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

Biomedicines. 2025 Jul 14;13(7):1717. doi: 10.3390/biomedicines13071717.

ABSTRACT

Background: Immune checkpoint inhibitors (ICIs) have transformed cancer treatment, yet severe immune-related adverse events (irAEs) often necessitate immunotherapy discontinuation and cause life-threatening complications. Circulating plasma proteins, dynamically accessible and functionally linked to immunity, may predict and offer novel targets for irAEs. Methods: Leveraging multi-omics integration, we conducted bidirectional two-sample Mendelian randomization (MR) using protein quantitative trait loci (pQTLs) from 4998 plasma proteins and genome-wide association data of irAE phenotypes. A causal inference framework combining colocalization analysis, multivariable MR (MVMR) adjusting for body mass index (BMI) confounding, and mediation MR elucidated BMI-independent pathways. Systems biology approaches including tissue-specific expression profiling, pathway enrichment, and protein interaction network analysis revealed spatial and functional drivers of irAE pathogenesis. Results: Proteome-wide MR mapping identified eight plasma proteins (CCL20, CSF1, CXCL9, CD40, TGFβ1, CLSTN2, TNFSF12, TGFα) causally associated with all-grade irAEs, and five (CCL20, CCL25, CXCL10, ADA, TGFα) with high-grade irAEs. Colocalization prioritized CD40/TNFSF12 (all-grade) and ADA/CCL25 (high-grade) as therapeutic targets (PPH4 > 0.7). CXCL9/TNFSF12 (all-grade) and CCL25 (high-grade) exerted BMI-independent effects, suggesting intrinsic immune dysregulation mechanisms. Tissue-specific gene expression patterns, CSF1, TGFβ1 in lung, TNFSF12 in the ileum may explain organ-specific irAE vulnerabilities. High-grade irAEs correlated with compartmentalized immune dysregulation and IL-17/immunodeficiency pathway activation. Conclusions: This study establishes the causal atlas of plasma proteins in irAE pathogenesis, bridging biomarker discovery with actionable therapeutic targets. These advances align with next-generation immunotherapy goals: maximizing efficacy while taming the immune storm.

PMID:40722787 | PMC:PMC12293052 | DOI:10.3390/biomedicines13071717

World J Clin Cases. 2025 Jul 26;13(21):105066. doi: 10.12998/wjcc.v13.i21.105066.

ABSTRACT

BACKGROUND: Circulating tumor DNA (ctDNA)-based liquid biopsy has been found to be effective for the detection of minimal residual disease and the evaluation of prognostic risk in various solid tumors, with good sensitivity and specificity for identifying patients at high risk of recurrence. However, use of its results as a biomarker for guiding the treatment and predicting the prognosis of nasopharyngeal carcinoma (NPC) has not been reported.

CASE SUMMARY: In this case study of a patient with stage IVb NPC, we utilized ctDNA as an independent biomarker to guide treatment. Chemotherapy was administered in the early stages of the disease, and local intensity-modulated radiation therapy was added when the patient tested positive for ctDNA, while radiation therapy was stopped and the patient was observed when the ctDNA test was negative. During the follow-up period, ctDNA signals became positive before tumor progression and became negative again at the end of treatment. We also explored the potential of ctDNA in combination with Epstein-Barr virus (EBV) DNA status to predict the prognosis of NPC patients, as well as the criteria for selecting genetic mutations and the testing cycle for ctDNA analysis.

CONCLUSION: The results of ctDNA-based liquid biopsy can serve as an independent biomarker, either independently or in conjunction with EBV DNA status, to guide the treatment and predict the prognosis of NPC.

PMID:40726932 | PMC:PMC12068179 | DOI:10.12998/wjcc.v13.i21.105066

Int J Surg. 2025 Jul 15. doi: 10.1097/JS9.0000000000003049. Online ahead of print.

