Prostate cancer (PCa), a common urinary malignancy, is the leading cause of mortality and morbidity among men worldwide. Curcuma amada extract has demonstrated antitumor properties in preclinical models of various cancers; however, its potential mechanism against PCa remains unclear. Therefore, the current study aimed to investigate the underlying mechanism of C. amada rhizome extract (CARE) in treating PCa through in silico and in vitro approaches. UHPLC-QTOF-HRMS/MS identified 16 phytoconstituents in C. amada, with 15 constituents passing drug-likeness. Public databases identified 1,311 CARE and 473 PCa related targets, with 59 shared targets. Protein–protein interaction analysis revealed P53, CTNNB1, EGFR, AKT1, ESR1, HIF1A, CCND1, PIK3CA, and BCL2 as hub targets. Further, 4-hydroxycinnamic acid, 13-hydroxylabda-8(17),14-dien-18-oic acid, labda-8(17),12-diene-15,16-dial, zederone, zedoarondiol, zerumin A, and caffeic acid were identified as core compounds with high degree values. GO and KEGG analysis-identified targets were primarily associated with apoptosis and the PI3K-AKT signalling pathway. Molecular docking revealed the good binding potential of core compounds with key hub targets. Molecular dynamics showed stable interactions and minimal fluctuations in the complexes throughout the simulation period. Additionally, immunohistochemistry data from the HPA database revealed that AKT1, CTNNB1, EGFR, and PIK3CA proteins showed marked cytoplasmic localization in malignant prostate tissues relative to the adjacent normal epithelium, indicating their potential spatial concentration in cancerous regions. Meanwhile, immune cell deconvolution via the TIMER 2.0 database confirmed that PIK3CA expression showed significant positive correlations with neutrophil and myeloid dendritic cell (r = 0.550 and 0.530, respectively) infiltration, indicating its involvement in establishing the innate immune landscape within the PCa microenvironment. CARE significantly inhibited the proliferation of PC-3 cells, induced apoptosis, and caused G2/M phase arrest. qRT-PCR experiments revealed that CARE suppressed mRNA expression of genes involved in the PI3K-AKT signalling pathway. Thus, this study highlights the therapeutic potential of CARE and elucidates its mechanistic relevance in PCa treatment.

Cinnamomum tamala (Buch.-Ham.) T.Nees & Eberm., commonly known as Indian Bay Leaf, is a traditionally valued Ayurvedic medicinal plant with wide therapeutic use, yet its molecular mechanism in non-small-cell lung cancer (NSCLC) remains largely uncharacterized. The present study employed an integrative systems biology pipeline combining spatial transcriptomics, network pharmacology, bioinformatics, molecular modeling, and experimental validation to elucidate the anticancer mechanisms of C. tamala essential oil (CTEO) in treating NSCLC. GC-MS profiling identified 49 phytoconstituents in CTEO, with 44 meeting drug-likeness criteria. Spatial transcriptomics data from NSCLC tissues (GSE21933) revealed 3,438 differentially expressed genes, including 1,894 upregulated and 1,548 downregulated genes. From public databases, 3,961 CTEO-related and 4,588 NSCLC-associated targets were retrieved, with 68 overlapping genes used to construct a protein–protein interaction network. CytoHubba analysis identified JUN, TP53, IL6, MAPK3, HIF1A, and CASP3 as key hub genes. Compound–target network analysis highlighted cinnamaldehyde, ethyl cinnamate, and acetophenone as core components of CTEO. Functional enrichment revealed pathways related to apoptosis, MAPK, TNF, IL-17 signaling, and cancer progression. Survival analysis showed that HIF1A and CASP3 were significantly associated with poor overall survival in lung adenocarcinoma patients. Immune cell deconvolution via TIMER revealed a strong correlation between neutrophil infiltration and hub genes HIF1A (cor = 0.500), IL6 (cor = 0.445), and CASP3 (cor = 0.325), indicating their role in modulating the NSCLC immune microenvironment. Molecular docking and dynamics studies demonstrated stable binding of cinnamaldehyde and ethyl cinnamate to MAPK3, supported by MM/PBSA analysis showing van der Waals forces as primary contributors. In vitro studies in A549 cells revealed that CTEO selectively inhibited cancer cell proliferation and induced apoptosis via ROS generation, mitochondrial depolarization, and caspase activation. These findings support the potential of C. tamala essential oil as a promising adjuvant therapy in NSCLC.

