate and interpretable vision backbone for predicting gene expressions from local histopathological image

Inferring gene expressions from histopathological images has long been a fascinating yet challenging task, primarily due to the substantial disparities between the two modality. Existing strategies using local or global features of histological images are suffering model complexity, GPU consumption, low interpretability, insufficient encoding of local features, and over-smooth prediction of gene expressions among neighboring sites. In this paper, we develop TCGN (Transformer with Convolution and Graph-Node co-embedding method) for gene expression estimation from H&E-stained pathological slide images. TCGN comprises a combination of convolutional layers, transformer encoders, and graph neural networks, and is the first to integrate these blocks in a general and interpretable computer vision backbone. Notably, TCGN uniquely operates with just a single spot image as input for histopathological image analysis, simplifying the process while maintaining interpretability. We validate TCGN on three publicly available spatial transcriptomic datasets. TCGN consistently exhibited the best performance (with median PCC 0.232). TCGN offers superior accuracy while keeping parameters to a minimum (just 86.241 million), and it consumes minimal memory, allowing it to run smoothly even on personal computers. Moreover, TCGN can be extended to handle bulk RNA-seq data while providing the interpretability. Enhancing the accuracy of omics information prediction from pathological images not only establishes a connection between genotype and phenotype, enabling the prediction of costly-to-measure biomarkers from affordable histopathological images, but also lays the groundwork for future multi-modal data modeling. Our results confirm that TCGN is a powerful tool for inferring gene expressions from histopathological images in precision health applications.

Designing Oligonucleotide-Based FISH Probe Sets with PaintSHOP

Fluorescent in situ hybridization (FISH) enables the visualization of the position and abundance of nucleic acid molecules in fixed cell and tissue samples. Many FISH-based methods employ sets of synthetic, computationally designed DNA oligonucleotide (oligo) FISH probes, including massively multiplexed imaging spatial transcriptomics and spatial genomics technologies. Oligo probes can either be designed de novo or accessed from an existing database of pre-discovered probe sequences. This chapter describes the use of PaintSHOP, a user-friendly, web-based platform for the design of sets of oligo-based FISH probes. PaintSHOP hosts large collections of pre-discovered probes from many model organisms and also provides collections of functional sequences such as primers and readout domains and interactive tools to add these functional sequences to selected probes. Detailed examples are provided for three common experimental scenarios.

ENGEP: advancing spatial transcriptomics with accurate unmeasured gene expression prediction

Imaging-based spatial transcriptomics techniques provide valuable spatial and gene expression information at single-cell resolution. However, their current capability is restricted to profiling a limited number of genes per sample, resulting in most of the transcriptome remaining unmeasured. To overcome this challenge, we develop ENGEP, an ensemble learning-based tool that predicts unmeasured gene expression in spatial transcriptomics data by using multiple single-cell RNA sequencing datasets as references. ENGEP outperforms current state-of-the-art tools and brings biological insight by accurately predicting unmeasured genes. ENGEP has exceptional efficiency in terms of runtime and memory usage, making it scalable for analyzing large datasets.