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Exploration of immune phenotypes in self-sampling citizens
KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science.ORCID iD: 0000-0003-1492-3052
KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science.ORCID iD: 0000-0001-9329-2353
KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.ORCID iD: 0000-0002-2669-7796
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science. KTH, Centres, Science for Life Laboratory, SciLifeLab.ORCID iD: 0009-0003-1985-7733
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2026 (English)In: iScience, E-ISSN 2589-0042, Vol. 29, no 2, article id 114611Article in journal (Refereed) Published
Abstract [en]

Blood proteins have provided essential insights into how humans responded to the recent pandemic. To expand our understanding beyond patients seeking medical care, we conducted a citizen-centric survey with 2,000 random residents (age: 18–69 years) from Sweden's two largest cities in 2021. With self-sampled dried blood spots (DBS) and health information from 437 (22%) volunteers, we performed multi-analyte COVID-19 serology, measured autoantibodies (AAbs) against 22 interferons, and quantified 502 circulating low-abundant immune-related blood proteins. Antibody assays confirmed self-reported infections (26%) and vaccinations (40%), showed timing-dependent discrepancies in the immune response, and revealed anti-type I interferon AAbs co-occurring frequently alongside natural infections. Proteomics data added plausible mechanistic insights into cell-mediated processes: data-driven analyses revealed 24% of participants presented deviating immune phenotypes linked to infections, immunity, respiratory effects, and age. Multi-molecular DBS analysis of random layperson samples captured the broader spectrum of immune system states, adding relevant insights for clinical and public health investigations.

Place, publisher, year, edition, pages
Elsevier BV , 2026. Vol. 29, no 2, article id 114611
Keywords [en]
Health sciences
National Category
Clinical Medicine
Identifiers
URN: urn:nbn:se:kth:diva-376429DOI: 10.1016/j.isci.2025.114611ISI: 001678914400001PubMedID: 41630906Scopus ID: 2-s2.0-105027974969OAI: oai:DiVA.org:kth-376429DiVA, id: diva2:2036086
Note

QC 20260206

Available from: 2026-02-06 Created: 2026-02-06 Last updated: 2026-05-29Bibliographically approved
In thesis
1. On data-driven affinity proteomics analysis
Open this publication in new window or tab >>On data-driven affinity proteomics analysis
2026 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Proteins are diverse biological macromolecules that play essential roles in many biological functions. The study of proteins has led to important biological and medical discoveries. With the advancement of technology, an increasing number of proteins can be measured in a single experiment. Today, from just a drop of blood, we can measure hundreds or thousands of proteins in the study of proteomes. This field of study is called proteomics and allows us to survey the vast array of proteins in our bodies to discover biological relationships that can improve our understanding of health and disease.

As the number of proteins that we can measure increases, so does the burden of analysing the increasingly large data sets. Data analysis pipelines must be crafted with care at every step, from data preprocessing to analysis, visualisation, and presentation. This thesis presents studies that showcase how one may go about interacting with large affinity proteomics data sets. 

To study the proteome we must be able to reliably measure proteins. Antibodies are used heavily in affinity proteomics for their excellent sensitivity. Their selectivity, however, must be validated thoroughly to ensure that we are measuring the correct protein. In study A, we validate antibodies targeting a clinically important family of proteins. The results have been published in an interactive web application open for anyone to browse.

The type of biological sample we use influences what proteins are present and what research questions we can answer. Blood is a practical sample type for its minimal invasiveness and its ability to provide a systemic view of an individual’s health. Dried blood spots (DBS) can be used as an alternative to venous blood draws that may also be performed without medical expertise, allowing remote self-sampling. In study B we perform a population study during the COVID-19 pandemic using DBS and demonstrate the feasibility of the sampling method for population proteomics. Study C expands on the previous study, establishing a general pipeline for immune phenotype exploration. These studies demonstrate the utility of the sampling and data analysis methods for profiling immune phenotypes through serology and proteomics. In study D we explore the biological differences between DBS and blood plasma in a large proteome survey using multiple proteomics technologies. Together, these studies provide insights into the dried blood proteome and how such data may be processed and analysed.