Spatial transcriptomics enables the in situ mapping of gene expression, revolutionizing our ability to study tissue organization and cellular interactions. However, as this technology is increasingly adopted across biological and clinical research, many groups struggle with practical barriers to implementation, including platform selection, sample quality, sequencing depth, and experimental scalability. Here, we provide a comprehensive, practical guide to spatial transcriptomics, informed by the processing and analysis of over 1000 spatial samples across Visium, Visium HD, and Xenium platforms. We outline the best practices for experimental design, tissue handling, sequencing, and computational analysis, with special attention paid to clinical samples and high-throughput settings. Key lessons include the following: assemble a multidisciplinary team from the outset; prioritize, but do not overly rely on RNA quality metrics like DV200 and RIN, as meaningful data can be obtained from below‑threshold samples; avoid under‑sequencing; weigh gene panel size carefully, especially for imaging platforms like Xenium, since larger panels may dilute the per‑gene signal; and proactively prevent batch effects through randomization, replication, and detailed metadata tracking rather than relying solely on data analysis correction.

Our goal is to translate hands-on experience into actionable recommendations that support robust, reproducible spatial workflows. This guide is designed to assist researchers at all levels, from those designing their first spatial experiment to groups aiming to integrate ST into translational pipelines and large-scale studies.

Spatial transcriptomics (ST) technologies are reshaping our understanding of tissue organization and cellular context in health and disease. However, technical benchmarking across platforms remains limited, particularly in formalin-fixed, paraffin-embedded (FFPE) clinical samples, which represent the most common tissue format in oncology. Here, we systematically benchmark five commercial ST platforms (Visium v1, Visium v2/CytAssist, Visium HD, Xenium, and CosMx) using matched FFPE human tumor sections from six cancer types. Uniquely, our study includes both sequencing-based and imaging-based platforms profiled on the same samples, enabling direct technical comparisons across spatial capture modalities. We evaluate platform performance across multiple dimensions, including transcript and UMI detection, spatial autocorrelation, cell-type recovery, and integration with spatial proteomics. We also quantify the impact of sampling strategies and area coverage on cell-type estimation, revealing trade-offs in spatial resolution versus tissue context. Notably, we present the first same-sample comparison of Xenium Multi-Tissue (377 genes) and Xenium Prime (5,000 genes), highlighting key differences in transcript recovery and spatial signal despite shared chemistry and imaging infrastructure. Finally, we integrate Visium spatial proteomics data with matched RNA profiles, uncovering widespread RNA–protein decoupling and spatial heterogeneity in concordance. Collectively, this work provides a harmonized dataset and technical reference for the spatial transcriptomics community, offering insight into the relative strengths, limitations, and design considerations associated with the high-throughput spatial profiling of FFPE tumors.

The treatment of the most aggressive primary brain tumor in adults, glioblastoma (GBM), is challenging due to its heterogeneous nature, invasive potential, and poor response to chemo and radiotherapy. As a result, GBM inevitably recurs, and only approximately 7% of patients survive 5 years post-diagnosis. GBM is characterized by extensive phenotypic and genetic heterogeneity, which creates a diversified genetic landscape and a network of biological interactions promoting tumor growth and therapeutic resistance. This includes spatial and temporal changes in the tumor microenvironment, which influence cellular behaviors and therapeutic responses.
Over the past several years, we have developed a fluorescence-guided multiple sampling scheme, allowing the objective identification of tumor areas in GBM patients. Using this scheme, we identified areas of residual disease in the sub-ventricular zone (SVZ) of the lateral ventricles and the infiltrative margin, which represents the interface between the tumor mass and the normal brain parenchyma.
This presentation will focus on our single-nucleus and spatial transcriptomic dataset of the SVZ microenvironment using tissue samples collected from 15 GBM patients. We comprehensively compared tumor mass samples isolated from the same patients and used two histologically normal SVZ samples as controls. We found that in GBM patients, the SVZ microenvironment is characterized by a ZEB1-centered mesenchymal signature and tumor-supportive microglia, which spatially coexist and establish cross-talks with tumor cells. Moreover, differential gene expression analyses, predictions of ligand–receptor, and incoming/outgoing interactions revealed that microglia interact with tumor cells through the IL-1β/IL-1RAcP and Wnt-5a/Frizzled-3 pathways. Lastly, in vitro targeting of these pathways significantly reduces the ability of SVZ-derived tumor cells to proliferate and migrate. Altogether, our data provide insights into the biology of the SVZ in GBM patients and identify potential targets of this microenvironment.

