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At least 19 recordsLinked to original sources

PubMed2026

A G-Quadruplex-Activated Near-Infrared Chemiluminescent Probe for In Situ Hepatic Imaging of the Hepatitis C Virus Genome.

Real-time monitoring of viral replication is essential for infectious disease diagnosis and antiviral drug development. The G-quadruplex (G4), a conserved regulatory element within viral genomes, represents a significant endogenous biomarker for tracking viral activity. However, imaging viral G4s in deep tissues remains a challenge for current optical technologies due to severe photon attenuation and autofluorescence. Herein, we report Lumin680, the first near-infrared (NIR) chemiluminescent probe directly activated by conserved viral G4 conformations. Its chemiluminescence was triggered by parallel G4, emitting in the NIR optical window (680 nm) with a 104.6-fold signal enhancement. Notably, the luminescence of Lumin680 could penetrate up to 1.2 cm of biological tissue, outperforming traditional G4 fluorescent probe. In vivo, Lumin680 enabled the rapid visualization of orthotopic hepatitis C virus (HCV) genome RNA-presenting mini-organ within 5 min post-intravenous administration. Furthermore, the chemiluminescent intensity of Lumin680 quantitatively mapped the therapeutic efficacy of clinical direct-acting antivirals (DAAs) at both the cellular and whole-animal levels, exhibiting high concordance with the gold-standard quantitative RT-PCR (qPCR). This study not only provides a powerful G4 specific chemiluminescent tool but also establishes a novel paradigm for the non-invasive, in situ diagnosis and precise therapeutic monitoring of viral infections.

G-Quadruplexes↗

PubMed2026

Ultrasound-Actuated Gene Editing in Human Kidney Organoids.

Efficient delivery of gene editing ribonucleoproteins (RNPs) into the interior of solid tissues remains a key hurdle to the clinical translation of non-viral CRISPR-Cas9 technologies. Here, we report acoustically-actuated peptide nanoemulsions (NPeps) that can be spatiotemporally guided and activated by ultrasound to ballistically deliver RNPs into cells within the bulk of dense 3D cellular structures. Using human kidney organoids as a model, we demonstrate NPep vectors improve the spatial profile of gene editing in the organoid mass relative to commercial lipofection reagents, without disruption of tissue structure or qualitative viability features. This technologic paradigm is poised to advance imaging-guided, deep tissue RNP delivery modalities to expand the clinical diagnostic and therapeutic potential of CRISPR-Cas9 editing strategies.

Humans↗

PubMed2025

Scalable Deep Learning of Histology Images Reveals Genetic and Phenotypic Determinants of Adipocyte Hypertrophy.

BACKGROUND: White adipose tissue dysfunction has emerged as a critical factor in cardiometabolic disease development, yet the cellular microstructure and genetic architecture of adipocyte morphology remain poorly explored. METHODS: We introduce Adipocyte U-Net 2.0, an advanced deep learning method for the semantic segmentation of adipose tissue histology, enabling analysis of over 27 million adipocytes from 2,667 individuals. FINDINGS: Our approach revealed that adipocyte hypertrophy associates with metabolic dysfunction, including increased fasting glucose, glycated hemoglobin, leptin, and triglycerides, with decreased adiponectin and HDL cholesterol levels. Through the largest genome-wide association study of adipocyte size to date (NSubcutaneous = 2,066, NVisceral = 1,878), we identified four genome-wide significant loci: two in sex-combined analysis (rs73184721 in NAALADL2 and rs200047724 in NRXN3) and two female-specific variants (rs140503338 and rs11656704 in ULK2). Notably, these genetic associations showed congruent relationships with cardiometabolic traits, suggesting shared biological mechanisms. INTERPRETATION: Our findings demonstrate the utility of deep learning for adipocyte phenotyping at scale and provide novel insights into the genetic basis of adipocyte morphology and its relationship to metabolic disease.

Journal Article↗

PubMed2025

Histology-Based Virtual RNA Inference Identifies Pathways Associated With Metastasis Risk in Colorectal Cancer.

