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  • 1. Aebersold, Ruedi
    et al.
    Agar, Jeffrey N.
    Amster, I. Jonathan
    Baker, Mark S.
    Bertozzi, Carolyn R.
    Boja, Emily S.
    Costello, Catherine E.
    Cravatt, Benjamin F.
    Fenselau, Catherine
    Garcia, Benjamin A.
    Ge, Ying
    Gunawardena, Jeremy
    Hendrickson, Ronald C.
    Hergenrother, Paul J.
    Huber, Christian G.
    Ivanov, Alexander R.
    Jensen, Ole N.
    Jewett, Michael C.
    Kelleher, Neil L.
    Kiessling, Laura L.
    Krogan, Nevan J.
    Larsen, Martin R.
    Loo, Joseph A.
    Loo, Rachel R. Ogorzalek
    Lundberg, Emma
    KTH, Centres, Science for Life Laboratory, SciLifeLab. Stanford Univ, Dept Genet, Stanford, CA 94305 USA.
    MacCoss, Michael J.
    Mallick, Parag
    Mootha, Vamsi K.
    Mrksich, Milan
    Muir, Tom W.
    Patrie, Steven M.
    Pesavento, James J.
    Pitteri, Sharon J.
    Rodriguez, Henry
    Saghatelian, Alan
    Sandoval, Wendy
    Schluter, Hartmut
    Sechi, Salvatore
    Slavoff, Sarah A.
    Smith, Lloyd M.
    Snyder, Michael P.
    Thomas, Paul M.
    Uhlen, Mathias
    Van Eyk, Jennifer E.
    Vidal, Marc
    Walt, David R.
    White, Forest M.
    Williams, Evan R.
    Wohlschlager, Therese
    Wysocki, Vicki H.
    Yates, Nathan A.
    Young, Nicolas L.
    Zhang, Bing
    How many human proteoforms are there?2018In: Nature Chemical Biology, ISSN 1552-4450, E-ISSN 1552-4469, Vol. 14, no 3, p. 206-214Article in journal (Refereed)
    Abstract [en]

    Despite decades of accumulated knowledge about proteins and their post-translational modifications (PTMs), numerous questions remain regarding their molecular composition and biological function. One of the most fundamental queries is the extent to which the combinations of DNA-, RNA-and PTM-level variations explode the complexity of the human proteome. Here, we outline what we know from current databases and measurement strategies including mass spectrometry-based proteomics. In doing so, we examine prevailing notions about the number of modifications displayed on human proteins and how they combine to generate the protein diversity underlying health and disease. We frame central issues regarding determination of protein-level variation and PTMs, including some paradoxes present in the field today. We use this framework to assess existing data and to ask the question, "How many distinct primary structures of proteins (proteoforms) are created from the 20,300 human genes?" We also explore prospects for improving measurements to better regularize protein-level biology and efficiently associate PTMs to function and phenotype.

  • 2. Ahmad, Yasmeen
    et al.
    Boisvert, Francois-Michel
    Lundberg, Emma
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lamond, Angus I.
    Systematic Analysis of Protein Pools, Isoforms, and Modifications Affecting Turnover and Subcellular Localization2012In: Molecular & Cellular Proteomics, ISSN 1535-9476, E-ISSN 1535-9484, Vol. 11, no 3Article in journal (Refereed)
    Abstract [en]

    In higher eukaryotes many genes encode protein isoforms whose properties and biological roles are often poorly characterized. Here we describe systematic approaches for detection of either distinct isoforms, or separate pools of the same isoform, with differential biological properties. Using information from ion intensities we have estimated protein abundance levels and using rates of change in stable isotope labeling with amino acids in cell culture isotope ratios we measured turnover rates and subcellular distribution for the HeLa cell proteome. Protein isoforms were detected using three data analysis strategies that evaluate differences between stable isotope labeling with amino acids in cell culture isotope ratios for specific groups of peptides within the total set of peptides assigned to a protein. The candidate approach compares stable isotope labeling with amino acids in cell culture isotope ratios for predicted isoform- specific peptides, with ratio values for peptides shared by all the isoforms. The rule of thirds approach compares the mean isotope ratio values for all peptides in each of three equal segments along the linear length of the protein, assessing differences between segment values. The three in a row approach compares mean isotope ratio values for each sequential group of three adjacent peptides, assessing differences with the mean value for all peptides assigned to the protein. Protein isoforms were also detected and their properties evaluated by fractionating cell extracts on one- dimensional SDS- PAGE prior to trypsin digestion and MS analysis and independently evaluating isotope ratio values for the same peptides isolated from different gel slices. The effect of protein phosphorylation on turnover rates was analyzed by comparing mean turnover values calculated for all peptides assigned to a protein, either including, or excluding, values for cognate phosphopeptides. Collectively, these experimental and analytical approaches provide a framework for expanding the func- tional annotation of the genome.

  • 3.
    Akan, Pelin
    et al.
    KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Alexeyenko, Andrey
    KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Costea, Paul Igor
    KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hedberg, Lilia
    KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Werne Solnestam, Beata
    KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lundin, Sverker
    KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hallman, Jimmie
    Lundberg, Emma
    KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lundeberg, Joakim
    KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Comprehensive analysis of the genome transcriptome and proteome landscapes of three tumor cell lines2012In: Genome Medicine, ISSN 1756-994X, E-ISSN 1756-994X, Vol. 4, p. 86-Article in journal (Refereed)
    Abstract [en]

    We here present a comparative genome, transcriptome and functional network analysis of three human cancer cell lines (A431, U251MG and U2OS), and investigate their relation to protein expression. Gene copy numbers significantly influenced corresponding transcript levels; their effect on protein levels was less pronounced. We focused on genes with altered mRNA and/or protein levels to identify those active in tumor maintenance. We provide comprehensive information for the three genomes and demonstrate the advantage of integrative analysis for identifying tumor-related genes amidst numerous background mutations by relating genomic variation to expression/protein abundance data and use gene networks to reveal implicated pathways.

  • 4. Alkasalias, Twana
    et al.
    Alexeyenko, Andrey
    Hennig, Katharina
    Danielsson, Frida
    Lebbink, Robert Jan
    Fielden, Matthew
    Turunen, S. Pauliina
    Lehti, Kaisa
    Kashuba, Vladimir
    Madapura, Harsha S.
    Bozoky, Benedek
    Lundberg, Emma
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Balland, Martial
    Guven, Hayrettin
    Klein, George
    Gad, Annica K. B.
    Pavlova, Tatiana
    RhoA knockout fibroblasts lose tumor-inhibitory capacity in vitro and promote tumor growth in vivo2017In: Proceedings of the National Academy of Sciences of the United States of America, ISSN 0027-8424, E-ISSN 1091-6490, Vol. 114, no 8, p. E1413-E1421Article in journal (Refereed)
    Abstract [en]

    Fibroblasts are a main player in the tumor-inhibitory microenvironment. Upon tumor initiation and progression, fibroblasts can lose their tumor-inhibitory capacity and promote tumor growth. The molecular mechanisms that underlie this switch have not been defined completely. Previously, we identified four proteins over-expressed in cancer-associated fibroblasts and linked to Rho GTPase signaling. Here, we show that knocking out the Ras homolog family member A (RhoA) gene in normal fibroblasts decreased their tumor-inhibitory capacity, as judged by neighbor suppression in vitro and accompanied by promotion of tumor growth in vivo. This also induced PC3 cancer cell motility and increased colony size in 2D cultures. RhoA knockout in fibroblasts induced vimentin intermediate filament reorganization, accompanied by reduced contractile force and increased stiffness of cells. There was also loss of wide F-actin stress fibers and large focal adhesions. In addition, we observed a significant loss of a-smooth muscle actin, which indicates a difference between RhoA knockout fibroblasts and classic cancer-associated fibroblasts. In 3D collagen matrix, RhoA knockout reduced fibroblast branching and meshwork formation and resulted in more compactly clustered tumor-cell colonies in coculture with PC3 cells, which might boost tumor stem-like properties. Coculturing RhoA knockout fibroblasts and PC3 cells induced expression of proinflammatory genes in both. Inflammatory mediators may induce tumor cell stemness. Network enrichment analysis of transcriptomic changes, however, revealed that the Rho signaling pathway per se was significantly triggered only after coculturing with tumor cells. Taken together, our findings in vivo and in vitro indicate that Rho signaling governs the inhibitory effects by fibroblasts on tumor-cell growth.

