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  • 1.
    Sialana, Fernando J.
    et al.
    Univ Vienna, Dept Pharmaceut Chem, Vienna, Austria.;Austrian Acad Sci, CeMM Res Ctr Mol Med, Vienna, Austria..
    Gulyassy, Peter
    Eotvos Lorand Univ, Inst Biol, Lab Prote, Budapest, Hungary..
    Majek, Peter
    Austrian Acad Sci, CeMM Res Ctr Mol Med, Vienna, Austria..
    Sjöstedt, Evelina
    KTH, Centres, Science for Life Laboratory, SciLifeLab. Uppsala Univ, Dept Immunol Genet & Pathol, Sci Life Lab, Uppsala, Sweden..
    Kis, Viktor
    Eotvos Lorand Univ, Dept Anat Cell & Dev Biol, Budapest, Hungary..
    Muller, Andre C.
    Austrian Acad Sci, CeMM Res Ctr Mol Med, Vienna, Austria.;ThermoFisher Sci, Vienna, Austria..
    Rudashevskaya, Elena L.
    Med Univ Vienna, Inst Med Chem, Vienna, Austria.;ISAS, Leibniz Inst Analyt Wissensch, Dortmund, Germany..
    Mulder, Jan
    Uppsala Univ, Dept Neurosci, Sci Life Lab, Uppsala, Sweden..
    Bennett, Keiryn L.
    Austrian Acad Sci, CeMM Res Ctr Mol Med, Vienna, Austria..
    Lubec, Gert
    Univ Vienna, Dept Pharmaceut Chem, Vienna, Austria..
    Mass spectrometric analysis of synaptosomal membrane preparations for the determination of brain receptors, transporters and channels2016In: Proteomics, ISSN 1615-9853, E-ISSN 1615-9861, Vol. 16, no 22, p. 2911-2920Article in journal (Refereed)
    Abstract [en]

    The molecular composition of synaptic signal transduction machineries shapes synaptic neurotransmission. The repertoire of receptors, transporters and channels (RTCs) comprises major signaling events in the brain. RTCs are conventionally studied by candidate immunohistochemistry and biochemistry, which are low throughput with resolution greatly affected by available immunoreagents and membrane interference. Therefore, a comprehensive resource of synaptic brain RTCs is still lacking. In particular, studies on the detergent-soluble synaptosomal fraction, known to contain transporters and channels, are limited. We, therefore, performed sub-synaptosomal fractionation of rat cerebral cortex, followed by trypsin/chymotrypsin sequential digestion of a detergent-soluble synaptosomal fraction and a postsynaptic density preparation, stable-isotope tryptic peptide labeling and liquid chromatography mass spectrometry. Based on the current study, a total of 4784 synaptic proteins were submitted to the ProteomExchange database (PXD001948), including 274 receptors, 394 transporters/channels and 1377 transmembrane proteins. Function-based classification assigned 1781 proteins as probable drug targets with 834 directly linked to brain disorders. The analytical approach identified 499 RTCs that are not listed in the largest, curated database for synaptosomal proteins (SynProt). This is a threefold RTC increase over all other data collected to date. Taken together, we present a protein discovery resource that can serve as a benchmark for future molecular interrogation of synaptic connectivity.

  • 2.
    Sjöstedt, Evelina
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Towards a deeper understanding of the human brain2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Identifying the proteome variation in different parts of the body provides fundamental molecular details, enabling further findings and mapping of tissue specific proteins. By combining quantitative transcriptomics with qualitative antibody based proteomics, the Human Protein Atlas (HPA) project aims to protein profile each human protein-coding gene. Genes with varying expression levels in the different tissue types are categorized as tissue elevated in one tissue compared to others, thus connecting genes to potential tissue specific functions. This thesis focuses on the most complex organ in the human body, the brain. With its billions of neurons specifically organized and interconnected, the ability of not only controlling the body but also responsible for higher cognitive functions, the brain is still not fully understood.