Colorectal cancer (CRC) remains a major health concern, with >150,000 new diagnoses and >50,000 deaths annually in the United States, underscoring an urgent need for improved screening, prognostication, disease management, and therapeutic approaches. The tumor microenvironment (TME)-comprising cancerous and immune cells interacting within the tumor's spatial architecture-plays a critical role in disease progression and treatment outcomes, reinforcing its importance as a prognostic marker for metastasis and recurrence risk. However, traditional methods for TME characterization, such as bulk transcriptomics and multiplex protein assays, lack sufficient spatial resolution. Although spatial transcriptomics (ST) allows for the high-resolution mapping of whole transcriptomes at near-cellular resolution, current ST technologies (eg, Visium and Xenium) are limited by high costs, low throughput, and issues with reproducibility, preventing their widespread application in large-scale molecular epidemiology studies. In this study, we refined and implemented virtual RNA inference (VRI) to derive ST-level molecular information directly from hematoxylin and eosin (H&E)-stained tissue images. Our VRI models were trained on the largest matched CRC ST data set to date, comprising 45 patients and >300,000 Visium spots from primary tumors. Using state-of-the-art deep learning models (UNI, ResNet-50, Vision Transformer, and Vision Mamba), we achieved a median Spearman's correlation coefficient of 0.546 between predicted and measured spot-level expression. As validation, VRI-derived gene signatures linked to specific tissue regions (tumor, interface, submucosa, stroma, serosa, muscularis, and inflammation) showed strong concordance with signatures generated via direct ST, and VRI performed accurately in estimating cell-type proportions spatially from H&E slides. In an expanded CRC cohort controlling for tumor invasiveness and clinical factors, we further identified VRI-derived gene signatures significantly associated with key prognostic outcomes, including metastasis status. Although certain tumor-related pathways are not fully captured by histology alone, our findings highlight the ability of VRI to infer a wide range of "histology-associated" biological pathways at near-cellular resolution without requiring ST profiling. Future efforts will extend this framework to expand TME phenotyping from standard H&E tissue images, with the potential to accelerate translational CRC research at scale.

Humans↗

PubMed2025

Ossifying Spindled and Epithelioid Tumor: A Novel Soft Tissue Tumor.

This investigation describes the clinicoradiologic, pathologic, and molecular features of a unique soft tissue tumor characterized by a peripheral shell of bone and composed of bland myoid spindle and epithelioid cells that are keratin-positive. Our study cohort consists of 6 men and 6 women, with a mean age of 32 years. The tumors arose in the extremities (n = 9) and proximal limb girdle (n = 3) and were equally distributed between deep and superficial soft tissues. Patients reported dull painless masses of several months to >10 years duration (mean: 2.9 years). Imaging demonstrated a complete or partial peripheral shell of bone that could extend centrally, and the tumor's mean size was 5.7 cm. Histologically, the tumors were composed of uniform, eosinophilic myoid spindled cells growing in sheets and intersecting fascicles, surrounded by mature lamellar and/or woven bone. Also present was an admixed component of intermediate-sized epithelioid cells with eosinophilic cytoplasm. Mitotic activity was consistently low. Immunohistochemistry showed strong multifocal staining for keratins, and 50% (5/10) showed focal staining for S100; however, all were negative for SMA, desmin, SOX10, ERG, and CD34. Genetic analysis by multiple targeted RNA sequencing panels was negative (n = 10); however, whole transcriptome sequencing (n = 8) revealed a recurrent and novel in-frame SRSF7::NFATC3 fusion in 4 tumors. Dual fluorescence in situ hybridization probes for SRSF7::NFATC3 successfully confirmed this fusion and identified a fifth case, which had not undergone whole transcriptome sequencing but was negative by a targeted RNA fusion panel. Methylation profiling (n = 8) demonstrated a shared epigenetic profile distinct from other entities. Clinical follow-up (n = 11) showed no evidence of recurrence after primary excision with a mean of 41.6 months. In summary, we describe a novel soft tissue tumor designated "ossifying spindled and epithelioid tumor" as a descriptive histologic term that also emphasizes its close radiologic mimic, ossifying fibromyxoid tumor. All cases have behaved in a benign fashion without recurrence following simple excision. Awareness of this entity is important, so that it can be distinguished from other neoplasms that have more aggressive biological potential.