Background and Aim

Intrahepatic cholangiocarcinoma (iCCA) is a highly aggressive and heterogeneous liver cancer with limited therapeutic options. Its pronounced intratumoral diversity at both molecular and cellular levels poses a significant barrier to effective treatment. To better understand the
complexity and spatial dynamics of the tumor microenvironment (TME), we employed spatial transcriptomics (ST) to resolve the architecture of clonal populations and their interactions with surrounding stromal components.

Methods

We combined Confetti-based lineage tracing in a genetically engineered mouse model of iCCA with 10x Genomics Visium CytAssist spatial transcriptomics across multiple tumors (n=4),
generating high-resolution spatial maps of tumor-stroma interactions. ST data were integrated with single-cell RNA sequencing (scRNA-seq) to identify cell types and reconstruct cell–cell
communication networks across clonal territories. Bioinformatic analyses included spatial
deconvolution (CARD), ligand–receptor inference (LIANA), copy number variation mapping
(inferCNV), dimensionality reduction (PaCMAP), and a custom random forest classifier for clonal identity. Findings were further validated in four human iCCA samples.

Results

Spatial transcriptomics revealed structured gradients of gene expression and niche-specific
transcriptional programs that aligned with histological features and clonal architecture.
Integration with scRNA-seq demonstrated that clonal identity not only drives intrinsic tumor cell programs but also actively shapes the surrounding microenvironment. Notably, cancer-
associated fibroblasts (CAFs) formed spatially restricted subpopulations that engaged in clone-specific signaling interactions, significantly influencing tumor cell proliferation and spatial
organization. This integrated spatial approach uncovered highly organized clonal ecosystems
coexisting within the same tumor mass.

Conclusion

Spatial transcriptomics was essential in decoding the spatial logic of iCCA. By mapping clonal-stromal interactions at high resolution, our study revealed how tumor clones shape—and are
shaped by—their microenvironment. These insights lay the groundwork for spatially targeted
and clonally informed therapeutic strategies in iCCA.

Background

Microsatellite stable (MSS) colon cancers (CCs) typically resist immunotherapy due to limited immune infiltration, unlike microsatellite instability (MSI) tumors. We previously identified a high-evolution MSS (HE-MSS) subtype marked by pronounced B/plasma cell infiltration and elevated mutational divergence between diagnosis and relapse (Martín-Arana et al., 2025). Spatial transcriptomics offers a powerful approach to map the spatial architecture and immune dynamics of the tumor microenvironment in this subset of patients.

Methods

We analyzed 14 primary CC tumors (9 MSS and 5 MSI) using 10x Genomics Visium HD. Tumor evolution was quantified based on genomic similarity between baseline and matched relapse plasma samples, stratifying MSS cases into HE-MSS (n=4) and low-evolution MSS (LE-MSS) (n=5). Data were processed with spaceranger v3.1.2. Cell segmentation on tissue images and 2×2 μm bin-to-cell assignment followed the ENACT pipeline. Cell types were annotated with CellTypist. Downstream analyses were conducted in Python using Squidpy, decoupler (for pathway activity), and stLearn (for ligand–receptor interactions).

Results

Compared to LE-MSS, HE-MSS tumor cells showed increased EGFR/MAPK and PI3K signaling, reduced oxidative stress response activity, and were surrounded by heightened peritumoral inflammation—a profile consistent with elevated genomic instability. Immune infiltrates were more prominent and partially resembled those in MSI tumors—most notably with increased IgG⁺ plasma cells that exhibited greater spatial co-localization than in LE-MSS tumors. B cells and IgG⁺, but not IgA⁺, plasma cells increased with tumor evolution, pointing to a role for reactive rather than resident immune activity. In HE-MSS tumors, IgG⁺ plasma cells engaged in enriched ligand–receptor interactions via integrin signaling and the MIF–CD74 and THBS2–CD47 axes, implying roles in spatial localization, tumor support, and immune suppression, respectively.

