scispace - formally typeset
Search or ask a question
Author

Annika Bendes

Bio: Annika Bendes is an academic researcher from Royal Institute of Technology. The author has contributed to research in topics: Population & Medicine. The author has an hindex of 5, co-authored 9 publications receiving 51 citations.

Papers
More filters
Journal ArticleDOI
TL;DR: In this article, a multianalyte and multiplexed approach was used 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).
Abstract: 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.

25 citations

Journal ArticleDOI
22 Mar 2020-Cancers
TL;DR: In this paper, the authors used affinity-based plasma proteomics to identify proteins with the predictive power to find radiosensitive patients and validated the two most predictive proteins (THPO and STIM1) by sandwich immunoassays.
Abstract: Nearly half of all cancers are treated with radiotherapy alone or in combination with other treatments, where damage to normal tissues is a limiting factor for the treatment. Radiotherapy-induced adverse health effects, mostly of importance for cancer patients with long-term survival, may appear during or long time after finishing radiotherapy and depend on the patient’s radiosensitivity. Currently, there is no assay available that can reliably predict the individual’s response to radiotherapy. We profiled two study sets from breast (n = 29) and head-and-neck cancer patients (n = 74) that included radiosensitive patients and matched radioresistant controls.. We studied 55 single nucleotide polymorphisms (SNPs) in 33 genes by DNA genotyping and 130 circulating proteins by affinity-based plasma proteomics. In both study sets, we discovered several plasma proteins with the predictive power to find radiosensitive patients (adjusted p < 0.05) and validated the two most predictive proteins (THPO and STIM1) by sandwich immunoassays. By integrating genotypic and proteomic data into an analysis model, it was found that the proteins CHIT1, PDGFB, PNKD, RP2, SERPINC1, SLC4A, STIM1, and THPO, as well as the VEGFA gene variant rs69947, predicted radiosensitivity of our breast cancer (AUC = 0.76) and head-and-neck cancer (AUC = 0.89) patients. In conclusion, circulating proteins and a SNP variant of VEGFA suggest that processes such as vascular growth capacity, immune response, DNA repair and oxidative stress/hypoxia may be involved in an individual’s risk of experiencing radiation-induced toxicity.

18 citations

Posted ContentDOI
02 Jul 2020-medRxiv
TL;DR: A translational approach combining home blood sampling by finger-pricking with multiplexed serology to assess the exposure to the SARS-CoV-2 virus in a general population and may serve to improve the understanding about the diversity of COVID-19 etiology.
Abstract: The COVID-19 pandemic has posed a tremendous challenge for the global community. We established a translational approach combining home blood sampling by finger-pricking with multiplexed serology to assess the exposure to the SARS-CoV-2 virus in a general population. The developed procedure determines the immune response in multiplexed assays against several spike (S, here denoted SPK), receptor binding domain (RBD) and nucleocapsid (NCP) proteins in eluates from dried capillary blood. The seroprevalence was then determined in two study sets by mailing 1000 blood sampling kits to random households in urban Stockholm during early and late April 2020, respectively. After receiving 55% (1097/2000) of the cards back within three weeks, 80% (878/1097) were suitable for the analyses of IgG and IgM titers. The data revealed diverse pattern of immune response, thus seroprevalence was dependent on the antigen, immunoglobulin class, stringency to include different antigens, as well as the required analytical performance. Applying unsupervised dimensionality reduction to the combined IgG and IgM data, 4.4% (19/435; 95% CI: 2.4%-6.3%) and 6.3% (28/443; 95% CI: 4.1%-8.6%) of the samples clustered with convalescent controls. Using overlapping scores from at least two SPK antigens, prevalence rates reached 10.1% (44/435; 95% CI: 7.3%-12.9%) in study set 1 and 10.8% (48/443; 95% CI: 7.9%-13.7%). Measuring the immune response against several SARS-CoV-2 proteins in a multiplexed workflow can provide valuable insights about the serological diversity and improve the certainty of the classification. Combining such assays with home-sampling of blood presents a viable strategy for individual-level diagnostics and towards an unbiased assessment of the seroprevalence in a population and may serve to improve our understanding about the diversity of COVID-19 etiology. One Sentence Summary A multiplexed serology assay was developed to determine antibodies against SARS-CoV-2 proteins in home-sampled dried blood spots collected by finger pricking.

15 citations

Journal ArticleDOI
TL;DR: A workflow is developed to build dual binder sandwich immunoassays (SIA) and for proteins predicted to be secreted into plasma and suggests using at least three antibodies per target, applicable for a wider range of targets of the plasma proteome.
Abstract: The plasma proteome offers a clinically useful window into human health. Recent advances from highly multiplexed assays now call for appropriate pipelines to validate individual candidates. Here, a workflow is developed to build dual binder sandwich immunoassays (SIA) and for proteins predicted to be secreted into plasma. Utilizing suspension bead arrays, ≈1800 unique antibody pairs are first screened against 209 proteins with recombinant proteins as well as EDTA plasma. Employing 624 unique antibodies, dilution-dependent curves in plasma and concentration-dependent curves of full-length proteins for 102 (49%) of the targets are obtained. For 22 protein assays, the longitudinal, interindividual, and technical performance is determined in a set of plasma samples collected from 18 healthy subjects every third month over 1 year. Finally, 14 of these assays are compared with with SIAs composed of other binders, proximity extension assays, and affinity-free targeted mass spectrometry. The workflow provides a multiplexed approach to screen for SIA pairs that suggests using at least three antibodies per target. This design is applicable for a wider range of targets of the plasma proteome, and the assays can be applied for discovery but also to validate emerging candidates derived from other platforms.

