Advances and limitations in angiogenesis assays: Integrating in vitro, in vivo, and emerging technologies.

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2570 Background: TIGIT is a promising emerging immunotherapeutic target. However, the specific sources of TIGIT expression within the tumor microenvironment are largely unknown. Here, we present an AI-powered spatial tumor-infiltrating lymphocyte (TIL) analyzer, Lunit SCOPE IO, to integrate image analysis from whole slide images with single-cell molecular profiling. Methods: We used The Cancer Genome Atlas (TCGA) RNA expression data across 23 cancer types (n=6,930). Lunit SCOPE IO was developed, trained, and validated based on >17k H&E whole-slide images, to segment cancer area (CA) and cancer-associated stroma (CS) and to detect tumor cells and TILs. The intra-tumoral TIL, stromal TIL, and tumor cell purity (TCP) in the CA+CS area were calculated. The public spatial transcriptomics (ST) dataset for breast cancer was downloaded from the 10X Visium web page. Lunit SCOPE IO was applied to the associated H&E WSIs to match distinct TIGIT expression to single cells identified in the WSIs. Results: TIGIT was highly expressed in TGCT (3.45±0.11; median±SEM), LUAD (3.07±0.05), and HNSC (2.89±0.06), and was highly enriched in samples with microsatellite instability-high or tumor mutational burden-high (≥ 10/Mb) compared to those without them (fold change = 1.30, p < 0.001). At a macroscopic, bulk-level in the TCGA dataset, TIGIT expression was positively correlated with intra-tumoral TIL density (R=0.37, p<0.001) and stromal TIL density (R=0.42, p<0.001), but it was negatively correlated with TCP (R=-0.27, p<0.001). Lunit SCOPE IO analyzed the images from ST analysis and calculated intra-tumoral TIL, stromal TIL, and TCP of each region of interest, containing 2 (IQR 0-7) cells. Interestingly, at a microscopic, cell-level, TIGIT expression was still higher in areas of enriched stromal TIL (P < 0.001) and lower in tumor cell-dense areas, but it was not significantly correlated with enriched intra-tumoral TIL areas, meaning that TIGIT expression is likely derived from the excluded TILs in the CS area. Conclusions: Interactive analysis of spatial transcriptomics with AI-powered pathology image analysis revealed that TIGIT expression in the tumor microenvironment is exclusive to confined areas with stromal TIL enrichment, reflecting the exclusion of TIL from the tumor nest. [Table: see text]

BackgroundInterferons (IFNs) are key cytokines that drive immune responses against infections and cancer, yet few therapies have successfully leveraged IFN signaling for cancer treatment. Long noncoding RNAs (lncRNAs) are emerging as promising therapeutic candidates, but their roles in immune modulation remain largely unexplored. Here, we functionally characterize a breast cancer-associated lncRNA, BRRIAR, which primes the IFN signaling pathway in specific cancer contexts and represents a potential therapeutic strategy for estrogen receptor-positive (ER+) breast cancer.MethodsBRRIAR expression and subcellular localization were examined using qPCR, in situ hybridization, single-cell RNA sequencing and spatial transcriptomics. BRRIAR target genes were identified through CRISPR interference, chromatin interaction assays and ChIP sequencing. Mechanistic studies in ER + breast cancer cells included CRISPR-Cas9 genome-wide screens, RNA sequencing, RNA pull-down followed by mass spectrometry, proliferation assays and Western blotting. The therapeutic potential of BRRIAR was evaluated via intratumoral delivery of lipid nanoparticle-encapsulated BRRIAR in ER + breast cancer xenograft models. Immune activation was assessed using flow cytometry and cytokine profiling of human peripheral blood mononuclear cells (PBMCs).ResultsWe demonstrate that BRRIAR is a key target gene at the 3p26 breast cancer risk region. Primarily expressed in ER + breast tumors, BRRIAR acts both in cis and in trans. Nuclear BRRIAR regulates BHLHE40 expression in cis through chromatin interactions, while cytoplasmic BRRIAR binds in trans to the pattern recognition receptor RIG-I, priming IFN signaling. Overexpression of BRRIAR RNA triggers RIG-I signaling, inducing IFN responses, drives rapid, dose-dependent apoptosis of ER + breast cancer cells in vitro and in vivo, and promotes immune activation in human PBMCs.ConclusionsThese findings establish lncRNAs as key regulators of tumor immunity and uncover a critical link between genetic risk, lncRNAs, cancer immunosurveillance and breast cancer development, positioning BRRIAR as a promising lncRNA-based RIG-I activator for ER + breast cancer therapy.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12943-025-02510-8.

