Spatial transcriptomics reveals distinct and conserved tumor core and edge architectures that predict survival and targeted therapy response

Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics

Single-cell transcriptomics has broadened our understanding of cellular diversity and gene expression dynamics in healthy and diseased tissues. Recently, spatial transcriptomics has emerged as a tool to contextualize single cells in multicellular neighbourhoods and to identify spatially recurrent phenotypes, or ecotypes. These technologies have generated vast datasets with targeted-transcriptome and whole-transcriptome profiles of hundreds to millions of cells. Such data have provided new insights into developmental hierarchies, cellular plasticity and diverse tissue microenvironments, and spurred a burst of innovation in computational methods for single-cell analysis. In this Review, we discuss recent advancements, ongoing challenges and prospects in identifying and characterizing cell states and multicellular neighbourhoods. We discuss recent progress in sample processing, data integration, identification of subtle cell states, trajectory modelling, deconvolution and spatial analysis. Furthermore, we discuss the increasing application of deep learning, including foundation models, in analysing single-cell and spatial transcriptomics data. Finally, we discuss recent applications of these tools in the fields of stem cell biology, immunology, and tumour biology, and the future of single-cell and spatial transcriptomics in biological research and its translation to the clinic.

Spatial transcriptomics reveals unique metabolic profile and key oncogenic regulators of cervical squamous cell carcinoma

BACKGROUND

As a prevalent and deadly malignant tumor, the treatment outcomes for late-stage patients with cervical squamous cell carcinoma (CSCC) are often suboptimal. Previous studies have shown that tumor progression is closely related with tumor metabolism and microenvironment reshaping, with disruptions in energy metabolism playing a critical role in this process. To delve deeper into the understanding of CSCC development, our research focused on analyzing the tumor microenvironment and metabolic characteristics across different regions of tumor tissue.

METHODS

Utilizing spatial transcriptomics (ST) sequencing technology, we conducted a study on FFPE (formalin-fixed paraffin-embedded) tumor samples from CSCC patients. Coupled with single-cell RNA sequencing (scRNA-seq) data after deconvolution, we described spatial distribution maps of tumor leading edge and core regions in detail. Tumor tissues were classified into hypermetabolic and hypometabolic regions to analyze the metabolism profiles and tumor differentiation degree across different spatial areas. We also employed The Cancer Genome Atlas (TCGA) database to examine the analysis results of ST data.

RESULTS

Our findings indicated a more complex tumor microenvironment in hypermetabolic regions. Cell-cell communication analysis showed that various cells in tumor microenvironment were influenced by the signalling molecule APP released by cancer cells and higher expression of APP was observed in hypermetabolic regions. Furthermore, our results revealed the correlation between APP and the transcription factor TRPS1. Both APP and TRPS1 demonstrated significant effects on cancer cell proliferation, migration, and invasion, potentially contributing to tumor progression.

CONCLUSIONS

Utilizing ST, scRNA-seq, and TCGA database, we examined the spatial metabolic profiles of CSCC tissues, including metabolism distribution, metabolic variations, and the relationship between metabolism and tumor differentiation degree. Additionally, potential cancer-promoting factors were proposed, offering a valuable foundation for the development of more effective treatment strategies for CSCC.

RBM4-mediated intron excision of Hsf1 induces BDNF for cerebellar foliation

Brain-derived neurotrophic factor (BDNF) plays important roles in brain development and neural function. Constitutive knockout of the splicing regulator RBM4 reduces BDNF expression in the developing brain and causes cerebellar hypoplasia, an autism-like feature. Here, we show that Rbm4 knockout induced intron 6 retention of Hsf1, leading to downregulation of HSF1 protein and its downstream target BDNF. RBM4-mediated Hsf1 intron excision regulated BDNF expression in cultured granule cells. Ectopic expression of HSF1 restored cerebellar foliation and motor learning of Rbm4-knockout mice, indicating a critical role for RBM4-HSF1-BDNF in cerebellar foliation. Moreover, N-methyl-D-aspartate receptor (NMDAR) signaling promoted the expression and nuclear translocation of RBM4, and hence increased the expression of both HSF and BDNF. A short CU-rich motif was responsible for NMDAR- and RBM4-mediated intron excision. Finally, RBM4 and polypyrimidine tract binding (PTB) proteins play antagonistic roles in intron excision, suggesting a role for splicing regulation in BDNF expression.

Molecular correlates for HPV-negative head and neck cancer engraftment prognosticate patient outcomes

There is a pressing need to improve risk stratification and treatment selection for HPV-negative head and neck squamous cell carcinoma (HNSCC) due to the adverse side effects of treatment. One of the most important prognostic features is lymph nodes involvement. Previously, we demonstrated that tumor formation in patient-derived xenografts (i.e. engraftment) was associated with poor clinical outcomes in patients with HPV-negative HNSCC. However, assessing engraftment is challenging in clinical settings. Here, we perform transcriptomic and proteomic profiling of 88 HNSCC patients and find the relationship between engraftment and clinical outcomes is recapitulated by molecular phenotype. We identify LAMC2 and TGM3 as candidate prognostic biomarkers and validated their utility in an independent cohort containing 404 HPV-negative HNSCC patients. Strikingly, these markers significantly improve prediction of outcomes beyond nodal status alone and can significantly stratify patients without any nodal involvement. Overall, our study demonstrates how the molecular characteristics of engraftment can inform patient prognostication.

Current state and future prospects of spatial biology in colorectal cancer

Over the past century, colorectal cancer (CRC) has become one of the most devastating cancers impacting the human population. To gain a deeper understanding of the molecular mechanisms driving this solid tumor, researchers have increasingly turned their attention to the tumor microenvironment (TME). Spatial transcriptomics and proteomics have emerged as a particularly powerful technology for deciphering the complexity of CRC tumors, given that the TME and its spatial organization are critical determinants of disease progression and treatment response. Spatial transcriptomics enables high-resolution mapping of the whole transcriptome. While spatial proteomics maps protein expression and function across tissue sections. Together, they provide a detailed view of the molecular landscape and cellular interactions within the TME. In this review, we delve into recent advances in spatial biology technologies applied to CRC research, highlighting both the methodologies and the challenges associated with their use, such as the substantial tissue heterogeneity characteristic of CRC. We also discuss the limitations of current approaches and the need for novel computational tools to manage and interpret these complex datasets. To conclude, we emphasize the importance of further developing and integrating spatial transcriptomics into CRC precision medicine strategies to enhance therapeutic targeting and improve patient outcomes.

SEMA7A-mediated juxtacrine stimulation of IGFBP-3 upregulates IL-17RB at pancreatic cancer invasive front

Tumor invasion is the hallmark of tumor malignancy. The invasive infiltration pattern of tumor cells located at the leading edge is highly correlated with metastasis and unfavorable patient outcomes. However, the regulatory mechanisms governing tumor malignancy at the invasive margin remain unclear. The IL-17B/IL-17RB pathway is known to promote pancreatic cancer invasion and metastasis, yet the specific mechanisms underlying IL-17RB upregulation during invasion are poorly understood. In this study, we unveiled a multistep process for IL-17RB upregulation at the invasive margin, which occurs through direct communication between tumor cells and fibroblasts. Tumor ATP1A1 facilitates plasma membrane expression of SEMA7A, which binds to and induces IGFBP-3 secretion from fibroblasts. The resulting gradient of IGFBP-3 influences the direction and enhances IL-17RB expression to regulate SNAI2 in invasion. These findings highlight the importance of local tumor-fibroblast interactions in promoting cancer cell invasiveness, potentially leading to the development of new therapeutic strategies targeting this communication.

