Molecular alterations in the fallopian tubes play a pivotal role in the development of cancer and reproductive disorders, yet their molecular landscape at the protein level remains poorly defined. Here, we map key fallopian tube proteins at single-cell resolution utilizing an integrated transcriptomics and proteomics approach. Based on RNA-seq analysis, we identify 310 genes with elevated expression in the fallopian tube, the majority of which are associated with motile cilia function. We spatially characterize 133 of the corresponding proteins in the fallopian tube and other human tissues with motile cilia to subcellular structures of ciliated cells, validating the findings with single-cell RNA-seq and mass-spectrometry data. Eleven proteins previously only studied on the transcript level without information in cilia databases are further analyzed in a hydrosalpinx patient, showing a thinner epithelium, lower density of FOXJ1 expression, and reduced expression of FHAD1, RIIAD1, and C2orf81. Our high-resolution spatial map aids in dissecting the pathways underlying infertility and diseases linked to cilia-specific functions.

Motile cilia are complex structures regulated by thousands of genes, essential for various physiological functions like respiration and reproduction. Their dysfunction can result in severe conditions like primary ciliary dyskinesia (PCD), highlighting the need for a deeper molecular understanding of their specific ciliary compartments. Interestingly, ciliated cells harbor multiple proteins with limited evidence on biological function, as defined by Functional Evidence (FE) scores, a grading system developed by the Human Proteome Project (HPP). Building upon the stringent antibody validation pipeline of the Human Protein Atlas (HPA) project, we developed a high-throughput workflow that combines a novel multiplex immunohistochemistry protocol with image analysis to investigate protein expression and subcellular localization in motile ciliated cells across five human tissues: nasopharynx, bronchus, fallopian tube, endometrium, and cervix. We spatially mapped >180 proteins, out of which 73% have FE scores 2–5, suggesting that further evidence is needed to establish these proteins’ biological function. Notably, expression patterns varied between tissues, suggesting that motile cilia proteins are not universally expressed across the different epithelia. Our pipeline constitutes a promising resource for comprehensive mapping of the motile cilia proteome, and a first step toward identifying cilia proteins for functional studies to understand the molecular mechanisms underlying ciliopathies.

The hippocampus comprises subregions of distinct cell types critical for memory and cognition, but their gene expression profiles and spatial distribution patterns remain to be clarified. Using single-cell spatial transcriptomic analysis and single-nucleus RNA sequencing, we obtained transcriptome-based atlases for the macaque, marmoset and mouse hippocampus. Cross-species comparison revealed primate-and lamina-specific glutamatergic cell types in the subicular complex, as well as enrichment of VIP-expressing GABAergic cells from mice to primates, including humans. Furthermore, we found reduced transcriptomic differences between CA3 and CA4 subregions and distinct longitudinal distributions of various cell types and expression of ion-channel genes, correlated with differences in electrophysiological properties of CA3, CA4 and CA1 neurons revealed by slice recording from marmosets and mice. Collectively, this cross-species study provides a molecular and cellular basis for understanding the evolution and function of the hippocampus.

Despite rapid advances in whole-genome sequencing (WGS), translating genomic findings into individualized insights remains challenging. We present GenRiskPro, a clinical decision-support and research platform, which automates WGS variant calling, annotation, prioritization, and reporting to deliver actionable findings and facilitate precision wellness. (To test the GenRiskPro platform, log on to https://www.phenomeportal.org/dashboard using the following credentials: Username: user@test.com; Password: test.) GenRiskPro integrates rare and common variant prioritization in a unified pipeline and in-house database, enabling both rare and complex disease and trait association analyses. Variant reporting is supported via LongevityCloud, which features a web portal for clinicians to review, adjust, and authorize the return of results in tabular and PDF formats, alongside a mobile app with artificial intelligence (AI) integration for sequenced individuals. Case studies using Turkish (TR, n = 275) and Swedish (SW, n = 101) WGS data assessed platform performance and variant prioritization: (a) predefined gene panels yielded a 1.82% positive rate for actionable findings per American College of Medical Genetics and Genomics (ACMG) secondary findings guidelines; (b) phenotype-driven support diagnosed cases including muscular dystrophy and microcephaly; (c) cohort-level ClinVar reassessment identified potentially misclassified pathogenic variants; (d) rare variant burden analysis revealed enrichment in ABCA4 for TR and SMPD1 in SW; and (e) population analysis highlighted carrier differences in trait-associated SNPs (rs12913832 and rs4988235) and PGx variants (CYP2B64 and CYP2B66). GenRiskPro unifies databases, literature, web development, and AI for rapid, user-friendly genomic analysis and reporting, which fosters collaboration among hospitals, researchers, clinicians, and patients.

Motivation: Exploration of large-scale biological datasets remains a central challenge in computational biology. While many tools are available, they are often developed in isolation, leading to fragmented workflows, duplicated efforts, and limited reproducibility. There is a pressing need for flexible, standardized solutions that unify exploratory data analysis and biomarker discovery across diverse platforms.

Results: We present HDAnalyzeR, a user-friendly and extensible R package for the streamlined analysis of high-dimensional biological data. HDAnalyzeR provides modular, reproducible workflows that support a range of analyses, from quality control and dimensionality reduction to differential expression and enrichment analysis. The package features built-in visualization, metadata-aware modeling, and seamless integration with interactive apps and learning resources. We also present two case studies, where HDAnalyzeR dramatically reduced analysis time and code complexity while providing biologically meaningful insights, such as classification of blood cancer types with AUC = 1.0 and identification of thousands of solid tumor-associated genes. HDAnalyzeR is designed to support both beginner users and experienced bioinformaticians, promoting transparency, reproducibility, and publication-quality output.