Humans↗

PubMed2026

Cross-Device Adaptation of Mirai for Mammography-Based Breast Cancer Risk Prediction.

Fine-tuning can adapt pretrained medical imaging models to new clinical datasets, but device-specific domain shifts may limit generalizability. We evaluated Mirai, a mammography-based deep learning model for breast cancer risk prediction, in a large screening cohort containing Hologic and General Electric (GE) full-field digital mammography systems, including GE Premium View (GE PV) and Tissue Equalization (GE TE) post-processing software. Native Mirai showed lower performance on TE images than on Hologic or PV images. Fine-tuning on TE images improved TE performance, particularly for short-term risk prediction, but substantially reduced performance on Hologic images, consistent with catastrophic forgetting. To mitigate this effect, we developed a device-invariant model using interleaved multi-device sampling and conditional adversarial training. This approach largely restored Hologic performance while maintaining improved TE performance, providing better robustness across heterogeneous imaging platforms. Comparison of cumulative and annual risk AUCs over a five-year time horizon further showed that performance gains were driven mainly by short- and intermediate-term predictions. These findings highlight both the value and dangers of device-specific fine-tuning and support balanced domain-adaptation strategies for deploying mammography-based risk models across diverse clinical imaging environments.

Journal Article↗

PubMed2026

DeepPlaque: a scalable multimodal platform for Aβ pathology and cell analysis in Alzheimer's disease.

Histological analysis is essential for understanding disease pathology and the microenvironment, particularly in Alzheimer's disease (AD), characterized by beta-amyloid (Aβ) plaques that exist as diffuse, fibrillar, and core species, with distinct toxicity levels. However, accurate classification of Aβ plaque types in postmortem brain tissues and profiling of surrounding cells present significant challenges. To address these challenges, we developed "DeepPlaque", an integrated system featuring "PlaqueNet", a deep learning model for automated classification of Aβ plaque species from diverse imaging platforms. DeepPlaque includes automated workflows for cellular phenotyping and proteomic profiling through targeted laser microdissection. PlaqueNet achieves expert-level accuracy (AUC > 90%) in classifying the 3 major Aβ plaque species, supporting consistent and large-scale annotation. By integrating spatial cellular phenotyping with laser microdissection, DeepPlaque enables high-throughput proteomic analysis of Aβ plaque niches, revealing that microglia are more abundant around core and fibrillar Aβ plaques, with increased expression of apolipoprotein E and amyloid precursor protein in core Aβ plaques. This customizable platform enhances the molecular and cellular characterization of Aβ plaque-associated environments, providing critical insights into AD pathology.

Alzheimer Disease↗

PubMed2026

Scalable, generalizable and uncertainty-aware integration of spatial multiomics across diverse modalities and platforms with SCIGMA.

Recent advances in spatial omics technologies have enabled simultaneous profiling of transcriptomic, proteomic, epigenomic, metabolomic and imaging data at high spatial resolution, offering unprecedented opportunities to dissect tissue complexity. However, integrating these diverse and large-scale spatial multimodal datasets remains a major computational challenge. We present SCIGMA, a scalable and generalizable deep learning framework for spatial multiomics integration. SCIGMA introduces an uncertainty-aware contrastive learning objective and multiview graph neural networks to preserve modality-specific signals while learning biologically meaningful joint representations. Unlike previous methods, SCIGMA provides spatially resolved uncertainty estimates, interpretably identifying regions of biological or technical heterogeneity. SCIGMA supports integration of up to five modalities, and its modular framework is extensible to future technologies with even more modalities. It also scales to more than 1 million spatial locations, enabling analysis of high-resolution datasets such as Visium HD and Xenium Prime. We evaluated SCIGMA across 19 datasets spanning 8 modalities, 10 tissues and 9 platforms. On benchmarkable datasets, SCIGMA outperformed other methods in spatial domain detection, modality preservation, feature reconstruction and reproducibility. SCIGMA identifies biologically meaningful structures, refined spatial domains and modality-specific regulatory programs, providing a robust, flexible and future-ready solution for scalable spatial multimodal integration.

