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  • 1. Andersson, Sandra
    et al.
    Nilsson, Kenneth
    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.
    Sundström, Christer
    Danielsson, Angelika
    Edlund, Karolina
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Asplund, Anna
    The Transcriptomic and Proteomic Landscapes of Bone Marrow and Secondary Lymphoid Tissues2014In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 9, no 12, p. e115911-Article in journal (Refereed)
    Abstract [en]

    Background: The sequencing of the human genome has opened doors for global gene expression profiling, and the immense amount of data will lay an important ground for future studies of normal and diseased tissues. The Human Protein Atlas project aims to systematically map the human gene and protein expression landscape in a multitude of normal healthy tissues as well as cancers, enabling the characterization of both housekeeping genes and genes that display a tissue-specific expression pattern. This article focuses on identifying and describing genes with an elevated expression in four lymphohematopoietic tissue types (bone marrow, lymph node, spleen and appendix), based on the Human Protein Atlas-strategy that combines high throughput transcriptomics with affinity-based proteomics. Results: An enriched or enhanced expression in one or more of the lymphohematopoietic tissues, compared to other tissue-types, was seen for 693 out of 20,050 genes, and the highest levels of expression were found in bone marrow for neutrophilic and erythrocytic genes. A majority of these genes were found to constitute well-characterized genes with known functions in lymphatic or hematopoietic cells, while others are not previously studied, as exemplified by C19ORF59. Conclusions: In this paper we present a strategy of combining next generation RNA-sequencing with in situ affinity-based proteomics in order to identify and describe new gene targets for further research on lymphatic or hematopoietic cells and tissues. The results constitute lists of genes with enriched or enhanced expression in the four lymphohematopoietic tissues, exemplified also on protein level with immunohistochemical images.

  • 2.
    Berglund, Lisa
    et al.
    KTH, School of Biotechnology (BIO).
    Björling, Erik
    KTH, School of Biotechnology (BIO).
    Jonasson, Kalle
    KTH, School of Biotechnology (BIO).
    Rockberg, Johan
    KTH, School of Biotechnology (BIO).
    Fagerberg, Linn
    KTH, School of Biotechnology (BIO).
    Al-Khalili Szigyarto, Cristina
    KTH, School of Biotechnology (BIO).
    Sivertsson, Åsa
    KTH, School of Biotechnology (BIO).
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO).
    A whole-genome bioinformatics approach to selection of antigens for systematic antibody generation2008In: Proteomics, ISSN 1615-9853, E-ISSN 1615-9861, Vol. 8, no 14, p. 2832-2839Article in journal (Refereed)
    Abstract [en]

    Here, we present an antigen selection strategy based on a whole-genome bioinformatics approach, which is facilitated by an interactive visualization tool displaying protein features from both public resources and in-house generated data. The web-based bioinformatics platform has been designed for selection of multiple, non-overlapping recombinant protein epitope signature tags by display of predicted information relevant for antigens, including domain- and epitope sized sequence similarities to other proteins, transmembrane regions and signal peptides. The visualization tool also displays shared and exclusive protein regions for genes with multiple splice variants. A genome-wide analysis demonstrates that antigens for approximately 80% of the human protein-coding genes can be selected with this strategy.

  • 3.
    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.

  • 4.
    Bergman, Julia
    et al.
    Uppsala University.
    Botling, Johan
    Uppsala University.
    Fagerberg, Linn
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    M Hallström, Björn
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Djureinovic, Dijana
    Uppsala University.
    Pontén, Fredrik
    Uppsala University.
    Mathias, Uhlén
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    The human adrenal gland proteome defined by transcriptomics and antibody-based profiling.2017In: Endocrinology, ISSN 0013-7227, E-ISSN 1945-7170, Vol. 158, no 2, p. 239-251Article in journal (Refereed)
    Abstract [en]

    The adrenal gland is a composite endocrine organ with vital functions that include the synthesis and release of glucocorticoids and catecholamines. To define the molecular landscape that underlies the specific functions of the adrenal gland, we combined a genome-wide transcriptomics approach using messenger RNA sequencing of human tissues with immunohistochemistry-based protein profiling on tissue microarrays. Approximately two-thirds of all putative protein coding genes were expressed in the adrenal gland, and the analysis identified 253 genes with an elevated pattern of expression in the adrenal gland, with only 37 genes showing a markedly greater expression level (more than fivefold) in the adrenal gland compared with 31 other normal human tissue types analyzed. The analyses allowed for an assessment of the relative expression levels for well-known proteins involved in adrenal gland function but also identified previously poorly characterized proteins in the adrenal cortex, such as the FERM (4.1 protein, ezrin, radixin, moesin) domain containing 5 and the nephroblastoma overexpressed (NOV) protein homolog. We have provided a global analysis of the adrenal gland transcriptome and proteome, with a comprehensive list of genes with elevated expression in the adrenal gland and spatial information with examples of protein expression patterns for corresponding proteins. These genes and proteins constitute important starting points for an improved understanding of the normal function and pathophysiology of the adrenal glands.