    In my search for brain important proteins, genes were classified at different stages based on expression levels. In Paper I and II the transcriptome of cerebral cortex was compared with peripheral organs to classify genes with elevated expression in the brain. Brain expression information was expanded by including external data (GTEx and FANTOM5) into the analysis, in Paper III. Thereafter, in Paper IV, the three datasets (HPA, GTEx and FANTOM5) were aligned and combined, enabling a consensus classification with an improved representation of the brain complexity. The most recent classification provided whole body gene expression profiles and out of the 19,670 protein-coding genes, 2,501 were expressed at elevated levels in the brain compared to the other tissue types. Twelve individual regions represented the brain as an organ, and were further analyzed and compared for regional classification of gene expression. One thousand genes showed regional variation in expression level, thus classified as regionally elevated within the brain. Interestingly, less than 500 of the genes classified as brain elevated on the whole body level, and were also regionally elevated in the brain. Many genes with regionally variable expression within the brain showed higher expression in a peripheral organ than in the brain when comparing whole body expression. Based on elevated expression in the brain or brain regions, more than 3,000 genes were suggested to be of high importance to the brain.

    In addition, this high-throughput approach to combine transcriptomics and protein profiles in tissues and cells further generated new knowledge in several different other aspects: better understanding of uncharacterized and “missing proteins” (Paper III), validation of an antibody improving classification of pituitary adenoma (Paper V) and in Paper VI the possibility to explore cancer specific expression in relation to clinical data and normal tissue expression.

    There are multiple diseases of the brain that are poorly understood on both a cellular and molecular level. While my work mainly focused on identifying and understanding the molecular organization of the normal brain, the ultimate goal of mapping and studying the normal expression baseline is to understand the molecular aspects of disease and identify ways to prevent, treat and cure diseases.

  • 3.
    Sjöstedt, Evelina
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Bollerslev, Jens
    Mulder, Jan
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lindskog, Cecilia
    Ponten, Fredrik
    Casar-Borota, Olivera
    A specific antibody to detect transcription factor T-Pit: a reliable marker of corticotroph cell differentiation and a tool to improve the classification of pituitary neuroendocrine tumours2017In: Acta Neuropathologica, ISSN 0001-6322, E-ISSN 1432-0533, Vol. 134, no 4, p. 675-677Article in journal (Refereed)
  • 4. Sjöstedt, Evelina
    et al.
    Fagerberg, Linn
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Hallström, Björn M.
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Häggmark, Anna
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Mitsios, Nicholas
    Nilsson, Peter
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Pontén, Fredrik
    Hökfelt, Tomas
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Mulder, Jan
    Defining the Human Brain Proteome Using Transcriptomics and Antibody-Based Profiling with a Focus on the Cerebral Cortex2015In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 10, no 6, article id UNSP e0130028Article in journal (Refereed)
    Abstract [en]

    The mammalian brain is a complex organ composed of many specialized cells, harboring sets of both common, widely distributed, as well as specialized and discretely localized proteins. Here we focus on the human brain, utilizing transcriptomics and public available Human Protein Atlas (HPA) data to analyze brain-enriched (frontal cortex) polyadenylated messenger RNA and long non-coding RNA and generate a genome-wide draft of global and cellular expression patterns of the brain. Based on transcriptomics analysis of altogether 27 tissues, we have estimated that approximately 3% (n=571) of all protein coding genes and 13% (n=87) of the long non-coding genes expressed in the human brain are enriched, having at least five times higher expression levels in brain as compared to any of the other analyzed peripheral tissues. Based on gene ontology analysis and detailed annotation using antibody-based tissue micro array analysis of the corresponding proteins, we found the majority of brain-enriched protein coding genes to be expressed in astrocytes, oligodendrocytes or in neurons with molecular properties linked to synaptic transmission and brain development. Detailed analysis of the transcripts and the genetic landscape of brainenriched coding and non-coding genes revealed brain-enriched splice variants. Several clusters of neighboring brain-enriched genes were also identified, suggesting regulation of gene expression on the chromatin level. This multi-angle approach uncovered the brainenriched transcriptome and linked genes to cell types and functions, providing novel insights into the molecular foundation of this highly specialized organ.

