Cell. 2025 Aug 22:S0092-8674(25)00922-5. doi: 10.1016/j.cell.2025.08.008. Online ahead of print.

ABSTRACT

Performing total RNA profiling on formalin-fixed, paraffin-embedded (FFPE) samples, the predominant sample conservation method in clinical practice, remains challenging for current spatial transcriptomics techniques. Here, we introduce Stereo-seq V2, which employs random primers to capture and sequence RNAs in situ on FFPE sections and provides single-cell resolution. The random-priming-based strategy offers unbiased transcript capturing and uniform gene body coverage, which increase the sensitivity to marker genes, the efficiency of non-polyadenylation (poly(A)) RNA profiling, and immune repertoire coverage. We demonstrated the robust performance of Stereo-seq V2 on clinical FFPE samples using triple-negative breast cancer (TNBC) sections and identified tumor-specific alternative splicing events. In a Mycobacterium tuberculosis (Mtb)-infected mouse model, we monitored gene expression dynamics of host and pathogen transcriptomes simultaneously by utilizing Stereo-seq V2. We also assembled immune repertoires and identified Mtb-specific BCR clones, which could also be observed in human tuberculous lung samples. These results highlight Stereo-seq V2's potential in biomedical research and personalized medicine.

PMID:40882628 | DOI:10.1016/j.cell.2025.08.008

Nat Commun. 2025 Aug 22;16(1):7827. doi: 10.1038/s41467-025-63146-2.

ABSTRACT

While dysregulation of polyamine metabolism is frequently observed in cancer, it is unknown how polyamines alter the tumor microenvironment (TME) and contribute to therapeutic resistance. Analysis of polyamines in the plasma of pancreatic cancer patients reveals that spermine levels are significantly elevated and correlate with poor prognosis. Using a multi-omics approach, we identify Serpinb9 as a vulnerability in spermine metabolism in pancreatic cancer. Serpinb9, a serine protease inhibitor, directly interacts with spermine synthase (SMS), impeding its lysosome-mediated degradation and thereby augmenting spermine production and secretion. Mechanistically, the accumulation of spermine in the TME alters the metabolic landscape of immune cells, promoting CD8⁺ T cell dysfunction and pro-tumor polarization of macrophages, thus creating an immunosuppressive microenvironment. Small peptides that disrupt the Serpinb9-SMS interaction significantly enhance the efficacy of immune checkpoint blockade therapy. Together, our findings suggest that targeting spermine metabolism is a promising strategy to improve pancreatic cancer immunotherapy.

PMID:40846845 | PMC:PMC12373741 | DOI:10.1038/s41467-025-63146-2

J Am Soc Mass Spectrom. 2025 Aug 6;36(8):1598-1608. doi: 10.1021/jasms.4c00505. Epub 2025 Jul 2.

ABSTRACT

Gastric signet ring cell carcinoma (GSRC) is an aggressive subtype of gastric cancer (GC) with a poor prognosis. The lack of a systematic molecular and metabolic heterogeneity overview has led to slow progress in clinical practice. This study used mass spectrometry imaging (MSI) to investigate the metabolic landscape of GSRC in GC tissue with various differentiation grades. Our comprehensive spatial profiling of metabolites and lipids unveiled distinct metabolic signatures across different tissue subregions. A substantial number of lipidomic biomarkers associated with GSRC were identified, including phosphatidylethanolamine N-methyl (PE-NMe), phosphatidylethanolamine (PE), sphingomyelin (SM), diacylglycerol (DG), phosphatidic acid (PA), and phosphatidylcholine (PC), which may provide insights into its pathogenesis and potential therapeutic targets. Furthermore, multi-omics network analysis revealed intricate metabolic pathways involved in GSRC progression. Our findings highlight the importance of understanding the metabolic heterogeneity of GSRC and pave the way for future studies exploring its clinical implications and therapeutic strategies.

PMID:40600435 | DOI:10.1021/jasms.4c00505

Radiother Oncol. 2025 Jan 10;204:110715. doi: 10.1016/j.radonc.2025.110715. Online ahead of print.

ABSTRACT

BACKGROUND AND PURPOSE: Quantifying tumor heterogeneity from various dimensions is crucial for precise treatment. This study aimed to develop and validate multi-omics models based on the computed tomography images, pathological images, dose and clinical information to predict treatment response and overall survival of non-small cell lung cancer (NSCLC) patients undergoing chemotherapy and radiotherapy.

MATERIALS AND METHODS: This retrospective study included 220 NSCLC patients from three centers. Following feature extraction and selection, single-omics and multi-omics models were built for treatment response and overall survival prediction. The performance of treatment response models was evaluated using the area under the curve (AUC) and box plots. For overall survival analysis, the model's evaluation included AUC, concordance index (C-index), Kaplan-Meier curves, and calibration curves. Shapley values were used to assess the contribution of different features to multi-omics models.

RESULTS: Multi-omics models consistently exhibited superior discriminative ability compared to single-omics models in predicting both treatment response and overall survival. For treatment response, the three all-modality models achieved AUC values of 0.87, 0.91, and 0.82 in the external validation set, respectively. In overall survival analysis, the three all-modality models demonstrated AUC values and C-index of 0.73/0.72, 0.80/0.77, 0.79/0.78 in the external validation set, respectively.

CONCLUSION: Multi-omics prediction models demonstrated superior predictive ability with robustness and interpretability. By predicting treatment response and overall survival in NSCLC patients, these models have the potential to assist clinician optimizing treatment plans, supporting individualized treatment strategies, improving the tumor control probability and prolonging the patients' survival.

PMID:39800269 | DOI:10.1016/j.radonc.2025.110715