Nat Cell Biol. 2025 Nov 26. doi: 10.1038/s41556-025-01811-w. Online ahead of print.

ABSTRACT

Spatial omics technologies have transformed biomedical research by enabling high-resolution molecular profiling while preserving the native tissue architecture. These advances provide unprecedented insights into tissue structure and function. However, the high cost and time-intensive nature of spatial omics experiments necessitate careful experimental design, particularly in selecting regions of interest (ROIs) from large tissue sections. Currently, ROI selection is performed manually, which introduces subjectivity, inconsistency and a lack of reproducibility. Previous studies have shown strong correlations between spatial molecular patterns and histological features, suggesting that readily available and cost-effective histology images can be leveraged to guide spatial omics experiments. Here we present Smart Spatial omics (S2-omics), an end-to-end workflow that automatically selects ROIs from histology images with the goal of maximizing molecular information content in the ROIs. Through comprehensive evaluations across multiple spatial omics platforms and tissue types, we demonstrate that S2-omics enables systematic and reproducible ROI selection and enhances the robustness and impact of downstream biological discovery.

PMID:41298871 | DOI:10.1038/s41556-025-01811-w

Nat Commun. 2025 Nov 26. doi: 10.1038/s41467-025-66644-5. Online ahead of print.

ABSTRACT

Single-cell technologies have transformed our understanding of cellular heterogeneity through multimodal data acquisition. However, robust cell alignment remains a major challenge for data integration and harmonization, including batch correction, label transfer, and multi-omics integration. Many existing methods constrain alignment based on rigid feature-wise distance metrics, limiting their ability to capture accurate cell correspondence across diverse cell populations and conditions. We introduce scGALA, a graph-based learning framework that redefines cell alignment by combining graph attention networks with a score-driven, task-independent optimization strategy. scGALA constructs enriched graphs of cell-cell relationships by integrating gene expression profiles with auxiliary information, such as spatial coordinates, and iteratively refines alignment via self-supervised graph link prediction, where a deep neural network is trained to identify and reinforce high-confidence correspondences across datasets. In extensive benchmarks, scGALA identifies over 25 percent more high-confidence alignments without compromising accuracy. By improving the core step of cell alignment, scGALA serves as a versatile enhancer for a wide range of single-cell data integration tasks.

PMID:41298467 | DOI:10.1038/s41467-025-66644-5

Curr Oncol. 2025 Oct 22;32(11):589. doi: 10.3390/curroncol32110589.

ABSTRACT

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest malignancies due to late diagnosis and limited treatment options. Circulating tumor DNA (ctDNA) is a promising, minimally invasive biomarker that could improve the clinical outcomes of patients with PDAC by enabling early disease detection, minimal residual disease (MRD) assessment, precise prognostication, and accurate treatment monitoring. CtDNA has prognostic as well as predictive value in both resectable and metastatic settings, with serial measurements enhancing risk stratification and recurrence prediction beyond CA19-9. However, despite the promise, the true potential of ctDNA has not yet been fulfilled in patients with PDAC. The current limitations include a low sensitivity of ctDNA assays in early stage PDAC, challenges in the assay interpretation due to the specific nature of ctDNA shedding in PDAC, inter-patient heterogeneity, and technical variability. As precision oncology advances, ctDNA will be a powerful tool for personalized care in PDAC, but rigorous validation of its use within specific clinical contexts is still needed before the true potential of ctDNA is realized for patients with PDAC.

PMID:41294651 | PMC:PMC12650963 | DOI:10.3390/curroncol32110589

Mol Biol Rep. 2025 Nov 26;53(1):125. doi: 10.1007/s11033-025-11302-8.

ABSTRACT

Alternative splicing (AS) is a fundamental mechanism of pre-mRNA processing that allows one gene to create numerous transcript and protein isoforms, thereby substantially increasing the diversity of the human proteome. AS occurs co-transcriptionally (when the nascent pre-mRNA is still being generated from chromatin) or post-transcriptionally after the release of the transcript, and both modalities contribute to the control of isoform expression in a tissue- and context-dependent manner. Under normal physiological conditions, AS is tightly regulated in a tissue- and context-dependent manner. However, in malignancies, this regulatory precision is often lost, leading to extensive splicing aberrations that promote oncogenic transformation, tumor progression, and resistance to therapy. Solid tumors, in particular, exhibit a high frequency of aberrant splicing events, which frequently give rise to oncogenic isoforms or the suppression of tumor-inhibitory variants. These disruptions contribute to key cancer hallmarks such as uncontrolled proliferation, resistance to apoptosis, neoangiogenesis, and epithelial-mesenchymal transition (EMT). Recent findings underscore the clinical relevance of splicing-derived molecular signatures. Distinct splicing profiles have been correlated with diagnostic, prognostic, and predictive outcomes in multiple solid tumors-including breast, prostate, lung, colorectal, and central nervous system malignancies. Notably, tumor-specific alternative splice variants often generate unique exon-exon junctions (neojunctions) that encode immunogenic peptides, representing a promising class of neoantigens for immunotherapy. These neoantigens are fueling the development of personalized treatment modalities such as splicing-directed vaccines and T cell-based therapies. The advent of advanced technologies-including long-read sequencing, single-cell transcriptomics, and proteogenomics-has enabled high-resolution mapping of cancer-specific splice variants and enhanced our understanding of their functional relevance. Therapeutic strategies targeting aberrant splicing are also advancing, with splice-switching oligonucleotides, small-molecule modulators, and CRISPR-based RNA-editing platforms emerging as innovative approaches. Despite these advances, challenges such as splicing heterogeneity, off-target effects, and incomplete protein-level validation continue to hinder clinical translation. This review offers an integrated overview of the molecular drivers and clinical implications of alternative splicing in cancer. It emphasizes the potential of AS-based diagnostics and therapeutics within precision oncology and highlights the importance of multi-omic integration and clinical validation to fully harness the therapeutic opportunities of splicing dysregulation.

PMID:41296088 | DOI:10.1007/s11033-025-11302-8