  • 5. Butler, L. M.
    et al.
    Hallström, Björn Mikael
    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.
    Pontén, F.
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Renné, T.
    Odeberg, Jacob
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Analysis of Body-wide Unfractionated Tissue Data to Identify a Core Human Endothelial Transcriptome2016In: Cell Systems, ISSN 2405-4712, Vol. 3, no 3, p. 287-301.e3Article in journal (Refereed)
    Abstract [en]

    Endothelial cells line blood vessels and regulate hemostasis, inflammation, and blood pressure. Proteins critical for these specialized functions tend to be predominantly expressed in endothelial cells across vascular beds. Here, we present a systems approach to identify a panel of human endothelial-enriched genes using global, body-wide transcriptomics data from 124 tissue samples from 32 organs. We identified known and unknown endothelial-enriched gene transcripts and used antibody-based profiling to confirm expression across vascular beds. The majority of identified transcripts could be detected in cultured endothelial cells from various vascular beds, and we observed maintenance of relative expression in early passage cells. In summary, we describe a widely applicable method to determine cell-type-specific transcriptome profiles in a whole-organism context, based on differential abundance across tissues. We identify potential vascular drug targets or endothelial biomarkers and highlight candidates for functional studies to increase understanding of the endothelium in health and disease.

  • 6. Danielsson, Angelika
    et al.
    Pontén, Fredrik
    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.
    Schwenk, Jochen M.
    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.
    Korsgren, Olle
    Lindskog, Cecilia
    The Human Pancreas Proteome Defined by Transcriptomics and Antibody-Based Profiling2014In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 9, no 12, p. e115421-Article in journal (Refereed)
    Abstract [en]

    The pancreas is composed of both exocrine glands and intermingled endocrine cells to execute its diverse functions, including enzyme production for digestion of nutrients and hormone secretion for regulation of blood glucose levels. To define the molecular constituents with elevated expression in the human pancreas, we employed a genome-wide RNA sequencing analysis of the human transcriptome to identify genes with elevated expression in the human pancreas. This quantitative transcriptomics data was combined with immunohistochemistry-based protein profiling to allow mapping of the corresponding proteins to different compartments and specific cell types within the pancreas down to the single cell level. Analysis of whole pancreas identified 146 genes with elevated expression levels, of which 47 revealed a particular higher expression as compared to the other analyzed tissue types, thus termed pancreas enriched. Extended analysis of in vitro isolated endocrine islets identified an additional set of 42 genes with elevated expression in these specialized cells. Although only 0.7% of all genes showed an elevated expression level in the pancreas, this fraction of transcripts, in most cases encoding secreted proteins, constituted 68% of the total mRNA in pancreas. This demonstrates the extreme specialization of the pancreas for production of secreted proteins. Among the elevated expression profiles, several previously not described proteins were identified, both in endocrine cells (CFC1, FAM159B, RBPJL and RGS9) and exocrine glandular cells (AQP12A, DPEP1, GATM and ERP27). In summary, we provide a global analysis of the pancreas transcriptome and proteome with a comprehensive list of genes and proteins with elevated expression in pancreas. This list represents an important starting point for further studies of the molecular repertoire of pancreatic cells and their relation to disease states or treatment effects.

  • 7. Djureinovic, D.
    et al.
    Fagerberg, Linn
    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.
    Danielsson, A.
    Lindskog, C.
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Pontén, F.
    The human testis-specific proteome defined by transcriptomics and antibody-based profiling2014In: Molecular human reproduction, ISSN 1360-9947, E-ISSN 1460-2407, Vol. 20, no 6, p. 476-488Article in journal (Refereed)
    Abstract [en]

    The testis' function is to produce haploid germ cells necessary for reproduction. Here we have combined a genome-wide transcriptomics analysis with immunohistochemistry-based protein profiling to characterize the molecular components of the testis. Deep sequencing (RNA-Seq) of normal human testicular tissue from seven individuals was performed and compared with 26 other normal human tissue types. All 20 050 putative human genes were classified into categories based on expression patterns. The analysis shows that testis is the tissue with the most tissue-specific genes by far. More than 1000 genes show a testis-enriched expression pattern in testis when compared with all other analyzed tissues. Highly testis enriched genes were further characterized with respect to protein localization within the testis, such as spermatogonia, spermatocytes, spermatids, sperm, Sertoli cells and Leydig cells. Here we present an immunohistochemistry-based analysis, showing the localization of corresponding proteins in different cell types and various stages of spermatogenesis, for 62 genes expressed at > 50-fold higher levels in testis when compared with other tissues. A large fraction of these genes were unexpectedly expressed in early stages of spermatogenesis. In conclusion, we have applied a genome-wide analysis to identify the human testis-specific proteome using transcriptomics and antibody-based protein profiling, providing lists of genes expressed in a tissue-enriched manner in the testis. The majority of these genes and proteins were previously poorly characterised in terms of localization and function, and our list provides an important starting point to increase our molecular understanding of human reproductive biology and disease.