Conclusion

A subset of high-evolution MSS CCs displays enriched, spatially organized IgG⁺ plasma cell infiltrates and MSI-like immune features, supporting spatial profiling as a tool for immunotherapy stratification.

Image-based spatial transcriptomics enables the visualization of RNA molecules within intact tissues at subcellular resolution. However, a substantial proportion of transcripts often remains unassigned during cell segmentation. These unassigned RNAs (uRNAs) are typically excluded from downstream analyses, despite their potential biological relevance.

In this study, we explore the origins and biological relevance of uRNA. We systematically investigate uRNAs across 14 datasets from different tissue types generated with diverse technologies. To facilitate broader exploration of uRNAs, we developed Troutpy, an open-source Python package that offers tools for exploring uRNA, including the detection of undersegmentation events, identification of cell–cell contact zones, and analysis of extrasomatic RNAs.

By quantifying the contributions of various technical factors, including undersegmentation, imaging noise, and molecular diffusion, we identify both technical and non-technical sources of uRNAs. Notably, a significant portion of uRNAs exhibits non-random spatial patterns and local enrichment in specific tissue structures, particularly in regions associated with distinct extrasomatic features. We demonstrate these patterns to be reproducible across datasets and platforms, suggesting a biological origin. Finally, we demonstrate that uRNAs exhibit distinct spatial expression signatures, divergent from intracellular profiles, that align with specific cellular architectures, thereby offering a framework to investigate the extrasomatic distribution of RNA across cell types.

Our findings highlight the biological relevance of unassigned RNAs (uRNA) in spatial transcriptomics. By proposing strategies to incorporate uRNA into existing analyses, our study extends the application of spatial transcriptomics to investigate extrasomatic RNA, potentially key in tissue organization, and cell–cell interactions or migration, among other topics.

Lymphoma comprises a highly heterogeneous group of neoplasms arising from diverse immune cell precursors. Subtypes differ markedly in their localization, cell of origin, molecular profiles and clinical outcomes. Understanding the origins of the disease is of great scientific interest since lymphoma affects millions of people globally, climbing to the top 10 most frequent cancers. In this light, we employed spatial transcriptomics using the 10X Genomics Visium platform on tissue samples from a cohort of 107 patients. Unlike bulk RNA-sequencing, spatial transcriptomics retains the tissue architecture, enabling high-resolution mapping of gene expression within the tumor microenvironment (TME). A major challenge in lymphoma is accurately delineating tumor cells from the surrounding TME, as malignant cells themselves originate from B and T lymphocytes, making classification a complex process. To address this, we applied a holistic, pre-trained cell deconvolution method, integrating gene expression and spatial context, and validated its performance against a single-cell RNA-seq reference. Our analysis revealed conserved cell-type frequencies across lymphoma subtypes as well as cell-type niches, which highlighted the role of macrophages in the lymphoma TME. This approach provides a granular perspective on the lymphoma ecosystem and lays the groundwork for identifying spatially informed biomarkers and therapeutic targets.

Lymphomas are clinically and genetically heterogeneous malignancies that arise from B or T‑cell precursors at different maturation stages. Although bulk omics identify driver mutations and support current classifications, patients with apparently similar genomic profiles often experience divergent outcomes. One likely cause of this variability is the spatial composition of the tumour micro‑environment (TME).

To characterise diverse TMEs, we built a 10x Visium spatial–transcriptomic atlas from 107 diagnostic lymph‑node biopsies that include Hodgkin lymphoma, diffuse large B‑cell lymphoma, follicular lymphoma, several rarer subtypes, and non‑tumour lymph nodes. Each sample was analysed with MEFISTO, a probabilistic framework that models spatial covariation to extract latent factors. Across the cohort, these factors consistently corresponded to micro‑regional gene programmes, such as germinal centre signalling, interferon response, hypoxia, and cellular respiration, together with additional pathways that were preferentially enriched in specific lymphoma subtypes.

Clinically relevant immune markers were also mapped. Aggressive tumours showed a tighter spatial association with CD86⁺ and CD80⁺ macrophages, whereas PD‑L1, CTLA‑4, and PD‑1 levels were comparable across entities, suggesting alternative immune‑modulatory circuits that bulk RNA‑seq cannot detect.

Our data show that biologically significant spatial programmes are present in lymphomas but remain undetectable by bulk RNA‑seq. This work therefore adds a crucial spatial dimension to lymphoma characterization.