The landscape of plasma proteomic links to human organ imaging

Nanoparticle-enriched mass spectrometry proteomics in British South Asians identifies links between genetic variants, plasma protein levels and disease risk

It is demonstrated how untargeted nanoparticle-enriched mass spectrometry (MS)-based plasma proteomics delivers quantitatively and qualitatively different insights compared to two affinity-based assays in a sample of ~1,400 British South Asian individuals.

Large-Scale Plasma Proteomics Reveals Preclinical Biomarkers of Incident Severe Liver Disease.

Beyond the classical plasma secretome: genetic architecture and disease associations of the expanded human plasma proteome in 13,445 Europeans

Circulating plasma proteins are key biomarkers and therapeutic targets, now measurable at scale through high-throughput technologies, yet whether expanding proteomics platforms beyond the classical plasma secretome enhances genetic discovery and causal inference remains poorly understood. Here, we use an expanded SomaScan 7k platform to map the genetic architecture of a broader segment of the plasma proteome and to evaluate how proteome expansion affects pQTL discovery, causal inference and therapeutic target prioritisation. After quality control, we analysed 7,144 aptamers targeting 6,267 proteins in the harmonised dataset of two European cohorts: INTERVAL (n = 9,251 participants) and CHRIS (n = 4,194), and conducted genome-wide pQTL association analyses followed by meta-analysis. We identified 7,870 significant pQTLs (P-value < 1.26 x 10E-11; 1,784 cis, 6,086 trans), of which 2,704 (34%) associations were not reported in five prior large-scale pQTL studies. Newly assessed proteins, which accounted for 53% (1,422/2,704) of the novel associations, were less likely to harbour cis-pQTLs associations (15%) than those in the previous platform version (28%), consistent with their lower expected plasma concentrations and predominantly intracellular localisation. Colocalization analyses revealed widespread sharing of genetic signals across proteins and characterised 22 pleiotropic trans-regulatory hotspots accounting for 68% of all trans-pQTLs. Through two-sample Mendelian randomization analyses on 2,003 phenotypes from the Million Veteran Program, UK Biobank, and FinnGen (combined N > 1.2 million), we identified 6,340 genetically supported protein-trait associations, highlighting disease mechanisms and potential therapeutic opportunities beyond currently drug-targeted circulating proteins. Together, these findings provide a systematic view of the genetic architecture of the expanded plasma proteome and demonstrate that plasma proteome expansion reveals genetically anchored disease biology beyond the classical secretome, while exposing inherent biological and technical constraints of studying low-abundance intracellular proteins in circulation.

Modeling Joint Reference Regions for Omics Biomarkers in UK Biobank Proteomics

Conventional univariate reference intervals (UniRIs) are widely used to identify abnormal biomarker values, but they evaluate each biomarker independently and do not account for coordinated deviations between biomarkers. We developed and evaluated a joint reference region (JRR) framework for plasma proteomics data using the Olink proteomics dataset generated by the UK Biobank Pharma Proteomics Project, covering approximately 3,000 plasma proteins. JRRs were estimated for selected protein pairs in a healthy reference subset, while UniRIs were estimated separately for individual proteins using the nonparametric method. Both approaches were then evaluated in ICD-defined disease subsets. Biomarker discovery revealed sparse and heterogeneous disease--protein associations, with some proteins recurring across multiple phenotypes and others showing more disease-specific patterns. The added value of JRRs varied across diseases and protein pairs. Across evaluated protein pairs, 56.5% showed higher sensitivity under the JRR framework than the UniRI of the first protein, and 47.3% showed higher sensitivity than the UniRI of the second protein. At the disease level, the median proportion of protein pairs with improved JRR sensitivity was 0.57. JRRs were most informative when univariate detection was limited but a subset of diseased observations was flagged only by the joint region. These findings suggest that JRRs provide a complementary approach to UniRIs by capturing abnormal joint biomarker configurations in high-dimensional proteomics data.

Identification and Validation of Plasma Protein Biomarkers for Abdominal Aortic Aneurysm Using Integrated Proteomics

Abdominal aortic aneurysm (AAA) is a progressive and often asymptomatic vascular disease associated with high mortality after rupture, but reliable circulating biomarkers for noninvasive detection remain limited. We aimed to identify and validate plasma protein biomarkers for AAA using an integrated proteomics-based approach. Plasma samples from 22 patients with AAA and 22 healthy controls were analyzed through data-independent acquisition (DIA) mass spectrometry. Differentially expressed proteins were subjected to bioinformatic analyses, including Gene Ontology enrichment, Kyoto Encyclopedia of Genes and Genomes pathway analysis, protein–protein interaction, and weighted gene coexpression network analyses. Candidate biomarkers were selected on the basis of differential abundance, diagnostic performance, and biological relevance and subsequently validated by enzyme-linked immunosorbent assay in an independent cohort comprising 93 patients with AAA and 83 non-AAA controls. DIA proteomics identified 111 differentially abundant proteins, revealing enrichment of pathways related to mitochondrial respiration, oxidative stress, inflammation, extracellular matrix remodeling, and proteostasis. Among the candidates, plasma CHRDL1 levels were significantly reduced, whereas OGN and CCL18 levels were significantly elevated in patients with AAA; these findings were consistently confirmed in the validation cohort. A combined three-protein model demonstrated strong diagnostic performance, with an area under the receiver operating characteristic curve of 0.890. These findings identify CHRDL1, OGN, and CCL18 as promising plasma biomarkers for AAA detection and further highlight mitochondrial dysfunction, chronic inflammation, ECM remodeling, and dysregulated proteostasis as key molecular features of AAA.

Paired plaque and plasma proteomics reveal molecular signatures of symptomatic atherosclerosis

Deep proteomic profiling of human carotid plaques identifies molecular signatures of symptomatic atherosclerosis that extend beyond conventional histopathology, which implicate neutrophil activation and inflammatory signaling pathways as key determinants of plaque vulnerability.