Spatial transcriptomics in breast cancer reveals tumour microenvironment-driven drug responses and clonal therapeutic heterogeneity

Breast cancer patients are categorized into three subtypes with distinct treatment approaches. Precision oncology has increased patient outcomes by targeting the specific molecular alterations of tumours, yet challenges remain. Treatment failure persists due to the coexistence of several malignant subpopulations with different drug sensitivities within the same tumour, a phenomenon known as intratumour heterogeneity (ITH). This heterogeneity has been extensively studied from a tumour-centric view, but recent insights underscore the role of the tumour microenvironment in treatment response. Our research utilizes spatial transcriptomics data from breast cancer patients to predict drug sensitivity. We observe diverse response patterns across tumour, interphase and microenvironment regions, unveiling a sensitivity and functional gradient from the tumour core to the periphery. Moreover, we find tumour therapeutic clusters with different drug responses associated with distinct biological functions driven by unique ligand-receptor interactions. Importantly, we identify genetically identical subclones with different responses depending on their location within the tumour ducts. This research underscores the significance of considering the distance from the tumour core and microenvironment composition when identifying suitable treatments to target ITH. Our findings provide critical insights into optimizing therapeutic strategies, highlighting the necessity of a comprehensive understanding of tumour biology for effective cancer treatment.

Spatial Transcriptomics Unravel the Tissue Complexity of Oral Pathogenesis

Spatial transcriptomics (ST) is a cutting-edge methodology that enables the simultaneous profiling of global gene expression and spatial information within histological tissue sections. Traditional transcriptomic methods lack the spatial resolution required to sufficiently examine the complex interrelationships between cellular regions in diseased and healthy tissue states. We review the general workflows for ST, from specimen processing to ST data analysis and interpretations of the ST dataset using visualizations and cell deconvolution approaches. We show how recent studies used ST to explore the development or pathogenesis of specific craniofacial regions, including the cranium, palate, salivary glands, tongue, floor of mouth, oropharynx, and periodontium. Analyses of cranial suture patency and palatal fusion during development using ST identified spatial patterns of bone morphogenetic protein in sutures and osteogenic differentiation pathways in the palate, in addition to the discovery of several genes expressed at critical locations during craniofacial development. ST of salivary glands from patients with Sjögren's disease revealed co-localization of autoimmune antigens with ductal cells and a subpopulation of acinar cells that was specifically depleted by the dysregulated autoimmune response. ST of head and neck lesions, such as premalignant leukoplakia progressing to established oral squamous cell carcinomas, oral cancers with perineural invasions, and oropharyngeal lesions associated with HPV infection spatially profiled the complex tumor microenvironment, showing functionally important gene signatures of tumor cell differentiation, invasion, and nontumor cell dysregulation within patient biopsies. ST also enabled the localization of periodontal disease-associated gene expression signatures within gingival tissues, including genes involved in inflammation, and the discovery of a fibroblast subtype mediating the transition between innate and adaptive immune responses in periodontitis. The increased use of ST, especially in conjunction with single-cell analyses, promises to improve our understandings of craniofacial development and pathogenesis at unprecedented tissue-level resolution in both space and time.

Computational Methods for Image Analysis in Craniofacial Development and Disease

Observation is at the center of all biological sciences. Advances in imaging technologies are therefore essential to derive novel biological insights to better understand the complex workings of living systems. Recent high-throughput sequencing and imaging techniques are allowing researchers to simultaneously address complex molecular variations spatially and temporarily in tissues and organs. The availability of increasingly large dataset sizes has allowed for the evolution of robust deep learning models, designed to interrogate biomedical imaging data. These models are emerging as transformative tools in diagnostic medicine. Combined, these advances allow for dynamic, quantitative, and predictive observations of entire organisms and tissues. Here, we address 3 main tasks of bioimage analysis, image restoration, segmentation, and tracking and discuss new computational tools allowing for 3-dimensional spatial genomics maps. Finally, we demonstrate how these advances have been applied in studies of craniofacial development and oral disease pathogenesis.

Spatial dissection of tumour microenvironments in gastric cancers reveals the immunosuppressive crosstalk between CCL2+ fibroblasts and STAT3-activated macrophages

BACKGROUND

A spatially resolved, niche-level analysis of tumour microenvironments (TME) can provide insights into cellular interactions and their functional impacts in gastric cancers (GC).

OBJECTIVE

Our goal was to translate the spatial organisation of GC ecosystems into a functional landscape of cellular interactions involving malignant, stromal and immune cells.

DESIGN

We performed spatial transcriptomics on nine primary GC samples using the Visium platform to delineate the transcriptional landscape and dynamics of malignant, stromal and immune cells within the GC tissue architecture, highlighting cellular crosstalks and their functional consequences in the TME.

RESULTS

GC spatial transcriptomes with substantial cellular heterogeneity were delineated into six regional compartments. Specifically, the fibroblast-enriched TME upregulates epithelial-to-mesenchymal transformation and immunosuppressive response in malignant and TME cells, respectively. Cell type-specific transcriptional dynamics revealed that malignant and endothelial cells promote the cellular proliferations of TME cells, whereas the fibroblasts and immune cells are associated with procancer and anticancer immunity, respectively. Ligand-receptor analysis revealed that CCL2-expressing fibroblasts promote the tumour progression via JAK-STAT3 signalling and inflammatory response in tumour-infiltrated macrophages. CCL2+ fibroblasts and STAT3-activated macrophages are co-localised and their co-abundance was associated with unfavourable prognosis. We experimentally validated that CCL2+ fibroblasts recruit myeloid cells and stimulate STAT3 activation in recruited macrophages. The development of immunosuppressive TME by CCL2+ fibroblasts were also validated in syngeneic mouse models.

CONCLUSION

GC spatial transcriptomes revealed functional cellular crosstalk involving multiple cell types among which the interaction between CCL2+ fibroblasts and STAT3-activated macrophages plays roles in establishing immune-suppressive GC TME with potential clinical relevance.

Multi-omics analysis deciphers intercellular communication regulating oxidative stress to promote oral squamous cell carcinoma progression

Oral squamous cell carcinoma (OSCC) is a common malignant tumor in the head and neck, associated with high recurrence and poor prognosis. We performed an integrated analysis of single-cell RNA and spatial transcriptomic data from cancerous and normal tissues to create a comprehensive atlas of epithelial cells and cancer-associated fibroblasts (CAFs). Our findings show that AKR1C3+ epithelial cells, located at the tumor's stromal front, exhibit significant copy number variation and poor prognostic indicators, suggesting a role in tumor invasion. We also identified a distinct group of early-stage CAFs (named OSCC_Normal, characterized by ADH1B+, MFAP4+, and PLA2G2A+) that interact with AKR1C3+ cells, where OSCC_Normal may inhibit the FOXO1 redox switch in these epithelial cells via the IGF1/IGF1R pathway, causing oxidative stress overload. Conversely, AKR1C3+ cells use ITGA6/ITGB4 receptor to counteract the effects of OSCC_Normal, promoting cancer invasion. This study unveils complex interactions within the OSCC tumor microenvironment.