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

Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST), as advanced omics technologies, have addressed critical challenges in liver transplantation (LT), the most effective treatment for end-stage liver disease. This review aims to summarize the applications and future directions of scRNA-seq and ST in the context of LT. We highlight their role in uncovering immune cell heterogeneity and related injury mechanisms post-transplantation. From a clinician's perspective, we also outline potential future developments in the application of advanced omics in LT. Specifically, we focus on key immune cells involved in LT, with an emphasis on post-transplant immune responses and ischemia-reperfusion injury (IRI), as revealed by scRNA-seq and ST. Furthermore, we underscore the importance of multi-omics approaches and dynamic omics analyses in clinical LT research. With ongoing technological advancements, the integration of cutting-edge omics technologies and artificial intelligence (AI) holds great promise for advancing precision medicine in LT. Emphasis should be placed on the value of single-cell and spatial omics technologies in improving precision therapy and clinical management for LT patients.

From tarsal anatomy to tear-film homeostasis: A history-informed review of the meibomian gland in ocular surface biology.

The advent of spatially resolved transcriptomics has revolutionized our understanding of tissue heterogeneity. While advanced spatial transcriptomics (ST) technologies offer unparalleled andunbiased, whole-slide molecular mapping, laser microdissection (LMD) remains a highly valuable and often superior approach for specific research questions, particularly when coupled with RNA sequencing (RNA-seq). LMD provides high spatial resolution through precise morphological annotation, allowing for the isolation of defined cell clusters or even rare populations from complex tissues. This enables deeper and more sensitive transcriptomic profiling than is often achievable with some high-throughput ST methods that depend on computational deconvolution. Crucially, LMD is well established for use with routine histological samples, including challenging formalin-fixed paraffin-embedded (FFPE) specimens, facilitating invaluable retrospective studies on large patient cohorts. Unlike emerging ST platforms that can present significant per-sample costs and substantial computational overhead, targeted LMD-RNA-seq offers a more cost-effective and scalable solution for analyzing numerous specific regions across large numbers of patients. Therefore, a robust protocol outlining the use of LMD for routine histological samples, coupled with RNA-seq, remains an invaluable tool for discovery, enabling highly specific molecular insights from clinically relevant biospecimens.

Neoadjuvant strategies in head and neck squamous cell carcinoma (HNSCC) are reshaping therapeutic paradigms by shifting emphasis from anatomical staging toward biology-driven response stratification. The transition from induction chemotherapy to immune checkpoint-based and combination regimens has transformed the perioperative setting into a translational platform that enables interrogation of tumor-immune interactions and clonal selection under therapeutic pressure prior to surgery. In this context, pathological response assessment has emerged as a robust surrogate endpoint, overcoming the limitations of radiologic evaluation, which often fails to capture immune-mediated pseudoprogression and spatially heterogeneous regression. Quantification of residual viable tumor (RVT) provides a reproducible metric of therapeutic efficacy, while characterization of immune-related regression beds, tertiary lymphoid structures, macrophage polarization states, and compartment-specific nodal responses offers mechanistic insight into tumor clearance and resistance evolution. Evidence from phase II trials, single-cell sequencing, spatial transcriptomics, and multiplex immune profiling supports the prognostic relevance of pathology-driven endpoints. Integration of digital pathology and artificial intelligence-assisted image analysis further enhances reproducibility and enables high-resolution mapping of residual disease and immune architecture. Within this modern oncologic framework, the neoadjuvant-treated specimen functions as a dynamic biomarker platform guiding response-adapted surgical strategies and biomarker-driven clinical trial design. This study was designed as a narrative review. A structured literature search was performed using PubMed and major oncology journals to identify relevant studies on pathology-driven response assessment in neoadjuvant-treated head and neck squamous cell carcinoma. The review focused on publications addressing histopathological response criteria, immune microenvironment remodeling, spatial profiling technologies, and computational pathology approaches.