While the full breadth of proteomics and the analysis of such data cannot be captured in this one thesis, the work presented herein provides important contributions toward the reproducible analysis of large-scale affinity proteomics data.

Abstract [sv]

Proteiner är mångfaldiga biologiska makromolekyler som spelar en livsviktig roll i många biologiska funktioner. Forskning på proteiner har lett till viktiga biologiska och medicinska upptäckter. Utveckling av teknologi har gjort att fler och fler proteiner kan mätas i ett enda experiment. Idag kan vi mäta hundratals eller tusentals av proteiner från en droppe blod för att studera proteom. Detta forskningsfält kallas proteomik och låter oss undersöka de stora mängderna proteiner som finns i våra kroppar för att upptäcka biologiska samband som kan förbättra vår förståelse av hälsa och sjukdom. 

Allteftersom antalet proteiner som vi kan mäta ökar, ökar bördan av att analysera de växande mängderna av data. Dataanalysflöden måste skapas med omsorg i varje steg, från förbehandling av data till analys, visualisering, och presentation. Denna avhandling presenterar forskningsstudier som visar hur vi kan interagera med stora dataset från affinitetsproteomik. 

För att studera proteom måste vi kunna mäta proteiner pålitligt. Antikroppar används flitigt inom affinitetsproteomik för deras utmärkta känslighet. Dock måste deras urskiljningsförmåga kontrolleras för att säkerställa att vi mäter rätt proteiner. I studie A validerar vi antikroppar mot en kliniskt viktig familj av proteiner. Resultaten har publicerats i en interaktiv webbapplikation som är öppen för alla att utforska. 

Sorten av biologiskt prov påverkar vilka proteiner vi kan mäta och vilka forskningsfrågor vi kan besvara. Blodprov är praktiska eftersom de är minimalt invasiva och ger en systemisk bild av en individs hälsotillstånd. Torkade blodfläckar (dried blood spots, DBS) kan användas som ett alternativ till venös provtagning som även kan genomföras utan medicinsk expertis, vilket möjliggör självprovtagning på distans. I studie B genomför vi en populationsstudie under COVID-19-pandemin med hjälp av DBS och påvisar rimligheten av provtagningsmetoden för populationsproteomik. Studie C utvecklar den förra studien och etablerar ett generellt arbetsflöde för att utforska immunfenotyper. Dessa studier demostrerar användbarheten av provtagningen och analysmetoderna för profilering av immunfenotyper med hjälp av serologi och proteomik. I studie D utforskar vi de biologiska skillnaderna mellan DBS och blodplasma i en stor kartläggning av proteom genom att använda flera proteomikteknologier. Sammantagna bidrar dessa studier med inblickar i torrblodsproteomet och hur sådana data kan behandlas och analyseras.

Denna enskilda avhandling kan inte täcka hela vidden av proteomik och analysen av sådan data. Dock innehåller arbetena i denna avhandling viktiga bidraganden för reproducerbar analys av storskalig affinitetsproteomikdata.

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2026. p. 101
Series
TRITA-CBH-FOU ; 2026:14
National Category
Bioinformatics and Computational Biology Medical Biotechnology
Research subject
Biotechnology
Identifiers
urn:nbn:se:kth:diva-381452 (URN)978-91-8106-643-2 (ISBN)
Public defence
2026-06-12, Air & Fire, via Zoom: https://kth-se.zoom.us/j/61953580734, Tomtebodavägen 23A, Stockholm, 09:00 (English)
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Note

QC 2026-05-19

Available from: 2026-05-19 Created: 2026-05-19 Last updated: 2026-05-25Bibliographically approved

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Dahl, LeoBendes, AnnikaBueno Alvez, MariaAlbrecht, VincentAghelpasand, HoomanMezger, AnjaKäller, MaxFredolini, ClaudiaRoxhed, NiclasSchwenk, Jochen M.

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Dahl, LeoBendes, AnnikaBueno Alvez, MariaAlbrecht, VincentAghelpasand, HoomanMezger, AnjaKäller, MaxFredolini, ClaudiaRoxhed, NiclasSchwenk, Jochen M.
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