Next-generation spatial transcriptomics: unleashing the power to gear up translational oncology.

The growing advances in spatial transcriptomics (ST) stand as the new frontier bringing unprecedented influences in the realm of translational oncology. This has triggered systemic experimental design, analytical scope, and depth alongside with thorough bioinformatics approaches being constantly developed in the last few years. However, harnessing the power of spatial biology and streamlining an array of ST tools to achieve designated research goals are fundamental and require real-world experiences. We present a systemic review by updating the technical scope of ST across different principal basis in a timeline manner hinting on the generally adopted ST techniques used within the community. We also review the current progress of bioinformatic tools and propose in a pipelined workflow with a toolbox available for ST data exploration. With particular interests in tumor microenvironment where ST is being broadly utilized, we summarize the up-to-date progress made via ST-based technologies by narrating studies categorized into either mechanistic elucidation or biomarker profiling (translational oncology) across multiple cancer types and their ways of deploying the research through ST. This updated review offers as a guidance with forward-looking viewpoints endorsed by many high-resolution ST tools being utilized to disentangle biological questions that may lead to clinical significance in the future.

Spatially Informed Graph Structure Learning Extracts Insights from Spatial Transcriptomics.

Embeddings derived from cell graphs hold significant potential for exploring spatial transcriptomics (ST) datasets. Nevertheless, existing methodologies rely on a graph structure defined by spatial proximity, which inadequately represents the diversity inherent in cell-cell interactions (CCIs). This study introduces STAGUE, an innovative framework that concurrently learns a cell graph structure and a low-dimensional embedding from ST data. STAGUE employs graph structure learning to parameterize and refine a cell graph adjacency matrix, enabling the generation of learnable graph views for effective contrastive learning. The derived embeddings and cell graph improve spatial clustering accuracy and facilitate the discovery of novel CCIs. Experimental benchmarks across 86 real and simulated ST datasets show that STAGUE outperforms 15 comparison methods in clustering performance. Additionally, STAGUE delineates the heterogeneity in human breast cancer tissues, revealing the activation of epithelial-to-mesenchymal transition and PI3K/AKT signaling in specific sub-regions. Furthermore, STAGUE identifies CCIs with greater alignment to established biological knowledge than those ascertained by existing graph autoencoder-based methods. STAGUE also reveals the regulatory genes that participate in these CCIs, including those enriched in neuropeptide signaling and receptor tyrosine kinase signaling pathways, thereby providing insights into the underlying biological processes.

Disruption of cellular plasticity by repeat RNAs in human pancreatic cancer.

Aberrant expression of repeat RNAs in pancreatic ductal adenocarcinoma (PDAC) mimics viral-like responses with implications on tumor cell state and the response of the surrounding microenvironment. To better understand the relationship of repeat RNAs in human PDAC, we performed spatial molecular imaging at single-cell resolution in 46 primary tumors, revealing correlations of high repeat RNA expression with alterations in epithelial state in PDAC cells and myofibroblast phenotype in cancer-associated fibroblasts (CAFs). This loss of cellular identity is observed with dosing of extracellular vesicles (EVs) and individual repeat RNAs of PDAC and CAF cell culture models pointing to cell-cell intercommunication of these viral-like elements. Differences in PDAC and CAF responses are driven by distinct innate immune signaling through interferon regulatory factor 3 (IRF3). The cell-context-specific viral-like responses to repeat RNAs provide a mechanism for modulation of cellular plasticity in diverse cell types in the PDAC microenvironment.

Resting natural killer cells promote the progress of colon cancer liver metastasis by elevating tumor-derived stem cell factor.