The immunosuppressive status of the tumor microenvironment (TME) remains poorly defined due to a lack of understanding regarding the function of tumor-associated macrophages (TAMs), which are abundant in the TME. TAMs are crucial drivers of tumor progression, metastasis, and resistance to therapy. Intra- and inter-tumoral spatial heterogeneities are potential keys to understanding the relationships between subpopulations of TAMs and their functions. Antitumor M1-like and pro-tumor M2-like TAMs coexist within tumors, and the opposing effects of these M1/M2 subpopulations on tumors directly impact current strategies to improve antitumor immune responses. Recent studies have found significant differences among monocytes or macrophages from distinct tumors, and other investigations have explored the existence of diverse TAM subsets at the molecular level. In this review, we discuss emerging evidence highlighting the redefinition of TAM subpopulations and functions in the TME and the possibility of separating macrophage subsets with distinct functions into antitumor M1-like and pro-tumor M2-like TAMs during the development of tumors. Such redefinition may relate to the differential cellular origin and monocyte and macrophage plasticity or heterogeneity of TAMs, which all potentially impact macrophage biomarkers and our understanding of how the phenotypes of TAMs are dictated by their ontogeny, activation status, and localization. Therefore, the detailed landscape of TAMs must be deciphered with the integration of new technologies, such as multiplexed immunohistochemistry (mIHC), mass cytometry by time-of-flight (CyTOF), single-cell RNA-seq (scRNA-seq), spatial transcriptomics, and systems biology approaches, for analyses of the TME.

Background/Objectives: Metastatic breast cancer (MBC) remains a daunting clinical challenge, accounting for approximately 90% of all breast cancer-related deaths. The management of MBC has shifted from traditional chemotherapy to a sophisticated, biomarker-driven model of precision oncology. This evolution is predicated on the ability of biomarkers to provide prognostic and predictive information. The objective of this review is to provide a comprehensive synthesis of the current landscape of biomarker testing in MBC, detailing which biomarkers to test and the clinical rationale for doing so. Methods: This is a comprehensive review based on current international clinical practice guidelines, peer-reviewed literature, and evidence regarding clinically actionable and emerging biomarkers in metastatic breast cancer. Results: Key biomarkers currently in routine use include the established estrogen receptor (ER), progesterone receptor (PR), and HER2, alongside newer, clinically actionable alterations such as mutations in PIK3CA and ESR1, germline/somatic BRCA1/2, and PD-L1 expression. Furthermore, liquid biopsy, particularly the analysis of circulating tumor DNA (ctDNA), is rapidly gaining prominence as a non-invasive tool for real-time disease monitoring and resistance detection, highlighting the critical need for re-testing at metastasis due to tumor heterogeneity. Conclusions: The future of personalized oncology in MBC will be defined by the seamless integration of dynamic biomarkers and cutting-edge technologies. The integration of AI and spatial transcriptomics will move the field of pathology beyond a static diagnosis to a more dynamic and predictive model, reinforcing the pathologist's role as the "molecular gatekeeper" for adaptive and personalized cancer care.

Tumor-infiltrating lymphocytes (TILs) play a critical role in the ability of the immune system to combat cancer, offering a foundation for personalized immunotherapies. However, the effectiveness of TILs is often reduced by problems like becoming less active, the tumor making the immune system weaker, and not lasting long in the tumor environment. Recent advancements in single-cell technologies, including single-cell RNA sequencing (scRNA-seq), single-cell T-cell receptor sequencing (scTCR-seq), and mass cytometry (CyTOF), have revolutionized our understanding of TIL heterogeneity and dynamics. These tools offer new perspectives on the diverse phenotypes, functional states, and spatial organization of TILs, enabling the identification of key exhaustion markers, regulatory pathways, and neoantigen-specific clones. Concurrently, genetic reprogramming strategies have emerged to address TIL limitations by reversing exhaustion, enhancing metabolic resilience, and improving persistence in vivo. This review explores the synergistic integration of single-cell technologies and genetic engineering in refining TIL-based therapies. We talk about how spatial transcriptomics can help us understand how TILs work in different areas of the body and how changing their epigenetics can help them become more effective at fighting cancer. Additionally, we highlight emerging approaches to overcome immunosuppressive barriers in the tumor microenvironment (TME), including targeting regulatory immune cells, neutralizing suppressive cytokines, and enhancing antigen presentation. Together, these strategies promise to unlock the full therapeutic potential of TILs, paving the way for more effective and durable cancer immunotherapy.