  • 5.
    Alm, Tove L.
    et al.
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Lundberg, Emma
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    The Affinity Binder Knockdown Initiative.2016In: Molecular Biology of the Cell, ISSN 1059-1524, E-ISSN 1939-4586, Vol. 27Article in journal (Refereed)
  • 6.
    Alm, Tove L.
    et al.
    KTH, School of Biotechnology (BIO).
    Lundberg, Emma
    KTH, School of Biotechnology (BIO).
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO).
    The Affinity Binder Knockdown Initiative2015In: Molecular Biology of the Cell, ISSN 1059-1524, E-ISSN 1939-4586, Vol. 26Article in journal (Other academic)
  • 7.
    Alm, Tove
    et al.
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lundberg, Emma
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Introducing the Affinity Binder Knockdown Initiative-A public-private partnership for validation of affinity reagents2016In: EuPA Open Proteomics, ISSN 0014-2328, E-ISSN 2212-9685, Vol. 10, p. 56-58Article in journal (Refereed)
    Abstract [en]

    The newly launched Affinity Binder Knockdown Initiative encourages antibody suppliers and users to join this public-private partnership, which uses crowdsourcing to collect characterization data on antibodies. Researchers are asked to share validation data from experiments where gene-editing techniques (such as siRNA or CRISPR) have been used to verify antibody binding. The initiative is launched under the aegis of Antibodypedia, a database designed to allow comparisons and scoring of publicly available antibodies towards human protein targets. What is known about an antibody is the foundation of the scoring and ranking system in Antibodypedia.

  • 8.
    Alm, Tove
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    von Feilitzen, Kalle
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lundberg, Emma
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Sivertsson, Åsa
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    A Chromosome-Centric Analysis of Antibodies Directed toward the Human Proteome Using Antibodypedia2014In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 13, no 3, p. 1669-1676Article in journal (Refereed)
    Abstract [en]

    Antibodies are crucial for the study of human proteins and have been defined as one of the three pillars in the human chromosome-centric Human Proteome Project (CHPP). In this article the chromosome-centric structure has been used to analyze the availability of antibodies as judged by the presence within the portal Antibodypedia, a database designed to allow comparisons and scoring of publicly available antibodies toward human protein targets. This public database displays antibody data from more than one million antibodies toward human protein targets. A summary of the content in this knowledge resource reveals that there exist more than 10 antibodies to over 70% of all the putative human genes, evenly distributed over the 24 human chromosomes. The analysis also shows that at present, less than 10% of the putative human protein-coding genes (n = 1882) predicted from the genome sequence lack antibodies, suggesting that focused efforts from the antibody-based and mass spectrometry-based proteomic communities should be encouraged to pursue the analysis of these missing proteins. We show that Antibodypedia may be used to track the development of available and validated antibodies to the individual chromosomes, and thus the database is an attractive tool to identify proteins with no or few antibodies yet generated.

  • 9. Amit, Ido
    et al.
    Lundberg, Emma
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Zhang, Tian
    et al.,
    Voices of biotech2016In: Nature Biotechnology, ISSN 1087-0156, E-ISSN 1546-1696, Vol. 34, no 3, p. 270-275Article in journal (Refereed)
  • 10.
    Anikeeva, Polina
    et al.
    MIT, Dept Mat Sci & Engn, Cambridge, MA 02139 USA.;MIT, Dept Brain & Cognit Sci, E25-618, Cambridge, MA 02139 USA.;MIT, Res Lab Elect, Cambridge, MA 02139 USA.;MIT, McGovern Inst Brain Res, 77 Massachusetts Ave, Cambridge, MA 02139 USA..
    Lundberg, Emma
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH). Stanford Univ, Dept Genet, Stanford, CA 94305 USA.;Chan Zuckerberg Biohub, San Francisco, CA USA..
    Zhuang, Xiaowei
    Harvard Univ, Dept Chem & Chem Biol, Cambridge, MA 02138 USA.;Harvard Univ, Dept Phys, Cambridge, MA 02138 USA.;Howard Hughes Med Inst, Cambridge, MA USA..
    Voices in methods development2019In: Nature Methods, ISSN 1548-7091, E-ISSN 1548-7105, Vol. 16, no 10, p. 945-951Article in journal (Other academic)
  • 11.
    Barbe, Laurent
    et al.
    KTH, School of Biotechnology (BIO).
    Lundberg, Emma
    KTH, School of Biotechnology (BIO), Proteomics.
    Oksvold, Per
    KTH, School of Biotechnology (BIO), Proteomics.
    Stenius, Anna
    KTH, School of Biotechnology (BIO).
    Lewin, Erland
    KTH, School of Engineering Sciences (SCI), Applied Physics, Cell Physics.
    Björling, Erik
    KTH, School of Biotechnology (BIO).
    Asplund, Anna
    Department of Genetics and Pathology, Rudbeck Laboratory, Uppsala University.
    Pontén, Fredrik
    Department of Genetics and Pathology, Rudbeck Laboratory, Uppsala University.
    Brismar, Hjalmar
    KTH, School of Engineering Sciences (SCI), Applied Physics, Cell Physics.
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics.
    Andersson-Svahn, Helene
    KTH, School of Biotechnology (BIO), Proteomics.
    Toward a confocal subcellular atlas of the human proteome2008In: Molecular and cellular proteomics, ISSN 1535-9476, Vol. 7, no 3, p. 499-508Article in journal (Refereed)
    Abstract [en]

    Information on protein localization on the subcellular level is important to map and characterize the proteome and to better understand cellular functions of proteins. Here we report on a pilot study of 466 proteins in three human cell lines aimed to allow large scale confocal microscopy analysis using protein-specific antibodies. Approximately 3000 high resolution images were generated, and more than 80% of the analyzed proteins could be classified in one or multiple subcellular compartment(s). The localizations of the proteins showed, in many cases, good agreement with the Gene Ontology localization prediction model. This is the first large scale antibody-based study to localize proteins into subcellular compartments using antibodies and confocal microscopy. The results suggest that this approach might be a valuable tool in conjunction with predictive models for protein localization.

  • 12.
    Berglund, Lisa
    et al.
    KTH, School of Biotechnology (BIO), Proteomics.
    Björling, Erik
    KTH, School of Biotechnology (BIO), Proteomics.
    Oksvold, Per
    KTH, School of Biotechnology (BIO), Proteomics.
    Fagerberg, Linn
    KTH, School of Biotechnology (BIO), Proteomics.
    Al-Khalili Szigyarto, Cristina
    KTH, School of Biotechnology (BIO), Proteomics.
    Persson, Anja
    KTH, School of Biotechnology (BIO), Proteomics.
    Ottosson, Jenny
    KTH, School of Biotechnology (BIO), Proteomics.
    Wernérus, Henrik
    KTH, School of Biotechnology (BIO), Proteomics.
    Nilsson, Peter
    KTH, School of Biotechnology (BIO), Proteomics.
    Lundberg, Emma
    KTH, School of Biotechnology (BIO), Proteomics.
    Sivertsson, Åsa
    KTH, School of Biotechnology (BIO), Proteomics.
    Hober, Sophia
    KTH, School of Biotechnology (BIO), Proteomics.
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics.
    et al.,
    A genecentric human protein atlas for expression profiles based on antibodies2008In: Molecular & Cellular Proteomics, ISSN 1535-9476, Vol. 7, no 10, p. 2019-2027Article in journal (Refereed)
    Abstract [en]

    An attractive path forward in proteomics is to experimentally annotate the human protein complement of the genome in a genecentric manner. Using antibodies, it might be possible to design protein-specific probes for a representative protein from every protein-coding gene and to subsequently use the antibodies for systematical analysis of cellular distribution and subcellular localization of proteins in normal and disease tissues. A new version (4.0) of the Human Protein Atlas has been developed in a genecentric manner with the inclusion of all human genes and splice variants predicted from genome efforts together with a visualization of each protein with characteristics such as predicted membrane regions, signal peptide, and protein domains and new plots showing the uniqueness (sequence similarity) of every fraction of each protein toward all other human proteins. The new version is based on tissue profiles generated from 6120 antibodies with more than five million immunohistochemistry-based images covering 5067 human genes, corresponding to similar to 25% of the human genome. Version 4.0 includes a putative list of members in various protein classes, both functional classes, such as kinases, transcription factors, G-protein-coupled receptors, etc., and project-related classes, such as candidate genes for cancer or cardiovascular diseases. The exact antigen sequence for the internally generated antibodies has also been released together with a visualization of the application-specific validation performed for each antibody, including a protein array assay, Western blot analysis, immunohistochemistry, and, for a large fraction, immunofluorescence-based confocal microscopy. New search functionalities have been added to allow complex queries regarding protein expression profiles, protein classes, and chromosome location. The new version of the protein atlas thus is a resource for many areas of biomedical research, including protein science and biomarker discovery.

