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.

Background & Aims: Primary sclerosing cholangitis (PSC) is a rare cholestatic liver disease with heterogeneous phenotypes and progression. Autoimmune traits, such as the presence of autoantibodies, are suspected to drive its heterogeneity. Methods: We performed a proteome-scale autoantibody screen of IgG and IgA isotypes using >42,100 protein fragments. This was followed by a validation of 1,153 selected autoantibodies, in serum samples from 466 patients with PSC in a longitudinal setting using the SUPRIM cohort and 214 controls. Results: We identified autoantibodies associated with clinical phenotypes, biochemical and clinical severity, comorbidities, and disease progression (e.g. alkaline phosphatase and albumin level p <e-10, presence of hepatobiliary malignancies p <0.001, seroconversion before transplantation p <0.001). Rather than a single universal autoantibody marker, small patient subgroups were positive for various autoantibodies with variable specificity. Global analysis of autoantigen targets revealed an overrepresentation of proteins normally expressed in immune-privileged sites, including the brain, testis, and retina. When interrogating tissue-specific autoantigen co-expression linked to expression and splicing quantitative trait loci of PSC risk variants, the thyroid emerged as an additional relevant tissue. We also detected increased autoantibody diversity associated with PSC duration and end-stage disease, already observable several years before liver transplantation. Multiomics analysis across body compartments confirmed neuroendocrine dysregulation in PSC. Our results are provided as a resource for further studies. Conclusions: Overall, our data support the cryptic antigen and epitope-drifting autoimmune theories and indicate that neuroendocrine dysregulation may contribute to PSC pathogenesis. Impact and implications: From a proteome-scale profiling of the SUPRIM cohort, we provide a short list of autoantibodies associated with clinical phenotypes and progression, along with the peptide sequences used to capture them. We identify across multiple datasets neuroendocrine deregulations in primary sclerosing cholangitis and provide a short list of related key plasma proteins. These data and technical details should facilitate validation studies, investigations of related pathophysiological mechanisms and development of low-cost tools for diagnostic or prognostic purposes.

The human blood proteome provides a holistic readout of health states through the assessment of thousands of circulating proteins. In this study, we present a pan-disease resource to enable the study of diverse disease phenotypes within a harmonized proteomics dataset. By profiling protein concentrations across 59 diseases and healthy cohorts, we identified proteins associated with age, sex, and body mass index, as well as disease-specific signatures. This study highlights shared and distinct protein patterns across conditions, demonstrating the power of a unified proteomics approach to uncover biological insights. The dataset, covering 8262 individuals and up to 5416 proteins, serves as an online resource for exploring disease-specific protein profiles and advancing precision medicine research.

New technologies enable single-cell transcriptome analysis, mapping genome-wide expression across the human body. Here, we present an extended analysis of protein-coding genes in all major human tissues and organs, combining single-cell and bulk transcriptomics. To enhance transcriptome depth, 31 tissues were analyzed using a pooling method, identifying 557 unique cell clusters, manually annotated by marker gene expression. Genes were classified by body-wide expression and validated through antibody-based profiling. All results are available in the updated open-access Single Cell Type section of the Human Protein Atlas for genome-wide exploration of genes, proteins, and their spatial distribution in cells.

Chronic liver disease poses significant challenges to healthcare systems, which frequently struggle to meet the needs of end-stage liver disease patients. Early detection and management are essential because liver damage and fibrosis are potentially reversible. However, the implementation of population-wide screenings is hindered by the asymptomatic nature of early chronic liver disease, along with the risks and costs associated with traditional diagnostics, such as liver biopsies. This study pioneers the development of innovative, minimally invasive methods capable of improving the outcomes of liver disease patients by identifying liver disease biomarkers using quantification methods with translational potential. A targeted mass spectrometry assay based on stable isotope standard protein epitope signature tags (SIS-PrESTs) was employed for the absolute quantification of 108 proteins in just two microliters of plasma. The plasma profiles were derived from patients of various liver disease stages and etiologies, including healthy controls. A set of potential biomarkers for stratifying liver fibrosis was identified through differential expression analysis and supervised machine learning. These findings offer promising alternatives for improved diagnostics and personalized treatment strategies in liver disease management. Moreover, our approach is fully compatible with existing technologies that facilitate the robust quantification of clinically relevant protein targets via minimally disruptive sampling methods.

Background: Becker muscular dystrophy (BMD) is a rare and heterogeneous form of dystrophinopathy caused by expression of altered dystrophin proteins, as a consequence of in-frame genetic mutations. The majority of the BMD biomarker studies employ targeted approaches and focus on translating findings from Duchenne Muscular Dystrophy (DMD), a more severe disease form with clinical similarities but caused by out-of-frame mutations in the dystrophin gene. Importantly, DMD therapies assume that disease progression can be slowed by promoting the expression of truncated dystrophin comparable to what occurs in BMD patients. In this study, we explore similarities and differences in protein trajectories over time between BMD and DMD serum, and explore proteins related to motor function performance.

Methods: Serum samples collected from 34 BMD patients, in a prospective longitudinal 3-year study, and 19 DMD patients, were analyzed by using Data Independent Acquisition Tandem Mass Spectrometry (DIA-MS). Subsequent normalization, linear mixed effects model was employed to identify proteins associated with physical tests and dystrophin expression in skeletal muscle. Analysis was also performed to explore the discrepancy between DMD and BMD biomarker abundance trajectories over time.

