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  1. Article: Interview: Mathias Uhlén zur "Antikörper-Validierung"

    Uhlén, Mathias

    Laborjournal

    2016  Volume 23, Issue 10, Page(s) 72

    Language German
    Document type Article
    ZDB-ID 1237282-1
    ISSN 1612-8354
    Database Current Contents Medicine

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 4842: Show issues Location:
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  2. Article ; Online: Sequential sequencing by synthesis and the next-generation sequencing revolution.

    Uhlen, Mathias / Quake, Stephen R

    Trends in biotechnology

    2023  Volume 41, Issue 12, Page(s) 1565–1572

    Abstract: The impact of next-generation sequencing (NGS) cannot be overestimated. The technology has transformed the field of life science, contributing to a dramatic expansion in our understanding of human health and disease and our understanding of biology and ... ...

    Abstract The impact of next-generation sequencing (NGS) cannot be overestimated. The technology has transformed the field of life science, contributing to a dramatic expansion in our understanding of human health and disease and our understanding of biology and ecology. The vast majority of the major NGS systems today are based on the concept of 'sequencing by synthesis' (SBS) with sequential detection of nucleotide incorporation using an engineered DNA polymerase. Based on this strategy, various alternative platforms have been developed, including the use of either native nucleotides or reversible terminators and different strategies for the attachment of DNA to a solid support. In this review, some of the key concepts leading to this remarkable development are discussed.
    MeSH term(s) Humans ; Sequence Analysis, DNA ; DNA/genetics ; Nucleotides ; DNA-Directed DNA Polymerase/genetics ; DNA-Directed DNA Polymerase/metabolism ; High-Throughput Nucleotide Sequencing
    Chemical Substances DNA (9007-49-2) ; Nucleotides ; DNA-Directed DNA Polymerase (EC 2.7.7.7)
    Language English
    Publishing date 2023-07-21
    Publishing country England
    Document type Journal Article ; Review ; Research Support, Non-U.S. Gov't
    ZDB-ID 47474-5
    ISSN 1879-3096 ; 0167-7799
    ISSN (online) 1879-3096
    ISSN 0167-7799
    DOI 10.1016/j.tibtech.2023.06.007
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 2750: Show issues Location:
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    Jg. 1995 - 2021: Lesesall (2.OG)
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  3. Article ; Online: Response to: Should we ignore western blots when selecting antibodies for other applications?

    Uhlen, Mathias

    Nature methods

    2017  Volume 14, Issue 3, Page(s) 215–216

    Language English
    Publishing date 2017-02-28
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2169522-2
    ISSN 1548-7105 ; 1548-7091
    ISSN (online) 1548-7105
    ISSN 1548-7091
    DOI 10.1038/nmeth.4194
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 6022: Show issues Location:
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  4. Article ; Online: Characterization of an

    Kim, Woonghee / Li, Mengzhen / Jin, Han / Yang, Hong / Türkez, Hasan / Uhlén, Mathias / Zhang, Cheng / Mardinoglu, Adil

    iScience

    2023  Volume 26, Issue 10, Page(s) 107727

    Abstract: ... ...

    Abstract Activated
    Language English
    Publishing date 2023-08-25
    Publishing country United States
    Document type Journal Article
    ISSN 2589-0042
    ISSN (online) 2589-0042
    DOI 10.1016/j.isci.2023.107727
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Addressing the Protease Bias in Quantitative Proteomics.

    Woessmann, Jakob / Kotol, David / Hober, Andreas / Uhlén, Mathias / Edfors, Fredrik

    Journal of proteome research

    2022  Volume 21, Issue 10, Page(s) 2526–2534

    Abstract: Protein quantification strategies using multiple proteases have been shown to deliver poor interprotease accuracy in label-free mass spectrometry experiments. By utilizing six different proteases with different cleavage sites, this study explores the ... ...