  • 8. Djureinovic, Dijana
    et al.
    Hallström, Björn M.
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Horie, Masafumi
    Mattsson, Johanna Sofia Margareta
    La Fleur, Linnea
    Fagerberg, Linn
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Brunnstrom, Hans
    Lindskog, Cecilia
    Madjar, Katrin
    Rahnenfuehrer, Joerg
    Ekman, Simon
    Stahle, Elisabeth
    Koyi, Hirsh
    Branden, Eva
    Edlund, Karolina
    Hengstler, Jan G.
    Lambe, Mats
    Saito, Akira
    Botling, Johan
    Ponten, Fredrik
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Micke, Patrick
    Profiling cancer testis antigens in non-small-cell lung cancer2016In: JCI INSIGHT, ISSN 2379-3708, Vol. 1, no 10, article id e86837Article in journal (Refereed)
    Abstract [en]

    Cancer testis antigens (CTAs) are of clinical interest as biomarkers and present valuable targets for immunotherapy. To comprehensively characterize the CTA landscape of non-small-cell lung cancer (NSCLC), we compared RNAseq data from 199 NSCLC tissues to the normal transcriptome of 142 samples from 32 different normal organs. Of 232 CTAs currently annotated in the Caner Testis Database (CTdatabase), 96 were confirmed in NSCLC. To obtain an unbiased CTA profile of NSCLC, we applied stringent criteria on our RNAseq data set and defined 90 genes as CTAs, of which 55 genes were not annotated in the CTdatabase, thus representing potential new CTAs. Cluster analysis revealed that CTA expression is histology dependent and concurrent expression is common. IHC confirmed tissue-specific protein expression of selected new CTAs (TKTL1, TGIF2LX, VCX, and CXORF67). Furthermore, methylation was identified as a regulatory mechanism of CTA expression based on independent data from The Cancer Genome Atlas. The proposed prognostic impact of CTAs in lung cancer was not confirmed, neither in our RNAseq cohort nor in an independent meta-analysis of 1,117 NSCLC cases. In summary, we defined a set of 90 reliable CTAs, including information on protein expression, methylation, and survival association. The detailed RNAseq catalog can guide biomarker studies and efforts to identify targets for immunotherapeutic strategies.

  • 9.
    Dusart, Philip
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Biotechnology (BIO).
    Fagerberg, Linn
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Biotechnology (BIO).
    Perisic, L.
    Civelek, M.
    Struck, Eike
    KTH, School of Biotechnology (BIO). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hedin, U.
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Biotechnology (BIO).
    Trégouët, D. -A
    Renné, T.
    Odeberg, Jacob
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Biotechnology (BIO). Coagulation Unit, Centre for Hematology, Karolinska University Hospital, SE-171 76, Stockholm, Sweden.
    Butler, Lynn M.
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Biotechnology (BIO). Clinical Chemistry and Blood Coagulation, Department of Molecular Medicine and Surgery, Karolinska Institute, SE-171 76, Stockholm, Sweden Institute for Clinical Chemistry and Laboratory Medicine, University Medical Centre Hamburg-Eppendorf, D-20246, Hamburg, Germany.
    A systems-approach reveals human nestin is an endothelial-enriched, angiogenesis-independent intermediate filament protein2018In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 8, no 1, article id 14668Article in journal (Refereed)
    Abstract [en]

    The intermediate filament protein nestin is expressed during embryonic development, but considered largely restricted to areas of regeneration in the adult. Here, we perform a body-wide transcriptome and protein-profiling analysis to reveal that nestin is constitutively, and highly-selectively, expressed in adult human endothelial cells (EC), independent of proliferative status. Correspondingly, we demonstrate that it is not a marker for tumour EC in multiple malignancy types. Imaging of EC from different vascular beds reveals nestin subcellular distribution is shear-modulated. siRNA inhibition of nestin increases EC proliferation, and nestin expression is reduced in atherosclerotic plaque neovessels. eQTL analysis reveals an association between SNPs linked to cardiovascular disease and reduced aortic EC nestin mRNA expression. Our study challenges the dogma that nestin is a marker of proliferation, and provides insight into its regulation and function in EC. Furthermore, our systems-based approach can be applied to investigate body-wide expression profiles of any candidate protein. 