ABSTRACT

Fatty acid-binding proteins (FABPs) are intracellular lipid chaperones with molecular weights of approximately 14-15 kDa. By binding and transporting fatty acids and lipid-related molecules, FABPs precisely regulate metabolic pathways, signal transduction, and gene expression, playing a central role in cancer initiation and progression. The 11 identified subtypes (FABP1-FABP12; FABP11 is identical to FABP3) exhibit tissue-specific expression and influence tumor progression through metabolic reprogramming, immune microenvironment modulation, and therapy resistance. Metabolically, FABPs enhance fatty acid uptake, β-oxidation, and synthesis, meeting the high proliferative demands of tumors. In immune regulation, FABP4+ macrophages secrete IL-6 to suppress T cell activity, while FABP6 downregulates MHC-I molecule expression to reduce CD8+ T cell infiltration, fostering an immunosuppressive microenvironment. Regarding therapy resistance, FABP4 enhances mitochondrial β-oxidation to reduce apoptosis in ovarian cancer, and FABP5 promotes chemoresistance in HCC via the HIF-1α pathway. Functional heterogeneity exists among subtypes: FABP7 drives glioblastoma stem cell migration via RXRα signaling, while FABP5 exhibits context-dependent roles, promoting HCC progression but suppressing colorectal cancer (CRC) through mTOR-mediated autophagy. Clinically, FABPs serve as diagnostic biomarkers and therapeutic targets. However, challenges such as insufficient target specificity, cross-cancer heterogeneity, and normal tissue toxicity remain. Future studies should integrate multi-omics and single-cell technologies to elucidate cell-specific mechanisms and develop precise combination therapies for clinical translation.

PMID:40717587 | DOI:10.1097/JS9.0000000000003049

Iran J Pharm Res. 2025 May 25;24(1):e159954. doi: 10.5812/ijpr-159954. eCollection 2025 Jan-Dec.

ABSTRACT

CONTEXT: Gastric cancer (GC) is a major global health burden, with drug resistance representing a critical barrier to effective treatment. Understanding the mechanisms underlying drug resistance and leveraging advanced technologies, such as artificial intelligence (AI), are essential for developing innovative therapeutic strategies.

EVIDENCE ACQUISITION: This review systematically examines the primary mechanisms of drug resistance in GC, organized into eight categories: Reduced drug uptake, enhanced drug efflux, impaired pro-drug activation or increased inactivation, molecular target alterations, enhanced DNA damage repair, imbalance in apoptotic regulation, tumor microenvironment modifications, and phenotypic changes. Additionally, the role of AI in addressing these challenges is explored, with a focus on omics-driven insights, pathway analysis, biomarker discovery, and modeling drug-response relationships.

RESULTS: The review highlights the transformative potential of AI in advancing precision therapy for GC. Key applications include therapeutic stratification, optimization of drug combinations, adaptive therapy design, and integration with clinical workflows. Challenges such as data quality, model interpretability, and the need for interdisciplinary collaboration are identified, along with strategies to address these barriers. Future directions emphasize the development of explainable AI models, integration of multi-omics and real-time patient data, and AI-driven drug discovery targeting resistance pathways.

CONCLUSIONS: By bridging research and clinical practice, AI offers a promising path to more effective, personalized, and adaptive therapeutic strategies for GC. Overcoming existing challenges and leveraging AI's potential can significantly improve treatment outcomes and address the pressing issue of drug resistance in GC.

PMID:40708930 | PMC:PMC12285678 | DOI:10.5812/ijpr-159954

STAR Protoc. 2025 Jul 24;6(3):103980. doi: 10.1016/j.xpro.2025.103980. Online ahead of print.

ABSTRACT

Single-cell nucleosome, methylome, and transcriptome (scNMT) sequencing is a recently developed method that allows multiomics profiling of single cells. In this scNMT protocol, we describe profiling of cells from mouse brain and pancreatic organoids, using liquid handling platforms to increase throughput from 96-well to 384-well plate format. Our approach miniaturizes reaction volumes and incorporates the latest Smart-seq3 protocol to obtain higher numbers of detected genes and genomic DNA (gDNA) CpGs per cell. We outline normalization steps to optimally distribute per-cell sequencing depth. For complete details on the use and execution of this protocol, please refer to Kremer et al. and other works.^{1,2,3,4,5,6,7} This protocol is an update to Cerrizuela et al.⁷.

PMID:40711871 | DOI:10.1016/j.xpro.2025.103980

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