  • 13. Bjork, L.
    et al.
    Ait Blal, C.
    Alm, Tove L.
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Bäckström, Anna
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Gnann, C.
    Hjelmare, Martin
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Schutten, Rutger
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Skogs, Marie
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Stadler, Charlotte
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Lundberg, Emma
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Application specific antibody validation. The Human Protein Atlas validation scheme and how to confirm subcellular protein localization.2016In: Molecular Biology of the Cell, ISSN 1059-1524, E-ISSN 1939-4586, Vol. 27Article in journal (Refereed)
  • 14. Bock, Thomas
    et al.
    Moest, Hansjoerg
    Omasits, Ulrich
    Dolski, Silvia
    Lundberg, Emma
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Frei, Andreas
    Hofmann, Andreas
    Bausch-Fluck, Damaris
    Jacobs, Andrea
    Krayenbuehl, Niklaus
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Aebersold, Ruedi
    Frei, Karl
    Wollscheid, Bernd
    Proteomic Analysis Reveals Drug Accessible Cell Surface N-Glycoproteins of Primary and Established Glioblastoma Cell Lines2012In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 11, no 10, p. 4885-4893Article in journal (Refereed)
    Abstract [en]

    Glioblastoma is the most common primary Glioblastoma Cell Surface Capturing brain tumor in adults with low average survival time after diagnosis. In order to improve glioblastoma treatment, new drug-accessible targets need to be identified. Cell surface glycoproteins are prime drug targets due to their accessibility at the surface of cancer cells. To overcome the limited availability of suitable antibodies for cell surface protein detection, we performed a comprehensive mass spectrometric investigation of the glioblastoma surfaceome. Our combined cell surface capturing analysis of primary ex vivo glioblastoma cell lines in combination with established glioblastoma cell lines revealed 633 N-glycoproteins, which vastly extends the known data of surfaceome drug targets at subcellular resolution. We provide direct evidence of common glioblastoma cell surface glycoproteins and an approximate estimate of their abundances, information that could not be derived from genomic and/or transcriptomic glioblastoma studies. Apart from our pharmaceutically valuable repertoire of already and potentially drug-accessible cell surface glycoproteins, we built a mass-spectrometry-based toolbox enabling directed, sensitive, and repetitive glycoprotein measurements for clinical follow-up studies. The included Skyline Glioblastoma SRM assay library provides an elevated starting point for parallel testing of the abundance level of the detected glioblastoma surfaceome members in future drug perturbation experiments.

  • 15. Borsics, Tamas
    et al.
    Lundberg, Emma
    KTH, School of Biotechnology (BIO), Centres, Albanova VinnExcellence Center for Protein Technology, ProNova.
    Geerts, Dirk
    Koomoa, Dana-Lynn T.
    Koster, Jan
    Wester, Kenneth
    Bachmann, Andres S.
    Subcellular distribution and expression of prenylated Rab acceptor 1 domain family, member 2 (PRAF2) in malignant glioma: Influence on cell survival and migration2010In: Cancer Science, ISSN 1347-9032, E-ISSN 1349-7006, Vol. 101, no 7, p. 1624-1631Article in journal (Refereed)
    Abstract [en]

    Our previous studies revealed that the expression of the 19-kDa protein prenylated Rab acceptor 1 domain family, member 2 (PRAF2) is elevated in cancer tissues of the breast, colon, lung, and ovary, when compared to noncancerous tissues of paired samples. PRAF2 mRNA expression also correlated with several genetic and clinical features and is a candidate prognostic marker in the pediatric cancer neuroblastoma. The PRAF2-related proteins, PRAF1 and PRAF3, play multiple roles in cellular processes, including endo/exocytic vesicle trafficking and glutamate uptake. PRAF2 shares a high sequence homology with these family members, but its function remains unknown. In this study, we examined PRAF2 mRNA and protein expression in 20 different human cancer types using Affymetrix microarray and human tissue microarray (TMA) analyses, respectively. In addition, we investigated the subcellular distribution of PRAF2 by immunofluorescence microscopy and cell fractionation studies. PRAF2 mRNA and protein expression was elevated in several cancer tissues with highest levels in malignant glioma. At the molecular level, we detected native PRAF2 in small, vesicle-like structures throughout the cytoplasm as well as in and around cell nuclei of U-87 malignant glioma cells. We further found that monomeric and dimeric forms of PRAF2 are associated with different cell compartments, suggesting possible functional differences. Importantly, PRAF2 down-regulation by RNA interference significantly reduced the cell viability, migration, and invasiveness of U-87 cells. This study shows that PRAF2 expression is elevated in various tumors with exceptionally high expression in malignant gliomas, and PRAF2 therefore presents a candidate molecular target for therapeutic intervention. (Cancer Sci 2010).

  • 16. Boström, Johan
    et al.
    Sramkova, Zuzana
    Salasova, Alena
    Johard, Helena
    Mahdessian, Diana
    Fedr, Radek
    Marks, Carolyn
    Medalova, Jirina
    Soucek, Karel
    Lundberg, Emma
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Biotechnology (BIO), Centres, Albanova VinnExcellence Center for Protein Technology, ProNova.
    Linnarsson, Sten
    Bryja, Vitezslav
    Sekyrova, Petra
    Altun, Mikael
    Andang, Michael
    Comparative cell cycle transcriptomics reveals synchronization of developmental transcription factor networks in cancer cells2017In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 12, no 12, article id e0188772Article in journal (Refereed)
    Abstract [en]

    The cell cycle coordinates core functions such as replication and cell division. However, cell-cycle-regulated transcription in the control of non-core functions, such as cell identity maintenance through specific transcription factors (TFs) and signalling pathways remains unclear. Here, we provide a resource consisting of mapped transcriptomes in unsynchro-nized HeLa and U2OS cancer cells sorted for cell cycle phase by Fucci reporter expression. We developed a novel algorithm for data analysis that enables efficient visualization and data comparisons and identified cell cycle synchronization of Notch signalling and TFs associated with development. Furthermore, the cell cycle synchronizes with the circadian clock, providing a possible link between developmental transcriptional networks and the cell cycle. In conclusion we find that cell cycle synchronized transcriptional patterns are temporally compartmentalized and more complex than previously anticipated, involving genes, which control cell identity and development.

  • 17.
    Boström, Tove
    et al.
    KTH, School of Biotechnology (BIO), Protein Technology.
    Danielsson, Frida
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Lundberg, Emma
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Johansson, Henrik J.
    Karlinska Institute, Cancer Proteomics Mass Spectrometry, Dep. of Oncology-Pathology.
    Tegel, Hanna
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Lehtiö, Janne
    Karolinska Institute, Cancer Proteomics Mass Spectrometry, Dep. of Oncology-Pathology.
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Hober, Sophia
    KTH, School of Biotechnology (BIO), Protein Technology.
    Ottosson Takanen, Jenny
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Investigating the correlation of protein and mRNA levels in human cell lines using quantitative proteomics and transcriptomicsManuscript (preprint) (Other academic)
    Abstract [en]

    An important topic of discussion in proteomics is the degree of correlation of RNA and protein levels in cells, tissues and organs. In this study, the difference in protein and mRNA levels for a number of selected gene targets were investigated across six human cell lines using quantitative proteomics and next generation sequencing-based transcriptomics. The copy numbers of 32 proteins were determined using an absolute quantitative proteomics approach (PrEST-SILAC), where heavy isotope-labeled protein fragments were used as internal standards. A cross evaluation of protein copy numbers determined by mass spectrometry and staining profiles using immunohistochemistry showed good correlation. The mRNA levels were determined using RNA sequencing based on digital counting of sequencing reads and the levels determined as FPKM values. Comparison of the relative variations in mRNA and protein levels for individual genes across the six cell lines showed correlation between protein and mRNA levels, including six genes with high variability in expression levels in the six cell lines resulting in an average correlation of 0.9 (Spearman's rank coefficient). In summary, the analysis of the selected protein targets supports the conclusion that the translation rate across cell lines correlates for a particular gene, suggesting that individual protein levels can be predicted from the respective mRNA levels by defining the relation between protein and mRNA, specific for each human gene.

  • 18. Carreras-Puigvert, J.
    et al.
    Zitnik, M.
    Jemth, A. -S
    Carter, M.
    Unterlass, J. E.
    Hallström, Björn M.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Loseva, O.
    Karem, Z.
    Calderón-Montanõ, J. M.
    Lindskog, C.
    Edqvist, P. -H
    Matuszewski, D. J.
    Ait Blal, Hammou
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Berntsson, R. P. A.
    Häggblad, M.
    Martens, U.
    Studham, M.
    Lundgren, B.
    Wählby, C.
    Sonnhammer, E. L. L.
    Lundberg, Emma
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Stenmark, P.
    Zupan, B.
    Helleday, T.
    A comprehensive structural, biochemical and biological profiling of the human NUDIX hydrolase family2017In: Nature Communications, ISSN 2041-1723, E-ISSN 2041-1723, Vol. 8, no 1, article id 1541Article in journal (Refereed)
    Abstract [en]

    The NUDIX enzymes are involved in cellular metabolism and homeostasis, as well as mRNA processing. Although highly conserved throughout all organisms, their biological roles and biochemical redundancies remain largely unclear. To address this, we globally resolve their individual properties and inter-relationships. We purify 18 of the human NUDIX proteins and screen 52 substrates, providing a substrate redundancy map. Using crystal structures, we generate sequence alignment analyses revealing four major structural classes. To a certain extent, their substrate preference redundancies correlate with structural classes, thus linking structure and activity relationships. To elucidate interdependence among the NUDIX hydrolases, we pairwise deplete them generating an epistatic interaction map, evaluate cell cycle perturbations upon knockdown in normal and cancer cells, and analyse their protein and mRNA expression in normal and cancer tissues. Using a novel FUSION algorithm, we integrate all data creating a comprehensive NUDIX enzyme profile map, which will prove fundamental to understanding their biological functionality.