  • 10.
    Edfors, Fredrik
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hober, Andreas
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Linderbäck, Klas
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Maddalo, Gianluca
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Azimi, Alireza
    Karolinska Inst, Karolinska Univ Hosp, Dept Oncol Pathol, SE-17177 Stockholm, Sweden..
    Sivertsson, Åsa
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Tegel, Hanna
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hober, Sophia
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Al-Khalili Szigyarto, Cristina
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Fagerberg, Linn
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    von Feilitzen, Kalle
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Oksvold, Per
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lindskog, Cecilia
    Uppsala Univ, Dept Immunol Genet & Pathol, SE-75185 Uppsala, Sweden..
    Forsström, Björn
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Uhlén, Mathias
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science. KTH, Centres, Science for Life Laboratory, SciLifeLab. Biosustainabil, DK-2970 Horsholm, Denmark..
    Enhanced validation of antibodies for research applications2018In: Nature Communications, ISSN 2041-1723, E-ISSN 2041-1723, Vol. 9, article id 4130Article in journal (Refereed)
    Abstract [en]

    There is a need for standardized validation methods for antibody specificity and selectivity. Recently, five alternative validation pillars were proposed to explore the specificity of research antibodies using methods with no need for prior knowledge about the protein target. Here, we show that these principles can be used in a streamlined manner for enhanced validation of research antibodies in Western blot applications. More than 6,000 antibodies were validated with at least one of these strategies involving orthogonal methods, genetic knockdown, recombinant expression, independent antibodies, and capture mass spectrometry analysis. The results show a path forward for efforts to validate antibodies in an application-specific manner suitable for both providers and users.

  • 11.
    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. 

  • 12. Edqvist, Per-Henrik D.
    et al.
    Fagerberg, Linn
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hallström, Bjorn M.
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Danielsson, Angelika
    Edlund, Karolina
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Pontén, Fredrik
    Expression of Human Skin-Specific Genes Defined by Transcriptomics and Antibody-Based Profiling2015In: Journal of Histochemistry and Cytochemistry, ISSN 0022-1554, E-ISSN 1551-5044, Vol. 63, no 2, p. 129-141Article in journal (Refereed)
    Abstract [en]

    To increase our understanding of skin, it is important to define the molecular constituents of the cell types and epidermal layers that signify normal skin. We have combined a genome-wide transcriptomics analysis, using deep sequencing of mRNA from skin biopsies, with immunohistochemistry-based protein profiling to characterize the landscape of gene and protein expression in normal human skin. The transcriptomics and protein expression data of skin were compared to 26 (RNA) and 44 (protein) other normal tissue types. All 20,050 putative protein-coding genes were classified into categories based on patterns of expression. We found that 417 genes showed elevated expression in skin, with 106 genes expressed at least five-fold higher than that in other tissues. The 106 genes categorized as skin enriched encoded for well-known proteins involved in epidermal differentiation and proteins with unknown functions and expression patterns in skin, including the C1orf68 protein, which showed the highest relative enrichment in skin. In conclusion, we have applied a genome-wide analysis to identify the human skin-specific proteome and map the precise localization of the corresponding proteins in different compartments of the skin, to facilitate further functional studies to explore the molecular repertoire of normal skin and to identify biomarkers related to various skin diseases.

  • 13.
    Fagerberg, Linn
    KTH, School of Biotechnology (BIO), Proteomics.
    Mapping the human proteome using bioinformatic methods2011Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The fundamental goal of proteomics is to gain an understanding of the expression and function of the proteome on the level of individual proteins, on the level of defined cell types and on the level of the entire organism. In this thesis, the human proteome is explored using membrane protein topology prediction methods to define the human membrane proteome and by global protein expression profiling, which relies on a complex study of the location and expression levels of proteins in tissues and cells.

    A whole-proteome analysis was performed based on the predicted protein-coding genes of humans using a selection of membrane protein topology prediction methods. The study used a majority decision-based method, which estimated that approximately 26% of the human genes encode for a membrane protein. The prediction results are displayed in a visualization tool to facilitate the selection of antigens to be used for antibody generation.

    Global protein expression profiles in a large number of cells and tissues in the human body were analyzed for more than 4000 protein targets, based on data from the antibody-based immunohistochemistry and immunofluorescence methods within the framework of the Human Protein Atlas project. The results revealed few cell-type specific proteins and a high fraction of human proteins expressed in most cells, suggesting that cell and tissue specificity is attained by a fine-tuned regulation of protein levels. The expression profiles were also used to analyze the relationship between 45 cell lines by hierarchical clustering and principal component analysis. The global protein expression patterns overall reflected the tumor origin of the cells, and also allowed for identification of proteins of importance for distinguishing different categories of cell lines, as defined by phenotype of progenitor cell. In addition, the protein distribution in 16 subcellular compartments in three of the human cell lines was mapped. A large fraction of proteins were localized in two or more compartments and, in line with previous results, a majority of proteins were detected in all three cell lines.

    Finally, mass spectrometry-based protein expression levels were compared to RNA-seq-based transcript expression levels in three cell lines. Highly ubiquitous mRNA expression was found and the changes of expression levels between the cell lines showed high correlations between proteins and transcripts. Large general differences in abundance of proteins from various functional classes were observed. A comparison between categories based on expression levels revealed that, in general, genes with varying expression levels between the cell lines or only expressed in one cell line were highly enriched for cell-surface proteins.

    These studies show a path for a systematic analysis to characterize the proteome in human cells, tissues and organs.