The abundance and biological contribution of natural killer (NK) cells in cancer are controversial. Here, we aim to uncover clinical relevance and cellular roles of NK cells in colon cancer liver metastasis (CCLM). Here, we integrated single-cell RNA-sequencing, spatial transcriptomics (ST), and bulk RNA-sequencing datasets to investigate NK cells' biological properties and functions in the microenvironment of primary and liver metastatic tumors. Results were validated through an in vitro co-culture experiment based on bioinformatics analysis. Useing single-cell RNA-sequencing and ST, we mapped the immune cellular landscape of colon cancer and well-matched liver metastatic cancer. We discovered that GZMK+ resting NK cells increased significantly in tumor tissues and were enriched in the tumor regions of both diseases. After combining bulk RNA and clinical data, we observed that these NK cell subsets contributed to a worse prognosis. Meanwhile, KIR2DL4+ activated NK cells exhibited the opposite position and relevance. Pseudotime cell trajectory analysis revealed the evolution of activated to resting NK cells. In vitro experiments further confirmed that tumor-cell-co-cultured NK cells exhibited a decidual-like status, as evidenced by remarkable increasing CD9 expression. Functional experiments finally revealed that NK cells exhibited tumor-activating characteristics by promoting the dissociation of SCF (stem cell factor) on the tumor cells membrane depending on cell-to-cell interaction, as the supernatant of the co-culture system enhanced tumor progression. In summary, our findings revealed resting NK cells exhibited a clinical relevance with CCLM, which may be exploited for novel strategies to improve therapeutic outcomes for patients with CCLM.

Single-cell RNA sequencing identifies a subtype of FN1 + tumor-associated macrophages associated with glioma recurrence and as a biomarker for immunotherapy.

BACKGROUND: Glioma is the most common primary malignant tumor in the brain, and even with standard treatments including surgical resection, radiotherapy, and chemotherapy, the long-term survival rate of patients remains unsatisfactory. Recurrence is one of the leading causes of death in glioma patients. The molecular mechanisms underlying glioma recurrence remain unclear. METHODS: Our study utilized single-cell sequencing, spatial transcriptomics, and RNA-seq data to identify a subtype of FN1 + tumor-associated macrophages (FN1 + TAMs) associated with glioma recurrence. RESULTS: This study revealed an increased abundance of FN1 + TAMs in recurrent gliomas, indicating their potential involvement as a critical factor in glioma recurrence. A negative correlation was observed between the abundance of FN1 + TAMs in primary gliomas and the interval time to recurrence, suggesting poor prognosis for glioma patients with high levels of FN1 + TAMs. Further investigation showed that FN1 + TAMs were enriched in hypoxic tumor regions, implying that metabolic changes in tumors drive the production and recruitment of FN1 + TAMs. Additionally, FN1 + TAMs were found to contribute to the regulation of an immunosuppressive microenvironment in gliomas, and their abundance might serve as an indicator of patients' sensitivity to immunotherapy. Finally, we developed a user-friendly website, PRIMEG ( http://www.szflab.site/PRIMEG/ ), for exploring the immune microenvironment of primary and recurrent gliomas. CONCLUSION: Our findings highlight a subtype of FN1 + TAMs associated with glioma recurrence, providing new insights into potential therapeutic targets. Moreover, the abundance of FN1 + TAMs hold promise for predicting immune therapy response and aiding in more precise risk stratification of recurrent glioma patients.

Spatial analyses revealed S100P + TFF1 + tumor cells in spread through air spaces samples correlated with undesirable therapy response in non-small cell lung cancer.

Spread through air spaces (STAS) is a recognized aggressive pattern in lung cancer, serving as a crucial risk factor for postoperative recurrence. However, its phenotype and related spatial structure have remained elusive. To address these limitations, we conducted a comprehensive study based on spatial data, analyzing over 30,000 spots from 14 non-STAS samples and one STAS sample. We observed increased proliferation activities and angiogenesis in STAS, identifying S100P as a potential biomarker for STAS. Furthermore, our investigation into the heterogeneity of STAS tumor cells revealed a subset identified as S100P + TFF1 +, exhibiting a negative impact on patients' survival in public datasets. This subtype exhibited the highest activities in the TGFb and hypoxia, suggesting its potential pro-tumor role within the tumor microenvironment. To assess the role of S100P + TFF1 + tumor cells in therapy response, we included data from two clinical trial cohorts (BPI-7711 for EGFR-TKI therapy and ORIENT-3 for immunotherapy). The presence of S100P + TFF1 + tumor cells correlated with worse responses to both EGFR-TKI therapy and immunotherapy. Notably, TFF1 emerged as a serum marker for predicting EGFR-TKI response. Cell-cell communication analysis revealed that the TGFb signaling pathway was the most activated in S100P + TFF1 + tumor cells, with TGFB2-TGFBR2 identified as the main ligand-receptor pair. This was further validated by multiplex immunofluorescence performed on twenty NSCLC samples. In summary, our study identified S100P as the biomarker for STAS and highlighted the adverse role of S100P + TFF1 + tumor cells in survival outcomes.