Multiomics↗

PubMed2026

A voyage of reprogrammable metabolic bioengineering reshapes plant defense: from editing tools to synthetic systems.

Metabolic bioengineering has emerged as a transformative approach for reshaping plant defense by targeting intrinsic biosynthetic pathways to enhance immunity in modern agriculture. Moving beyond proof-of-concept metabolomics to broad-spectrum programmable pathway engineering addresses gaps in plant rational design and optimizes resilience in response to diverse environmental cues. This review aims to comprehensively highlight the transition of innovative approaches to phenolics, alkaloids, flavonoids, terpenoids, and benzoxazinoids, inferring adaptive reprogramming that mediates the growth-defense balance and functions as molecular sentinels in plants. Furthermore, decoding the volatile metabolome reveals a dynamic signaling interface that influences defense responses and stress-induced plant-microbe interactions, with the shikimate, jasmonate, and salicylate pathways functioning as central hubs for microbial deterrence and priming immune memory. Recent developments in multi-scalar genome-editing strategies, including CRISPR-driven combinatorial edits, enzyme orthogonalization, fluxomics, and spatially resolved multi-omics, reconfigure central and specialized metabolic fluxes toward improved defense function and regulation. Additionally, emerging tools, such as WUSCHEL2 and BABY BOOM transcriptional modules, and artificial engineering strategies integrating deep learning model-driven predictions facilitate rapid development of synthetic genetic circuits and support a predictive engineering of plants. Moreover, Mass spectrometry imaging (MSI) in spatial metabolomics enables to obtain structures and locations of unidentified endogenous metabolites within cells and tissues. Overall, this review emphasizes a diverse array of primary and secondary metabolites, spanning molecular concepts to recent advances in plant immune mechanisms. It also illustrates new frontiers in programmable metabolic engineering that accelerate the understanding of plant-microbe-metabolite cross-talks, offering strategies to improve plant resistance and advance sustainable agricultural solutions.

metabolic bioengineering↗

PubMed2026

stDyer-image improves clustering analysis of spatially resolved transcriptomics and proteomics with morphological images.

MOTIVATION: Spatially resolved transcriptomics (SRT) and spatially resolved proteomics (SRP) data enable the study of gene expression and protein abundances within their precise spatial and cellular contexts in tissues. Certain SRT and SRP technologies also capture corresponding morphology images, adding another layer of valuable information. However, few existing methods developed for SRT data effectively leverage these supplementary images to enhance clustering performance. RESULTS: Here, we introduce stDyer-image, an end-to-end deep learning framework designed for clustering for SRT and SRP datasets with images. Unlike existing methods that utilize images to complement gene expression data, stDyer-image directly links image features to cluster labels. This approach draws inspiration from pathologists, who can visually identify specific cell types or tumor regions from morphological images without relying on gene expression or protein abundances. Benchmarks against state-of-the-art tools demonstrate that stDyer-image achieves superior performance in clustering. Moreover, it is capable of handling large-scale datasets across diverse technologies, making it a versatile and powerful tool for spatial omics analysis. AVAILABILITY AND IMPLEMENTATION: The source code of stDyer-image and detailed tutorials are available at https://github.com/ericcombiolab/stDyer-image.

Proteomics↗

PubMed2025

Complex IV deficiency due to COX4I1 deep intronic and de novo variants results in progressive motor impairment and Leigh syndrome.