10 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: In this article, the authors investigated the effect of disease modifying therapies on immune response to severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) vaccines in people with MS.
Abstract: Objective The purpose of this study was to investigate the effect of disease modifying therapies on immune response to severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) vaccines in people with multiple sclerosis (MS). Methods Four hundred seventy-three people with MS provided one or more dried blood spot samples. Information about coronavirus disease 2019 (COVID-19) and vaccine history, medical, and drug history were extracted from questionnaires and medical records. Dried blood spots were eluted and tested for antibodies to SARS-CoV-2. Antibody titers were partitioned into tertiles with people on no disease modifying therapy as a reference. We calculated the odds ratio of seroconversion (univariate logistic regression) and compared quantitative vaccine response (Kruskal Wallis) following the SARS-CoV-2 vaccine according to disease modifying therapy. We used regression modeling to explore the effect of vaccine timing, treatment duration, age, vaccine type, and lymphocyte count on vaccine response. Results Compared to no disease modifying therapy, the use of anti-CD20 monoclonal antibodies (odds ratio = 0.03, 95% confidence interval [CI] = 0.01–0.06, p [less than] 0.001) and fingolimod (odds ratio = 0.04; 95% CI = 0.01–0.12) were associated with lower seroconversion following the SARS-CoV-2 vaccine. All other drugs did not differ significantly from the untreated cohort. Both time since last anti-CD20 treatment and total time on treatment were significantly associated with the response to the vaccination. The vaccine type significantly predicted seroconversion, but not in those on anti-CD20 medications. Preliminary data on cellular T-cell immunity showed 40% of seronegative subjects had measurable anti-SARS-CoV-2 T cell responses. Interpretation Some disease modifying therapies convey risk of attenuated serological response to SARS-CoV-2 vaccination in people with MS. We provide recommendations for the practical management of this patient group. ANN NEUROL 2021

91 citations

Journal ArticleDOI
TL;DR: In this paper, the authors focus on mass spectrometry (MS)-based proteomics and describe how machine learning and, in particular, deep learning now predicts experimental peptide measurements from amino acid sequences alone.
Abstract: There is an avalanche of biomedical data generation and a parallel expansion in computational capabilities to analyze and make sense of these data. Starting with genome sequencing and widely employed deep sequencing technologies, these trends have now taken hold in all omics disciplines and increasingly call for multi-omics integration as well as data interpretation by artificial intelligence technologies. Here, we focus on mass spectrometry (MS)-based proteomics and describe how machine learning and, in particular, deep learning now predicts experimental peptide measurements from amino acid sequences alone. This will dramatically improve the quality and reliability of analytical workflows because experimental results should agree with predictions in a multi-dimensional data landscape. Machine learning has also become central to biomarker discovery from proteomics data, which now starts to outperform existing best-in-class assays. Finally, we discuss model transparency and explainability and data privacy that are required to deploy MS-based biomarkers in clinical settings.

66 citations

Journal ArticleDOI
TL;DR: In this paper, mass spectrometry (MS)-based proteomics was applied to measure serum proteomes of COVID-19 patients and symptomatic, but PCR-negative controls, in a time-resolved manner.
Abstract: A deeper understanding of COVID-19 on human molecular pathophysiology is urgently needed as a foundation for the discovery of new biomarkers and therapeutic targets. Here we applied mass spectrometry (MS)-based proteomics to measure serum proteomes of COVID-19 patients and symptomatic, but PCR-negative controls, in a time-resolved manner. In 262 controls and 458 longitudinal samples of 31 patients, hospitalized for COVID-19, a remarkable 26% of proteins changed significantly. Bioinformatics analyses revealed co-regulated groups and shared biological functions. Proteins of the innate immune system such as CRP, SAA1, CD14, LBP, and LGALS3BP decreased early in the time course. Regulators of coagulation (APOH, FN1, HRG, KNG1, PLG) and lipid homeostasis (APOA1, APOC1, APOC2, APOC3, PON1) increased over the course of the disease. A global correlation map provides a system-wide functional association between proteins, biological processes, and clinical chemistry parameters. Importantly, five SARS-CoV-2 immunoassays against antibodies revealed excellent correlations with an extensive range of immunoglobulin regions, which were quantified by MS-based proteomics. The high-resolution profile of all immunoglobulin regions showed individual-specific differences and commonalities of potential pathophysiological relevance.

65 citations