The activity of tertiary lymphoid structures in high grade serous ovarian cancer is governed by site, stroma, and cellular interactions.

Most high grade serous ovarian cancers (HGSOC) originate in the fallopian tube but spread to the ovary and peritoneal cavity, highlighting the need to understand antitumor immunity across HGSOC sites. Using spatial analyses, we discover that tertiary lymphoid structures (TLSs) within ovarian tumors are less developed compared with TLSs in fallopian tube or omental tumors. We reveal transcriptional differences across a spectrum of lymphoid structures, demonstrating that immune cell activity increases when residing in more developed TLSs and produce a prognostic, spatially derived TLS signature from HGSOC tumors. We interrogate TLS-adjacent stroma and assess how normal mesenchymal stem cells MSCs (nMSCs) may support B cell function and TLS, contrary to cancer-educated MSCs (CA-MSCs) which negate the prognostic benefit of our TLS signature, suggesting that pro-tumorigenic stroma could limit TLS formation.

From single-cell to spatial transcriptomics: decoding the glioma stem cell niche and its clinical implications.

Background: Gliomas are aggressive brain tumors associated with a poor prognosis. Cancer stem cells (CSCs) play a significant role in tumor recurrence and resistance to therapy. This study aimed to identify and characterize glioma stem cells (GSCs), analyze their interactions with various cell types, and develop a prognostic signature. Methods: Single-cell RNA sequencing data from 44 primary glioma samples were analyzed to identify GSC populations. Spatial transcriptomics and gene regulatory network analyses were performed to investigate GSC localization and transcription factor activity. CellChat analysis was conducted to infer cell-cell communication patterns. A GSC signature (GSCS) was developed using machine learning algorithms applied to bulk RNA sequencing data from multiple cohorts. In vitro and in vivo experiments were conducted to validate the role of TUBA1C, a key gene within the signature. Results: A distinct GSC population was identified, characterized by high proliferative potential and an enrichment of E2F1, E2F2, E2F7, and BRCA1 regulons. GSCs exhibited spatial proximity to myeloid-derived suppressor cells (MDSCs). CellChat analysis revealed an active MIF signaling pathway between GSCs and MDSCs. A 26-gene GSCS demonstrated superior performance compared to existing prognostic models. Knockdown of TUBA1C significantly inhibited glioma cell migration, and invasion in vitro, and reduced tumor growth in vivo. Conclusion: This study offers a comprehensive characterization of GSCs and their interactions with MDSCs, while presenting a robust GSCS. The findings offer new insights into glioma biology and identify potential therapeutic targets, particularly TUBA1C, aimed at improving patient outcomes.

An initial game-theoretic assessment of enhanced tissue preparation and imaging protocols for improved deep learning inference of spatial transcriptomics from tissue morphology.

