Front Mol Biosci. 2025 Jul 15;12:1614288. doi: 10.3389/fmolb.2025.1614288. eCollection 2025.

ABSTRACT

Recent advances in spatial transcriptomics (ST) and spatial proteomics (SP) technologies have enabled high-dimensional molecular profiling at single-cell resolution, providing deeper insights into the tumour-immune microenvironment. However, these modalities are typically applied to separate tissue sections, limiting direct comparisons across molecular layers. We developed a wet-lab and computational framework to perform and integrate ST and SP from the same tissue section, as demonstrated on human lung cancer samples. Applying ST, SP, and hematoxylin and eosin (H&E) staining from the same section ensured consistency in tissue morphology and spatial context. Computational registration using Weave software allowed accurate alignment and annotation transfer across modalities. This co-registered dataset enabled single-cell level comparisons of RNA and protein expression, revealed segmentation accuracy and transcript-protein correlation analyses within individual cells. Notably, we observed systematic low correlations between transcript and protein levels-consistent with prior findings-now resolved at cellular resolution. Our approach highlights the feasibility and utility of performing spatially-resolved multi-omics analysis on the same section without compromising data quality, facilitating concordance studies and region-specific analysis of immune and tumour markers, and ultimately advancing our understanding of disease heterogeneity at the molecular level.

PMID:40735471 | PMC:PMC12304548 | DOI:10.3389/fmolb.2025.1614288

Cell Rep. 2025 Jul 29;44(8):116065. doi: 10.1016/j.celrep.2025.116065. Online ahead of print.

ABSTRACT

Genome-wide association studies (GWASs) have identified over 100 signals associated with type 1 diabetes (T1D). However, it has been challenging to translate any given T1D GWAS signal into mechanistic insights, such as causal variants, their target genes, and the specific cell types involved. Here, we present a comprehensive multi-omic integrative analysis of single-cell/nucleus resolution profiles of gene expression and chromatin accessibility in human pancreatic islets under baseline and T1D-stimulating conditions. We nominate effector cell types for all T1D GWAS signals and the regulatory elements and genes for three independent T1D signals acting through β cells at the DLK1/MEG3, RASGRP1, and TOX loci. Subsequently, we validated the functional impact of these genes and regulatory regions using isogenic human embryonic stem cells (hESCs). We found that loss of RASGRP1 or DLK1, as well as disruption of their corresponding regulatory regions, led to increased β cell apoptosis. Furthermore, β cells derived from isogenic hESCs carrying the T1D risk allele of rs3783355 associated with DLK1 showed elevated β cell death. Through additional RNA sequencing (RNA-seq) and assay for transposase-accessible chromatin using sequencing (ATAC-seq) analyses, we identified five genes upregulated in both RASGRP1^-/- and DLK1^-/- β-like cells, four of which are near T1D GWAS signals. This integrative approach combining single-cell multi-omics, GWASs, and isogenic human pluripotent stem cell (hPSC)-derived β-like cells illuminates cell type context, genes, single nucleotide polymorphisms (SNPs), and regulatory elements underlying T1D-associated signals, providing insights into the biological functions and molecular mechanisms involved.

PMID:40737125 | DOI:10.1016/j.celrep.2025.116065

Diabetologia. 2025 Jul 30. doi: 10.1007/s00125-025-06462-y. Online ahead of print.

ABSTRACT

This article summarises the current understanding of the heterogeneity of type 1 diabetes from a June 2024 international Expert Forum organised by the editors of Diabetes, Diabetes Care, and Diabetologia. The Forum reviewed key factors contributing to the development and progression of type 1 diabetes and outlined specific, high-priority research questions. Knowledge gaps were identified and, notably, opportunities to harness disease heterogeneity to develop personalised therapies were outlined. Herein, we summarise our discussions and review the heterogeneity of genetic risk and immunologic and metabolic phenotypes that influence and characterise type 1 diabetes progression (presented as a palette of risk factors). We discuss how these age-related factors determine disease aggressiveness (along gradients) and describe how variable immunogenetic pathways aggregate (into networks) to affect beta cell and other pancreatic pathologies to cause clinical disease at different ages and with variable severity (described as disease-related thresholds). Heterogeneity of pathogenesis and clinical severity opens avenues to prevention and intervention, including the potential of disease-modifying immunotherapy and islet cell replacement. We conclude with a call for (1) continued research to identify more factors contributing to the disease, both overall and in specific subgroups; (2) investigations focusing on both individuals who surpass metabolic and immune thresholds and develop diabetes and those who remain disease free with the same level of immunogenetic risk; and (3) efforts to identify where the current type 1 diabetes staging system may fall short and determine how it can be improved to capture and leverage heterogeneity in prevention and intervention strategies.