SPathDB: a comprehensive database of spatial pathway activity atlas

Spatial transcriptomics sequencing technology deepens our understanding of the diversity of cell behaviors, fates and states within complex tissue, which is often determined by the fine-tuning of regulatory network functional activities. Therefore, characterizing the functional activity within tissue space is helpful for revealing the functional features that drive spatial heterogeneity, and understanding complex biological processes. Here, we describe a database, SPathDB (http://bio-bigdata.hrbmu.edu.cn/SPathDB/), which aims to dissect the pathway-mediated multidimensional spatial heterogeneity in the context of functional activity. We manually curated spatial transcriptomics datasets and biological pathways from public data resources. SPathDB consists of 1689 868 spatial spots of 695 slices from 84 spatial transcriptome datasets of human and mouse, which involves 36 tissues, and also diseases such as cancer, and provides interactive analysis and visualization of the functional activities of 114 998 pathways across these spatial spots. SPathDB provides five flexible interfaces to retrieve and analyze pathways with highly variable functional activity across spatial spots, the distribution of pathway functional activities along pseudo-space axis, pathway-mediated spatial intercellular communications and the associations between spatial pathway functional activity and the occurrence of cell types. SPathDB will serve as a foundational resource for identifying functional features and elucidating underlying mechanisms of spatial heterogeneity.

Digital profiling of gene expression from histology images with linearized attention

Cancer is a heterogeneous disease requiring costly genetic profiling for better understanding and management. Recent advances in deep learning have enabled cost-effective predictions of genetic alterations from whole slide images (WSIs). While transformers have driven significant progress in non-medical domains, their application to WSIs lags behind due to high model complexity and limited dataset sizes. Here, we introduce SEQUOIA, a linearized transformer model that predicts cancer transcriptomic profiles from WSIs. SEQUOIA is developed using 7584 tumor samples across 16 cancer types, with its generalization capacity validated on two independent cohorts comprising 1368 tumors. Accurately predicted genes are associated with key cancer processes, including inflammatory response, cell cycles and metabolism. Further, we demonstrate the value of SEQUOIA in stratifying the risk of breast cancer recurrence and in resolving spatial gene expression at loco-regional levels. SEQUOIA hence deciphers clinically relevant information from WSIs, opening avenues for personalized cancer management.

Revealing molecular mechanisms of early-onset tongue cancer by spatial transcriptomics

Tongue cancer at a young age demonstrates an increase in incidence, aggressiveness, and poor response to therapy. Classic etiological factors for head and neck tumors such as tobacco, alcohol, and human papillomavirus are not related to early-onset tongue cancer. Mechanisms of development and progression of this cancer remain unclear. In this study, we performed spatial whole-transcriptome profiling of tongue cancer in young adults compared with older patients. Nine patients with tongue squamous cell carcinoma (T2-3N0-1M0) were included and divided into two groups: younger (n = 5) and older than 45 years (n = 4). Formalin-fixed paraffin-embedded (FFPE) and fresh frozen (FF) samples of tumor tissue from 4 young and 5 older patients, respectively, were used for spatial transcriptomic profiling using the 10 × Genomics Visium. The findings were validated using SeekGene single cell full-length RNA sequencing (1 young vs 1 older patient) and TCGA data (15 young vs 70 older patients). As a result, we performed the first successful integration of spatial transcriptomics data from FF and FFPE samples and revealed distinctive features of tongue cancer in young adults. Oxidative stress, vascular mimicry, and MAPK and JAK-STAT pathways were enriched in early-onset tongue cancer. Tumor microenvironment demonstrated increased gene signatures corresponding to myeloid-derived suppressor cells, tumor-associated macrophages, and plasma cells. The invasive front was accompanied by vascular mimicry with arrangement of tumor-associated macrophages and aggregations of plasma cells and lymphocytes organized into tertiary lymphoid structures. Taken together, these results indicate that early-onset tongue cancer has distinct transcriptomic features and molecular mechanisms compared to older patients.

Comprehensive mapping of somatotroph pituitary neuroendocrine tumour heterogeneity using spatial and single-cell transcriptomics

BACKGROUND

Pituitary neuroendocrine tumours (PitNETs) are common intracranial tumours that are highly heterogeneous with unpredictable growth patterns. The driver genes and mechanisms that are crucial for tumour progression in somatotroph PitNETs are poorly understood.

METHODS

In this study, we performed integrative spatial transcriptomics (ST) and single-cell RNA sequencing (scRNA-seq) analysis on somatotroph tumours and normal pituitary samples to comprehensively characterize the differences in cellular characteristics.

RESULTS

By analyzing combined copy number variations (CNVs), tumour tissues were divided into two regions, which included the CNVhigh and CNVlow areas. The protumour genes DLK1 and RCN1 were highly expressed in the CNVhigh area, which might be related to tumour progression and could be targeted for precision therapy. We also found that the transforming growth factor beta signalling pathway participated in tumour progression and identified heterogeneity in the expression profiles of key genes. We assessed the intertumoral and intratumoral heterogeneity in somatotroph PitNETs and emphasized the importance of individualized treatment.

CONCLUSION

In summary, we visualized the cellular distribution and transcriptional differences in normal pituitary and somatotroph PitNETs by ST and scRNA-seq for the first time. This study provides a strong theoretical foundation to comprehensively understand the crucial mechanisms involved in tumour progression and develop new strategies to treat somatotroph PitNETs.

KEY POINTS

The first-ever visualization of cellular distributions in normal and tumor pituitary tissues. The inter- and intra-tumoral transcriptomic heterogeneity of somatotroph PitNETs was comprehensively revealed. Identification of potential protumor factors and critical signaling pathways, opening new avenues for therapeutic intervention.

Spatial oncology: Translating contextual biology to the clinic

Microscopic examination of cells in their tissue context has been the driving force behind diagnostic histopathology over the past two centuries. Recently, the rise of advanced molecular biomarkers identified through single cell profiling has increased our understanding of cellular heterogeneity in cancer but have yet to significantly impact clinical care. Spatial technologies integrating molecular profiling with microenvironmental features are poised to bridge this translational gap by providing critical in situ context for understanding cellular interactions and organization. Here, we review how spatial tools have been used to study tumor ecosystems and their clinical applications. We detail findings in cell-cell interactions, microenvironment composition, and tissue remodeling for immune evasion and therapeutic resistance. Additionally, we highlight the emerging role of multi-omic spatial profiling for characterizing clinically relevant features including perineural invasion, tertiary lymphoid structures, and the tumor-stroma interface. Finally, we explore strategies for clinical integration and their augmentation of therapeutic and diagnostic approaches.

Phenotype remodelling of HNSCC cells in the muscle invasion environment

BACKGROUND

Tumour invading muscle in head and neck squamous cell carcinoma (HNSCC) is often associated with destructive growth and poor prognosis. However, the phenotypic functions and pathological mechanisms of muscle-invasive cancer cells in tumour progress remains unknown. In this study, we aimed to investigate the phenotypic functions of muscle-invasive cancer cells of HNSCC and their potential crosstalk with tumour microenvironment.