COX4I1 gene encodes cytochrome c oxidase subunit 4 isoform 1, involved in the early assembly stages of mitochondrial respiratory chain complex IV. To date, COX4I1 pathogenic variants have been reported in only a few cases, each exhibiting heterogeneous clinical phenotypes and limited functional data. Here, we describe the fourth reported case of COX4I1 deficiency associated with human disease, expanding the phenotypic and genetic spectrum of this rare mitochondrial disorder and providing novel clinical, molecular, and functional data. The herein reported individual presented with progressive deterioration of motor skills, intellectual disability and brain imaging abnormalities compatible with Leigh syndrome. Genetic studies combining short and long read next generation sequencing uncovered a peculiar genetic combination in this patient, harboring a de novo COX4I1 nonsense substitution in trans with an inherited deep intronic variant (c.[64C>T];[73+1511A>G]; p.[Arg22Ter];[Glu25ValfsTer9]). Functional studies performed in patient's tissues and transiently transfected cell lines demonstrated that the identified variants mainly exert their pathogenic effect by targeting COX4I1 protein levels, thereby impairing the proper assembly and activity of complex IV.Additionally, proteomic data in patient's fibroblasts suggested an underlying pathomechanism that involves not only the regulation of complex IV function but also the levels of mitoribosomal proteins. In summary, our findings shed light to clarify some of the main clinical features associated with COX4I1 deficiency and the molecular mechanisms involved in the pathogenesis of this disorder.

Humans↗

PubMed2026

A framework for delivering real-time, instrument-relative navigation in transoral robotic surgery.

Transoral robotic surgery (TORS) is a minimally invasive, inside-out technique that, compared with traditional open approaches, provides fewer post-operative complications, shorter hospital stays, and improved survival for early-stage head and neck cancer. However, TORS is limited by its steep learning curve and poor visualization of deep tumor margins. This randomized crossover study evaluated a surgical navigation system's potential to enhance accuracy and user experience with real-time, instrument-relative feedback. Seven Teflon beads (d = 2.381 mm) were embedded at the tongue base of a porcine pharynx-and-larynx model. Tongue blade compression and retraction were applied to the model to mimic intraoperative tissue deformation, reproducing the anatomical shifts that occur relative to preoperative imaging. Eight participants used the da Vinci Surgical system to localize the beads by placing pins under two conditions: (a) preoperative computed tomography with no navigation; (b) model-based visual navigation with quantitative instrument-to-target metrics. Surgical accuracy was determined by calculating the target localization error (TLE, pin-to-bead Euclidean distance) and the angular error (AE, pin axis trajectory to bead). Accounting for training level and bead depth, surgical navigation reduced TLE by 5.44 mm (95% CI, 4.02-6.86 mm; p = 2.00e-11) and AE by 8.47 degrees (95% CI, 6.21-10.72 degrees; p = 5.17e-11). Impressions of the system were generally favorable using a 5-point Likert survey and task duration (p = 0.26) or cognitive workload via the NASA-Task Load Index (p = 0.22) were not significantly affected. The navigation system demonstrated translational promise, offering improved target localization accuracy and more consistent performance across experience levels, two critical determinants of surgical quality in TORS.

Robotic Surgical Procedures↗

PubMed2026

Spatial Multiomics Reveal Insights Into ADC Efficacy.

Antibody-drug conjugates (ADCs) have transformed the therapeutic landscape of solid tumors; however, responses remain heterogeneous and complex to predict. In addition, a growing number of multiple ADC targets are either approved or in late-stage clinical development, such as NECTIN-4, HER2, or TROP2 for metastatic urothelial cancer. Spatial multiomics-representing next-generation methods that couple high-plex RNA sequencing and multiplex protein imaging with precise x-y-z coordinates within tissues-offer a direct way to correlate (ADC) antigen expression, cell state information, and micro-anatomical context with patient treatment outcomes. In this review, we highlight suitability and technological advancements in current spatial transcriptomics and proteomics approaches to decode modes of action and resistance to ADCs and extract biological insights, particularly in metastatic urothelial cancer-and propose an integrative framework that combines spatial readouts with machine and/or deep learning-driven analytics to stratify patients, forecast on- and off-target toxicities, and guide next-generation linker-payload designs or combination therapies.

Humans↗

PubMed2026

Digital pathology, image analysis, and artificial intelligence in liver disease.