PMID:40736750 | DOI:10.1007/s00125-025-06462-y

bioRxiv [Preprint]. 2025 Jun 8:2025.06.08.658506. doi: 10.1101/2025.06.08.658506.

ABSTRACT

Advances in spatial profiling have resulted in the generation of multi-omic atlases that span biological scales. In general, multiple workflows are required for image registration, coordinate registration, and spot deconvolution to integrate modalities. To improve the throughput of registration of multi-omic cohorts, we introduce PIVOT, a user-friendly and open-source interface for streamlined nonlinear registration. We demonstrate PIVOT's strengths through registration of three multi-omic datasets, and show comparison of its performance to existing workflows.

PMID:40661390 | PMC:PMC12259011 | DOI:10.1101/2025.06.08.658506

bioRxiv [Preprint]. 2025 May 5:2025.05.01.651678. doi: 10.1101/2025.05.01.651678.

ABSTRACT

Pancreatic cancer remains as one of the most challenging malignancies to diagnose and treat due to the late development of symptoms and limited early diagnostic options. Intraductal papillary mucinous neoplasms (IPMNs) are non-invasive precursors to invasive pancreatic ductal adenocarcinoma (PDAC)and an understanding of the changes in patterns of protein expression that accompany the progression from normal ductal (ND) cell, to IPMN to PDAC may provide avenues for improved earlier detection. In this study, we present an optimized spatial tissue proteomics workflow, termed SP-Max (Spatial Proteomics Optimized for Maximum Sensitivity and Reproducibility in Minimal Sample), designed to maximize protein recovery and quantification from limited laser micro dissected (LMD) samples. Our workflow enabled the identification of more than 6,000 proteins and the quantification of over 5,200 protein groups from FFPE tissue contours of pancreatic tissues. Comparative analyses across ND, IPMN, and PDAC revealed critical molecular differences in protein pathways and potential markers of progression. SP-Max provides a systematic, reproducible approach that significantly enhances our ability to study precancerous lesions and cancer progression in pancreatic tissues at unprecedented resolution.

PMID:40654937 | PMC:PMC12247709 | DOI:10.1101/2025.05.01.651678

Discov Oncol. 2025 Jul 23;16(1):1391. doi: 10.1007/s12672-025-03243-2.

ABSTRACT

We developed the first multi-omics prognostic signature integrating 19 programmed cell death (PCD) pathways and organelle functions (mitochondria, lysosomes, Golgi apparatus) to predict prognosis and immunotherapy response in non-small cell lung cancer (NSCLC). (2) Methods: By combining single-cell RNA-seq, bulk transcriptomics, and deep neural networks (DNN), we identified a 23-gene signature validated across four cohorts (AUC 0.696–0.812). Conducted MR analysis to explore causal links between signature genes and NSCLC incidence, providing biological insights. (3) Results: A prognostic signature was developed, including 23 prognostic genes related to 19 PCD patterns and three organelle functions. The signature demonstrated powerful performance in predicting NSCLC prognosis, immune in-filtration, and therapeutic response. Established DNN models showed high value in predicting risk score groupings of NSCLC. MR analysis for combined SNP information of the 23 prognostic genes suggested a link to the high incidence of NSCLC. Individual MR analysis showed that HIF1A and SQLE expression had a causal effect on NSCLC incidence. (4) Conclusion: This signature stratifies high-risk patients with immunosuppressive microenvironments and predicts enhanced sensitivity to gemcitabine and PD-1 inhibitors, offering a roadmap for personalized NSCLC management.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12672-025-03243-2.

PMID:40699399 | PMC:PMC12287486 | DOI:10.1007/s12672-025-03243-2

Int J Surg. 2025 Jul 22. doi: 10.1097/JS9.0000000000002999. Online ahead of print.

ABSTRACT

BACKGROUND: Breast cancer remains the most prevalent malignancy among women, and patients presenting with both breast and lung cancer pose significant challenges in clinical diagnosis and treatment. Currently, comprehensive multi-omics analyses for such multiple malignancies are lacking.

METHODS: An integrated multi-omics analysis was performed, incorporating quantitative proteomics and radiomics data from patients with single primary breast cancer as well as those with multiple primary tumors (breast and lung cancer).

RESULTS: Quantitative proteomics analysis revealed four distinct molecular signatures (Types I-IV). Patients with single breast cancer exhibited driving pathways primarily linked to cell proliferation (e.g., HER2), whereas those with multiple breast cancers showed enrichment in ER-related and proliferative pathways. In contrast, patients with multiple lung cancers displayed pathways associated with immune response and immune escape. Additionally, immune subtyping identified three distinct immune landscapes (Types I-III). Radiomic analysis demonstrated strong correlations between these molecular/immune subtypes and imaging findings. Patients with high imaging information scores exhibited pronounced tumor heterogeneity and reduced immune infiltration.