METHODS

We obtained scRNA-seq data (SC) from GSE103322 (N = 18) and GSE181919 (N = 37), spatial RNA-seq data (ST) from GSE208253 and GSE181300 (N = 4), transcriptomics of human HNSCC samples from GSE42743 (N = 12) and GSE41613 (N = 97). Utilizing the TCGA-HNSC dataset, we conducted univariate and multivariate Cox analyses to investigate the prognostic impact of muscle-invasion in HNSCC, with validation in an additional cohort. Through Stutility and AUCell approaches, we identified and characterized muscle-invasive cancer cell clusters, including their functional phenotypes and gene-specific profiles. Integration of SC and ST data was achieved using Seurat analysis, multimodal intersection analysis, and spatial deconvolution. The results were further validated via in vitro and in vivo experiments.

RESULTS

Our analyses of the TCGA-HNSC cohort revealed the presence of muscle-invasion was associated with a poor prognosis. By combining ST and SC, we identified muscle-invasive cancer cells exhibiting epithelial-to-mesenchymal transition (EMT) and myoepithelial-like transcriptional programs, which were correlated with a poor prognosis. Furthermore, we identified G0S2 as a novel marker of muscle-invasive malignant cells that potentially promotes EMT and the acquisition of myoepithelium-like phenotypes. These findings were validated through in vitro assays and chorioallantoic membranes experiments. Additionally, we demonstrated that G0S2-overexpressing cancer cells might attract human ECs via VEGF signalling. Subsequent in vitro and in vivo experiments revealed G0S2 plays key roles in promoting the proliferation and invasion of cancer cells.

CONCLUSIONS

In this study, we profiled the transcriptional programs of muscle-invasive HNSCC cell populations and characterized their EMT and myoepithelial-like phenotypes. Furthermore, our findings highlight the presence of muscle-invasion as a predictive marker for HNSCC patients. G0S2 as one of the markers of muscle-invasive cancer cells is involved in HNSCC intravasation, probably via VEGF signalling.

Biomarker discovery in hepatocellular carcinoma (HCC) for personalized treatment and enhanced prognosis

Hepatocellular carcinoma (HCC) is a leading contributor to cancer-related deaths worldwide and presents significant challenges in diagnosis and treatment due to its heterogeneous nature. The discovery of biomarkers has become crucial in addressing these challenges, promising early detection, precise diagnosis, and personalized treatment plans. Key biomarkers, such as alpha fetoprotein (AFP) glypican 3 (GPC3) and des gamma carboxy prothrombin (DCP) have shown potential in improving clinical results. Progress in proteomic technologies, including next-generation sequencing (NGS), mass spectrometry, and liquid biopsies detecting circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA), has deepened our understanding of HCC's molecular landscape. Immunological markers, like PD-L1 expression and tumor-infiltrating lymphocytes (TILs), also play a crucial role in guiding immunotherapy decisions. Despite these advancements, challenges remain in biomarker validation, standardization, integration into clinical practice, and cost-related barriers. Emerging technologies like single-cell sequencing and machine learning offer promising avenues for further exploration. Continued investment in research and collaboration among researchers, healthcare providers, and policymakers is vital to harness the potential of biomarkers fully, ultimately revolutionizing HCC management and improving patient outcomes through personalized treatment approaches.

Defining and modeling dynamic spatial heterogeneity within tumor microenvironments

Many solid tumors exhibit significant genetic, cellular, and biophysical heterogeneity which dynamically evolves during disease progression and after treatment. This constant flux in cell composition, phenotype, spatial relationships, and tissue properties poses significant challenges in accurately diagnosing and treating patients. Much of the complexity lies in unraveling the molecular changes in different tumor compartments, how they influence one another in space and time and where vulnerabilities exist that might be appropriate to target therapeutically. Recent advances in spatial profiling tools and technologies are enabling new insight into the underlying biology of complex tumors, creating a greater understanding of the intricate relationship between cell types, states, and the microenvironment. Here we reflect on some recent discoveries in this area, where the key knowledge and technology gaps lie, and the advancements in spatial measurements and in vitro models for the study of spatial intratumoral heterogeneity.

EBF1-COX4I2 signaling axis promotes a myofibroblast-like phenotype in cancer-associated fibroblasts (CAFs) and is associated with an immunosuppressive microenvironment

Immunotherapy has limited response rates in colorectal cancer (CRC) due to an immunosuppressive tumor microenvironment (TME). Combining transcriptome sequencing, clinical specimens, and functional experiments, we identified a unique group of CAF subpopulations (COX4I2 + ) with inhibited mitochondrial respiration and enhanced glycolysis. Through bioinformatics predictions and luciferase reporter assays, we determined that EBF1 can upstreamly regulate COX4I2 transcription. COX4I2 + CAFs functionally and phenotypically resemble myofibroblasts, are important for the formation of the fibrotic TME, and are capable of activating the M2 phenotype of macrophages. In vitro experiments demonstrated that COX4I2 + CAFs promote immunosuppressive TME by blocking CD8 + T cell infiltration and inducing CD8 + T cell dysfunction. Using multiple independent cohorts, we also found a strong correlation between the immunotherapy response rate of CRC patients and COX4I2 expression in their tumors. Our results identify a CAF subpopulation characterized by activation of the EBF1-COX4I2 axis, and this group of CAFs can be targeted to improve cancer immunotherapy outcomes.

SpaTopic: A statistical learning framework for exploring tumor spatial architecture from spatially resolved transcriptomic data

Tumor tissues exhibit a complex spatial architecture within the tumor microenvironment (TME). Spatially resolved transcriptomics (SRT) is promising for unveiling the spatial structures of the TME at both cellular and molecular levels, but identifying pathology-relevant spatial domains remains challenging. Here, we introduce SpaTopic, a statistical learning framework that harmonizes spot clustering and cell-type deconvolution by integrating single-cell transcriptomics and SRT data. Through topic modeling, SpaTopic stratifies the TME into spatial domains with coherent cellular organization, facilitating refined annotation of the spatial architecture with improved performance. We assess SpaTopic across various tumor types and show accurate prediction of tertiary lymphoid structures and tumor boundaries. Moreover, marker genes derived from SpaTopic are transferrable and can be applied to mark spatial domains in other datasets. In addition, SpaTopic enables quantitative comparison and functional characterization of spatial domains across SRT datasets. Overall, SpaTopic presents an innovative analytical framework for exploring, comparing, and interpreting tumor SRT data.

A robust statistical approach for finding informative spatially associated pathways

Spatial transcriptomics offers deep insights into cellular functional localization and communication by mapping gene expression to spatial locations. Traditional approaches that focus on selecting spatially variable genes often overlook the complexity of biological pathways and the interactions among genes. Here, we introduce a novel framework that shifts the focus towards directly identifying functional pathways associated with spatial variability by adapting the Brownian distance covariance test in an innovative manner to explore the heterogeneity of biological functions over space. Unlike most other methods, this statistical testing approach is free of gene selection and parameter selection and allows nonlinear and complex dependencies. It allows for a deeper understanding of how cells coordinate their activities across different spatial domains through biological pathways. By analyzing real human and mouse datasets, the method found significant pathways that were associated with spatial variation, as well as different pathway patterns among inner- and edge-cancer regions. This innovative framework offers a new perspective on analyzing spatial transcriptomic data, contributing to our understanding of tissue architecture and disease pathology. The implementation is publicly available at https://github.com/tianlq-prog/STpathway.