  • 14.
    Fagerberg, Linn
    et al.
    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.
    Oksvold, Per
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Kampf, C.
    Djureinovic, D.
    Odeberg, Jacob
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Habuka, Masato
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Tahmasebpoor, S.
    Danielsson, A.
    Edlund, K.
    Asplund, A.
    Sjöstedt, E.
    Lundberg, E.
    Szigyarto, Cristina Al-Khalili
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Skogs, Marie
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Ottosson Takanen, J.
    Berling, H.
    Tegel, Hanna
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Mulder, J.
    Nilsson, Peter
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Schwenk, Jochen M.
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lindskog, C.
    Danielsson, Frida
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Mardinoglu, A.
    Sivertsson, Åsa
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Von Feilitzen, Kalle
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Forsberg, Mattias
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Zwahlen, Martin
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Olsson, I.
    Navani, S.
    Huss, Mikael
    Nielsen, Jens
    KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Pontén, F.
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Analysis of the human tissue-specific expression by genome-wide integration of transcriptomics and antibody-based proteomics2014In: Molecular & Cellular Proteomics, ISSN 1535-9476, E-ISSN 1535-9484, Vol. 13, no 2, p. 397-406Article in journal (Refereed)
    Abstract [en]

    Global classification of the human proteins with regards to spatial expression patterns across organs and tissues is important for studies of human biology and disease. Here, we used a quantitative transcriptomics analysis (RNA-Seq) to classify the tissue-specific expression of genes across a representative set of all major human organs and tissues and combined this analysis with antibody- based profiling of the same tissues. To present the data, we launch a new version of the Human Protein Atlas that integrates RNA and protein expression data corresponding to 80% of the human protein-coding genes with access to the primary data for both the RNA and the protein analysis on an individual gene level. We present a classification of all human protein-coding genes with regards to tissue-specificity and spatial expression pattern. The integrative human expression map can be used as a starting point to explore the molecular constituents of the human body.

  • 15.
    Fagerberg, Linn
    et al.
    KTH, School of Biotechnology (BIO), Proteomics.
    Jonasson, Kalle
    KTH, School of Biotechnology (BIO), Proteomics.
    von Heijne, Gunnar
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics.
    Berglund, Lisa
    KTH, School of Biotechnology (BIO), Proteomics.
    Prediction of the human membrane proteome2010In: Proteomics, ISSN 1615-9853, E-ISSN 1615-9861, Vol. 10, no 6, p. 1141-1149Article in journal (Refereed)
    Abstract [en]

    Membrane proteins are key molecules in the cell, and are important targets for pharmaceutical drugs. Few three-dimensional structures of membrane proteins have been obtained, which makes computational prediction of membrane proteins crucial for studies of these key molecules. Here, seven membrane protein topology prediction methods based on different underlying algorithms, such as hidden Markov models, neural networks and support vector machines, have been used for analysis of the protein sequences from the 21 416 annotated genes in the human genome. The number of genes coding for a protein with predicted cc-helical transmembrane region(s) ranged from 5508 to 7651, depending on the method used. Based on a majority decision method, we estimate 5539 human genes to code for membrane proteins, corresponding to approximately 26% of the human protein-coding genes. The largest fraction of these proteins has only one predicted transmembrane region, but there are also many proteins with seven predicted transmembrane regions, including the G-protein coupled receptors. A visualization tool displaying the topologies suggested by the eight prediction methods, for all predicted membrane proteins, is available on the public Human Protein Atlas portal (www.proteinatlas.org).

  • 16.
    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).

  • 17.
    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.

  • 18.
    Fagerberg, Linn
    et al.
    KTH, School of Biotechnology (BIO), Proteomics.
    Stromberg, Sara
    El-Obeid, Adila
    Gry, Marcus
    KTH, School of Biotechnology (BIO), Proteomics.
    Nilsson, Kenneth
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics.
    Ponten, Fredrik
    Asplund, Anna
    Large-Scale Protein Profiling in Human Cell Lines Using Antibody-Based Proteomics2011In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 10, no 9, p. 4066-4075Article in journal (Refereed)
    Abstract [en]

    Human cancer cell lines grown in vitro are frequently used to decipher basic cell biological phenomena and to also specifically study different forms of cancer. Here we present the first large-scale study of protein expression patterns in cell lines using an antibody-based proteomics approach. We analyzed the expression pattern of 5436 proteins in 45 different cell lines using hierarchical clustering, principal component analysis, and two-group comparisons for the identification of differentially expressed proteins. Our results show that immunohistochemically determined protein profiles can categorize cell lines into groups that overall reflect the tumor tissue of origin and that hematological cell lines appear to retain their protein profiles to a higher degree than cell lines established from solid tumors. The two-group comparisons reveal well-characterized proteins as well as previously unstudied proteins that could be of potential interest for further investigations. Moreover, multiple myeloma cells and cells of myeloid origin were found to share a protein profile, relative to the protein profile of lymphoid leukemia and lymphoma cells, possibly reflecting their common dependency of bone marrow microenvironment. This work also provides an extensive list of antibodies, for which high-resolution images as well as validation data are available on the Human Protein Atlas (www.proteinatlas.org), that are of potential use in cell line studies.