  • 5.
    Sjöstedt, Evelina
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Fagerberg, Linn
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Mitsios, Nicholas
    Karolinska Institutet.
    Adori, Csaba
    Karolinska Institutet.
    Oksvold, Per
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Limiszewka, Agnieszka
    Karolinska Insititutet.
    Kheder, Sania
    Karolinska Insitutiet.
    Norradin, Feria Hikmet
    Department of Immunology, Genetics and Pathology, Uppsala University.
    Lindskog, Cecilia
    Department of Immunology, Genetics and Pathology, Uppsala University.
    Pontén, Fredrik
    Department of immunology, genetics and pathology, Uppsala Univesity.
    Hökfelt, Tomas
    Karolinska Institutet.
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Mulder, Jan
    Karolinska institutet.
    The transcriptomic landscape of mammalian brainManuscript (preprint) (Other academic)
  • 6.
    Sjöstedt, Evelina
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Sivertsson, Åsa
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Norradin, Feria Hikmet
    Department of Immunology, Genetics and Pathology, Uppsala University.
    Katona, Borbala
    Department of Immunology, Genetics and Pathology, Uppsala University.
    Näsström, Åsa
    Rudbeck Laboratory, Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Sweden.
    Vuu, Jimmy
    Department of Immunology, Genetics and Pathology, Uppsala university.
    Kesti, Dennis
    Department of Immunology, Genetics and Pathology, Uppsala University.
    Oksvold, Per
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Edqvist, Per-Henrik
    Department of Immunology, Genetics and Pathology, Uppsala University.
    Olsson, Ingmarie
    Department of Immunology, Genetics and Pathology, Uppsala University.
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Lindskog, Cecilia
    Department of Immunology, Genetics and Pathology, Uppsala University.
    Integration of Transcriptomics and Antibody-Based Proteomics for Exploration of Proteins Expressed in Specialized Tissues2018In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 17, no 12, p. 4127-4137Article in journal (Refereed)
    Abstract [en]

    A large portion of human proteins are referred to as missing proteins, defined as protein-coding genes that lack experimental data on the protein level due to factors such as temporal expression, expression in tissues that are difficult to sample, or they actually do not encode functional proteins. In the present investigation, an integrated omics approach was used for identification and exploration of missing proteins. Transcriptomics data from three different sourcesthe Human Protein Atlas (HPA), the GTEx consortium, and the FANTOM5 consortiumwere used as a starting point to identify genes selectively expressed in specialized tissues. Complementing the analysis with profiling on more specific tissues based on immunohistochemistry allowed for further exploration of cell-type-specific expression patterns. More detailed tissue profiling was performed for >300 genes on complementing tissues. The analysis identified tissue-specific expression of nine proteins previously listed as missing proteins (POU4F1, FRMD1, ARHGEF33, GABRG1, KRTAP2-1, BHLHE22, SPRR4, AVPR1B, and DCLK3), as well as numerous proteins with evidence of existence on the protein level that previously lacked information on spatial resolution and cell-type- specific expression pattern. We here present a comprehensive strategy for identification of missing proteins by combining transcriptomics with antibody-based proteomics. The analyzed proteins provide interesting targets for organ-specific research in health and disease.

  • 7.
    Uhlén, Mathias
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Fagerberg, Linn
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hallström, Björn M
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Lindskog, Cecilia
    Oksvold, Per
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Mardinoglu, Adil
    Sivertsson, Åsa
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Kampf, Caroline
    Sjöstedt, Evelina
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Asplund, Anna
    Olsson, IngMarie
    Edlund, Karolina
    Lundberg, Emma
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Navani, Sanjay
    Szigyarto, Cristina Al-Khalili
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Odeberg, Jacob
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Djureinovic, Dijana
    Takanen, Jenny Ottosson
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Hober, Sophia
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Alm, Tove
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Edqvist, Per-Henrik
    Berling, Holger
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Tegel, Hanna
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Mulder, Jan
    Rockberg, Johan
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Nilsson, Peter
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Schwenk, Jochen M
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hamsten, Marica
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    von Feilitzen, Kalle
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Forsberg, Mattias
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Persson, Lukas
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Johansson, Fredric
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Zwahlen, Martin
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    von Heijne, Gunnar
    Nielsen, Jens
    Pontén, Fredrik
    Tissue-based map of the human proteome2015In: Science, ISSN 0036-8075, E-ISSN 1095-9203, Vol. 347, no 6220, p. 1260419-Article in journal (Refereed)
    Abstract [en]

    Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body.