Detecting anomalous anatomic regions in spatial transcriptomics with STANDS

Detection and Dissection of Anomalous Tissue Domains (DDATD) from multi-sample spatial transcriptomics (ST) data provides unprecedented opportunities to characterize anomalous tissue domains (ATDs), revealing both population-level and individual-specific pathogenic factors for understanding pathogenic heterogeneities behind diseases. However, no current methods can perform de novo DDATD from ST data, especially in the multi-sample context. Here, we introduce STANDS, an innovative framework based on Generative Adversarial Networks which integrates three core tasks in multi-sample DDATD: detecting, aligning, and subtyping ATDs. STANDS incorporates multimodal-learning, transfer-learning, and style-transfer techniques to effectively address major challenges in multi-sample DDATD, including complications caused by unalignable ATDs, under-utilization of multimodal information, and scarcity of normal ST datasets necessary for comparative analysis. Extensive benchmarks from diverse datasets demonstrate STAND's superiority in identifying both common and individual-specific ATDs and further dissecting them into biologically distinct subdomains. STANDS also provides clues to developing ATDs visually indistinguishable from surrounding normal tissues.

Spatial microenvironments tune immune response dynamics in the Drosophila larval fat body

Immune responses in tissues display intricate patterns of gene expression that vary across space and time. While such patterns have been increasingly linked to disease outcomes, the mechanisms that generate them and the logic behind them remain poorly understood. As a tractable model of spatial immune responses, we investigated heterogeneous expression of antimicrobial peptides in the larval fly fat body, an organ functionally analogous to the liver. To capture the dynamics of immune response across the full tissue at single-cell resolution, we established live light sheet fluorescence microscopy of whole larvae. We discovered that expression of antimicrobial peptides occurs in a reproducible spatial pattern, with enhanced expression in the anterior and posterior lobes of the fat body. This pattern correlates with microbial localization via blood flow but is not caused by it: loss of heartbeat suppresses microbial transport but leaves the expression pattern unchanged. This result suggests that regions of the tissue most likely to encounter microbes via blood flow are primed to produce antimicrobials. Spatial transcriptomics revealed that these immune microenvironments are defined by genes spanning multiple biological processes, including lipid-binding proteins that regulate host cell death by the immune system. In sum, the larval fly fat body exhibits spatial compartmentalization of immune activity that resembles the strategic positioning of immune cells in mammals, such as in the liver, gut, and lymph nodes. This finding suggests that tissues may share a conserved spatial organization that optimizes immune responses for antimicrobial efficacy while preventing excessive self-damage.

The impact of elective cervical lymph node treatment on the tumour immune response in head and neck squamous cell carcinoma: time for a change in treatment strategy?

The elective ablation of cervical lymph nodes, via surgery or irradiation, is a mainstay in the treatment of head and neck squamous cell carcinoma (HNSCC). In this setting, the decision to treat the clinically node negative neck is based upon risk analysis of various factors, primarily derived from tumour features. However, the impact of ablation of tumour-draining lymph nodes upon the tumour-immune response and immunocompetence is largely unknown. In this review we highlight recent evidence of the communication between tumour and tumour-draining lymph nodes and the fundamental importance of this axis. We will provide a perspective of how recent cancer biology discoveries may juxtapose with current treatment pathways, with potential translational line of site for future research. In particular, neo-adjuvant therapy or biomarkers from tumour-draining lymph nodes may present opportunities to preserve lymphatics and harness improved immunocompetence in HNSCC patients.

PoweREST: Statistical Power Estimation for Spatial Transcriptomics Experiments to Detect Differentially Expressed Genes Between Two Conditions

Recent advancements in Spatial Transcriptomics (ST) have significantly enhanced biological research in various domains. However, the high cost of current ST data generation techniques restricts its application in large-scale population studies. Consequently, there is a pressing need to maximize the use of available resources to achieve robust statistical power. One fundamental question in ST analysis is to detect differentially expressed genes (DEGs) among different conditions using ST data. Such DEG analysis is often performed but the associated power calculation is rarely discussed in the literature. To address this gap, we introduce, PoweREST (https://github.com/lanshui98/PoweREST), a power estimation tool designed to support power calculation of DEG detection with 10X Genomics Visium data. PoweREST enables power estimation both before any ST experiments or after preliminary data are collected, making it suitable for a wide variety of power analyses in ST studies. We also provide a user-friendly, program-free web application (https://lanshui.shinyapps.io/PoweREST/), allowing users to interactively calculate and visualize the study power along with relevant the parameters.

The extracellular matrix in solid tumor immunotherapy

The extracellular matrix (ECM) of solid tumors impacts the antitumor activities of CD8+ T and natural killer (NK) cells in a variety of ways. Cell motility is restricted by the tumor ECM which creates physical barriers. The tumor ECM directly alter the phenotypes and functions of cytotoxic lymphocytes, and indirectly influences immunological synapse-mediated interactions between cytotoxic lymphocytes and cancer cells. Therefore, strategies to improve solid tumor immunotherapy should be established by considering complex ternary interactions between cytotoxic lymphocytes, cancer cells, and the tumor ECM. Novel bioengineering tools approximating key characteristics of the tumor ECM, such as in vitro reconstituted 3D ECMs and microfluidics are valuable from a fundamental study viewpoint and from a translational perspective, aiming to enable systematic screening approaches.

The spatial distribution of intermediate fibroblasts and myeloid-derived cells dictate lymph node metastasis dynamics in oral cancer

BACKGROUND

Oral cancer poses a significant health challenge due to limited treatment protocols and therapeutic targets. We aimed to investigate the invasive margins of gingivo-buccal oral squamous cell carcinoma (GB-OSCC) tumors in terms of the localization of genes and cell types within the margins at various distances that could lead to nodal metastasis.

METHODS

We collected tumor tissues from 23 resected GB-OSCC samples for gene expression profiling using digital spatial transcriptomics. We monitored differential gene expression at varying distances between the tumor and its microenvironvent (TME), and performed a deconvulation study and immunohistochemistry to identify the cells and genes regulating the TME.

RESULTS

We found that the tumor-stromal interface (a distance up to 200 µm between tumor and immune cells) is the most active region for disease progression in GB-OSCC. The most differentially expressed apex genes, such as FN1 and COL5A1, were located at the stromal ends of the margins, and together with enrichment of the extracellular matrix (ECM) and an immune-suppressed microenvironment, were associated with lymph node metastasis. Intermediate fibroblasts, myocytes, and neutrophils were enriched at the tumor ends, while cancer-associated fibroblasts (CAFs) were enriched at the stromal ends. The intermediate fibroblasts transformed into CAFs and relocated to the adjacent stromal ends where they participated in FN1-mediated ECM modulation.

CONCLUSION

We have generated a functional organization of the tumor-stromal interface in GB-OSCC and identified spatially located genes that contribute to nodal metastasis and disease progression. Our dataset might now be mined to discover suitable molecular targets in oral cancer.