  • 19.
    Fagerberg, Linn
    et al.
    KTH, School of Biotechnology (BIO), Proteomics.
    Strömberg, Sara
    El-Obeid, Adila
    Gry, Marcus
    KTH, School of Biotechnology (BIO), Proteomics.
    Nilsson, Kenneth
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics.
    Ponten, Fredrik
    Adplund, Anna
    The Global Protein Expression Pattern in Human Cell LinesManuscript (preprint) (Other academic)
    Abstract [en]

    Human cancer cell lines grown in vitro are frequently used to decipher basic cell biological phenomena but also to specifically study different forms of cancer. Here we present the first large-scale study of protein expression patterns in cell lines using an antibody-based proteomics approach. We analyzed the expression pattern of 5436 proteins in 45 different cell lines using hierarchical clustering, principal component analysis and two-group comparisons for the identification of differentially expressed proteins. The results show that protein profiles of cell lines, as determined using immunohistochemistry, allow for a hierarchical clustering that overall reflects tumor tissues of origin. Hematological cell lines appear to retain their protein profiles to a higher degree than cell lines established from solid tumors, resulting in a clustering that well reflects progenitor cell types. The discrepancy may reflect different levels of in vitro induced alterations in adherent and suspension grown cell lines, respectively. In addition, multiple myeloma cells and cells of myeloid origin were found to share a protein profile, relative the protein profile of lymphoid leukemia and lymphoma cells, possibly reflecting their common dependency of bone marrow microenvironment.

     

  • 20. Gremel, Gabriela
    et al.
    Wanders, Alkwin
    Cedernaes, Jonathan
    Fagerberg, Linn
    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.
    Edlund, Karolina
    Sjostedt, Evelina
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Ponten, Fredrik
    The human gastrointestinal tract-specific transcriptome and proteome as defined by RNA sequencing and antibody-based profiling2015In: Journal of gastroenterology, ISSN 0944-1174, E-ISSN 1435-5922, Vol. 50, no 1, p. 46-57Article in journal (Refereed)
    Abstract [en]

    The gastrointestinal tract (GIT) is subdivided into different anatomical organs with many shared functions and characteristics, but also distinct differences. We have combined a genome-wide transcriptomics analysis with immunohistochemistry-based protein profiling to describe the gene and protein expression patterns that define the human GIT. RNA sequencing data derived from stomach, duodenum, jejunum/ileum and colon specimens were compared to gene expression levels in 23 other normal human tissues analysed with the same method. Protein profiling based on immunohistochemistry and tissue microarrays was used to sub-localize the corresponding proteins with GIT-specific expression into sub-cellular compartments and cell types. Approximately 75 % of all human protein-coding genes were expressed in at least one of the GIT tissues. Only 51 genes showed enriched expression in either one of the GIT tissues and an additional 83 genes were enriched in two or more GIT tissues. The list of GIT-enriched genes with validated protein expression patterns included various well-known but also previously uncharacterised or poorly studied genes. For instance, the colon-enriched expression of NXPE family member 1 (NXPE1) was established, while NLR family, pyrin domain-containing 6 (NLRP6) expression was primarily found in the human small intestine. We have applied a genome-wide analysis based on transcriptomics and antibody-based protein profiling to identify genes that are expressed in a specific manner within the human GIT. These genes and proteins constitute important starting points for an improved understanding of the normal function and the different states of disease associated with the GIT.

  • 21.
    Habuka, Masato
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab. Karolinska University Hospital, Sweden .
    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.
    Kampf, Caroline
    Edlund, Karolina
    Sivertsson, Åsa
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Yamamoto, Tadashi
    Pontén, Fredrik
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Odeberg, Jacob
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab. Karolinska University Hospital, Sweden.
    The Kidney Transcriptome and Proteome Defined by Transcriptomics and Antibody-Based Profiling2014In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 9, no 12, p. e116125-Article in journal (Refereed)
    Abstract [en]

    To understand renal functions and disease, it is important to define the molecular constituents of the various compartments of the kidney. Here, we used comparative transcriptomic analysis of all major organs and tissues in the human body, in combination with kidney tissue micro array based immunohistochemistry, to generate a comprehensive description of the kidney-specific transcriptome and proteome. A special emphasis was placed on the identification of genes and proteins that were elevated in specific kidney subcompartments. Our analysis identified close to 400 genes that had elevated expression in the kidney, as compared to the other analysed tissues, and these were further subdivided, depending on expression levels, into tissue enriched, group enriched or tissue enhanced. Immunohistochemistry allowed us to identify proteins with distinct localisation to the glomeruli (n=11), proximal tubules (n=120), distal tubules (n=9) or collecting ducts (n=8). Among the identified kidney elevated transcripts, we found several proteins not previously characterised or identified as elevated in kidney. This description of the kidney specific transcriptome and proteome provides a resource for basic and clinical research to facilitate studies to understand kidney biology and disease.