The application of deep learning to spatial transcriptomics (ST) can reveal relationships between gene expression and tissue architecture. Prior work has demonstrated that inferring gene expression from tissue histomorphology can discern these spatial molecular markers to enable population scale studies, reducing the fiscal barriers associated with large-scale spatial profiling. However, while most improvements in algorithmic performance have focused on improving model architectures, little is known about how the quality of tissue preparation and imaging can affect deep learning model training for spatial inference from morphology and its potential for widespread clinical adoption. Prior studies for ST inference from histology typically utilize manually stained frozen sections with imaging on non-clinical grade scanners. Training such models on ST cohorts is also costly. We hypothesize that adopting tissue processing and imaging practices that mirror standards for clinical implementation (permanent sections, automated tissue staining, and clinical grade scanning) can significantly improve model performance. An enhanced specimen processing and imaging protocol was developed for deep learning-based ST inference from morphology. This protocol featured the Visium CytAssist assay to permit automated hematoxylin and eosin staining (e.g. Leica Bond), 40×-resolution imaging, and joining of multiple patients' tissue sections per capture area prior to ST profiling. Using a cohort of 13 pathologic T Stage-III stage colorectal cancer patients, we compared the performance of models trained on slide prepared using enhanced versus traditional (i.e. manual staining and low-resolution imaging) protocols. Leveraging Inceptionv3 neural networks, we predicted gene expression across serial, histologically-matched tissue sections using whole slide images (WSI) from both protocols. The data Shapley was used to quantify and compare marginal performance gains on a patient-by-patient basis attributed to using the enhanced protocol versus the actual costs of spatial profiling. Findings indicate that training and validating on WSI acquired through the enhanced protocol as opposed to the traditional method resulted in improved performance at lower fiscal cost. In the realm of ST, the enhancement of deep learning architectures frequently captures the spotlight; however, the significance of specimen processing and imaging is often understated. This research, informed through a game-theoretic lens, underscores the substantial impact that specimen preparation/imaging can have on spatial transcriptomic inference from morphology. It is essential to integrate such optimized processing protocols to facilitate the identification of prognostic markers at a larger scale.

The burgeoning spatial multi-omics in human gastrointestinal cancers.

The development and progression of diseases in multicellular organisms unfold within the intricate three-dimensional body environment. Thus, to comprehensively understand the molecular mechanisms governing individual development and disease progression, precise acquisition of biological data, including genome, transcriptome, proteome, metabolome, and epigenome, with single-cell resolution and spatial information within the body's three-dimensional context, is essential. This foundational information serves as the basis for deciphering cellular and molecular mechanisms. Although single-cell multi-omics technology can provide biological information such as genome, transcriptome, proteome, metabolome, and epigenome with single-cell resolution, the sample preparation process leads to the loss of spatial information. Spatial multi-omics technology, however, facilitates the characterization of biological data, such as genome, transcriptome, proteome, metabolome, and epigenome in tissue samples, while retaining their spatial context. Consequently, these techniques significantly enhance our understanding of individual development and disease pathology. Currently, spatial multi-omics technology has played a vital role in elucidating various processes in tumor biology, including tumor occurrence, development, and metastasis, particularly in the realms of tumor immunity and the heterogeneity of the tumor microenvironment. Therefore, this article provides a comprehensive overview of spatial transcriptomics, spatial proteomics, and spatial metabolomics-related technologies and their application in research concerning esophageal cancer, gastric cancer, and colorectal cancer. The objective is to foster the research and implementation of spatial multi-omics technology in digestive tumor diseases. This review will provide new technical insights for molecular biology researchers.

Scoping Review: Methods and Applications of Spatial Transcriptomics in Tumor Research.

Spatial transcriptomics (ST) examines gene expression within its spatial context on tissue, linking morphology and function. Advances in ST resolution and throughput have led to an increase in scientific interest, notably in cancer research. This scoping study reviews the challenges and practical applications of ST, summarizing current methods, trends, and data analysis techniques for ST in neoplasm research. We analyzed 41 articles published by the end of 2023 alongside public data repositories. The findings indicate cancer biology is an important focus of ST research, with a rising number of studies each year. Visium (10x Genomics, Pleasanton, CA, USA) is the leading ST platform, and SCTransform from Seurat R library is the preferred method for data normalization and integration. Many studies incorporate additional data types like single-cell sequencing and immunohistochemistry. Common ST applications include discovering the composition and function of tumor tissues in the context of their heterogeneity, characterizing the tumor microenvironment, or identifying interactions between cells, including spatial patterns of expression and co-occurrence. However, nearly half of the studies lacked comprehensive data processing protocols, hindering their reproducibility. By recommending greater transparency in sharing analysis methods and adapting single-cell analysis techniques with caution, this review aims to improve the reproducibility and reliability of future studies in cancer research.

