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Serological testing is essential to curb the consequences of the COVID-19 pandemic. However, most assays are still limited to single analytes and samples collected within healthcare. Thus, we establish a multianalyte and multiplexed approach to reliably profile IgG and IgM levels against several versions of SARS-CoV-2 proteins (S, RBD, N) in home-sampled dried blood spots (DBS). We analyse DBS collected during spring of 2020 from 878 random and undiagnosed individuals from the population in Stockholm, Sweden, and use classification approaches to estimate an accumulated seroprevalence of 12.5% (95% CI: 10.3%–14.7%). This includes 5.4% of the samples being IgG + IgM + against several SARS-CoV-2 proteins, as well as 2.1% being IgG − IgM + and 5.0% being IgG + IgM − for the virus’ S protein. Subjects classified as IgG + for several SARS-CoV-2 proteins report influenza-like symptoms more frequently than those being IgG + for only the S protein (OR = 6.1; p  < 0.001). Among all seropositive cases, 30% are asymptomatic. Our strategy enables an accurate individual-level and multiplexed assessment of antibodies in home-sampled blood, assisting our understanding about the undiagnosed seroprevalence and diversity of the immune response against the coronavirus. Here, Roxhed et al. develop a multiplexed approach to screen IgG and IgM levels against several SARS-CoV-2 proteins in home-sampled dried blood spots and estimate seroprevalence of 12.5% in Stockholm in spring of 2020.

Blood biomarkers of Alzheimer’s disease (AD) are promising for dementia prediction, but their association with progression across intermediate stages of cognitive decline in the general population remains unclear. We followed 2148 dementia-free individuals from a Swedish population-based cohort for up to 16 years. Associations between baseline AD blood biomarkers and transitions between normal cognition, mild cognitive impairment (MCI), and dementia were examined. Lower amyloid-β42/40 ratio and higher phosphorylated-tau181 (p-tau181), p-tau217, total-tau, neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP) were associated with faster progression from MCI to all-cause and AD dementia, with the strongest associations for NfL and p-tau217. Elevated NfL and GFAP were linked to reduced MCI reversion to normal cognition, whereas no biomarker was associated with MCI development from normal cognition. These findings show robust group-level associations and indicate that AD blood biomarkers may help stratify dementia risk at the MCI stage in the community. Blood biomarkers of Alzheimer’s disease, including p-tau217, are linked with faster progression from mild cognitive impairment to dementia, supporting their potential for risk stratification at the stage of mild cognitive impairment in the community.

Nonalcoholic fatty liver disease (NAFLD) refers to excess fat accumulation in the liver. In animal experiments and human kinetic study, we found that administration of combined metabolic activators (CMAs) promotes the oxidation of fat, attenuates the resulting oxidative stress, activates mitochondria, and eventually removes excess fat from the liver. Here, we tested the safety and efficacy of CMA in NAFLD patients in a placebo‐controlled 10‐week study. We found that CMA significantly decreased hepatic steatosis and levels of aspartate aminotransferase, alanine aminotransferase, uric acid, and creatinine, whereas found no differences on these variables in the placebo group after adjustment for weight loss. By integrating clinical data with plasma metabolomics and inflammatory proteomics as well as oral and gut metagenomic data, we revealed the underlying molecular mechanisms associated with the reduced hepatic fat and inflammation in NAFLD patients and identified the key players involved in the host–microbiome interactions. In conclusion, we showed that CMA can be used to develop a pharmacological treatment strategy in NAFLD patients. SYNOPSIS A placebo‐controlled human study shows that oral administration of Combined Metabolic Activators (CMA) reduces liver fat in nonalcoholic fatty liver disease (NAFLD) patients. CMA, consisting of L‐serine, nicotinamide riboside, N‐acetyl‐L‐cysteine, and L‐carnitine tartrate, has a profound effect on hepatic steatosis after only 70 days of treatment in NAFLD patients. CMA supplementation improved clinical parameters in NAFLD patients, such as reductions in aspartate aminotransferase, alanine aminotransferase, uric acid, and creatinine. The underlying mechanisms associated with the beneficial effect of CMA were revealed by a comprehensive analysis of plasma metabolomics and inflammatory proteomics as well as oral and gut metagenomics. Graphical Abstract A placebo‐controlled human study shows that oral administration of Combined Metabolic Activators (CMA) reduces liver fat in nonalcoholic fatty liver disease (NAFLD) patients.