  • 8.
    Uhlén, Mathias
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. Center for Biosustainability, Danish Technical University, Copenhagen, Denmark..
    Zhang, Cheng
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lee, Sunjae
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Sjöstedt, Evelina
    KTH, Centres, Science for Life Laboratory, SciLifeLab. Department of Immunology Genetics and Pathology, Uppsala University, Uppsala, Sweden..
    Fagerberg, Linn
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Bidkhori, Gholamreza
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Benfeitas, Rui
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Arif, Muhammad
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Liu, Zhengtao
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Edfors, Fredrik
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Sanli, Kemal
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    von Feilitzen, Kalle
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Oksvold, Per
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lundberg, Emma
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hober, Sophia
    KTH, School of Biotechnology (BIO).
    Nilsson, Peter
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Mattsson, Johanna
    Schwenk, Jochen M.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Brunnstrom, Hans
    Glimelius, Bengt
    Sjoblom, Tobias
    Edqvist, Per-Henrik
    Djureinovic, Dijana
    Micke, Patrick
    Lindskog, Cecilia
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Ponten, Fredrik
    A pathology atlas of the human cancer transcriptome2017In: Science, ISSN 0036-8075, E-ISSN 1095-9203, Vol. 357, no 6352, p. 660-+Article in journal (Refereed)
    Abstract [en]

    Cancer is one of the leading causes of death, and there is great interest in understanding the underlying molecular mechanisms involved in the pathogenesis and progression of individual tumors. We used systems-level approaches to analyze the genome-wide transcriptome of the protein-coding genes of 17 major cancer types with respect to clinical outcome. A general pattern emerged: Shorter patient survival was associated with up-regulation of genes involved in cell growth and with down-regulation of genes involved in cellular differentiation. Using genome-scale metabolic models, we show that cancer patients have widespread metabolic heterogeneity, highlighting the need for precise and personalized medicine for cancer treatment. All data are presented in an interactive open-access database (www.proteinatlas.org/pathology) to allow genome-wide exploration of the impact of individual proteins on clinical outcomes.

  • 9.
    Zieba, Agata
    et al.
    Uppsala Univ, Dept Immunol Genet & Pathol, Sci Life Lab, S-75185 Uppsala, Sweden..
    Sjöstedt, Evelina
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Olovsson, Matts
    Uppsala Univ, Dept Womens & Childrens Hlth, S-75185 Uppsala, Sweden..
    Fagerberg, Linn
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hallström, Björn M.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Oskarsson, Linda
    Uppsala Univ, Dept Immunol Genet & Pathol, Sci Life Lab, S-75185 Uppsala, Sweden..
    Edlund, Karolina
    Uppsala Univ, Dept Immunol Genet & Pathol, Sci Life Lab, S-75185 Uppsala, Sweden..
    Tolf, Anna
    Uppsala Univ, Dept Immunol Genet & Pathol, Sci Life Lab, S-75185 Uppsala, Sweden..
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Ponten, Fredrik
    Uppsala Univ, Dept Immunol Genet & Pathol, Sci Life Lab, S-75185 Uppsala, Sweden..
    The Human Endometrium-Specific Proteome Defined by Transcriptomics and Antibody-Based Profiling2015In: Omics, ISSN 1536-2310, E-ISSN 1557-8100, Vol. 19, no 11, p. 659-668Article in journal (Refereed)
    Abstract [en]

    The human uterus includes the complex endometrial mucosa, the endometrium that undergoes dynamic, hormone-dependent alterations throughout the life of fertile females. Here we have combined a genome-wide transcriptomics analysis with immunohistochemistry-based protein profiling to analyze gene expression patterns in the normal endometrium. Human endometrial tissues from five women were used for deep sequencing (RNA-Seq). The mRNA and protein expression data from the endometrium were compared to 31 (RNA) and 44 (protein) other normal tissue types, to identify genes with elevated expression in the endometrium and to localize the expression of corresponding proteins at a cellular resolution. Based on the expression levels of transcripts, we could classify all putative human protein coding genes into categories defined by expression patterns and found altogether 101 genes that showed an elevated pattern of expression in the endometrium, with only four genes showing more than five-fold higher expression levels in the endometrium compared to other tissues. In conclusion, our analysis based on transcriptomics and antibody-based protein profiling reports here comprehensive lists of genes with elevated expression levels in the endometrium, providing important starting points for a better molecular understanding of human reproductive biology and disease.

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