Key publications

Zhong, W., Altay, O., Arif, M., Edfors, F., Doganay, L., Mardinoglu, A., … & Fagerberg, L. (2021). Next generation plasma proteome profiling of COVID-19 patients with mild to moderate symptoms. EBioMedicine, 74, 103723.

Zhong, W., Edfors, F., Gummesson, A., Bergstrom, G., Fagerberg, L., & Uhlen, M. (2021). Next generation plasma proteome profiling to monitor health and disease. Nat Commun, 12(1), 2493.

Tebani, A., Gummesson, A., Zhong, W., Koistinen, I. S., Lakshmikanth, T., Olsson, L. M., … & Fagerberg, L. (2020). Integration of molecular profiles in a longitudinal wellness profiling cohort. Nat Commun, 11(1), 4487.

Sjöstedt, E., Zhong, W., Fagerberg, L., … Mulder, J. (2020). An atlas of the protein-coding genes in the human, pig, and mouse brain. Science (New York, N.Y.), 367(6482), eaay5947.

Uhlen, M., Karlsson, M. J., Zhong, W., Tebani, A., Pou, C., Mikes, J., … & Fagerberg, L*., Brodin, P*. (2019). A genome-wide transcriptomic analysis of protein-coding genes in human blood cells. Science, 366(6472).

Uhlen, M., Fagerberg, L., Hallstrom, B. M., Lindskog, C., Oksvold, P., Mardinoglu, A., … & Ponten, F. (2015). Proteomics. Tissue-based map of the human proteome. Science, 347(6220), 1260419.

Linn Fagerberg

The main research focus of my group is integrative multi-omics where large-scale biological data is analyzed using advanced bioinformatic methods. I am head of Bioinformatics and Integrative Omics within the Human Protein Atlas program and my research is both part of the large effort to systematically map the human proteome as well as focused on precision medicine. The Human Protein Atlas program allows for a genome-wide exploration of the protein-coding genes expressed across human cells, tissues and organs. My group is responsible for the quantitative transcriptomics-based classification of the human proteome, as well the integrative approaches involving proteomics and transcriptomic data.

An important aspect of precision medicine is to probe and define the differences in molecular profiles among healthy and diseased individuals. I am the main responsible for the data integration within the Swedish SCAPIS SciLifeLab Wellness Profiling Program (S3WP), which started out by following 100 healthy individuals longitudinally for two years with extensive omics profiling, aiming to study the longitudinal effects of lifestyle variation based on personalized omics profiles. Next, we focused on profiling disease cohorts including type 2 diabetes, cardiovascular disease and COVID-19, and in addition, we also studied the blood proteome in premature children to obtain a deeper understanding of the preterm infant.

We are now expanding the omics-based profiling across a wide range of diseases based on well-characterized cohorts, to provide a comprehensive map of plasma protein levels in the context of disease. 1463 proteins have been measured in plasma using the Proximity Extension Assay (PEA) technology, resulting in individual profiles for 10,000 patients from more than 80 different diseases. We apply machine-learning prediction models to reveal common protein signals to facilitate classification of different patient groups. The effort aims to provide an open-access resource which will be made available through the Human Protein Atlas portal.


Last updated: 2023-08-10

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