Availability and implementation: HDAnalyzeR is freely available both as an open-source R package at https://github.com/kantonopoulos/HDAnalyzeR and a web application at https://hdanalyzer.serve.scilifelab.se.

Proteomic research enhances our understanding of health- and disease-related biological processes. Protein profiling during healthy childhood provides important insights into normal physiological development. We longitudinally measured 5416 plasma proteins at four follow-ups during childhood (4-, 8-, 16 years) and early adulthood (24 years) in 100 randomly selected subjects participating in a population-based Swedish cohort, using Olink Explore HT. In total, 3509 proteins were included in the analysis. 54% of the proteins were found to be associated with age, and we observed several protein trajectories from childhood to adulthood based on clustering. In addition to proteins involved in bone, teeth and cartilage formation, we identified differences in proteins involved in neural function, drug metabolism, and hormonal control. There were pronounced sex-related differences in protein levels, particularly at follow-ups 16 and 24, characterized by, for example, growth, response to stimuli and regulation of catabolic processes. We demonstrate dynamic age- and sex-related changes in protein levels during the first two decades of life. Our study results may serve as an important resource in understanding human physiological development, disease etiology, and for future protein biomarker research.

While single-omics analyses of Parkinson's Disease (PD) have demonstrated their ability in revealing the underlying molecular mechanisms, they often fail to provide a comprehensive view of the complete disease mechanisms. In this study, we leveraged multi-omics data from 64 heterogeneous, well-phenotyped PD patients, generated plasma metabolomics data and Olink proteomics data together with the gut and saliva metagenomics data, and investigated the altered molecular mechanisms and their interactions in association with the severity of motor function disorders in PD patients. Based on our multi-omics approach, we identified a panel of 58 biomarkers comprising one clinical variable, 10 proteins, and 17 metabolites from plasma, 26 gut species, and 4 saliva species for PD severity. These biomarkers exhibited superior predictive performance for assessing PD severity compared to those derived from single-omics datasets. The predictive power of our machine learning models based on these biomarkers was validated using additional multi-omics data from the same group of PD patients after a 3-month follow-up. The contribution of each omics dataset was evaluated by both supervised and unsupervised machine learning approaches, highlighting the importance of plasma metabolomics in disease stratification. Our study unveiled disease-related molecular alterations across multiple omics datasets, offering potential diagnostic and therapeutic insights for PD. Moreover, it underpinned the significance of employing multi-omics analyses when studying complex diseases like PD.

Achieving minimally invasive and rapid detection is a crucial goal in modern medicine. The comprehensive characterization of the blood proteome holds great promise in advancing our understanding of disease etiology, facilitating early diagnosis, risk stratification, and improved monitoring across various diseases and their subtypes. In this study, we collected plasma proteomes from over 3000 patients, representing 23 distinct diseases, encompassing a total of 1462 proteins. Based on histological knowledge, we developed a two-stage hierarchical multi-disease classifier and applied it to perform multi-disease classification on the collected proteomic data. Our results demonstrate that this empirically guided two-stage hierarchical multi-disease classifier outperforms traditional machine learning algorithms in terms of prediction performance, showing better balance and more meaningful feature selections. This finding highlights the positive role that domain expertise can play in machine learning-based disease detection, and underscores the potential of plasma proteomics for multi-disease screening.

Multiple sclerosis (MS) is a neuroinflammatory disease of the CNS characterized by demyelinating lesions. Lesion expansion contributes to disability progression, whereas remyelination may restore neurological function. How these divergent outcomes relate to microglial states remains incompletely understood. Using single-cell-resolution spatial transcriptomics, we compare lesions containing foamy to those containing ramified microglia in postmortem human brain tissue. We find distinct cellular and molecular signatures spatially associated with microglial morphology. Lesions with ramified microglia display gene expression profiles associated with myelin stability and axonal support, consistent with an environment permissive for repair. In contrast, lesions with foamy microglia exhibit immune activation, immunoglobulin production, complement activity, iron dysregulation, immune-oligodendrocytes, and demyelination. These findings show that molecular programs linked to lesion pathology are spatially segregated in association with microglial state, indicating distinct immune-glial niches associated with lesion expansion and repair.

Obesity’s metabolic heterogeneity is not fully captured by body mass index (BMI). Here we show that deep multi-omics phenotyping of 1,408 individuals defines a metabolome-informed obesity metric (metBMI) that captures adipose tissue-related dysfunction across organ systems. In an external cohort (n = 466), metBMI explained 52% of BMI variance and more accurately reflected adiposity than other omics models. Individuals with higher-than-expected metBMI had 2–5-fold higher odds of fatty liver disease, diabetes, severe visceral fat accumulation and attenuation, insulin resistance, hyperinsulinemia and inflammation and, in bariatric surgery (n = 75), achieved 30% less weight loss. This obesogenic signature aligned with reduced microbiome richness, altered ecology and functional potential. A 66-metabolite panel retained 38.6% explanatory power, with 90% covarying with the microbiome. Mediation analysis revealed a bidirectional, metabolite-centered host–microbiome axis, mediated by lipids, amino acids and diet-derived metabolites. These findings define an adipose-linked, microbiome-connected metabolic signature that outperforms BMI in stratifying cardiometabolic risk and guiding precision interventions.