  • 19. Clevers, Hans
    et al.
    Rafelski, Susanne
    Elowitz, Michael
    Klein, Allon
    Shendure, Jay
    Trapnell, Cole
    Lein, Ed
    Lundberg, Emma
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Uhlén, Matthias
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Martinez-Arias, Alfonso
    Sanes, Joshua R.
    Blainey, Paul
    Eberwine, James
    Kim, Junhyong
    Love, J. Christopher
    What Is Your Conceptual Definition of "Cell Type'' in the Context of a Mature Organism?2017In: CELL SYSTEMS, ISSN 2405-4712, Vol. 4, no 3, p. 255-259Article in journal (Refereed)
  • 20.
    Danielsson, Frida
    et al.
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Akesson, L.
    Skogs, Marie
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Lundberg, Emma
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Profiling changes in response to hypoxia in a four-step cell line model for malignant transformation.2016In: Molecular Biology of the Cell, ISSN 1059-1524, E-ISSN 1939-4586, Vol. 27Article in journal (Refereed)
  • 21.
    Danielsson, Frida
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Fasterius, Erik
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101).
    Sullivan, Devin
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hases, Linnea
    KTH, Centres, Science for Life Laboratory, SciLifeLab. Karolinska Institute, Huddinge, Sweden.
    Sanli, Kemal
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Zhang, Cheng
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Al-Khalili Szigyarto, Cristina
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101).
    Huss, M.
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101). KTH, Centres, Science for Life Laboratory, SciLifeLab. Technical University of Denmark, Hørsholm, Denmark.
    Williams, Cecilia
    KTH, Centres, Science for Life Laboratory, SciLifeLab. Karolinska Institute, Huddinge, Sweden.
    Lundberg, Emma
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Transcriptome profiling of the interconnection of pathways involved in malignant transformation and response to hypoxia2018In: OncoTarget, ISSN 1949-2553, E-ISSN 1949-2553, Vol. 9, no 28, p. 19730-19744Article in journal (Refereed)
    Abstract [en]

    In tumor tissues, hypoxia is a commonly observed feature resulting from rapidly proliferating cancer cells outgrowing their surrounding vasculature network. Transformed cancer cells are known to exhibit phenotypic alterations, enabling continuous proliferation despite a limited oxygen supply. The four-step isogenic BJ cell model enables studies of defined steps of tumorigenesis: the normal, immortalized, transformed, and metastasizing stages. By transcriptome profiling under atmospheric and moderate hypoxic (3% O2) conditions, we observed that despite being highly similar, the four cell lines of the BJ model responded strikingly different to hypoxia. Besides corroborating many of the known responses to hypoxia, we demonstrate that the transcriptome adaptation to moderate hypoxia resembles the process of malignant transformation. The transformed cells displayed a distinct capability of metabolic switching, reflected in reversed gene expression patterns for several genes involved in oxidative phosphorylation and glycolytic pathways. By profiling the stage-specific responses to hypoxia, we identified ASS1 as a potential prognostic marker in hypoxic tumors. This study demonstrates the usefulness of the BJ cell model for highlighting the interconnection of pathways involved in malignant transformation and hypoxic response.

  • 22.
    Danielsson, Frida
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lundberg, Emma
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Uhlen, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Gad, A. K.
    Profiling the Molecular changes during malignant transformation and response to different oxygen levels, using a combined transcriptomics and proteomics approach2014In: Molecular Biology of the Cell, ISSN 1059-1524, E-ISSN 1939-4586, Vol. 25, article id P1845Article in journal (Other academic)
  • 23.
    Danielsson, Frida
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Skogs, Marie
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Huss, Mikael
    Rexhepaj, Elton
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    O'Hurley, Gillian
    Klevebring, Daniel
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Pontén, Fredrik
    Gad, Annica K. B.
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lundberg, Emma
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Majority of differentially expressed genes are down-regulated during malignant transformation in a four-stage model2013In: Proceedings of the National Academy of Sciences of the United States of America, ISSN 0027-8424, E-ISSN 1091-6490, Vol. 110, no 17, p. 6853-6858Article in journal (Refereed)
    Abstract [en]

    The transformation of normal cells to malignant, metastatic tumor cells is a multistep process caused by the sequential acquirement of genetic changes. To identify these changes, we compared the transcriptomes and levels and distribution of proteins in a four-stage cell model of isogenically matched normal, immortalized, transformed, and metastatic human cells, using deep transcriptome sequencing and immunofluorescence microscopy. The data show that similar to 6% (n = 1,357) of the human protein-coding genes are differentially expressed across the stages in the model. Interestingly, the majority of these genes are down-regulated, linking malignant transformation to dedifferentiation. The up-regulated genes are mainly components that control cellular proliferation, whereas the down-regulated genes consist of proteins exposed on or secreted from the cell surface. As many of the identified gene products control basic cellular functions that are defective in cancers, the data provide candidates for follow-up studies to investigate their functional roles in tumor formation. When we further compared the expression levels of four of the identified proteins in clinical cancer cohorts, similar differences were observed between benign and cancer cells, as in the cell model. This shows that this comprehensive demonstration of the molecular changes underlying malignant transformation is a relevant model to study the process of tumor formation.

  • 24.
    Danielsson, Frida
    et al.
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Skogs, Marie
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Åkesson, L.
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Mahdessian, Diana
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Sullivan, Devin P.
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Thul, Peter
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Wiking, Mikaela
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Björk, L.
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Schutten, Rutger
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Ait Blal, Carl
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Hjelmare, Martin
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Gnann, Christian
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Lundberg, Emma
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    An image-based view of the microtubule proteome2016In: Molecular Biology of the Cell, ISSN 1059-1524, E-ISSN 1939-4586, Vol. 27Article in journal (Refereed)
  • 25.
    Danielsson, Frida
    et al.
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Wiking, Mikaela
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Mahdessian, Diana
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Skogs, Marie
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Ait Blal, Hammou
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hjelmare, Martin
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Stadler, Charlotte
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lundberg, Emma
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    RNA Deep Sequencing as a Tool for Selection of Cell Lines for Systematic Subcellular Localization of All Human Proteins2013In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 12, no 1, p. 231-239Article in journal (Refereed)
    Abstract [en]

    One of the major challenges of a chromosome-centric proteome project is to explore in a systematic manner the potential proteins identified from the chromosomal genome sequence, but not yet characterized on a protein level. Here, we describe the use of RNA deep sequencing to screen human cell lines for RNA profiles and to use this information to select cell lines suitable for characterization of the corresponding gene product. In this manner, the subcellular localization of proteins can be analyzed systematically using antibody-based confocal microscopy. We demonstrate the usefulness of selecting cell lines with high expression levels of RNA transcripts to increase the likelihood of high quality immunofluorescence staining and subsequent successful subcellular localization of the corresponding protein. The results show a path to combine transcriptomics with affinity proteomics to characterize the proteins in a gene- or chromosome-centric manner.

  • 26. Dengjel, Joern
    et al.
    Hoyer-Hansen, Maria
    Nielsen, Maria O.
    Eisenberg, Tobias
    Harder, Lea M.
    Schandorff, Soren
    Farkas, Thomas
    Kirkegaard, Thomas
    Becker, Andrea C.
    Schroeder, Sabrina
    Vanselow, Katja
    Lundberg, Emma
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Nielsen, Mogens M.
    Kristensen, Anders R.
    Akimov, Vyacheslav
    Bunkenborg, Jakob
    Madeo, Frank
    Jaattela, Marja
    Andersen, Jens S.
    Identification of Autophagosome-associated Proteins and Regulators by Quantitative Proteomic Analysis and Genetic Screens2012In: Molecular & Cellular Proteomics, ISSN 1535-9476, E-ISSN 1535-9484, Vol. 11, no 3Article in journal (Refereed)
    Abstract [en]

    Autophagy is one of the major intracellular catabolic pathways, but little is known about the composition of autophagosomes. To study the associated proteins, we isolated autophagosomes from human breast cancer cells using two different biochemical methods and three stimulus types: amino acid deprivation or rapamycin or concanamycin A treatment. The autophagosome- associated proteins were dependent on stimulus, but a core set of proteins was stimulus- independent. Remarkably, proteasomal proteins were abundant among the stimulus- independent common autophagosome- associated proteins, and the activation of autophagy significantly decreased the cellular proteasome level and activity supporting interplay between the two degradation pathways. A screen of yeast strains defective in the orthologs of the human genes encoding for a common set of autophagosome- associated proteins revealed several regulators of autophagy, including subunits of the retromer complex. The combined spatiotemporal proteomic and genetic data sets presented here provide a basis for further characterization of autophagosome biogenesis and cargo selection.

