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.

Introduction: Advances in various proteomics technologies, especially high-throughput and reproducibility, have enabled the systematic exploration of the circulating thrombosis proteome. This includes dissecting biological systems and pathways imperative to thrombosis, such as platelet activation, coagulation cascade, complement system, and endothelial cells. These insights strengthen our understanding of the cause and effect of thrombosis and improve precision medicine by identifying better biomarkers and biomarker panels, which may aid clinicians in decision-making in venous thromboembolism (VTE) and other thrombotic patients. This progress has the potential to reduce thrombosis-related morbidity and mortality, ultimately improving patient quality of life. Areas covered: This review highlights recent advances and applications of mass spectrometry and affinity-based proteomics in thrombosis over the past three years (2022–2024), focusing on the thrombotic proteome signature related to VTE. Expert opinion: Plasma proteomics, predominantly driven by mass spectrometry and affinity-based proteomics, has shown promise in identifying novel disease biomarkers and pathways. With the recent advances in the field, proteomics holds the potential to revolutionize precision medicine. As thrombosis is an intravascular disease, analysis of the blood proteome can capture environmental, genetic, and epigenetic contributors to risk variation in thrombosis, revealing novel protein biomarkers for diagnosis and risk prediction and new biological pathways.

Malaria presents with varying degrees of severity. To improve clinical management and prevention, it is crucial to understand the pathogenesis and host response. We analyzed 1,463 plasma proteins during and after acute Plasmodium falciparum malaria in adult travelers and linked responses to peripheral immune cells by integrating with single-cell RNA sequencing (RNA-seq) data from a subset of donors. We identified extensive perturbations in over 250 proteins with diverse origins, including many not previously analyzed in malaria patients, such as hormones, circulating receptors, and intracellular or membrane-bound proteins from affected tissues. The protein profiles clustered participants according to disease severity, enabling the identification of a compressed 11-protein signature enriched in severe malaria. Conceptually, this study advances our understanding of malaria by linking systemic proteomic changes to immune cell communication and organ-specific responses. This resource, which includes an interactive platform to explore data, opens new avenues for hypothesis generation, biomarker discovery, and therapeutic target identification.

The creatine-phosphocreatine cycle serves as a crucial temporary energy buffering system in the brain, regulated by brain creatine kinase (CKB), in maintaining Adenosine triphosphate (ATP) levels. Alzheimer's disease (AD) has been linked to increased CKB oxidation and loss of its regulatory function, although specific pathological processes and affected cell types remain unclear. In our study, cerebral cortex samples from individuals with AD, dementia with Lewy bodies (DLB), and age-matched controls were analyzed using antibody-based methods to quantify CKB levels and assess alterations associated with disease processes. Two independently validated antibodies exclusively labeled astrocytes in the human cerebral cortex. Combining immunofluorescence (IF) and mass spectrometry (MS), we explored CKB availability in AD and DLB cases. IF and Western blot analysis demonstrated a loss of CKB immunoreactivity correlated with increased plaque load, severity of tau pathology, and Lewy body pathology. However, transcriptomics data and targeted MS demonstrated unaltered total CKB levels, suggesting posttranslational modifications (PTMs) affecting antibody binding. This aligns with altered efficiency at proteolytic cleavage sites indicated in the targeted MS experiment. These findings highlight that the proper function of astrocytes, understudied in the brain compared with neurons, is highly affected by PTMs. Reduction in ATP levels within astrocytes can disrupt ATP-dependent processes, such as the glutamate-glutamine cycle. As CKB and the creatine-phosphocreatine cycle are important in securing constant ATP availability, PTMs in CKB, and astrocyte dysfunction may disturb homeostasis, driving excitotoxicity in the AD brain. CKB and its activity could be promising biomarkers for monitoring early-stage energy deficits in AD.

Chemical exposomes can now be comprehensively measured in human blood, but knowledge of their variability and longitudinal stability is required for robust application in cohort studies. Here, we applied high-resolution chemical exposomics to plasma of 46 adults, each sampled 6 times over 2 years in a multiomic cohort, resulting in 276 individual exposomes. In addition to quantitative analysis of 83 priority target analytes, we discovered and semiquantified substances that have rarely or never been reported in humans, including personal care products, pesticide transformation products, and polymer additives. Hierarchical cluster analysis for 519 confidently annotated substances revealed unique and distinctive coexposures, including clustered pesticides, poly(ethylene glycols), chlorinated phenols, or natural substances from tea and coffee; interactive heatmaps were publicly deposited to support open exploration of the complex (meta)data. Intraclass correlation coefficients (ICC) for all annotated substances demonstrated the relatively low stability of the exposome compared to that of proteome, microbiome, and endogenous small molecules. Implications are that the chemical exposome must be measured more frequently than other omics in longitudinal studies and four longitudinal exposure types are defined that can be considered in study design. In this small cohort, mixed-effect models nevertheless revealed significant associations between testosterone and perfluoroalkyl substances, demonstrating great potential for longitudinal exposomics in precision health research.

Background: Developing and implementing new patient-centric strategies for drug trials lowers the barrier to participation for some patients by reducing the need to travel to research sites. In early chronic kidney disease (CKD) trials, albuminuria is the key measure for determining treatment effect prior to pivotal kidney outcome trials. Methods: To facilitate albuminuria sample collection outside of a clinical research site, we developed 2 quantitative microsampling methods to determine the urinary albumin to creatinine ratio (UACR). Readout was performed by LC-MS/MS. Results: For the Mitra device the within-batch precision (CV%) was 2.8% to 4.6% and the between-batch precision was 5.3% to 6.1%. Corresponding data for the Capitainer device were 4.0% to 8.6% and 6.7% to 9.0%, respectively. The storage stability at room temperature for 3 weeks was 98% to 103% for both devices. The recovery for the Mitra and Capitainer devices was 104% (SD 7.0%) and 95 (SD 7.4%), respectively. The inter-assay comparison of UACR assessment generated results that were indistinguishable regardless of microsampling technique. The accuracy based on LC-MS/MS vs analysis of neat urine using a clinical chemistry analyzer was assessed in a clinical setting, resulting in 102 ± 8.0% for the Mitra device and 95 ± 10.0% for the Capitainer device. Conclusions: Both UACR microsampling measurements exhibit excellent accuracy and precision compared to a clinical chemistry analyzer using neat urine. We applied our patient-centric sampling strategy to subjects with heart failure in a clinical setting. Precise UACR measurements using quantitative microsampling at home would be beneficial in clinical drug development for kidney therapies.