Background Identification of non-sputum diagnostic markers for tuberculosis (TB) is urgently needed. This exploratory study aimed to discover potential serum protein biomarkers for the diagnosis of active pulmonary TB (PTB). Method We employed Proximity Extension Assay (PEA) to measure levels of 92 protein biomarkers related to inflammation in serum samples from three patient groups: 30 patients with active PTB, 29 patients with other respiratory diseases with latent TB (ORD with LTBI+), and 29 patients with other respiratory diseases without latent TB (ORD with LTBI-). To understand the functional mechanisms associated with differentially expressed proteins, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Least absolute shrinkage and selection operator (LASSO) regression was employed to identify potential TB diagnostic protein biomarkers. Network interactions among the identified candidate diagnostic markers were then analyzed, and their diagnostic performance was evaluated using logistic regression and receiver operating characteristic (ROC) analysis. Result The analysis revealed 37 differentially expressed proteins (DEPs) in the active PTB group compared to both ORD with LTBI + and ORD with LTBI- groups. Gene Ontology analysis indicated that these DEPs were primarily involved in the inflammatory response, while KEGG enrichment analysis highlighted the cytokine-cytokine receptor interaction pathway as the top significant hit. LASSO regression identified eight promising candidate protein biomarkers: IFN-gamma, LIF, uPA, CSF-1, SCF, SIRT2, 4E-BP1, and GDNF. The combined set of these eight proteins yielded an AUC of 0.943 for differentiating active PTB from ORD with LTBI+, and an AUC of 0.927 for distinguishing PTB from ORD with LTBI-. Conclusion We have identified eight protein markers that reliably differentiate active PTB from ORD irrespective of LTBI presence. Further large-scale validation and translation of these protein markers into a user-friendly and affordable point-of-care test hold the potential to significantly enhance TB control in high-burden regions.

There is a strong need for procedures that enable context and application dependent validation of antibodies. Here, we applied a magnetic bead assisted workflow and immunoprecipitation mass spectrometry (IP-MS/MS) to assess antibody selectivity for the detection of proteins in human plasma. A resource was built on 414 IP experiments using 157 antibodies (targeting 120 unique proteins) in assays with heat-treated or untreated EDTA plasma. For each protein we determined their antibody related degrees of enrichment using z-scores and their frequencies of identification across all IP assays. Out of 1,313 unique endogenous proteins, 426 proteins (33%) were detected in >20% of IPs, and these background components were mainly comprised of proteins from the complement system. For 45% (70/157) of the tested antibodies, the expected target proteins were enriched (z-score ≥ 3). Among these 70 antibodies, 59 (84%) co-enriched other proteins beside the intended target and mainly due to sequence homology or protein abundance. We also detected protein interactions in plasma, and for IGFBP2 confirmed these using several antibodies and sandwich immunoassays. The protein enrichment data with plasma provide a very useful and yet lacking resource for the assessment of antibody selectivity. Our insights will contribute to a more informed use of affinity reagents for plasma proteomics assays.

Background The lack of specificity and high degree of false positive and false negative rates when using mammographic screening for detecting early-stage breast cancer is a critical issue. Blood-based molecular assays that could be used in adjunct with mammography for increased specificity and sensitivity could have profound clinical impact. Our objective was to discover and independently verify a panel of candidate blood-based biomarkers that could identify the earliest stages of breast cancer and complement current mammographic screening approaches. Methods We used affinity hydrogel nanoparticles coupled with LC-MS/MS analysis to enrich and analyze low-abundance proteins in serum samples from 20 patients with invasive ductal carcinoma (IDC) breast cancer and 20 female control individuals with positive mammograms and benign pathology at biopsy. We compared these results to those obtained from five cohorts of individuals diagnosed with cancer in organs other than breast (ovarian, lung, prostate, and colon cancer, as well as melanoma) to establish IDC-specific protein signatures. Twenty-four IDC candidate biomarkers were then verified by multiple reaction monitoring (LC-MRM) in an independent validation cohort of 60 serum samples specifically including earliest-stage breast cancer and benign controls (19 early-stage (T1a) IDC and 41 controls). Results In our discovery set, 56 proteins were increased in the serum samples from IDC patients, and 32 of these proteins were specific to IDC. Verification of a subset of these proteins in an independent cohort of early-stage T1a breast cancer yielded a panel of 4 proteins, ITGA2B (integrin subunit alpha IIb), FLNA (Filamin A), RAP1A (Ras-associated protein-1A), and TLN-1 (Talin-1), which classified breast cancer patients with 100% sensitivity and 85% specificity (AUC of 0.93). Conclusions Using a nanoparticle-based protein enrichment technology, we identified and verified a highly specific and sensitive protein signature indicative of early-stage breast cancer with no false positives when assessing benign and inflammatory controls. These markers have been previously reported in cell-ECM interaction and tumor microenvironment biology. Further studies with larger cohorts are needed to evaluate whether this biomarker panel improves the positive predictive value of mammography for breast cancer detection.