  • 27.
    Edfors, Fredrik
    et al.
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Boström, Tove
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Forsström, Björn
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Zeiler, Marlis
    Johansson, Henrik J.
    Karlinska Institute.
    Lundberg, Emma
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hober, Sophia
    KTH, School of Biotechnology (BIO), Protein Technology.
    Lehtiö, Janne
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Mann, Matthias
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Immunoproteomics using polyclonal antibodies and stable isotope-labeled affinity-purified recombinant proteins2014In: Molecular & Cellular Proteomics, ISSN 1535-9476, E-ISSN 1535-9484, Vol. 13, no 6, p. 1611-1624Article in journal (Refereed)
    Abstract [en]

    AThe combination of immuno-based methods and mass spectrometry detection has great potential in the field of quantitative proteomics. Here, we describe a new method (immuno-SILAC) for the absolute quantification of proteins in complex samples based on polyclonal antibodies and stable isotope-labeled recombinant protein fragments to allow affinity enrichment prior to mass spectrometry analysis and accurate quantification. We took advantage of the antibody resources publicly available from the Human Protein Atlas project covering more than 80% of all human protein-coding genes. Epitope mapping revealed that a majority of the polyclonal antibodies recognized multiple linear epitopes, and based on these results, a semi-automated method was developed for peptide enrichment using polyclonal antibodies immobilized on protein A-coated magnetic beads. A protocol based on the simultaneous multiplex capture of more than 40 protein targets showed that approximately half of the antibodies enriched at least one functional peptide detected in the subsequent mass spectrometry analysis. The approach was further developed to also generate quantitative data via the addition of heavy isotope-labeled recombinant protein fragment standards prior to trypsin digestion. Here, we show that we were able to use small amounts of antibodies (50 ng per target) in this manner for efficient multiplex analysis of quantitative levels of proteins in a human HeLa cell lysate. The results suggest that polyclonal antibodies generated via immunization of recombinant protein fragments could be used for the enrichment of target peptides to allow for rapid mass spectrometry analysis taking advantage of a substantial reduction in sample complexity. The possibility of building up a proteome-wide resource for immuno-SILAC assays based on publicly available antibody resources is discussed.

  • 28.
    Edfors, Fredrik
    et al.
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Danielsson, Frida
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hallström, Björn
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Käll, Lukas
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lundberg, Emma
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Ponten, Fredrik
    Department of Immunology, Genetics and Pathology, Rudbeck Laboratory, Uppsala University, SE-751 85 Uppsala, Sweden.
    Forsström, Björn
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab. Technical University of Denmark, Denmark.
    Gene specific correlation of RNA and protein levels in human cells and tissues2016In: Molecular Systems Biology, ISSN 1744-4292, E-ISSN 1744-4292Article in journal (Refereed)
    Abstract [en]

    An important issue for molecular biology is to establish if transcript levels of a given gene can be used as proxies for the corresponding protein levels. Here, we have developed a targeted proteomics approach for a set of human non-secreted proteins based on Parallel Reaction Monitoring to measure, at steady-state conditions, absolute protein copy numbers across human tissues and cell lines and compared these levels with the corresponding mRNA levels using transcriptomics. The study shows that the transcript and protein levels do not correlate well unless a gene-specific RNA-to-protein (RTP) conversion factor independent of the tissue-type is introduced, thus significantly enhancing the predictability of protein copy numbers from RNA levels. The results show that the RTP-ratio varies significantly with a few hundred copies per mRNA molecule for some genes to several hundred thousands protein copies per mRNA molecule for others. In conclusion, our data suggests that transcriptome analysis can be used as a tool to predict the protein copy numbers per cell, thus forming an attractive link between the field of genomics and proteomics. 

  • 29.
    Edfors, Fredrik
    et al.
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Danielsson, Frida
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hallström, Björn M.
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Käll, Lukas
    KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lundberg, Emma
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Ponten, Fredrik
    Forsström, Björn
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab. Technical University of Denmark, Denmark.
    Gene-specific correlation of RNA and protein levels in human cells and tissues2016In: Molecular Systems Biology, ISSN 1744-4292, E-ISSN 1744-4292, Vol. 12, no 10, article id 883Article in journal (Refereed)
    Abstract [en]

    An important issue for molecular biology is to establish whether transcript levels of a given gene can be used as proxies for the corresponding protein levels. Here, we have developed a targeted proteomics approach for a set of human non-secreted proteins based on parallel reaction monitoring to measure, at steady-state conditions, absolute protein copy numbers across human tissues and cell lines and compared these levels with the corresponding mRNA levels using transcriptomics. The study shows that the transcript and protein levels do not correlate well unless a gene-specific RNA-to-protein (RTP) conversion factor independent of the tissue type is introduced, thus significantly enhancing the predictability of protein copy numbers from RNA levels. The results show that the RTP ratio varies significantly with a few hundred copies per mRNA molecule for some genes to several hundred thousands of protein copies per mRNA molecule for others. In conclusion, our data suggest that transcriptome analysis can be used as a tool to predict the protein copy numbers per cell, thus forming an attractive link between the field of genomics and proteomics.

  • 30.
    Edfors, Fredrik
    et al.
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Linderbäck, Klas
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Sivertsson, Åsa
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Danielsson, Frida
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Fagerberg, Linn
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lundberg, Emma
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Alm, Tove
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Forsström, Björn
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab. Technical University of Denmark, Denmark.
    Validation of antibodies for Western blot applications using orthogonal methodsManuscript (preprint) (Other academic)
    Abstract [en]

    There is a great need for standardized validation methods for antibody specificity and selectivity. Here, we describe the use of orthogonal methods in which the specificity of an antibody in a particular application is determined based on correlation of protein abundance across several samples using an antibody-independent method. We show that pair-wise correlation between orthogonal samples can be used to score the specificity of antibodies in a standardized manner using a test panel of human cell lines. Here, we investigated two independent methods for validation of antibodies in Western blot applications, namely transcriptomics and targeted proteomics and we show that the two methods yield similar, but not identical results. The orthogonal methods can also be used to investigate on- and off- target binding for antibodies with multiple bands in the Western blot assay. In conclusion, orthogonal methods for antibody validation provide an attractive strategy for systematic validation of antibodies in a quantitative manner. 

  • 31.
    Fagerberg, Linn
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Oksvold, Per
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Skogs, Marie
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Älgenäs, C.
    Lundberg, Emma
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Pontén, F.
    Sivertsson, Åsa
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Odeberg, Jacob
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Klevebring, Daniel
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Kampf, C.
    Asplund, A.
    Sjöstedt, E.
    Al-Khalili Szigyarto, C.
    Edqvist, P. -H
    Olsson, I.
    Rydberg, U.
    Hudson, P.
    Ottosson Takanen, J.
    Berling, H.
    Björling, Lisa
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Tegel, Hanna
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Rockberg, J.
    Nilsson, Peter
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Navani, S.
    Jirström, K.
    Mulder, J.
    Schwenk, Jochen M.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Zwahlen, Martin
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hober, Sophia
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Forsberg, Mattias
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Von Feilitzen, Kalle
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Contribution of antibody-based protein profiling to the human chromosome-centric proteome project (C-HPP)2013In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 12, no 6, p. 2439-2448Article in journal (Refereed)
    Abstract [en]

    A gene-centric Human Proteome Project has been proposed to characterize the human protein-coding genes in a chromosome-centered manner to understand human biology and disease. Here, we report on the protein evidence for all genes predicted from the genome sequence based on manual annotation from literature (UniProt), antibody-based profiling in cells, tissues and organs and analysis of the transcript profiles using next generation sequencing in human cell lines of different origins. We estimate that there is good evidence for protein existence for 69% (n = 13985) of the human protein-coding genes, while 23% have only evidence on the RNA level and 7% still lack experimental evidence. Analysis of the expression patterns shows few tissue-specific proteins and approximately half of the genes expressed in all the analyzed cells. The status for each gene with regards to protein evidence is visualized in a chromosome-centric manner as part of a new version of the Human Protein Atlas (www.proteinatlas.org).

  • 32.
    Fagerberg, Linn
    et al.
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101).
    Sandler, Charlotte
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Skogs, Marie
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hjelmare, Martin
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Jonasson, Kalle
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101).
    Wiking, Mikaela
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Åbergh, Annica
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101).
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lundberg, Emma
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Mapping the subcellular protein distribution in three human cell lines2011In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 10, no 8, p. 3766-3777Article in journal (Refereed)
    Abstract [en]

    The subcellular locations of proteins are closely related to their function and constitute an essential aspect for understanding the complex machinery of living cells. A systematic effort has been initiated to map the protein distribution in three functionally different cell lines with the aim to provide a subcellular localization index for at least one representative protein from all human protein-encoding genes. Here, we present the results of over 4,000 proteins mapped to 16 subcellular compartments. The results indicate a ubiquitous protein expression with a majority of the proteins found in all three cell lines and a large portion localized to two or more compartments. The inter-relationships between the subcellular compartments are visualized in a protein-compartment network based on all detected proteins. Hierarchical clustering was performed to determine how closely related the organelles are in terms of protein constituents and compare the proteins detected in each cell type. Our results show distinct organelle proteomes, well conserved across the cell types, and demonstrate that biochemically similar organelles are grouped together.

