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

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

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

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

J Clin Invest. 2025 Jul 15;135(14):e191937. doi: 10.1172/JCI191937. eCollection 2025 Jul 15.

ABSTRACT

Pancreatic ductal adenocarcinoma (PDAC) is known to progress from one of two main precursor lesions: pancreatic intraepithelial neoplasia (PanIN) or intraductal papillary mucinous neoplasm (IPMN). The poor survival rates for patients with PDAC, even those diagnosed with localized disease, highlight the need for pancreatic cancer interception at the precursor stage. Although their basic biological drivers are well characterized, practical strategies for PanIN and IPMN interception remain elusive due to difficulties with detection, risk stratification, and low-morbidity intervention. Recently, advances in liquid biopsy, spatial multiomics analysis, and machine learning technology have provided deeper understanding of the molecular landscapes underlying pancreatic precursor development and progression. In this Review, we outline the different histologic phenotypes, clinical characteristics, and neoplastic cell-intrinsic and -extrinsic drivers of PanINs and IPMNs, with particular focus on current and potential future opportunities for pancreatic precancer interception.

PMID:40662372 | PMC:PMC12259249 | DOI:10.1172/JCI191937

Brief Bioinform. 2025 Jul 2;26(4):bbaf312. doi: 10.1093/bib/bbaf312.

ABSTRACT

Advancements in high-throughput sequencing technologies and artificial intelligence (AI) offer unprecedented opportunities for groundbreaking discoveries in bioinformatics research. However, the challenges of exponential growth of omics data and the rapid development of AI technologies require automated big biological data analysis capability and interdisciplinary knowledge-driven scientific insight. Here, we propose a data-intelligence-intensive bioinformatics copilot (Bio-Copilot) system that synergizes AI capabilities with human researchers to facilitate hypothesis-free exploratory research and inspire novel scientific insights in large-scale omics studies. Bio-Copilot forms high-quality intensive intelligence through close collaboration between multiple agents, driven by large language models (LLMs), and human researchers. To augment the capabilities of Bio-Copilot, this study devises an agent group management strategy, an effective human-agent interaction mechanism, a shared interdisciplinary knowledge database, and continuous learning strategies for the agents. We comprehensively compare Bio-Copilot against GPT-4o and several leading AI agents across diverse bioinformatics tasks, using a broad range of evaluation metrics. Bio-Copilot achieves overall state-of-the-art performance across all tasks, while showcasing exceptional task completeness. Furthermore, on application to constructing a large-scale human lung cell atlas, Bio-Copilot not only reproduces the intricate data integration process detailed in a seminal study but also introduces a recursive, multilevel annotation strategy to capture the continuous nature of cellular states and uncovers the characteristics of rare cell types, highlighting its potential to unravel hidden complexities in biological systems. Beyond the technical achievements, this study also underscores the profound implications of integrating AI capabilities with expert knowledge in accelerating impactful biological discoveries and exploring uncharted territories.

PMID:40639418 | PMC:PMC12245162 | DOI:10.1093/bib/bbaf312

Eur J Trauma Emerg Surg. 2025 Jun 27;51(1):238. doi: 10.1007/s00068-025-02922-7.

ABSTRACT

PURPOSE: Obesity is a prevalent disease, but its influence on post-injury biology remains unclear. In this study, we aimed to characterize the independent effect of obesity on the proteomic and metabolomic signatures of trauma.

METHODS: Plasma was obtained on arrival from injured patients at a Level 1 Trauma Center and analyzed with modern mass spectrometry-based proteomics and metabolomics. Samples obtained after start of transfusion were excluded. Patients were stratified by "obesity" (body mass index [BMI]≥30 kg/m²) vs. "no obesity" (BMI < 30 kg/m²). In sub-group analyses, patients were sub-stratified by Low Injury/Low Shock (ISS < 15, base excess [BE]≥-6mEq/L) and High Injury/High Shock (ISS≥15, BE<-6). Multiple regression was used to adjust the omics data for significant covariates prior to performing ome-wide analyses.

