Publication highlights November 2025

Recent proteomic breakthroughs using Olink® Explore HT

Technologies that allow the simultaneous analysis of thousands of proteins  at population scale are empowering breakthrough discoveries across the biomedical research spectrum.  Olink’s flagship, high-throughput NGS proteomics platform, Olink Explore HT enables the measurement  of >5,400 proteins from just 2µL of blood and is scalable for any size of study.  Here we summarize some recent high-impact publications from studies using this powerful proteomics solution.

DID YOU KNOW?

Olink Explore HT was selected for the world’s largest ever  population proteomics study? This ongoing project will measure over 5,400 proteins in 600,000 samples from participants in the UK Biobank.

Proteomics identifies presymptomatic changes in multiple sclerosis

The timing of the biological onset of multiple sclerosis (MS) is unclear.  Researchers from the University of California at San Francisco compared the proteomics of presymptomatic patients with MS and matched healthy controls to define the neurological onset of disease development and understand the mechanisms involved. The data was generated using Olink Explore HT and the data analyzed using Olink Analyze software.

For MS patients analyzed at the presymptomatic stage , 38 differentially expressed proteins were identified that  discriminated between participants who later developed MS and control subjects.  Nine of the top ten proteins in this regard were unique additions to the Explore HT library that were not available in previous iterations of the Explore platform.

Longitudinal analysis showed that levels of the myelin injury marker, MOG were elevated vs controls up to 7 years prior to clinical manifestation, preceding elevation of the axonal damage marker NfL by 1 year. In contrast, the astrocyte marker GFAP did not show elevation until around the time of diagnosis. This maps a clear trajectory of myelin damage followed by axonal injury during the presymptomatic phase, with astrocyte involvement only at clinical manifestation. A machine learning-derived multi-protein model was also developed, which could discriminate presymptomatic MS from healthy controls with 74% sensitivity, 76% specificity and an AUC of 0.79.

By identifying substantial changes in the serum proteome years before the clinical onset of MS, this study highlights the utility of protein biomarker discovery to define multiple components during disease development and to identify potential tools for early detection.

The proteomic response to feminizing gender-affirming hormone therapy

The phenotypic changes that occur during gender-affirming hormone therapy (GAHT) are well documented but the molecular profiles underlying physiological and biochemical changes are less well understood. Researchers from the Murdoch Children’s Research Institute in Victoria, Australia used the Olink® Explore HT platform to measure over 5,000 proteins in transgender individuals undergoing feminizing GAHT.

The study cohort comprised of subjects given estradiol plus one of two antiandrogens, cyproterone (CPA) or spironolactone (SPIRO). The findings from this analysis were then assessed in the context of sex-specific proteomics data obtained from mainly cis-individuals in the UK Biobank Pharma Proteomics Project (UKB-PPP).

• From baseline to 6 months’ treatment, 245 proteins changed in the CPA group, while 91 changed in the SPIRO group, with 37 in common.
• GAHT-dependent physiological changes such as body fat percentage and breast volume were also mirrored by proteomic changes, such as an increase in leptin levels.
• Comparison with UKB-PPP data indicated that GAHT remodels the proteome towards that of a cis-female profile, with 36 and 22 of the top 100 sex-associated proteins in UK Biobank dataset altered in response to the CPA and SPIRO regimens, respectively.

A multi-protein signature discriminates malignant and benign ovarian tumors

Scientists at Uppsala University used the Olink® Explore HT platform to measure >5,400 proteins in two cohorts of women surgically diagnosed with benign or malignant ovarian tumors. Protein levels were compared between individuals with benign tumors and early stage (I–II), late stage (III–IV) or any stage (I–IV) ovarian cancer. Machine learning was used to train risk-score reporting multivariate models and their performances were verified in the independent replication cohort.

• 327 protein associations with different tumor status/stage comparisons were observed (191 unique proteins), of which 326 were replicated in the independent cohort.
• Machine learning identified an 8-protein signature that discriminated malignant vs benign tumors with AUC=0.96 (97% sensitivity at 68% specificity).
• The new Explore HT-derived signature had a significantly higher specificity in the 0.9-1.0 sensitivity range compared to one identified previously in overlapping samples by the same group based on fewer proteins analyzed, making it potentially much more useful in the clinical setting.
• The data indicates that up to a third of benign ovarian tumors could be identified by molecular measures, reducing the need for diagnostic surgery

A human pan-disease blood atlas of the circulating proteome

The Human Protein Atlas project used Olink Explore HT to profile 9,027 samples from across multiple cohorts of healthy and diseased individuals. Differential protein expression was analyzed across 59 different diseases (including cancer, immunological, cardiovascular). Proteomic changes in response to demographic factors were also profiled longitudinally in a healthy cohort.

Key findings included that in the longitudinal cohort, each individual had a unique and stable protein profile over two years. This provides a wellness baseline that could be used to detect disease-associated deviations. The broad analysis of >5,400 proteins revealed both shared and unique signatures across the 59 diseases, providing a bigger picture of disease biology while crucially identifying specific markers for each condition.  The dataset was made available as an online resource for exploring disease-specific protein profiles and advancing precision medicine research.