Spatial transcriptomics reveals tumor-derived SPP1 induces fibroblast chemotaxis and activation in the hepatocellular carcinoma microenvironment.

BACKGROUND: The tumor microenvironment (TME) exerts profound effects on tumor progression and therapeutic efficacy. In hepatocellular carcinoma (HCC), the TME is enriched with cancer-associated fibroblasts (CAFs), which secrete a plethora of cytokines, chemokines, and growth factors that facilitate tumor cell proliferation and invasion. However, the intricate architecture of the TME in HCC, as well as the mechanisms driving interactions between tumor cells and CAFs, remains largely enigmatic. METHODS: We analyzed 10 spatial transcriptomics and 12 single-cell transcriptomics samples sourced from public databases, complemented by 20 tumor tissue samples from liver cancer patients obtained in a clinical setting. RESULTS: Our findings reveal that tumor cells exhibiting high levels of SPP1 are preferentially localized adjacent to hepatic stellate cells (HSCs). The SPP1 secreted by these tumor cells interacts with the CD44 receptor on HSCs, thereby activating the PI3K/AKT signaling pathway, which promotes the differentiation of HSCs into CAFs. Notably, blockade of the CD44 receptor effectively abrogates this interaction. Furthermore, in vivo studies demonstrate that silencing SPP1 expression in tumor cells significantly impairs HSC differentiation into CAFs, leading to a reduction in tumor volume and collagen deposition within the tumor stroma. CONCLUSIONS: This study delineates the SPP1-CD44 signaling axis as a pivotal mechanism underpinning the interaction between tumor cells and CAFs. Targeting this pathway holds potential to mitigate liver fibrosis and offers novel therapeutic perspectives for liver cancer management.

Spatial resolved transcriptomics reveals distinct cross-talk between cancer cells and tumor-associated macrophages in intrahepatic cholangiocarcinoma.

BACKGROUND: Multiple studies have shown that tumor-associated macrophages (TAMs) promote cancer initiation and progression. However, the reprogramming of macrophages in the tumor microenvironment (TME) and the cross-talk between TAMs and malignant subclones in intrahepatic cholangiocarcinoma (iCCA) has not been fully characterized, especially in a spatially resolved manner. Deciphering the spatial architecture of variable tissue cellular components in iCCA could contribute to the positional context of gene expression containing information pathological changes and cellular variability. METHODS: Here, we applied spatial transcriptomics (ST) and digital spatial profiler (DSP) technologies with tumor sections from patients with iCCA. RESULTS: The results reveal that spatial inter- and intra-tumor heterogeneities feature iCCA malignancy, and tumor subclones are mainly driven by physical proximity. Tumor cells with TME components shaped the intra-sectional heterogenetic spatial architecture. Macrophages are the most infiltrated TME component in iCCA. The protein trefoil factor 3 (TFF3) secreted by the malignant subclone can induce macrophages to reprogram to a tumor-promoting state, which in turn contributes to an immune-suppressive environment and boosts tumor progression. CONCLUSIONS: In conclusion, our description of the iCCA ecosystem in a spatially resolved manner provides novel insights into the spatial features and the immune suppressive landscapes of TME for iCCA.

Single-cell and spatial transcriptome assays reveal heterogeneity in gliomas through stress responses and pathway alterations.

Background: Glioma is a highly heterogeneous malignancy of the central nervous system. This heterogeneity is driven by various molecular processes, including neoplastic transformation, cell cycle dysregulation, and angiogenesis. Among these biomolecular events, inflammation and stress pathways in the development and driving factors of glioma heterogeneity have been reported. However, the mechanisms of glioma heterogeneity under stress response remain unclear, especially from a spatial aspect. Methods: This study employed single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) to explore the impact of oxidative stress response genes in oligodendrocyte precursor cells (OPCs). Our analysis identified distinct pathways activated by oxidative stress in two different types of gliomas: high- and low- grade (HG and LG) gliomas. Results: In HG gliomas, oxidative stress induced a metabolic shift from oxidative phosphorylation to glycolysis, promoting cell survival by preventing apoptosis. This metabolic reprogramming was accompanied by epithelial-to-mesenchymal transition (EMT) and an upregulation of stress response genes. Furthermore, SCENIC (Single-Cell rEgulatory Network Inference and Clustering) analysis revealed that oxidative stress activated the AP1 transcription factor in HG gliomas, thereby enhancing tumor cell survival and proliferation. Conclusion: Our findings provide a novel perspective on the mechanisms of oxidative stress responses across various grades of gliomas. This insight enhances our comprehension of the evolutionary processes and heterogeneity within gliomas, potentially guiding future research and therapeutic strategies.