RESULTS: There were 183 patients included (48 [26%] with obesity and 135 [74%] without). After covariate-adjustment, multiple proteins and metabolites were correlated with ISS and/or BE and were significantly different from Low Injury/Low Shock to High Injury/High Shock only in patients with obesity. This obesity-specific omics response to injury was characterized by increased inflammation, hypercoagulability, altered nitrogen metabolism, and mitochondrial dysfunction. Patients with obesity also exhibited excessive injury-provoked tissue destruction and organ damage compared to patients without obesity. In injury severity-adjusted analyses, the obesity signature consistently displayed markers of hemolysis, likely reflecting a pre-injury hemolytic propensity.

CONCLUSION: Obesity is independently associated with altered post-injury biology, which likely underlies unique pathology in trauma patients with obesity. Identifying this aberrant response to injury is the first step in developing personalized therapies for this patient population.

PMID:40576654 | DOI:10.1007/s00068-025-02922-7

Biomolecules. 2025 May 31;15(6):803. doi: 10.3390/biom15060803.

ABSTRACT

Lung cancer is responsible for 2.21 million annual cancer cases and is the leading worldwide cause of cancer-related deaths. Specifically, lung adenocarcinoma (LUAD) is the most prevalent lung cancer subtype resulting from genetic causes; LUAD has a 15% patient survival rate due to it commonly being detected in its advanced stages. This study aimed to identify a novel biomarker signature of early-stage LUAD utilizing gene expression analysis of human lung tissue samples. Using 22 pairs of LUAD and matched normal lung microarrays, 229 differentially expressed genes were identified. These genes were networked for their protein-protein interactions, and 44 hub genes were determined from protein essentiality. Survival analysis of 478 LUAD patient samples identified four statistically significant candidates. These candidate genes' expression profiles were validated from GTEx and TCGA (347 normal, 483 LUAD samples); immunohistochemistry validated the subsequent protein presence. Through intensive bioinformatic identification and multiple validations of the four-biomarker gene signature, AGER, MGP, and PECAM1 were identified as downregulated in LUAD; SLC2A1 was identified as upregulated in LUAD. These four biologically significant genes are involved in tumorigenesis and poor LUAD prognosis, meriting their use as a clinical biomarker signature and therapeutic targets for early-stage LUAD.

PMID:40563443 | PMC:PMC12191159 | DOI:10.3390/biom15060803

JCI Insight. 2025 Jun 26:e191595. doi: 10.1172/jci.insight.191595. Online ahead of print.

ABSTRACT

Pancreatic ductal adenocarcinoma (PDAC) has a poor survival rate due to late detection. PDAC arises from precursor microscopic lesions, termed pancreatic intraepithelial neoplasia (PanIN), that develop at least a decade before overt disease--this provides an opportunity to intercept PanIN-to-PDAC progression. However, immune interception strategies require full understanding of PanIN and PDAC cellular architecture. Surgical specimens containing PanIN and PDAC lesions from a unique cohort of five treatment-naïve patients with PDAC were surveyed using spatial-omics (proteomic and transcriptomic). Findings were corroborated by spatial proteomics of PanIN and PDAC from tamoxifen-inducible KPC (tiKPC) mice. We uncovered the organization of lymphoid cells into tertiary lymphoid structures (TLSs) adjacent to PanIN lesions. These TLSs lacked CD21+CD23+ B cells compared to more mature TLSs near the PDAC border. PanINs harbored mostly CD4+ T cells with fewer Tregs and exhausted T cells than PDAC. Peri-tumoral space was enriched with naïve CD4+ and central memory T cells. These observations highlight the opportunity to modulate the immune microenvironment in PanINs before immune exclusion and immunosuppression emerge during progression into PDAC.

PMID:40569674 | DOI:10.1172/jci.insight.191595