Advances in digital pathology, image analysis, and artificial intelligence (AI) are rapidly transforming how pathologists and researchers interact with tissue samples and enable the development of diagnostic tools that harness high-resolution whole-slide images; these advances are in turn creating new opportunities for research, education, and routine clinical care globally. Liver disease is no exception, and digital pathology and AI have many applications in the diagnosis of liver cancer and liver diseases and in the assessment and management of transplantation. Although quantitative image analysis techniques have been applied to liver disease in research settings for over 50 years, recent improvements in image resolution, data storage, and the availability of advanced AI methods such as deep learning have driven multiple exciting developments. In this Review, we summarise the advancements in digital pathology, image analysis, and AI in liver disease. Key challenges such as access to and the logistics of using digital solutions, quality issues, and appropriate guidance in research and clinical use are reviewed, along with potential solutions to these challenges in the context of liver pathology and liver disease. Digital technologies are well established in liver pathology research, and access in clinical practice is increasing, with potential to address current laboratory challenges. Further evaluation is required to assess real-world effectiveness, clinical safety, and implementation of AI tools in liver pathology.

Journal Article↗

PubMed2026

Quantitative susceptibility mapping in neurodegenerative diseases: An umbrella review of iron-related biomarkers and mechanisms.

Pathological iron accumulation is a common pathophysiological hallmark across multiple neurodegenerative diseases (NDDs), motivating the need for accurate, non-invasive quantification methods. Quantitative susceptibility mapping (QSM) is an advanced magnetic resonance imaging (MRI) technique that enables in vivo measurement of tissue magnetic susceptibility (χ), providing a sensitive proxy for iron content. This umbrella review systematically evaluates the diagnostic accuracy, clinical correlations, and distinct iron distribution patterns of QSM in major NDDs, such as Parkinson's disease (PD), Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS), and atypical Parkinsonism. We included 15 (13/15 were rated Low or Critically Low on AMSTAR 2) systematic reviews and meta-analyses (through July 15, 2026); however, the findings should be interpreted cautiously because of heterogeneity and the low methodological quality. A Corrected Covered Area (CCA) analysis demonstrated only slight overlap of primary studies across the included reviews (CCA = 5.42%). Collectively, the evidence indicates that QSM provides comparable or higher diagnostic sensitivity and reliability than conventional R2* and SWI techniques, particularly for deep gray matter structures. The findings support significant iron overload in the substantia nigra, particularly in the pars compacta, as a robust biomarker for PD that correlates with motor severity and disease duration. Furthermore, regional iron profiling in the basal ganglia is critical for differential diagnosis; specifically, elevated χ in the putamen and globus pallidus effectively distinguishes multiple system atrophy and progressive supranuclear palsy from idiopathic PD. Distinctively, AD and ALS exhibit specific χ alterations in the thalamus, motor cortex, and hippocampus, reflecting divergent iron-related pathophysiological mechanisms, which correlate with cognitive impairment and upper motor neuron signs. Overall, QSM shows diagnostic promise and offers mechanistic insights into iron-related neurodegenerative processes.

Humans↗

PubMed2026

Integrating histology and spatial transcriptomics via multimodal transformers and contrastive representation learning for accurate gene expression prediction.

Predicting spatial gene expression from Histological images is a fundamental task in understanding tissue organization and molecular phenotypes. However, existing methods often rely on single-model representations or lack effective alignment between image and transcriptomic features. To address these limitations, we propose a unified multimodal learning framework that integrates histological imaging and spatial transcriptomics through a shared latent representation space. Specifically, histological H&E images are encoded by a ResNet50-based convolutional stem and a MobileViT Transformer backbone to extract hierarchical visual representations. Both modalities are projected into a shared latent space via linear-GELU-dropout transformation blocks, enabling cross-modal alignment through a contrastive learning objective that maximizes agreement between the corresponding image and the spot embeddings. Experimental results on the 10x Genomics Visium dataset of human liver tissue demonstrate that MViTGene achieves significantly higher prediction accuracy than existing methods across multiple gene subsets, with improvements of 20%, 33%, and 12% in predicting marker genes, highly expressed genes, and highly variable genes, respectively. The significant improvement in relevance indicates that the model can more accurately capture the true correspondence between tissue morphology and gene expression, therefore enabling more reliable biological interpretation. It provides a computational tool for high-throughput spatial gene expression prediction that balances performance and interpretability.