Single-cell transcriptome profiles the heterogeneity of tumor cells and microenvironments for different pathological endometrial cancer and identifies specific sensitive drugs

Endometrial cancer (EC) is a highly heterogeneous malignancy characterized by varied pathology and prognoses, and the heterogeneity of its cancer cells and the tumor microenvironment (TME) remains poorly understood. We conducted single-cell RNA sequencing (scRNA-seq) on 18 EC samples, encompassing various pathological types to delineate their specific unique transcriptional landscapes. Cancer cells from diverse pathological sources displayed distinct hallmarks labeled as immune-modulating, proliferation-modulating, and metabolism-modulating cancer cells in uterine clear cell carcinomas (UCCC), well-differentiated endometrioid endometrial carcinomas (EEC-I), and uterine serous carcinomas (USC), respectively. Cancer cells from the UCCC exhibited the greatest heterogeneity. We also identified potential effective drugs and confirmed their effectiveness using patient-derived EC organoids for each pathological group. Regarding the TME, we observed that prognostically favorable CD8+ Tcyto and NK cells were prominent in normal endometrium, whereas CD4+ Treg, CD4+ Tex, and CD8+ Tex cells dominated the tumors. CXCL3+ macrophages associated with M2 signature and angiogenesis were exclusively found in tumors. Prognostically relevant epithelium-specific cancer-associated fibroblasts (eCAFs) and SOD2+ inflammatory CAFs (iCAFs) predominated in EEC-I and UCCC groups, respectively. We also validated the oncogenic effects of SOD2+ iCAFs in vitro. Our comprehensive study has yielded deeper insights into the pathogenesis of EC, potentially facilitating personalized treatments for its varied pathological types.

Integrating spatially-resolved transcriptomics data across tissues and individuals: challenges and opportunities

Advances in spatially-resolved transcriptomics (SRT) technologies have propelled the development of new computational analysis methods to unlock biological insights. As the cost of generating these data decreases, these technologies provide an exciting opportunity to create large-scale atlases that integrate SRT data across multiple tissues, individuals, species, or phenotypes to perform population-level analyses. Here, we describe unique challenges of varying spatial resolutions in SRT data, as well as highlight the opportunities for standardized preprocessing methods along with computational algorithms amenable to atlas-scale datasets leading to improved sensitivity and reproducibility in the future.

Open-ST: High-resolution spatial transcriptomics in 3D

Spatial transcriptomics (ST) methods unlock molecular mechanisms underlying tissue development, homeostasis, or disease. However, there is a need for easy-to-use, high-resolution, cost-efficient, and 3D-scalable methods. Here, we report Open-ST, a sequencing-based, open-source experimental and computational resource to address these challenges and to study the molecular organization of tissues in 2D and 3D. In mouse brain, Open-ST captured transcripts at subcellular resolution and reconstructed cell types. In primary head-and-neck tumors and patient-matched healthy/metastatic lymph nodes, Open-ST captured the diversity of immune, stromal, and tumor populations in space, validated by imaging-based ST. Distinct cell states were organized around cell-cell communication hotspots in the tumor but not the metastasis. Strikingly, the 3D reconstruction and multimodal analysis of the metastatic lymph node revealed spatially contiguous structures not visible in 2D and potential biomarkers precisely at the 3D tumor/lymph node boundary. All protocols and software are available at https://rajewsky-lab.github.io/openst.

Assessment of Concentration KRT6 Proteins in Tumor and Matching Surgical Margin from Patients with Head and Neck Squamous Cell Carcinoma

Head and neck squamous cell carcinomas (HNSCCs) are one of the most frequently detected cancers in the world; not all mechanisms related to the expression of keratin in this type of cancer are known. The aim of this study was to evaluate type II cytokeratins (KRT): KRT6A, KRT6B, and KRT6C protein concentrations in 54 tumor and margin samples of head and neck squamous cell carcinoma (HNSCC). Moreover, we examined a possible association between protein concentration and the clinical and demographic variables. Protein concentrations were measured using enzyme-linked immunosorbent assay (ELISA). Significantly higher KRT6A protein concentration was found in HNSCC samples compared to surgical margins. An inverse relationship was observed for KRT6B and KRT6C proteins. We showed an association between the KRT6C protein level and clinical parameters T and N in tumor and margin samples. When analyzing the effect of smoking and drinking on KRT6A, KRT6B, and KRT6C levels, we demonstrated a statistically significant difference between regular or occasional tobacco and alcohol habits and patients who do not have any tobacco and alcohol habits in tumor and margin samples. Moreover, we found an association between KRT6B and KRT6C concentration and proliferative index Ki-67 and HPV status in tumor samples. Our results showed that concentrations of KRT6s were different in the tumor and the margin samples and varied in relation to clinical and demographic parameters. We add information to the current knowledge about the role of KRT6s isoforms in HNSCC. We speculate that variations in the studied isoforms of the KRT6 protein could be due to the presence and development of the tumor and its microenvironment. It is important to note that the analyses were performed in tumor and surgical margins and can provide more accurate information on the function in normal and cancer cells and regulation in response to various factors.

Development and validation of a novel disulfidptosis-related lncRNAs signature in patients with HPV-negative oral squamous cell carcinoma

Disulfidptosis is a recently identified mode of regulated cell death. Regulating disulfidptosis in carcinoma is a promising therapeutic approach. Long non-coding RNAs (lncRNAs) have been reported to be related to the occurrence and development of many cancers. Disulfidptosis-related lncRNAs (DRLs) in HPV-negative oral squamous cell carcinoma (OSCC) have not been studied. Based on The Cancer Genome Atlas (TCGA) database, least absolute shrinkage selection operator (LASSO) analysis and Cox regression analysis were used to identify overall survival related DRLs and construct the signature. Kaplan-Meier, time-dependent receiver operating characteristics (ROC) and principal component analyses (PCA) were explored to demonstrate the prediction potential of the signature. Subgroup analysis stratified by different clinicopathological characteristics were conducted. Nomogram was established by DRLs signature and independent clinicopathological characteristics. The calibration plots were performed to reveal the accuracy of nomogram. Immune cell subset infiltration, immunotherapy response, drug sensitivity analysis, and tumor mutation burden (TMB) were conducted. Underlying functions and pathways were explored by Gene Set Enrichment Analysis (GSEA) analysis. Previous lncRNA signatures of OSCC were retrieved from PubMed for further validation. Gene expression omnibus (GEO) datasets (GSE41613 and GSE85446) were merged as an external validation for DRLs signature. Consensus clustering analysis of DRLs signature and experimental validation of DRLs were also explored. This research sheds light on the robust performance of DRLs signature in survival prediction, immune cell infiltration, immune escape, and immunotherapy of HPV-negative OSCC.