Bladder cancer remains one of the most common malignancies of the urinary tract. Although the treatment landscape has expanded rapidly in recent years, gemcitabine still occupies a central position in intravesical treatment for non-muscle-invasive bladder cancer, in perioperative systemic therapy for muscle-invasive disease, and in platinum-based first-line regimens for advanced urothelial carcinoma. Yet the long-term benefit of gemcitabine is frequently curtailed by primary non-response or acquired resistance. In practice, this problem is often recognized only after radiographic progression or clear clinical deterioration has occurred. This review summarizes recent progress in bladder cancer therapy and translational research, with a particular emphasis on the biological basis and hierarchical evolution of gemcitabine resistance. We establish a 3-stage operational model of resistance, distinguishing: (1) early pharmacologic resistance driven by impaired drug uptake/activation or enhanced inactivation; (2) intermediate resistance driven by enhanced DNA damage repair, replication stress tolerance, and pro-survival autophagy signaling; and (3) late adaptive resistance driven by epithelial-mesenchymal transition (EMT), stemness maintenance, metabolic reprogramming, non-coding RNA-mediated epigenetic regulation, inflammatory microenvironmental remodeling, and extracellular vesicle-based intercellular transmission. These layers function as an interactive network, with sequential emergence under treatment pressure and parallel activation in context-dependent clinical settings. We stratify key mechanistic nodes (including the HYAL4-V1/CD44/JAK2-STAT3/CDA axis, AKR1C3, AP1M2-RAD54B, PRPF19-DDB1, AKT/mTOR signaling, Beclin-1-dependent autophagy, the MINCR/ZEB1/PHGDH axis, and IL-6-associated inflammatory states) by their clinical evidence quality and translational readiness, explicitly distinguishing preclinical discovery from clinically validated findings. Critically, most mechanistic findings remain at the preclinical or retrospective validation stage, with no markers yet approved for routine clinical use. Future work must prioritize longitudinal paired clinical samples, standardized analytic assays for dynamic biomarkers, and the integration of functional models (organoids, microfluidic systems), multi-omics technologies (single-cell sequencing, spatial transcriptomics), and liquid-biopsy approaches to translate mechanistic discoveries into clinically actionable predictive tools and therapeutic strategies.

Non-small cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality globally, driven by marked molecular and cellular heterogeneity that complicates diagnosis and treatment. Despite advances in targeted therapies and immunotherapies, treatment resistance frequently emerges, and clinical benefits remain limited to specific molecular subtypes. To improve early detection and dynamic monitoring, novel diagnostic strategies—including liquid biopsy, low-dose computed tomography scans (CT) with radiomic analysis, and AI-integrated multi-modal platforms—are under active investigation. Non-invasive sampling of exhaled breath, saliva, and sputum, and high-throughput profiling of peripheral T-cell receptors and immune signatures offer promising, patient-friendly biomarker sources. In parallel, multi-omic technologies such as single-cell sequencing, spatial transcriptomics, and proteomics are providing granular insights into tumor evolution and immune interactions. The integration of these data with real-world clinical evidence and machine learning is refining predictive models and enabling more adaptive treatment strategies. Emerging therapeutic modalities—including antibody–drug conjugates, bispecific antibodies, and cancer vaccines—further expand the therapeutic landscape. This review synthesizes recent advances in NSCLC diagnostics and treatment, outlines key challenges, and highlights future directions to improve long-term outcomes. These advancements collectively improve personalized and effective management of NSCLC, offering hope for better-quality survival. Continued research and integration of cutting-edge technologies will be crucial to overcoming current challenges and achieving long-term clinical success.

The integration of single-cell sequencing and organoid technologies has been transformative for biomedical research, enabling investigations of organ development, disease mechanisms, and therapeutic innovation at even finer resolutions. Organoids serve as 3D in vitro models that replicate the structural and functional complexity of human tissues, while single-cell sequencing can resolve cellular heterogeneity, transcriptional dynamics, and lineage trajectories at high resolution. This review systematically explores the synergistic potential of these two technologies across multiple domains. First, it describes their application in studying the developmental mechanisms of organs including the brain, lungs, heart, liver, intestines, and kidneys, revealing key signaling pathways and cellular interaction networks. Then, it details their application in studying in vitro models of various diseases, including neurodegenerative disorders, genetic diseases, infectious diseases, metabolic syndrome, and tumors, advancing the in-depth analysis of pathological mechanisms. By leveraging patient-derived organoid biobanks, combining these two technologies can accelerate drug screening and precision, while utilizing transplantable tissue constructs to pioneer regenerative medicine strategies. This review also highlights the strengths of combining these two technologies in dynamically decoding cellular behavior and communication networks. By constructing physiologically relevant multifunctional research platforms, the integration of single-cell sequencing with organoid models will accelerate the elucidation of disease mechanisms and drive innovative breakthroughs in precision medicine and regenerative medicine. Looking ahead, the deep integration of single-cell sequencing with organoids, combined with cutting-edge technologies such as spatial transcriptomics and gene editing, will continue to propel life sciences toward a transformative leap from descriptive research to mechanism-driven, precision-oriented, and personalized approaches.

Investigation of human aging at the single-cell level