    Abstract Protein quantification strategies using multiple proteases have been shown to deliver poor interprotease accuracy in label-free mass spectrometry experiments. By utilizing six different proteases with different cleavage sites, this study explores the protease bias and its effect on accuracy and precision by using recombinant protein standards. We established 557 SRM assays, using a recombinant protein standard resource, toward 10 proteins in human plasma and determined their concentration with multiple proteases. The quantified peptides of these plasma proteins spanned 3 orders of magnitude (0.02-70 μM). In total, 60 peptides were used for absolute quantification and the majority of the peptides showed high robustness. The retained reproducibility was achieved by quantifying plasma proteins using spiked stable isotope standard recombinant proteins in a targeted proteomics workflow.
    MeSH term(s) Blood Proteins/analysis ; Endopeptidases ; Humans ; Isotope Labeling/methods ; Isotopes ; Peptide Hydrolases ; Peptides/analysis ; Proteomics/methods ; Recombinant Proteins ; Reproducibility of Results
    Chemical Substances Blood Proteins ; Isotopes ; Peptides ; Recombinant Proteins ; Endopeptidases (EC 3.4.-) ; Peptide Hydrolases (EC 3.4.-)
    Language English
    Publishing date 2022-08-31
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2078618-9
    ISSN 1535-3907 ; 1535-3893
    ISSN (online) 1535-3907
    ISSN 1535-3893
    DOI 10.1021/acs.jproteome.2c00491
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 6189: Show issues Location:
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  6. Article ; Online: Plasma proteomics for prediction of subclinical coronary artery calcifications in primary prevention.

    Royer, Patrick / Björnson, Elias / Adiels, Martin / Álvez, María Bueno / Fagerberg, Linn / Bäckhed, Fredrik / Uhlén, Mathias / Gummesson, Anders / Bergström, Göran

    American heart journal

    2024  Volume 271, Page(s) 55–67

    Abstract: Background and aims: Recent developments in high-throughput proteomic technologies enable the discovery of novel biomarkers of coronary atherosclerosis. The aims of this study were to test if plasma protein subsets could detect coronary artery ... ...

    Abstract Background and aims: Recent developments in high-throughput proteomic technologies enable the discovery of novel biomarkers of coronary atherosclerosis. The aims of this study were to test if plasma protein subsets could detect coronary artery calcifications (CAC) in asymptomatic individuals and if they add predictive value beyond traditional risk factors.
    Methods: Using proximity extension assays, 1,342 plasma proteins were measured in 1,827 individuals from the Impaired Glucose Tolerance and Microbiota (IGTM) study and 883 individuals from the Swedish Cardiopulmonary BioImage Study (SCAPIS) aged 50-64 years without history of ischaemic heart disease and with CAC assessed by computed tomography. After data-driven feature selection, extreme gradient boosting machine learning models were trained on the IGTM cohort to predict the presence of CAC using combinations of proteins and traditional risk factors. The trained models were validated in SCAPIS.
    Results: The best plasma protein subset (44 proteins) predicted CAC with an area under the curve (AUC) of 0.691 in the validation cohort. However, this was not better than prediction by traditional risk factors alone (AUC = 0.710, P = .17). Adding proteins to traditional risk factors did not improve the predictions (AUC = 0.705, P = .6). Most of these 44 proteins were highly correlated with traditional risk factors.
    Conclusions: A plasma protein subset that could predict the presence of subclinical CAC was identified but it did not outperform nor improve a model based on traditional risk factors. Thus, support for this targeted proteomics platform to predict subclinical CAC beyond traditional risk factors was not found.
    MeSH term(s) Humans ; Middle Aged ; Coronary Artery Disease/blood ; Coronary Artery Disease/diagnostic imaging ; Coronary Artery Disease/diagnosis ; Coronary Artery Disease/epidemiology ; Female ; Proteomics/methods ; Male ; Vascular Calcification/blood ; Vascular Calcification/diagnostic imaging ; Biomarkers/blood ; Blood Proteins/analysis ; Primary Prevention/methods ; Machine Learning ; Risk Factors ; Predictive Value of Tests ; Tomography, X-Ray Computed/methods ; Sweden/epidemiology
    Chemical Substances Biomarkers ; Blood Proteins
    Language English
    Publishing date 2024-02-05
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 80026-0
    ISSN 1097-6744 ; 0002-8703
    ISSN (online) 1097-6744
    ISSN 0002-8703
    DOI 10.1016/j.ahj.2024.01.011
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article: Machine Learning Analysis Reveals Biomarkers for the Detection of Neurological Diseases.

    Lam, Simon / Arif, Muhammad / Song, Xiya / Uhlén, Mathias / Mardinoglu, Adil

    Frontiers in molecular neuroscience

    2022  Volume 15, Page(s) 889728

    Abstract: It is critical to identify biomarkers for neurological diseases (NLDs) to accelerate drug discovery for effective treatment of patients of diseases that currently lack such treatments. In this work, we retrieved genotyping and clinical data from 1,223 UK ...