  • 33. Fasano, M.
    et al.
    Alberio, T.
    Babu, M.
    Lundberg, Emma
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Urbani, A.
    Towards a functional definition of the mitochondrial human proteome2016In: EuPA Open Proteomics, ISSN 0014-2328, E-ISSN 2212-9685, Vol. 10, p. 24-27Article in journal (Refereed)
    Abstract [en]

    The mitochondrial human proteome project (mt-HPP) was initiated by the Italian HPP group as a part of both the chromosome-centric initiative (C-HPP) and the "biology and disease driven" initiative (B/D-HPP). In recent years several reports highlighted how mitochondrial biology and disease are regulated by specific interactions with non-mitochondrial proteins. Thus, it is of great relevance to extend our present view of the mitochondrial proteome not only to those proteins that are encoded by or transported to mitochondria, but also to their interactors that take part in mitochondria functionality. Here, we propose a graphical representation of the functional mitochondrial proteome by retrieving mitochondrial proteins from the NeXtProt database and adding to the network their interactors as annotated in the IntAct database. Notably, the network may represent a reference to map all the proteins that are currently being identified in mitochondrial proteomics studies.

  • 34.
    Grimm, Sebastian
    et al.
    KTH, School of Biotechnology (BIO).
    Lundberg, Emma
    KTH, School of Biotechnology (BIO).
    Shibasaki, Seiji
    KTH, School of Biotechnology (BIO).
    Vernet, Erik
    KTH, School of Biotechnology (BIO).
    Skogs, Marie
    KTH, School of Biotechnology (BIO).
    Nygren, Per-Åke
    KTH, School of Biotechnology (BIO).
    Gräslund, Torbjörn
    KTH, School of Biotechnology (BIO).
    Selection and characterization of affibody molecules interfering with the interaction between Ras and RafManuscript (Other academic)
  • 35.
    Grimm, Sebastian
    et al.
    KTH, School of Biotechnology (BIO), Proteomics.
    Lundberg, Emma
    KTH, School of Biotechnology (BIO), Proteomics.
    Yu, Feifan
    KTH, School of Biotechnology (BIO), Proteomics.
    Shibasaki, Seiji
    Vernet, Erik
    KTH, School of Biotechnology (BIO), Proteomics.
    Skogs, Marie
    KTH, School of Biotechnology (BIO), Proteomics.
    Nygren, Per-Åke
    KTH, School of Biotechnology (BIO), Molecular Biotechnology.
    Gräslund, Torbjörn
    KTH, School of Biotechnology (BIO), Molecular Biotechnology.
    Selection and characterisation of affibody molecules inhibiting the interaction between Ras and Raf in vitro2010In: NEW BIOTECHNOL, ISSN 1871-6784, Vol. 27, no 6, p. 766-773Article in journal (Refereed)
    Abstract [en]

    Development of molecules with the ability to selectively inhibit particular protein-protein interactions is important in providing tools for understanding cell biology In this work, we describe efforts to select small Ras- and Raf-specific three-helix bundle affibody binding proteins capable of inhibiting the interaction between H-Ras and Raf-1, from a combinatorial library displayed on bacteriophage Target-specific variants with typically high nanomolar or low micromolar affinities (K-D) could be selected successfully against both proteins, as shown by dot blot, ELISA and real-time biospecific interaction analyses Affibody molecule variants selected against H-Ras were shown to bind epitopes overlapping each other at a site that differed from that at which H-Ras interacts with Raf-1 In contrast, an affibody molecule isolated during selection against Raf-1 was shown to effectively inhibit the interaction between H-Ras and Raf-1 in a dose-dependent manner Possible intracellular applications of the selected affibody molecules are discussed

  • 36.
    Hjelm, Barbara
    et al.
    KTH, School of Biotechnology (BIO), Centres, Albanova VinnExcellence Center for Protein Technology, ProNova.
    Forsström, Björn
    KTH, School of Biotechnology (BIO), Centres, Albanova VinnExcellence Center for Protein Technology, ProNova.
    Igel, Ulrika
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Johannesson, Henrik
    Stadler, Charlotte
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lundberg, Emma
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Ponten, Fredrik
    Sjoberg, Anna
    Rockberg, Johan
    KTH, School of Biotechnology (BIO), Centres, Albanova VinnExcellence Center for Protein Technology, ProNova.
    Schwenk, Jochen M.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Nilsson, Peter
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Johansson, Christine
    Uhlen, Mathias
    KTH, School of Biotechnology (BIO), Centres, Albanova VinnExcellence Center for Protein Technology, ProNova. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Generation of monospecific antibodies based on affinity capture of polyclonal antibodies2011In: Protein Science, ISSN 0961-8368, E-ISSN 1469-896X, Vol. 20, no 11, p. 1824-1835Article in journal (Refereed)
    Abstract [en]

    A method is described to generate and validate antibodies based on mapping the linear epitopes of a polyclonal antibody followed by sequential epitope-specific capture using synthetic peptides. Polyclonal antibodies directed towards four proteins RBM3, SATB2, ANLN, and CNDP1, potentially involved in human cancers, were selected and antibodies to several non-overlapping epitopes were generated and subsequently validated by Western blot, immunohistochemistry, and immunofluorescence. For all four proteins, a dramatic difference in functionality could be observed for these monospecific antibodies directed to the different epitopes. In each case, at least one antibody was obtained with full functionality across all applications, while other epitope-specific fractions showed no or little functionality. These results present a path forward to use the mapped binding sites of polyclonal antibodies to generate epitope-specific antibodies, providing an attractive approach for large-scale efforts to characterize the human proteome by antibodies.

  • 37. Horvatovich, Peter
    et al.
    Lundberg, Emma K.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Chen, Yu-Ju
    Sung, Ting-Yi
    He, Fuchu
    Nice, Edouard C.
    Goode, Robert J.
    Yu, Simon
    Ranganathan, Shoba
    Baker, Mark S.
    Domont, Gilberto B.
    Velasquez, Erika
    Li, Dong
    Liu, Siqi
    Wang, Quanhui
    He, Qing-Yu
    Menon, Rajasree
    Guan, Yuanfang
    Corrales, Fernando J.
    Segura, Victor
    Casal, J. Ignacio
    Pascual-Montano, Alberto
    Albar, Juan P.
    Fuentes, Manuel
    Gonzalez-Gonzalez, Maria
    Diez, Paula
    Ibarrola, Nieves
    Degano, Rosa M.
    Mohammed, Yassene
    Borchers, Christoph H.
    Urbani, Andrea
    Soggiu, Alessio
    Yamamoto, Tadashi
    Salekdeh, Ghasem Hosseini
    Archakov, Alexander
    Ponomarenko, Elena
    Lisitsa, Andrey
    Lichti, Cheryl F.
    Mostovenko, Ekaterina
    Kroes, Roger A.
    Rezeli, Melinda
    Vegvari, Akos
    Fehniger, Thomas E.
    Bischoff, Rainer
    Vizcaino, Juan Antonio
    Deutsch, Eric W.
    Lane, Lydie
    Nilsson, Carol L.
    Marko-Varga, Gyorgy
    Omenn, Gilbert S.
    Jeong, Seul-Ki
    Lim, Jong-Sun
    Paik, Young-Ki
    Hancock, William S.
    Quest for Missing Proteins: Update 2015 on Chromosome-Centric Human Proteome Project2015In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 14, no 9, p. 3415-3431Article in journal (Other academic)
    Abstract [en]

    This paper summarizes the recent activities of the Chromosome-Centric Human Proteome Project (C-HPP) consortium, which develops new technologies to identify yet-to-be annotated proteins (termed "missing proteins") in biological samples that lack sufficient experimental evidence at the protein level for confident protein identification. The C-HPP also aims to identify new protein forms that may be caused by genetic variability, post-translational modifications, and alternative splicing. Proteogenomic data integration forms the basis of the C-HPP's activities; therefore, we have summarized some of the key approaches and their roles in the project. We present new analytical technologies that improve the chemical space and lower detection limits coupled to bioinformatics tools and some publicly available resources that can be used to improve data analysis or support the development of analytical assays. Most of this paper's content has been compiled from posters, slides, and discussions presented in the series of C-HPP workshops held during 2014. All data (posters, presentations) used are available at the C-HPP Wild (http://c-hpp.webhosting.rug.nl/) and in the Supporting Information.

  • 38. Jakobsen, Lis
    et al.
    Schroder, Jacob Morville
    Larsen, Katja M.
    Lundberg, Emma
    KTH, School of Biotechnology (BIO), Proteomics.
    Andersen, Jens S.
    Centrosome Isolation and Analysis by Mass Spectrometry-Based Proteomics2013In: CILIA, PART B, Academic Press, 2013, p. 371-393Chapter in book (Refereed)
    Abstract [en]

    Centrioles are microtubule-based scaffolds that are essential for the formation of centrosomes, cilia, and flagella with important functions throughout the cell cycle, in physiology and during development. The ability to purify centriole-containing organelles on a large scale, combined with advances in protein identification using mass spectrometry-based proteomics, have revealed multiple centriole-associated proteins that are conserved during evolution in eukaryotes. Despite these advances, the molecular basis for the plethora of processes coordinated by cilia and centrosomes is not fully understood. Considering the complexity and dynamics of centriole-related proteomes and the first-pass analyses reported so far, it is likely that further insight might come from more thorough proteome analyses under various cellular and physiological conditions. To this end, we here describe methods to isolate centrosomes from human cells and strategies to selectively identify and study the properties of the associated proteins using quantitative mass spectrometry-based proteomics.