  • 22. 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.
    Danielsson, Angelika
    Nielsen, Jens
    KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab. Chalmers University of Technology, Sweden.
    Pontén, Fredrik
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Defining the human gallbladder proteome by transcriptomics and affinity proteomics2014In: Proteomics, ISSN 1615-9853, E-ISSN 1615-9861, Vol. 14, no 21-22, p. 2498-2507Article in journal (Refereed)
    Abstract [en]

    Global protein analysis of human gallbladder tissue is vital for identification of molecular regulators and effectors of its physiological activity. Here, we employed a genome-wide deep RNA sequencing analysis in 28 human tissues to identify the genes overrepresented in the gallbladder and complemented it with antibody-based immunohistochemistry in 48 human tissues. We characterized human gallbladder proteins and identified 140 gallbladder-specific proteins with an elevated expression in the gallbladder as compared to the other analyzed tissues. Five genes were categorized as enriched, with at least fivefold higher levels in gallbladder, 60 genes were categorized as group enriched with elevated transcript levels in gallbladder shared with at least one other tissue and 75 genes were categorized as enhanced with higher expression than the average expression in other tissues. We explored the localization of the genes within the gallbladder through cell-type specific antibody-based protein profiling and the subcellular localization of the genes through immunofluorescent-based profiling. Finally, we revealed the biological processes and metabolic functions carried out by these genes through the use of GO, KEGG Pathway, and HMR2.0 that is compilation of the human metabolic reactions. We demonstrated the results of the combined analysis of the transcriptomics and affinity proteomics.

  • 23. 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.

  • 24.
    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.

  • 25. Lindskog, Cecilia
    et al.
    Fagerberg, Linn
    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.
    Edlund, Karolina
    Hellwig, Birte
    Rahnenführer, Jörg
    Kampf, Caroline
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Pontén, Fredrik
    Micke, Patrick
    The lung-specific proteome defined by integration of transcriptomics and antibody-based profiling2014In: The FASEB Journal, ISSN 0892-6638, E-ISSN 1530-6860, Vol. 28, no 12, p. 5184-5196Article in journal (Refereed)
    Abstract [en]

    The combined action of multiple cell types is essential for the physiological function of the lung, and increased awareness of the molecular constituents characterizing each cell type is likely to advance the understanding of lung biology and disease. In the current study, we used genome-wide RNA sequencing of normal lung parenchyma and 26 additional tissue types, combined with antibody-based protein profiling, to localize the expression to specific cell types. Altogether, 221 genes were found to be elevated in the lung compared with their expression in other analyzed tissues. Among the gene products were several well-known markers, but also several proteins previously not described in the context of the lung. To link the lungspecific molecular repertoire to human disease, survival associations of pneumocyte-specific genes were assessed by using transcriptomics data from 7 non-small-cell lung cancer (NSCLC) cohorts. Transcript levels of 10 genes (SFTPB, SFTPC, SFTPD, SLC34A2, LAMP3, CACNA2D2, AGER, EMP2, NKX2-1, and NAPSA) were significantly associated with survival in the adenocarcinoma subgroup, thus qualifying as promising biomarker candidates. In summary, based on an integrated omics approach, we identified genes with elevated expression in lung and localized corresponding protein expression to different cell types. As biomarker candidates, these proteins may represent intriguing starting points for further exploration in health and disease.-Lindskog, C., Fagerberg, L., Hallstrom, B., Edlund, K., Hellwig, B., Rahnenfuhrer, J., Kampf, C., Uhlen, M., Ponten, F., Micke, P. The lung-specific proteome defined by integration of transcriptomics and antibody-based profiling.

  • 26. Lindskog, Cecilia
    et al.
    Linne, Jerker
    Fagerberg, Linn
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hallström, Björn M.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Sundberg, Carl Johan
    Lindholm, Malene
    Huss, Mikael
    Kampf, Caroline
    Choi, Howard
    Liem, David A.
    Ping, Peipei
    Varemo, Leif
    Mardinoglu, Adil
    Nielsen, Jens
    Larsson, Erik
    Ponten, Fredrik
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    The human cardiac and skeletal muscle proteomes defined by transcriptomics and antibody-based profiling2015In: BMC Genomics, ISSN 1471-2164, E-ISSN 1471-2164, Vol. 16, article id 475Article in journal (Refereed)
    Abstract [en]

    Background: To understand cardiac and skeletal muscle function, it is important to define and explore their molecular constituents and also to identify similarities and differences in the gene expression in these two different striated muscle tissues. Here, we have investigated the genes and proteins with elevated expression in cardiac and skeletal muscle in relation to all other major human tissues and organs using a global transcriptomics analysis complemented with antibody-based profiling to localize the corresponding proteins on a single cell level. Results: Our study identified a comprehensive list of genes expressed in cardiac and skeletal muscle. The genes with elevated expression were further stratified according to their global expression pattern across the human body as well as their precise localization in the muscle tissues. The functions of the proteins encoded by the elevated genes are well in line with the physiological functions of cardiac and skeletal muscle, such as contraction, ion transport, regulation of membrane potential and actomyosin structure organization. A large fraction of the transcripts in both cardiac and skeletal muscle correspond to mitochondrial proteins involved in energy metabolism, which demonstrates the extreme specialization of these muscle tissues to provide energy for contraction. Conclusions: Our results provide a comprehensive list of genes and proteins elevated in striated muscles. A number of proteins not previously characterized in cardiac and skeletal muscle were identified and localized to specific cellular subcompartments. These proteins represent an interesting starting point for further functional analysis of their role in muscle biology and disease.