CONCLUSIONS: This study provides new insights into the molecular pathogenesis of multiple primary malignancies, particularly breast and lung cancer.

PMID:40694032 | DOI:10.1097/JS9.0000000000002999

J Liq Biopsy. 2025 Jul 8;9:100310. doi: 10.1016/j.jlb.2025.100310. eCollection 2025 Sep.

ABSTRACT

Bladder cancer is the ninth most common malignancy worldwide, with two clinically distinct forms: non-muscle-invasive disease, characterized by high recurrence and excellent long-term survival, and muscle-invasive disease, associated with poorer outcomes. Current surveillance-cystoscopy and urine cytology-offers high specificity but is invasive, costly, and insensitive to low-grade tumors, underscoring the need for reliable, non-invasive biomarkers. Liquid biopsy approaches in urine and blood have demonstrated promise for real-time assessment of tumor burden, molecular heterogeneity, and early recurrence. Circulating tumor DNA (ctDNA) assays detect tumor-derived genetic and epigenetic alterations, enabling dynamic monitoring of minimal residual disease and treatment response. Methylation-based tests and CpG-targeted sequencing in urine achieve high diagnostic accuracy, potentially reducing dependence on cystoscopy. Molecular classification of bladder tumors into luminal and basal subtypes has refined therapeutic strategies: FGFR inhibitors for luminal-papillary tumors, EGFR-targeted and chemotherapy approaches for basal/squamous cases, and immune-checkpoint inhibitors guided by immune-infiltration profiles. Integration of artificial intelligence with multi-omic liquid biopsy data further enhances predictive modeling for recurrence, treatment response, and minimal residual disease detection. Despite these advances, clinical implementation faces challenges including pre-analytical variability, lack of standardized assays, limited prospective validation, and unclear cost-effectiveness. Harmonized protocols, large multicenter trials, and health-economic evaluations are essential to translate liquid biopsy technologies into routine practice. Future integration with advanced imaging, tissue biopsy, and digital pathology-supported by multidisciplinary collaboration and formal guideline endorsement-holds the potential to personalize bladder cancer management, reduce invasive procedures, and improve patient outcomes.

PMID:40698358 | PMC:PMC12281373 | DOI:10.1016/j.jlb.2025.100310

J Hepatocell Carcinoma. 2025 Jul 17;12:1453-1469. doi: 10.2147/JHC.S493907. eCollection 2025.

ABSTRACT

BACKGROUND: Hepatocellular carcinoma (HCC) necessitates novel immunotherapeutic targets. PBK, a cancer/testis antigen (CTA), was identified as a pivotal hub gene influencing prognosis, tumor mutation burden (TMB), and immune microenvironment remodeling.

METHODS: PBK was prioritized using weighted gene co-expression network analysis (WGCNA) and differential expression screening in the TCGA-LIHC cohort, intersected with curated CTAs. Analyses assessed correlations with clinicopathological features (TNM stage, survival), genomic characterization (mutation frequencies), and functional validation via siRNA-mediated PBK knockdown in Huh7 cells (migration assay). Single-cell RNA sequencing (scRNA-seq) profiled of the tumor immune microenvironment.

RESULTS: PBK overexpression was significantly correlated with advanced TNM stage (P < 0.05) and poor survival (log-rank P = 0.003). Genomic analysis revealed distinct mutation profiles: high-PBK tumors exhibited increased TP53 mutation frequency (39% vs 17%) but decreased CTNNB1 mutations (20% vs 31%). Patients exhibiting with combined PBK overexpression and high TMB demonstrated the poorest prognosis. Functional validation confirmed that PBK knockdown significantly inhibited Huh7 cell migration capacity (P < 0.05). scRNA-seq analysis showed PBK-enriched tumors contained elevated proportions of immunosuppressive SPP1(+) macrophages (22.33% vs 6.6%, FDR corrected P < 0.001) and CD8(+) SLC4A10(+) MAIT cells (9.82% vs 4.7%, FDR corrected P < 0.001).

CONCLUSION: PBK synergistically drives HCC progression through three synergistic mechanisms: (1) promoting oncogenic mutation accumulation (eg, TP53), (2) increasing metastatic potential, and (3) reprogramming an immune-suppressive microenvironment enriched for SPP1(+) macrophages and CD8(+)SLC4A10(+) MAIT cells. This establishes PBK as a dual-purpose biomarker for prognostic stratification and immunotherapy resistance prediction, providing a mechanistic rationale for developing PBK-targeted therapies in HCC.

PMID:40697330 | PMC:PMC12279550 | DOI:10.2147/JHC.S493907

ACS Omega. 2025 Jun 30;10(27):28782-28796. doi: 10.1021/acsomega.4c10637. eCollection 2025 Jul 15.