Humans↗

PubMed2026

Linking cortical structure and delirium in the elderly: insights from cohort study and shared genetic risk analysis.

BACKGROUND: This study aimed to assess the association between regional cortical changes measured via baseline magnetic resonance imaging (MRI) and the incidence of delirium. METHODS: Observational associations were assessed using a prospective cohort from the UK Biobank and an independent clinical cohort. The population-based study included participants aged 60 years or older who had undergone structural brain MRI since 2014. Regional cortical volume, mean thickness, and surface area were extracted based on the Desikan-Killiany cortical atlas. Delirium was defined using ICD-10 diagnostic codes. Additionally, preoperative brain MRI images from participants in another cohort were collected and automatically segmented using deep learning algorithms to obtain cortical measurements. Logistic analysis was performed to investigate the associations between cerebral cortical structure and delirium risk. Lastly, genome-wide association study data derived from the ENIGMA Consortium and FinnGen Biobank were utilized to conduct conditional/conjunctional false discovery rate (cond/conjFDR) analyses to identify shared genetic loci associated with cortical structures and delirium. RESULTS: This observational analysis included 31,890 participants from the UK Biobank and 152 participants from an independent cohort. In the UK Biobank cohort, decreased cortical thickness in the 17 regions was associated with a significantly increased risk of delirium. Similarly, a preoperative reduction in cortical volume in 7 regions was associated with an increased risk of delirium in the independent cohort. Besides, 100 single-nucleotide polymorphisms (SNPs) were identified as significantly associated with cortical structures when conditioned on delirium. Finally, colocalization analysis demonstrated that these pleiotropic risk loci modulated the expression of NT5C2, RGP1, CCDC25, TPM2, EEF1AKMT2, IQANK1 and LHPP in blood and brain tissues. CONCLUSION: Regional cortical atrophy is associated with an increased risk of delirium in the elderly. Brain MRI examinations may be beneficial for preoperative delirium risk assessment in elderly individuals undergoing elective surgery.

Humans↗

PubMed2026

Single-section multiplex spatial proteomics of immune microenvironments in kidney transplantation.

Characterizing kidney disease is challenged by marked cellular heterogeneity and limited tissue availability from renal biopsies. Conventional diagnostic workflows rely on multiple serial sections for parallel staining, increasing tissue consumption, sampling bias, and loss of spatial information, thereby constraining molecular characterization within intact tissue architecture. High-plex spatial proteomics may overcome these limitations by enabling comprehensive molecular profiling on a single section. Here, we present and evaluate a high-plex cyclic immunofluorescence imaging workflow (MACSima™, Miltenyi Biotec) applied to kidney transplant biopsies, including BK virus nephropathy (BKVN) and focal segmental glomerulosclerosis (FSGS), to characterize spatial immune organization with a focus on complement system components. Feasibility and subcellular resolution were first assessed in a lupus nephritis section, demonstrating compatibility with diagnostic immune panels and preservation of tissue morphology. A 48-marker multiplex panel interrogating immunity, oxidative stress, senescence, and fibrosis was then applied to BKVN samples, including paired pre- and post-treatment biopsies, revealing distinct proteomic patterns and dynamic changes following therapy. In FSGS, a glomerulus-focused panel identified spatially resolved innate and adaptive immune signatures, including complement-related patterns supporting exploratory analysis of glomerular immune architecture. Structural, nuclear, membrane, and phosphorylated signaling markers enabled precise delineation of renal compartments and assessment of cellular states such as proliferation, DNA damage, and pathway activation. The workflow also supported detection of extracellular vesicles in cultured renal cells, highlighting its versatility. Overall, this approach provides a robust, tissue-sparing platform for integrated spatial and molecular profiling of renal biopsies, reducing sampling bias while enabling discovery-level phenotyping from a single section. This unified strategy is particularly suited to kidney transplantation, where diagnosis, therapeutic decision-making, and longitudinal monitoring are closely interconnected.

Kidney Transplantation↗