Plasma proteomics technologies are advancing rapidly, offering new opportunities for biomarker discovery and precision medicine. Direct comparisons of available technologies are needed to understand how platform selection affects downstream findings. We compared the performance of a peptide fractionation-based mass spectrometry method (HiRIEF LC-MS/MS) and the Olink Explore 3072 proximity extension assays on 88 plasma samples, analyzing 1129 proteins with both methods. The platforms exhibited complementary proteome coverage, high precision, and concordance in estimating sex differences in protein levels. Quantitative agreement between platforms was moderate (median correlation 0.59, interquartile range 0.33-0.75), mainly influenced by technical factors. Finally, we present a publicly available tool for peptide-level analysis of platform agreement and demonstrate its utility in clarifying cross-platform discrepancies in protein and proteoform measurements. Our findings provide insights for platform selection and study design, and highlight the value of combining mass spectrometry and affinity-based approaches for more comprehensive and reliable plasma proteome profiling. Advancements in plasma proteomics have opened new avenues for biomarker discovery, necessitating a clear understanding of technological capabilities. Here, the authors compare HiRIEF LC-MS/MS and Olink Explore 3072, revealing complementary strengths and moderate quantitative agreement, and introduce PeptAffinity, a resource facilitating detailed peptide-level exploration of differences in protein quantification between platforms.

Background Self-sampling of dried blood spots (DBS) offers new routes to gather valuable health-related information from the general population. Yet, the utility of using deep proteome profiling from home-sampled DBS to obtain clinically relevant insights about SARS-CoV-2 infections remains largely unexplored. Methods Our study involved 228 individuals from the general Swedish population who used a volumetric DBS sampling device and completed questionnaires at home during spring 2020 and summer 2021. Using multi-analyte COVID-19 serology, we stratified the donors by their response phenotypes, divided them into three study sets, and analyzed 276 proteins by proximity extension assays (PEA). After normalizing the data to account for variances in layman-collected samples, we investigated the association of DBS proteomes with serology and self-reported information. Results Our three studies display highly consistent variance of protein levels and share associations of proteins with sex (e.g., MMP3) and age (e.g., GDF-15). Studying seropositive (IgG + ) and seronegative (IgG - ) donors from the first pandemic wave reveals a network of proteins reflecting immunity, inflammation, coagulation, and stress response. A comparison of the early-infection phase (IgM + IgG - ) with the post-infection phase (IgM - IgG + ) indicates several proteins from the respiratory system. In DBS from the later pandemic wave, we find that levels of a virus receptor on B-cells differ between seropositive (IgG + ) and seronegative (IgG - ) donors. Conclusions Proteome analysis of volumetric self-sampled DBS facilitates precise analysis of clinically relevant proteins, including those secreted into the circulation or found on blood cells, augmenting previous COVID-19 reports with clinical blood collections. Our population surveys support the usefulness of DBS, underscoring the role of timing the sample collection to complement clinical and precision health monitoring initiatives. Plain language summary The COVID-19 pandemic has posed multiple challenges to healthcare systems. A significant gap that remains is a lack of understanding of the impact of SARS-CoV-2 on individuals who did not seek or require hospitalization. To address this, we distribute self-sampling devices to random citizens, aiming to analyze how blood protein levels are affected in people who have had COVID-19 but had no or mild symptoms. Conducting multiple molecular measurements in dried blood, our study confirms clinically known markers and their relationship to infection stages, even if the donors themselves collect the sample. Our work highlights the potential of combining self-sampling with laboratory methods to provide useful information on human health. This convenient patient-centric sampling approach may potentially be useful when studying other diseases. Fredolini et al. present a proteomics analysis of home-sampled dried blood spots taken from the general population in Stockholm during the COVID-19 pandemic. The study provides insights into the molecular effects of SARS-CoV-2 infection in non-hospitalized individuals and demonstrates the compatibility of self-sampled blood spots with proteomics.

Extracellular vesicles (EVs) are mediators of intercellular communication and a promising class of biomarkers. Surface proteins of EVs play decisive roles in establishing a connection with recipient cells, and they are putative targets for diagnostic assays. Analysis of the surface proteins can thus both illuminate the biological functions of EVs and help identify potential biomarkers. We developed a strategy combining high-resolution mass spectrometry (HRMS) and  proximity ligation assays (PLA) to first identify and then validate surface proteins discovered on EVs. We applied our workflow to investigate surface proteins of small EVs found in seminal fluid (SF-sEV). We identified 1,014 surface proteins and verified the presence of a subset of these on the surface of SF-sEVs. Our work demonstrates a general strategy for deep analysis of EVs’ surface proteins across patients and pathological conditions, proceeding from unbiased screening by HRMS to ultra-sensitive targeted analyses via PLA. Surface biotinylation, proximity ligation assays and proteomics are used to identify surface markers of prostate-derived small extracellular vesicles.