    Abstract It is critical to identify biomarkers for neurological diseases (NLDs) to accelerate drug discovery for effective treatment of patients of diseases that currently lack such treatments. In this work, we retrieved genotyping and clinical data from 1,223 UK Biobank participants to identify genetic and clinical biomarkers for NLDs, including Alzheimer's disease (AD), Parkinson's disease (PD), motor neuron disease (MND), and myasthenia gravis (MG). Using a machine learning modeling approach with Monte Carlo randomization, we identified a panel of informative diagnostic biomarkers for predicting AD, PD, MND, and MG, including classical liver disease markers such as alanine aminotransferase, alkaline phosphatase, and bilirubin. A multinomial model trained on accessible clinical markers could correctly predict an NLD diagnosis with an accuracy of 88.3%. We also explored genetic biomarkers. In a genome-wide association study of AD, PD, MND, and MG patients, we identified single nucleotide polymorphisms (SNPs) implicated in several craniofacial disorders such as apnoea and branchiootic syndrome. We found evidence for shared genetic risk loci among NLDs, including SNPs in cancer-related genes and SNPs known to be associated with non-brain cancers such as Wilms tumor, leukemia, and colon cancer. This indicates overlapping genetic characterizations among NLDs which challenges current clinical definitions of the neurological disorders. Taken together, this work demonstrates the value of data-driven approaches to identify novel biomarkers in the absence of any known or promising biomarkers.
    Language English
    Publishing date 2022-05-31
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2452967-9
    ISSN 1662-5099
    ISSN 1662-5099
    DOI 10.3389/fnmol.2022.889728
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  8. Article ; Online: Revisiting the role of serine metabolism in hepatic lipogenesis.

    Yang, Hong / Zhang, Cheng / Turkez, Hasan / Uhlen, Mathias / Boren, Jan / Mardinoglu, Adil

    Nature metabolism

    2023  Volume 5, Issue 5, Page(s) 760–761

    MeSH term(s) Humans ; Lipogenesis ; Serine/metabolism ; Non-alcoholic Fatty Liver Disease/metabolism
    Chemical Substances Serine (452VLY9402)
    Language English
    Publishing date 2023-05-11
    Publishing country Germany
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Comment
    ISSN 2522-5812
    ISSN (online) 2522-5812
    DOI 10.1038/s42255-023-00792-0
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article: Revealing the Metabolic Alterations during Biofilm Development of

    Altay, Ozlem / Zhang, Cheng / Turkez, Hasan / Nielsen, Jens / Uhlén, Mathias / Mardinoglu, Adil

    Metabolites

    2021  Volume 11, Issue 4

    Abstract: Burkholderia ... ...

    Abstract Burkholderia cenocepacia
    Language English
    Publishing date 2021-04-05
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2662251-8
    ISSN 2218-1989
    ISSN 2218-1989
    DOI 10.3390/metabo11040221
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article: Genome-Scale Metabolic Modeling of Glioblastoma Reveals Promising Targets for Drug Development.

    Larsson, Ida / Uhlén, Mathias / Zhang, Cheng / Mardinoglu, Adil

    Frontiers in genetics

    2020  Volume 11, Page(s) 381

    Abstract: Glioblastoma (GBM) is an aggressive type of brain cancer with a poor prognosis for affected patients. The current line of treatment only gives the patients a survival time of on average 15 months. In this work, we use genome-scale metabolic models (GEMs) ...

    Abstract Glioblastoma (GBM) is an aggressive type of brain cancer with a poor prognosis for affected patients. The current line of treatment only gives the patients a survival time of on average 15 months. In this work, we use genome-scale metabolic models (GEMs) together with other systems biology tools to examine the global transcriptomics-data of GBM-patients obtained from The Cancer Genome Atlas (TCGA). We reveal the molecular mechanisms underlying GBM and identify potential therapeutic targets for effective treatment of patients. The work presented consists of two main parts. The first part stratifies the patients into two groups, high and low survival, and compares their gene expression. The second part uses GBM and healthy brain tissue GEMs to simulate gene knockout in a GBM cell model to find potential therapeutic targets and predict their side effect in healthy brain tissue. We (1) find that genes upregulated in the patients with low survival are linked to various stages of the glioma invasion process, and (2) identify five essential genes for GBM, whose inhibition is non-toxic to healthy brain tissue, therefore promising to investigate further as therapeutic targets.
    Language English
    Publishing date 2020-04-17
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2606823-0
    ISSN 1664-8021
    ISSN 1664-8021
    DOI 10.3389/fgene.2020.00381
    Database MEDical Literature Analysis and Retrieval System OnLINE

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