Results: Linear mixed effects models identified 20 proteins with altered longitudinal signatures between DMD and BMD, including creatine kinase M-type (CKM) pyruvate kinase (PKM), fibrinogen gamma chain (FGG), lactate dehydrogenase B (LDHB) and alpha-2-macroglobulin (A2M). Furthermore, several proteins related to innate immune response were associated with motor function in BMD patients. In particular, A2M displayed an altered time-dependent decline in relation to dystrophin expression in the tibialis anterior muscle.

Conclusions: Our study revealed differences in the serum proteome between BMD and DMD, which comprises proteins involved in the immune response, extracellular matrix organization and hemostasis but not muscle leakage proteins significantly associated with disease progression in DMD. If further evaluated and validated, these biomarker candidates may offer means to monitor disease progression in BMD patients. A2M is of particular interest due to its association with dystrophin expression in BMD muscle and higher abundance in DMD patients in comparison to BMD. If validated, A2M could be used as a pharmacodynamic biomarker in therapeutic clinical trials aiming to restore dystrophin expression.

Background: Understanding how genetics and environmental factors shape human metabolic profiles is crucial for advancing metabolic health. Variability in metabolic profiles, influenced by genetic makeup, lifestyle, and environmental exposures, plays a critical role in disease susceptibility and progression. Methods: We conducted a two-year longitudinal study involving 101 clinically healthy individuals aged 50 to 65, integrating genomics, metabolomics, lipidomics, proteomics, clinical measurements, and lifestyle questionnaire data from repeat sampling. We evaluated the influence of both external and internal factors, including genetic predispositions, lifestyle factors, and physiological conditions, on individual metabolic profiles. Additionally, we developed an integrative metabolite-protein network to analyze protein-metabolite associations under both genetic and environmental regulations. Results: Our findings highlighted the significant role of genetics in determining metabolic variability, identifying 22 plasma metabolites as genetically predetermined. Environmental factors such as seasonal variation, weight management, smoking, and stress also significantly influenced metabolite levels. The integrative metabolite-protein network comprised 5,649 significant protein-metabolite pairs and identified 87 causal metabolite-protein associations under genetic regulation, validated by showing a high replication rate in an independent cohort. This network revealed stable and unique protein-metabolite profiles for each individual, emphasizing metabolic individuality. Notably, our results demonstrated the importance of plasma proteins in capturing individualized metabolic variabilities. Key proteins related to individual metabolic profiles were identified and validated in the UK Biobank, showing great potential for metabolic risk assessment. Conclusions: Our study provides longitudinal insights into how genetic and environmental factors shape human metabolic profiles, revealing unique and stable individual metabolic profiles. Plasma proteins emerged as key indicators for capturing the variability in human metabolism and assessing metabolic risks. These findings offer valuable tools for personalized medicine and the development of diagnostics for metabolic diseases.

Venous thromboembolism (VTE), including deep vein thrombosis (DVT) and pulmonary embolism (PE), is the third leading cause of morbidity and mortality among cardiovascular diseases. The current diagnostic approach assesses clinical probability through decision rules (e.g., Wells score) and measuring plasma D-dimer levels. While D-dimer helps rule out VTE in low-probability cases, patients with medium to high probability require imaging to confirm diagnosis. However, fewer than 20% of computed tomography pulmonary angiograms (CTPAs) performed for suspected PE actually confirm the diagnosis. Additionally, 25% of patients with unprovoked VTE experience a recurrence within 5 years after stopping anticoagulation. There is a clear need for better plasma marker-based tools that could enable early decision-making without relying on imaging, increasing patient safety and reducing costs. Such tools could also help identify patients at the highest risk of recurrence after unprovoked VTE, supporting decisions on extended anticoagulation where the benefits outweigh bleeding risks. As VTE is an intravascular disease, blood properties, endothelial function, and their interactions are critical in thrombosis.Proteomic analysis can help identify new biomarkers for diagnosis and risk assessment. Recent advancements in proteomics, particularly mass spectrometry and affinity-based proximity extension assays, have enabled deeper characterization of the plasma proteome and the identification of changes in protein profiles in VTE that could improve diagnosis. In our study, we have profiled plasma samples from patients with acute VTE and after a first VTE event. We used the high-throughput proximity extension assay, as part of the generation of a comprehensive blood atlas covering 59 diseases, designed to explore the circulating human proteome and identify plasma proteins as potential signatures for disease diagnosis, prognosis, and treatment management. We semi-quantified 1161 unique proteins in 48 acute VTE and 98 post-VTE plasma samples. Using differential expression analysis and machine learning, we identified VTE-related protein signatures by comparing them with circulating protein profiles from healthy, cardiovascular, and pan-disease cohorts. Our results highlight significant up- and downregulation of proteins involved in the regulation of coagulation, complement activation, endothelial function, and fibrinolysis. The most relevant proteins were selected and applied to predictive models. Our findings demonstrate the potential of proteomics technology and the open-access blood atlas for identifying proteomic signatures associated with VTE and other diseases. In this context, our study provides novel insights into the dynamics of the plasma proteome in venous thrombosis and could contribute to improved clinical decision-making in patient risk stratification and personalized treatments for VTE.