Identifying High Recurrence Risk in Breast Carcinoma Patients Through Spatial Transcriptomic Analysis.

BACKGROUND/AIM: Comparing gene expression profiles according to recurrence risk using spatially resolved transcriptomic analysis has not been reported. This study aimed to identify distinct genetic features of breast carcinoma associated with a high Oncotype DX Recurrence Score (ORS). PATIENTS AND METHODS: Patients were categorized into two groups, ORS-high (ORS-H; two patients) and ORS-non-high (ORS-NH; five patients). We performed digital spatial profiling and bioinformatic analyses to investigate the spatial transcriptomic profiles. RESULTS: Lysozyme (LYZ), complement C1q C chain (C1QC), and complement C1q B chain (C1QB) exhibited the highest fold changes in the stromal compartment of the ORS-H group. Gene ontology enrichment analysis of the ORS-H group revealed significant up-regulation of genes associated with immune response in the stromal compartment, including lymphocyte-mediated immunity, adaptive immune response related to the immunoglobulin superfamily, and leukocyte-mediated immunity. Gene set enrichment analysis showed significant positive enrichment of gene sets associated with interferon (IFN) response and complement pathways in the stromal compartment. CONCLUSION: This study highlights significant differences in gene expression profiles and spatially resolved transcriptional activities between ORS-H and ORS-NH breast carcinomas. The significant up-regulation of genes and pathways associated with cell-mediated immunity, IFN response, and complement C1q in the stromal compartment of the ORS-H group warrants further evaluation with larger population cohorts.

Deep learning-based multimodal spatial transcriptomics analysis for cancer.

The advent of deep learning (DL) and multimodal spatial transcriptomics (ST) has revolutionized cancer research, offering unprecedented insights into tumor biology. This book chapter explores the integration of DL with ST to advance cancer diagnostics, treatment planning, and precision medicine. DL, a subset of artificial intelligence, employs neural networks to model complex patterns in vast datasets, significantly enhancing diagnostic and treatment applications. In oncology, convolutional neural networks excel in image classification, segmentation, and tumor volume analysis, essential for identifying tumors and optimizing radiotherapy. The chapter also delves into multimodal data analysis, which integrates genomic, proteomic, imaging, and clinical data to offer a holistic understanding of cancer biology. Leveraging diverse data sources, researchers can uncover intricate details of tumor heterogeneity, microenvironment interactions, and treatment responses. Examples include integrating MRI data with genomic profiles for accurate glioma grading and combining proteomic and clinical data to uncover drug resistance mechanisms. DL's integration with multimodal data enables comprehensive and actionable insights for cancer diagnosis and treatment. The synergy between DL models and multimodal data analysis enhances diagnostic accuracy, personalized treatment planning, and prognostic modeling. Notable applications include ST, which maps gene expression patterns within tissue contexts, providing critical insights into tumor heterogeneity and potential therapeutic targets. In summary, the integration of DL and multimodal ST represents a paradigm shift towards more precise and personalized oncology. This chapter elucidates the methodologies and applications of these advanced technologies, highlighting their transformative potential in cancer research and clinical practice.

Spatial transcriptomics reveal tumor microenvironment and SLCO2A1 correlated with tumor suppression in hypopharyngeal squamous cell carcinoma.