STimage-1K4M: A histopathology image-gene expression dataset for spatial transcriptomics

Recent advances in multi-modal algorithms have driven and been driven by the increasing availability of large image-text datasets, leading to significant strides in various fields, including computational pathology. However, in most existing medical image-text datasets, the text typically provides high-level summaries that may not sufficiently describe sub-tile regions within a large pathology image. For example, an image might cover an extensive tissue area containing cancerous and healthy regions, but the accompanying text might only specify that this image is a cancer slide, lacking the nuanced details needed for in-depth analysis. In this study, we introduce STimage-1K4M, a novel dataset designed to bridge this gap by providing genomic features for sub-tile images. STimage-1K4M contains 1,149 images derived from spatial transcriptomics data, which captures gene expression information at the level of individual spatial spots within a pathology image. Specifically, each image in the dataset is broken down into smaller sub-image tiles, with each tile paired with 15,000 - 30,000 dimensional gene expressions. With 4,293,195 pairs of sub-tile images and gene expressions, STimage-1K4M offers unprecedented granularity, paving the way for a wide range of advanced research in multi-modal data analysis an innovative applications in computational pathology, and beyond.

Spatial transcriptomics in cancer research and potential clinical impact: a narrative review

Spatial transcriptomics (ST) provides novel insights into the tumor microenvironment (TME). ST allows the quantification and illustration of gene expression profiles in the spatial context of tissues, including both the cancer cells and the microenvironment in which they are found. In cancer research, ST has already provided novel insights into cancer metastasis, prognosis, and immunotherapy responsiveness. The clinical precision oncology application of next-generation sequencing (NGS) and RNA profiling of tumors relies on bulk methods that lack spatial context. The ability to preserve spatial information is now possible, as it allows us to capture tumor heterogeneity and multifocality. In this narrative review, we summarize precision oncology, discuss tumor sequencing in the clinic, and review the available ST research methods, including seqFISH, MERFISH (Vizgen), CosMx SMI (NanoString), Xenium (10x), Visium (10x), Stereo-seq (STOmics), and GeoMx DSP (NanoString). We then review the current ST literature with a focus on solid tumors organized by tumor type. Finally, we conclude by addressing an important question: how will spatial transcriptomics ultimately help patients with cancer?

Spatial Transcriptomics: A Powerful Tool in Disease Understanding and Drug Discovery

Recent advancements in modern science have provided robust tools for drug discovery. The rapid development of transcriptome sequencing technologies has given rise to single-cell transcriptomics and single-nucleus transcriptomics, increasing the accuracy of sequencing and accelerating the drug discovery process. With the evolution of single-cell transcriptomics, spatial transcriptomics (ST) technology has emerged as a derivative approach. Spatial transcriptomics has emerged as a hot topic in the field of omics research in recent years; it not only provides information on gene expression levels but also offers spatial information on gene expression. This technology has shown tremendous potential in research on disease understanding and drug discovery. In this article, we introduce the analytical strategies of spatial transcriptomics and review its applications in novel target discovery and drug mechanism unravelling. Moreover, we discuss the current challenges and issues in this research field that need to be addressed. In conclusion, spatial transcriptomics offers a new perspective for drug discovery.

Insights and Opportunity Costs in Applying Spatial Biology to Study the Tumor Microenvironment

SUMMARY

The recent development of high-dimensional spatial omics tools has revealed the functional importance of the tumor microenvironment in driving tumor progression. Here, we discuss practical factors to consider when designing a spatial biology cohort and offer perspectives on the future of spatial biology research.

Redefining clinical practice through spatial profiling: a revolution in tissue analysis

Spatial biology, which combines molecular biology and advanced imaging, enhances our understanding of tissue cellular organisation. Despite its potential, spatial omics encounters challenges related to data complexity, computational requirements and standardisation of analysis. In clinical applications, spatial omics has the potential to revolutionise biomarker discovery, disease stratification and personalised treatments. It can identify disease-specific cell patterns, and could help risk stratify patients for clinical trials and disease-appropriate therapies. Although there are challenges in adopting it in clinical practice, spatial omics has the potential to significantly enhance patient outcomes. In this paper, we discuss the recent evolution of spatial biology, and its potential for improving our tissue level understanding and treatment of disease, to help advance precision and effectiveness in healthcare interventions.

Review of Molecular Technologies for Investigating Canine Cancer

Genetic molecular testing is starting to gain traction as part of standard clinical practice for dogs with cancer due to its multi-faceted benefits, such as potentially being able to provide diagnostic, prognostic and/or therapeutic information. However, the benefits and ultimate success of genomic analysis in the clinical setting are reliant on the robustness of the tools used to generate the results, which continually expand as new technologies are developed. To this end, we review the different materials from which tumour cells, DNA, RNA and the relevant proteins can be isolated and what methods are available for interrogating their molecular profile, including analysis of the genetic alterations (both somatic and germline), transcriptional changes and epigenetic modifications (including DNA methylation/acetylation and microRNAs). We also look to the future and the tools that are currently being developed, such as using artificial intelligence (AI) to identify genetic mutations from histomorphological criteria. In summary, we find that the molecular genetic characterisation of canine neoplasms has made a promising start. As we understand more of the genetics underlying these tumours and more targeted therapies become available, it will no doubt become a mainstay in the delivery of precision veterinary care to dogs with cancer.

Molecular, Metabolic, and Subcellular Mapping of the Tumor Immune Microenvironment via 3D Targeted and Non-Targeted Multiplex Multi-Omics Analyses

Most platforms used for the molecular reconstruction of the tumor-immune microenvironment (TIME) of a solid tumor fail to explore the spatial context of the three-dimensional (3D) space of the tumor at a single-cell resolution, and thus lack information about cell-cell or cell-extracellular matrix (ECM) interactions. To address this issue, a pipeline which integrated multiplex spatially resolved multi-omics platforms was developed to identify crosstalk signaling networks among various cell types and the ECM in the 3D TIME of two FFPE (formalin-fixed paraffin embedded) gynecologic tumor samples. These platforms include non-targeted mass spectrometry imaging (glycans, metabolites, and peptides) and Stereo-seq (spatial transcriptomics) and targeted seqIF (IHC proteomics). The spatially resolved imaging data in a two- and three-dimensional space demonstrated various cellular neighborhoods in both samples. The collection of spatially resolved analytes in a voxel (3D pixel) across serial sections of the tissue was also demonstrated. Data collected from this analytical pipeline were used to construct spatial 3D maps with single-cell resolution, which revealed cell identity, activation, and energized status. These maps will provide not only insights into the molecular basis of spatial cell heterogeneity in the TIME, but also novel predictive biomarkers and therapeutic targets, which can improve patient survival rates.

The Reign of Follistatin in Tumors and Their Microenvironment: Implications for Drug Resistance

Follistatin (FST) is a potent neutralizer of the transforming growth factor-β superfamily and is associated with normal cellular programs and various hallmarks of cancer, such as proliferation, migration, angiogenesis, and immune evasion. The aberrant expression of FST by solid tumors is a well-documented observation, yet how FST influences tumor progression and therapy response remains unclear. The recent surge in omics data has revealed new insights into the molecular foundation underpinning tumor heterogeneity and its microenvironment, offering novel precision medicine-based opportunities to combat cancer. In this review, we discuss these recent FST-centric studies, thereby offering an updated perspective on the protean role of FST isoforms in shaping the complex cellular ecosystem of tumors and in mediating drug resistance.