  • 39. Jakobsen, Lis
    et al.
    Vanselow, Katja
    Skogs, Marie
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Toyoda, Yusuke
    Lundberg, Emma
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Poser, Ina
    Falkenby, Lasse G.
    Bennetzen, Martin
    Westendorf, Jens
    Nigg, Erich A.
    Uhlen, Mathias
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101).
    Hyman, Anthony A.
    Andersen, Jens S.
    Novel asymmetrically localizing components of human centrosomes identified by complementary proteomics methods2011In: EMBO Journal, ISSN 0261-4189, E-ISSN 1460-2075, Vol. 30, no 8, p. 1520-1535Article in journal (Refereed)
    Abstract [en]

    Centrosomes in animal cells are dynamic organelles with a proteinaceous matrix of pericentriolar material assembled around a pair of centrioles. They organize the microtubule cytoskeleton and the mitotic spindle apparatus. Mature centrioles are essential for biogenesis of primary cilia that mediate key signalling events. Despite recent advances, the molecular basis for the plethora of processes coordinated by centrosomes is not fully understood. We have combined protein identification and localization, using PCP-SILAC mass spectrometry, BAC transgeneOmics, and antibodies to define the constituents of human centrosomes. From a background of non-specific proteins, we distinguished 126 known and 40 candidate centrosomal proteins, of which 22 were confirmed as novel components. An antibody screen covering 4000 genes revealed an additional 113 candidates. We illustrate the power of our methods by identifying a novel set of five proteins preferentially associated with mother or daughter centrioles, comprising genes implicated in cell polarity. Pulsed labelling demonstrates a remarkable variation in the stability of centrosomal protein complexes. These spatiotemporal proteomics data provide leads to the further functional characterization of centrosomal proteins.

  • 40. Kampf, Caroline
    et al.
    Bergman, Julia
    Oksvold, Per
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Asplund, Anna
    Navani, Sanjay
    Wiking, Mikaela
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lundberg, Emma
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Pontén, Fredrik
    A tool to facilitate clinical biomarker studies - a tissue dictionary based on the Human Protein Atlas2012In: BMC Medicine, ISSN 1741-7015, E-ISSN 1741-7015, Vol. 10, p. 103-Article in journal (Refereed)
    Abstract [en]

    The complexity of tissue and the alterations that distinguish normal from cancer remain a challenge for translating results from tumor biological studies into clinical medicine. This has generated an unmet need to exploit the findings from studies based on cell lines and model organisms to develop, validate and clinically apply novel diagnostic, prognostic and treatment predictive markers. As one step to meet this challenge, the Human Protein Atlas project has been set up to produce antibodies towards human protein targets corresponding to all human protein coding genes and to map protein expression in normal human tissues, cancer and cells. Here, we present a dictionary based on microscopy images created as an amendment to the Human Protein Atlas. The aim of the dictionary is to facilitate the interpretation and use of the image-based data available in the Human Protein Atlas, but also to serve as a tool for training and understanding tissue histology, pathology and cell biology. The dictionary contains three main parts, normal tissues, cancer tissues and cells, and is based on high-resolution images at different magnifications of full tissue sections stained with H & E. The cell atlas is centered on immunofluorescence and confocal microscopy images, using different color channels to highlight the organelle structure of a cell. Here, we explain how this dictionary can be used as a tool to aid clinicians and scientists in understanding the use of tissue histology and cancer pathology in diagnostics and biomarker studies.

  • 41. Kampf, Caroline
    et al.
    Mardinoglu, Adil
    Fagerberg, Linn
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hallström, Björn M.
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Edlund, Karolina
    Lundberg, Emma
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Pontén, Fredrik
    Nielsen, Jens
    KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    The human liver-specific proteome defined by transcriptomics and antibody-based profiling2014In: The FASEB Journal, ISSN 0892-6638, E-ISSN 1530-6860, Vol. 28, no 7, p. 2901-2914Article in journal (Refereed)
    Abstract [en]

    Human liver physiology and the genetic etiology of the liver diseases can potentially be elucidated through the identification of proteins with enriched expression in the liver. Here, we combined data from RNA sequencing (RNA-Seq) and antibody-based immunohistochemistry across all major human tissues to explore the human liver proteome with enriched expression, as well as the cell type-enriched expression in hepatocyte and bile duct cells. We identified in total 477 protein-coding genes with elevated expression in the liver: 179 genes have higher expression as compared to all the other analyzed tissues; 164 genes have elevated transcript levels in the liver shared with at least one other tissue type; and an additional 134 genes have a mild level of increased expression in the liver. We identified the precise localization of these proteins through antibody-based protein profiling and the subcellular localization of these proteins through immunofluorescent-based profiling. We also identified the biological processes and metabolic functions associated with these proteins, investigated their contribution in the occurrence of liver diseases, and identified potential targets for their treatment. Our study demonstrates the use of RNA-Seq and antibody-based immunohistochemistry for characterizing the human liver proteome, as well as the use of tissue-specific proteins in identification of novel drug targets and discovery of biomarkers.

  • 42.
    Klevebring, Daniel
    et al.
    KTH, School of Biotechnology (BIO), Gene Technology.
    Fagerberg, Linn
    KTH, School of Biotechnology (BIO), Proteomics.
    Lundberg, Emma
    KTH, School of Biotechnology (BIO), Proteomics.
    Emanuelsson, Olof
    KTH, School of Biotechnology (BIO), Gene Technology.
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics.
    Lundeberg, Joakim
    KTH, School of Biotechnology (BIO), Gene Technology.
    Analysis of transcript and protein overlap in a human osteosarcoma cell line2010In: BMC Genomics, ISSN 1471-2164, E-ISSN 1471-2164, Vol. 11, no 1, p. 684-Article in journal (Refereed)
    Abstract [en]

    Background: An interesting field of research in genomics and proteomics is to compare the overlap between the transcriptome and the proteome. Recently, the tools to analyse gene and protein expression on a whole-genome scale have been improved, including the availability of the new generation sequencing instruments and high-throughput antibody-based methods to analyze the presence and localization of proteins. In this study, we used massive transcriptome sequencing (RNA-seq) to investigate the transcriptome of a human osteosarcoma cell line and compared the expression levels with in situ protein data obtained in-situ from antibody-based immunohistochemistry (IHC) and immunofluorescence microscopy (IF).

    Results: A large-scale analysis based on 2749 genes was performed, corresponding to approximately 13% of the protein coding genes in the human genome. We found the presence of both RNA and proteins to a large fraction of the analyzed genes with 60% of the analyzed human genes detected by all three methods. Only 34 genes (1.2%) were not detected on the transcriptional or protein level with any method. Our data suggest that the majority of the human genes are expressed at detectable transcript or protein levels in this cell line. Since the reliability of antibodies depends on possible cross-reactivity, we compared the RNA and protein data using antibodies with different reliability scores based on various criteria, including Western blot analysis. Gene products detected in all three platforms generally have good antibody validation scores, while those detected only by antibodies, but not by RNA sequencing, generally consist of more low-scoring antibodies.

    Conclusion: This suggests that some antibodies are staining the cells in an unspecific manner, and that assessment of transcript presence by RNA-seq can provide guidance for validation of the corresponding antibodies.

  • 43. Lane, Lydie
    et al.
    Bairoch, Amos
    Beavis, Ronald C.
    Deutsch, Eric W.
    Gaudet, Pascale
    Lundberg, Emma
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Omenn, Gilbert S.
    Metrics for the Human Proteome Project 2013-2014 and Strategies for Finding Missing Proteins2014In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 13, no 1, p. 15-20Article in journal (Refereed)
    Abstract [en]

    One year ago the Human Proteome Project (HPP) leadership designated the baseline metrics for the Human Proteome Project to be based on neXtProt with a total of 13 664 proteins validated at protein evidence level 1 (PE1) by mass spectrometry, antibody-capture, Edman sequencing, or 3D structures. Corresponding chromosome-specific data were provided from PeptideAtlas, GPMdb, and Human Protein Atlas. This year, the neXtProt total is 15 646 and the other resources, which are inputs to neXtProt, have high-quality identifications and additional annotations for 14 012 in PeptideAtlas, 14 869 in GPMdb, and 10 976 in HPA. We propose to remove 638 genes from the denominator that are "uncertain" or "dubious" in Ensembl, UniProt/SwissProt, and neXtProt. That leaves 3844 "missing proteins", currently having no or inadequate documentation, to be found from a new denominator of 19 490 protein-coding genes. We present those tabulations and web links and discuss current strategies to find the missing proteins.