BACKGROUND: Hypopharyngeal squamous cell carcinoma (HSCC) is a type of head and neck tumor with malignant behavior and poor prognosis. Spatial transcriptomics is a method that spatially analyzes gene expression patterns in tissues and has been used to discover tumor microenvironment and molecular markers in various tumors. However, there are no published reports on spatial transcriptomic analysis of HSCC. METHODS: In this study, spatial transcriptomic analysis was performed on tumor tissues in situ, peritumoral tissues, and lymphatic metastatic tissues of four patients with HSCC. Morphological markers, including panCK, SMA, and CD45, were used to identify epithelial, fibroblast, and immune cells, respectively. By analyzing the expression of more than 18, 000 genes within the transcriptome of all ROIs, differentially expressed genes of three cell types in different tissues were identified, and differentially expressed signaling pathways and immune infiltration were analyzed. RESULTS: The spatial distribution of cells suggests that fibroblast cells in tumor tissues may be involved in the genesis and development of tumors, and the immune infiltration of lymphatic tumor metastasis is lower than that of tumors in situ. For epithelial cells, SLCO2A1, which is a favorable prognosis marker in head and neck squamous cell carcinoma (HNSCC), was significantly down-regulated in tumor tissues and lymphatic metastatic tissues compared with adjacent normal tissues. For immune cells, KANK3, which is a favorable prognosis markers in HNSCC, was significantly down-regulated in lymphatic metastatic tissues compared with adjacent normal tissues. For fibroblast cells, AQP1, CLEC3B and SLCO2A1, which are favorable prognosis markers in HNSCC, were significantly down-regulated in tumor tissues compared with adjacent normal tissues. ITGA8, which is a favorable prognosis markers in HNSCC, was significantly down-regulated in lymphatic metastatic tissues compared with normal lymphatic tissues. CSRP1, DES, and SLCO2A1 positively correlate with immune infiltration in HNSCC. Moreover, SLCO2A1 overexpression suppressed Fadu cells proliferation and metastasis and significantly correlated with favorable survival overcome in HSCC. CONCLUSIONS: We investigated tumor and fibroblast heterogeneity, as well as the immune microenvironment in HSCC by using spatial transcriptomics. SLCO2A1 may be a tumor suppressor gene and correlates with immune infiltration for HSCC and could serve as a potential target for its diagnosis and treatment.

Recovering single-cell expression profiles from spatial transcriptomics with scResolve.

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

Applications of spatial transcriptomics and artificial intelligence to develop integrated management of pancreatic cancer.

Cancer is a complex disease intrinsically associated with cellular processes and gene expression. With the development of techniques such as single-cell sequencing and sequential fluorescence in situ hybridization (seqFISH), it was possible to map the location of cells based on their gene expression with more precision. Moreover, in recent years, many tools have been developed to analyze these extensive datasets by integrating machine learning and artificial intelligence in a comprehensive manner. Since these tools analyze sequencing data, they offer the chance to analyze any tissue regardless of its origin. By applying this to cancer settings, spatial transcriptomic analysis based on artificial intelligence may help us understand cell-cell communications within the tumor microenvironment. Another advantage of this analysis is the identification of new biomarkers and therapeutic targets. The integration of such analysis with other omics data and with routine exams such as magnetic resonance imaging can help physicians with the earlier diagnosis of tumors as well as establish a more personalized treatment for pancreatic cancer patients. In this review, we give an overview description of pancreatic cancer, describe how spatial transcriptomics and artificial intelligence have been used to study pancreatic cancer and provide examples of how integrating these tools may help physicians manage pancreatic cancer in a more personalized approach.

Crosstalk between tumor and microenvironment: Insights from spatial transcriptomics.

Cancer is a dynamic disease, and clonal heterogeneity plays a fundamental role in tumor development, progression, and resistance to therapies. Single-cell and spatial multimodal technologies can provide a high-resolution molecular map of underlying genomic, epigenomic, and transcriptomic alterations involved in inter- and intra-tumor heterogeneity and interactions with the microenvironment. In this review, we provide a perspective on factors driving cancer heterogeneity, tumor evolution, and clonal states. We briefly describe spatial transcriptomic technologies and summarize recent literature that sheds light on the dynamical interactions between tumor states, cell-to-cell communication, and remodeling local microenvironment.