SRT-Server: powering the analysis of spatial transcriptomic data

BACKGROUND

Spatial resolved transcriptomics (SRT) encompasses a rapidly developing set of technologies that enable the measurement of gene expression in tissue while retaining spatial localization information. SRT technologies and the enabled SRT studies have provided unprecedent insights into the structural and functional underpinnings of complex tissues. As SRT technologies have advanced and an increasing number of SRT studies have emerged, numerous sophisticated statistical and computational methods have been developed to facilitate the analysis and interpretation of SRT data. However, despite the growing popularity of SRT studies and the widespread availability of SRT analysis methods, analysis of large-scale and complex SRT datasets remains challenging and not easily accessible to researchers with limited statistical and computational backgrounds.

RESULTS

Here, we present SRT-Server, the first webserver designed to carry out comprehensive SRT analyses for a wide variety of SRT technologies while requiring minimal prior computational knowledge. Implemented with cutting-edge web development technologies, SRT-Server is user-friendly and features multiple analytic modules that can perform a range of SRT analyses. With a flowchart-style interface, these different analytic modules on the SRT-Server can be dragged into the main panel and connected to each other to create custom analytic pipelines. SRT-Server then automatically executes the desired analyses, generates corresponding figures, and outputs results-all without requiring prior programming knowledge. We demonstrate the advantages of SRT-Server through three case studies utilizing SRT data collected from two common platforms, highlighting its versatility and values to researchers with varying analytic expertise.

CONCLUSIONS

Overall, SRT-Server presents a user-friendly, efficient, effective, secure, and expandable solution for SRT data analysis, opening new doors for researchers in the field. SRT-Server is freely available at https://spatialtranscriptomicsanalysis.com/ .

Digital profiling of cancer transcriptomes from histology images with grouped vision attention

Cancer is a heterogeneous disease that demands precise molecular profiling for better understanding and management. Recently, deep learning has demonstrated potentials for cost-efficient prediction of molecular alterations from histology images. While transformer-based deep learning architectures have enabled significant progress in non-medical domains, their application to histology images remains limited due to small dataset sizes coupled with the explosion of trainable parameters. Here, we develop SEQUOIA, a transformer model to predict cancer transcriptomes from whole-slide histology images. To enable the full potential of transformers, we first pre-train the model using data from 1,802 normal tissues. Then, we fine-tune and evaluate the model in 4,331 tumor samples across nine cancer types. The prediction performance is assessed at individual gene levels and pathway levels through Pearson correlation analysis and root mean square error. The generalization capacity is validated across two independent cohorts comprising 1,305 tumors. In predicting the expression levels of 25,749 genes, the highest performance is observed in cancers from breast, kidney and lung, where SEQUOIA accurately predicts the expression of 11,069, 10,086 and 8,759 genes, respectively. The accurately predicted genes are associated with the regulation of inflammatory response, cell cycles and metabolisms. While the model is trained at the tissue level, we showcase its potential in predicting spatial gene expression patterns using spatial transcriptomics datasets. Leveraging the prediction performance, we develop a digital gene expression signature that predicts the risk of recurrence in breast cancer. SEQUOIA deciphers clinically relevant gene expression patterns from histology images, opening avenues for improved cancer management and personalized therapies.

Deciphering head and neck cancer microenvironment: Single-cell and spatial transcriptomics reveals human papillomavirus-associated differences

Human papillomavirus (HPV) is a major causative factor of head and neck squamous cell carcinoma (HNSCC), and the incidence of HPV- associated HNSCC is increasing. The role of tumor microenvironment in viral infection and metastasis needs to be explored further. We studied the molecular characteristics of primary tumors (PTs) and lymph node metastatic tumors (LNMTs) by stratifying them based on their HPV status. Eight samples for single-cell RNA profiling and six samples for spatial transcriptomics (ST), composed of matched primary tumors (PT) and lymph node metastases (LNMT), were collected from both HPV- negative (HPV- ) and HPV-positive (HPV+ ) patients. Using the 10x Genomics Visium platform, integrative analyses with single-cell RNA sequencing were performed. Intracellular and intercellular alterations were analyzed, and the findings were confirmed using experimental validation and publicly available data set. The HPV+ tissues were composed of a substantial amount of lymphoid cells regardless of the presence or absence of metastasis, whereas the HPV- tissue exhibited remarkable changes in the number of macrophages and plasma cells, particularly in the LNMT. From both single-cell RNA and ST data set, we discovered a central gene, pyruvate kinase muscle isoform 1/2 (PKM2), which is closely associated with the stemness of cancer stem cell-like populations in LNMT of HPV- tissue. The consistent expression was observed in HPV- HNSCC cell line and the knockdown of PKM2 weakened spheroid formation ability. Furthermore, we found an ectopic lymphoid structure morphology and clinical effects of the structure in ST slide of the HPV+ patients and verified their presence in tumor tissue using immunohistochemistry. Finally, the ephrin-A (EPHA2) pathway was detected as important signals in angiogenesis for HPV- patients from single-cell RNA and ST profiles, and knockdown of EPHA2 declined the cell migration. Our study described the distinct cellular composition and molecular alterations in primary and metastatic sites in HNSCC patients based on their HPV status. These results provide insights into HNSCC biology in the context of HPV infection and its potential clinical implications.

DDX5 Functions as a Tumor Suppressor in Tongue Cancer

DEAD-box polypeptide 5 (DDX5), a DEAD-box RNA helicase, is a multifunctional protein that plays important roles in many physiological and pathological processes. Contrary to its documented oncogenic role in a wide array of cancers, we herein demonstrate that DDX5 serves as a tumor suppressor in tongue cancer. The high expression of DDX5 is correlated with better prognosis for clinical tongue cancer patients. DDX5 downregulates the genes associated with tongue cancer progression. The knockdown of DDX5 promotes, while the overexpression of DDX5 inhibits, tongue cancer proliferation, development, and cisplatin resistance. Furthermore, the expression of DDX5 in tongue cancer is associated with immune cell infiltration in the tumor microenvironment. Specifically, the expression of DDX5 is associated with the reduced infiltration of M2 macrophages and increased infiltration of T cell clusters, which may contribute to anticancer effects in the tumor microenvironment. In this study, we establish DDX5 as a valuable prognostic biomarker and an important tumor suppressor in tongue cancer.

Macrophage-driven cardiac inflammation and healing: insights from homeostasis and myocardial infarction

Early and prompt reperfusion therapy has markedly improved the survival rates among patients enduring myocardial infarction (MI). Nonetheless, the resulting adverse remodeling and the subsequent onset of heart failure remain formidable clinical management challenges and represent a primary cause of disability in MI patients worldwide. Macrophages play a crucial role in immune system regulation and wield a profound influence over the inflammatory repair process following MI, thereby dictating the degree of myocardial injury and the subsequent pathological remodeling. Despite numerous previous biological studies that established the classical polarization model for macrophages, classifying them as either M1 pro-inflammatory or M2 pro-reparative macrophages, this simplistic categorization falls short of meeting the precision medicine standards, hindering the translational advancement of clinical research. Recently, advances in single-cell sequencing technology have facilitated a more profound exploration of macrophage heterogeneity and plasticity, opening avenues for the development of targeted interventions to address macrophage-related factors in the aftermath of MI. In this review, we provide a summary of macrophage origins, tissue distribution, classification, and surface markers. Furthermore, we delve into the multifaceted roles of macrophages in maintaining cardiac homeostasis and regulating inflammation during the post-MI period.