ABSTRACT

Despite the high prevalence of type 2 diabetes (T2D), the mechanisms driving pathology in pancreatic islet β cells remain poorly understood. We utilized a multiomics approach to evaluate the transcriptional and biochemical makeup of islets from human organ donors with T2D and nondiabetic controls. Transcriptomic (N = 10), proteomic (N = 6), and untargeted high-resolution metabolomic (N = 10) data were analyzed individually and then integrated using sparse partial least-squares regression, and differential network analysis was performed. In individual data sets, 25 transcripts, 30 proteins, and 30 metabolites were differentially abundant between T2D and nondiabetic islets, representing some pathways not previously characterized in T2D islets including purine and pyrimidine, branched-chain amino acid, and histidine metabolism. Network analysis of integrated data sets highlighted disrupted relationships among features in T2D islets compared to those from nondiabetic individuals. Fatty and amino acid metabolism and immune activity were identified as prominent drivers of the distinctions in biochemical interactions in T2D networks. Our findings also suggested greater abundance and influence of industrial chemicals, including polychlorinated and polybrominated biphenyls, in T2D islets. This pilot study demonstrates that multiomics profiling can identify candidate molecules and mechanisms impacting islet cell activity in T2D, which could represent targets for therapeutic intervention.

PMID:40687044 | PMC:PMC12268419 | DOI:10.1021/acsomega.4c10637

Int J Med Sci. 2025 Jun 12;22(12):2906-2918. doi: 10.7150/ijms.113736. eCollection 2025.

ABSTRACT

ACTL6A, a core subunit of the SWI/SNF chromatin remodeling complex, has emerged as a critical oncogenic driver across multiple malignancies. Recent studies reveal that aberrant ACTL6A overexpression promotes tumor initiation, progression, and metastasis by orchestrating chromatin remodeling, transcriptional reprogramming, and crosstalk with key signaling pathways (e.g., Hippo/YAP, Notch, and PI3K/AKT). This review systematically synthesizes evidence from in vitro, in vivo, and clinical studies spanning hepatocellular carcinoma, breast cancer, glioblastoma, and 10 other cancer types, highlighting ACTL6A's dual role as a chromatin remodeler and an independent oncogenic effector. Key mechanisms include sustaining cancer stemness, suppressing apoptosis, enhancing DNA repair, and driving metabolic reprogramming. Clinically, ACTL6A overexpression correlates with advanced tumor stage, therapy resistance, and poor prognosis, positioning it as a promising prognostic biomarker and therapeutic target. We further discuss emerging strategies to inhibit ACTL6A (e.g., siRNA, small-molecule inhibitors) and propose combinatorial approaches to overcome drug resistance. By integrating multi-omics data and preclinical models, this review not only clarifies ACTL6A's context-dependent oncogenic networks but also bridges mechanistic insights to translational challenges, offering a roadmap for future research and therapeutic development.

PMID:40657395 | PMC:PMC12243864 | DOI:10.7150/ijms.113736

MedComm (2020). 2025 Jul 11;6(7):e70292. doi: 10.1002/mco2.70292. eCollection 2025 Jul.

ABSTRACT

Cancer-associated fibroblasts (CAFs) are functionally diverse stromal regulators that orchestrate tumor progression, metastasis, and therapy resistance through dynamic crosstalk within the tumor microenvironment (TME). Recent advances in single-cell multiomics and spatial transcriptomics have identified conserved CAF subtypes with distinct molecular signatures, spatial distributions, and context-dependent roles, highlighting their dual capacity to promote immunosuppression or restrain tumor growth. However, therapeutic strategies struggle to reconcile this functional duality, hindering clinical translation. This review systematically categorizes CAF subtypes by origin, biomarkers, and TME-specific functions, focusing on their roles in chemoresistance, maintenance of stemness, and formation of immunosuppressive niches. We evaluate emerging targeting approaches, including selective depletion of tumor-promoting subsets (e.g., fibroblast activation protein+ CAFs), epigenetic reprogramming toward antitumor phenotypes, and inhibition of CXCL12/CXCR4 or transforming growth factor-beta signaling pathways. Spatial multiomics-driven combinatorial therapies, such as the synergistic use of CAFs and immune checkpoint inhibitors, are highlighted as strategies to overcome microenvironment-driven resistance. By integrating CAF biology with translational advances, this work provides a roadmap for developing subtype-specific biomarkers and precision stromal therapies, directly informing efforts to disrupt tumor-stroma coevolution. Key concepts include spatial transcriptomics, stromal reprogramming, and tumor-stroma coevolution, offering actionable insights for both mechanistic research and clinical innovation.

PMID:40656546 | PMC:PMC12246558 | DOI:10.1002/mco2.70292