  • 44. Larance, Mark
    et al.
    Kirkwood, Kathryn J.
    Xirodimas, Dimitris P.
    Lundberg, Emma
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lamond, Angus I.
    Characterization of MRFAP1 Turnover and Interactions Downstream of the NEDD8 Pathway2012In: Molecular & Cellular Proteomics, ISSN 1535-9476, E-ISSN 1535-9484, Vol. 11, no 3Article in journal (Refereed)
    Abstract [en]

    The NEDD8-Cullin E3 ligase pathway plays an important role in protein homeostasis, in particular the degradation of cell cycle regulators and transcriptional control networks. To characterize NEDD8-cullin target proteins, we performed a quantitative proteomic analysis of cells treated with MLN4924, a small molecule inhibitor of the NEDD8 conjugation pathway. MRFAP1 and its interaction partner, MORF4L1, were among the most up-regulated proteins after NEDD8 inhibition in multiple human cell lines. We show that MRFAP1 has a fast turnover rate in the absence of MLN4924 and is degraded via the ubiquitin- proteasome system. The increased abundance of MRFAP1 after MLN4924 treatment results from a decreased rate of degradation. Characterization of the binding partners of both MRFAP1 and MORF4L1 revealed a complex protein-protein interaction network. MRFAP1 bound to a number of E3 ubiquitin ligases, including CUL4B, but not to components of the NuA4 complex, including MRGBP, which bound to MORF4L1. These data indicate that MRFAP1 may regulate the ability of MORF4L1 to interact with chromatin-modifying enzymes by binding to MORF4L1 in a mutually exclusive manner with MRGBP. Analysis of MRFAP1 expression in human tissues by immunostaining with a MRFAP1-specific antibody revealed that it was detectable in only a small number of tissues, in particular testis and brain. Strikingly, analysis of the seminiferous tubules of the testis showed the highest nuclear staining in the spermatogonia and much weaker staining in the spermatocytes and spermatids. MRGBP was inversely correlated with MRFAP1 expression in these cell types, consistent with an exchange of MORF4L1 interaction partners as cells progress through meiosis in the testis. These data highlight an important new arm of the NEDD8cullin pathway.

  • 45. Li, J.
    et al.
    Newberg, J. Y.
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lundberg, Emma
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Murphy, R. F.
    Automated Analysis and Reannotation of Subcellular Locations in Confocal Images from the Human Protein Atlas2012In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 7, no 11, p. e50514-Article in journal (Refereed)
    Abstract [en]

    The Human Protein Atlas contains immunofluorescence images showing subcellular locations for thousands of proteins. These are currently annotated by visual inspection. In this paper, we describe automated approaches to analyze the images and their use to improve annotation. We began by training classifiers to recognize the annotated patterns. By ranking proteins according to the confidence of the classifier, we generated a list of proteins that were strong candidates for reexamination. In parallel, we applied hierarchical clustering to group proteins and identified proteins whose annotations were inconsistent with the remainder of the proteins in their cluster. These proteins were reexamined by the original annotators, and a significant fraction had their annotations changed. The results demonstrate that automated approaches can provide an important complement to visual annotation.

  • 46. Li, J.
    et al.
    Shariff, A.
    Wiking, Mikaela
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lundberg, Emma
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Rohde, G. K.
    Murphy, R. F.
    Estimating Microtubule Distributions from 2D Immunofluorescence Microscopy Images Reveals Differences among Human Cultured Cell Lines2012In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 7, no 11, p. e50292-Article in journal (Refereed)
    Abstract [en]

    Microtubules are filamentous structures that are involved in several important cellular processes, including cell division, cellular structure and mechanics, and intracellular transportation. Little is known about potential differences in microtubule distributions within and across cell lines. Here we describe a method to estimate information pertaining to 3D microtubule distributions from 2D fluorescence images. Our method allows for quantitative comparisons of microtubule distribution parameters (number of microtubules, mean length) between different cell lines. Among eleven cell lines compared, some showed differences that could be accounted for by differences in the total amount of tubulin per cell while others showed statistically significant differences in the balance between number and length of microtubules. We also observed that some cell lines that visually appear different in their microtubule distributions are quite similar when the model parameters are considered. The method is expected to be generally useful for comparing microtubule distributions between cell lines and for a given cell line after various perturbations. The results are also expected to enable analysis of the differences in gene expression underlying the observed differences in microtubule distributions among cell types.

  • 47.
    Li, Jingjing
    et al.
    KTH, School of Biotechnology (BIO).
    Lundberg, Emma
    KTH, School of Biotechnology (BIO).
    Vernet, Erik
    KTH, School of Biotechnology (BIO).
    Höidén-Guthenberg, Ingmarie
    KTH, School of Biotechnology (BIO).
    Gräslund, Torbjörn
    KTH, School of Biotechnology (BIO).
    Selection of affibody molecules blocking hormone-binding to the insulin-like growth factor 1 receptorManuscript (Other academic)
  • 48.
    Li, Jingjing
    et al.
    KTH, School of Biotechnology (BIO), Molecular Biotechnology (closed 20130101).
    Lundberg, Emma
    KTH, School of Biotechnology (BIO), Molecular Biotechnology (closed 20130101).
    Vernet, Erik
    KTH, School of Biotechnology (BIO), Molecular Biotechnology (closed 20130101).
    Larsson, Barbro
    Höidén-Guthenberg, Ingmarie
    Gräslund, Torbjörn
    KTH, School of Biotechnology (BIO), Molecular Biotechnology (closed 20130101).
    Selection of affibody molecules to the ligand-binding site of the insulin-like growth factor-1 receptor2010In: Biotechnology and applied biochemistry, ISSN 0885-4513, E-ISSN 1470-8744, Vol. 55, p. 99-109Article in journal (Refereed)
    Abstract [en]

    Affibody molecules binding to the site of hormone interaction in IGF-IR (insulin-like growth factor-I receptor) were successfully selected by phage-display technology employing a competitive-elution strategy during biopanning, whereby release of receptor-bound phagemids was accomplished by competition with IGFI (insulin-like growth factor-I). In non-competitive selections, the elution of receptor-bound phagemids was performed by imidazole or low-pH incubation, which also resulted in the isolation of affibody molecules that could bind to the receptor. An ELISA-based assay showed that the affibody molecules generated by IGF-I competition during elution, in addition to affibody molecules generated in the noncompetitive selections, could compete with IGF-I for binding to the receptor. The affinities of the isolated variants to IGF-IR-overexpressing MCF-7 cells were determined and ranged from high nanomolar to 2.3 nM. The most promising variant, Z(4;40), was shown to recognize IGF- IR efficiently in several different contexts: in analyses based on flow cytometry, fluorescence microscopy and receptor pull-down from cell extracts. In addition, when Z, was added to the medium of MCF-7 cells that were dependent on IGF-I for efficient growth, it was found to have a dose-dependent growth-inhibitory effect on the cells. Applications of affibody-based reagents for quantitative and qualitative analyses of IGF- I R status, as well as applications of affibody-based reagents for therapy, are discussed.

  • 49. Liem, David A.
    et al.
    Nsair, Ali
    Setty, Shaun P.
    Cadeiras, Martin
    Wang, Ding
    Maclellan, Robb
    Lotz, Chris
    Lin, Amanda J.
    Tabaraki, Jason
    Li, Hua
    Ge, Junbo
    Odeberg, Jacob
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Pontén, Fredrik
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Larson, Erik
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Mulder, Jan
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lundberg, Emma
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Weiss, James N.
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Ping, Peipei
    Deng, Mario C.
    Molecular- and Organelle-Based Predictive Paradigm Underlying Recovery by Left Ventricular Assist Device Support2014In: Circulation Heart Failure, ISSN 1941-3289, E-ISSN 1941-3297, Vol. 7, no 2, p. 359-366Article in journal (Refereed)
  • 50. Liu, Fei
    et al.
    Koval, Michael
    Ranganathan, Shoba
    Fanayan, Susan
    Hancock, William S.
    Lundberg, Emma Käller
    KTH, School of Biotechnology (BIO). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Beavis, Ronald C.
    Lane, Lydie
    Duek, Paula
    McQuade, Leon
    Kelleher, Neil L.
    Baker, Mark S.
    Systems Proteomics View of the Endogenous Human Claudin Protein Family2016In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 15, no 2, p. 339-359Article, review/survey (Refereed)
    Abstract [en]

    Claudius are the major transmembrane protein components of tight junctions in human endothelia and epithelia. Tissue-specific expression of claudin members suggests that this protein family is not only essential for sustaining the role of tight junctions in cell permeability control but also vital in organizing cell contact signaling by protein protein interactions. How this protein family is collectively processed and regulated is key to understanding the role of junctional proteins in preserving cell identity and tissue integrity. The focus of this review is to first provide a brief overview of the functional context, on the basis of the extensive body of claudin biology research that has been thoroughly reviewed, for endogenous human claudin members and then ascertain existing and future proteomics techniques that may be applicable to systematically characterizing the chemical forms and interacting protein partners of this protein family in human. The ability to elucidate claudin-based signaling networks may provide new insight into cell development and differentiation programs that are crucial to tissue stability and manipulation.

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