  • 27. Liu, Zihe
    et al.
    Liu, Lifang
    Osterlund, Tobias
    Hou, Jin
    Huang, Mingtao
    Fagerberg, Linn
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, School of Biotechnology (BIO), Centres, Albanova VinnExcellence Center for Protein Technology, ProNova.
    Petranovic, Dina
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, School of Biotechnology (BIO), Centres, Albanova VinnExcellence Center for Protein Technology, ProNova. Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Denmark .
    Nielsen, Jens
    Improved Production of a Heterologous Amylase in Saccharomyces cerevisiae by Inverse Metabolic Engineering2014In: Applied and Environmental Microbiology, ISSN 0099-2240, E-ISSN 1098-5336, Vol. 80, no 17, p. 5542-5550Article in journal (Refereed)
    Abstract [en]

    The increasing demand for industrial enzymes and biopharmaceutical proteins relies on robust production hosts with high protein yield and productivity. Being one of the best-studied model organisms and capable of performing posttranslational modifications, the yeast Saccharomyces cerevisiae is widely used as a cell factory for recombinant protein production. However, many recombinant proteins are produced at only 1% (or less) of the theoretical capacity due to the complexity of the secretory pathway, which has not been fully exploited. In this study, we applied the concept of inverse metabolic engineering to identify novel targets for improving protein secretion. Screening that combined UV-random mutagenesis and selection for growth on starch was performed to find mutant strains producing heterologous amylase 5-fold above the level produced by the reference strain. Genomic mutations that could be associated with higher amylase secretion were identified through whole-genome sequencing. Several single-point mutations, including an S196I point mutation in the VTA1 gene coding for a protein involved in vacuolar sorting, were evaluated by introducing these to the starting strain. By applying this modification alone, the amylase secretion could be improved by 35%. As a complement to the identification of genomic variants, transcriptome analysis was also performed in order to understand on a global level the transcriptional changes associated with the improved amylase production caused by UV mutagenesis.

  • 28.
    Lundberg, Emma
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Biotechnology (BIO), Proteomics (closed 20130101).
    Fagerberg, Linn
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101).
    Klevebring, Daniel
    KTH, School of Biotechnology (BIO), Gene Technology.
    Matic, Ivan
    Geiger, Tamar
    Cox, Juergen
    Älgenäs, Cajsa
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101).
    Lundeberg, Joakim
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Biotechnology (BIO), Gene Technology.
    Mann, Matthias
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Biotechnology (BIO), Proteomics (closed 20130101).
    Defining the transcriptome and proteome in three functionally different human cell lines2010In: Molecular Systems Biology, ISSN 1744-4292, E-ISSN 1744-4292, Vol. 6, p. 450-Article in journal (Refereed)
    Abstract [en]

    An essential question in human biology is how cells and tissues differ in gene and protein expression and how these differences delineate specific biological function. Here, we have performed a global analysis of both mRNA and protein levels based on sequence-based transcriptome analysis (RNA-seq), SILAC-based mass spectrometry analysis and antibody-based confocal microscopy. The study was performed in three functionally different human cell lines and based on the global analysis, we estimated the fractions of mRNA and protein that are cell specific or expressed at similar/different levels in the cell lines. A highly ubiquitous RNA expression was found with > 60% of the gene products detected in all cells. The changes of mRNA and protein levels in the cell lines using SILAC and RNA ratios show high correlations, even though the genome-wide dynamic range is substantially higher for the proteins as compared with the transcripts. Large general differences in abundance for proteins from various functional classes are observed and, in general, the cell-type specific proteins are low abundant and highly enriched for cell-surface proteins. Thus, this study shows a path to characterize the transcriptome and proteome in human cells from different origins.

  • 29. Mardinoglu, Adil
    et al.
    Kampf, Caroline
    Asplund, Anna
    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
    Blüher, Matthias
    Pontén, Fredrik
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Nielsen, Jens
    KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab. Chalmers University of Technology, Sweden.
    Defining the Human Adipose Tissue Proteome To Reveal Metabolic Alterations in Obesity2014In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 13, no 11, p. 5106-5119Article in journal (Refereed)
    Abstract [en]

    White adipose tissue (WAT) has a major role in the progression of obesity. Here, we combined data from RNA-Seq and antibody-based immunohistochemistry to describe the normal physiology of human WAT obtained from three female subjects and explored WAT-specific genes by comparing WAT to 26 other major human tissues. Using the protein evidence in WAT, we validated the content of a genome-scale metabolic model for adipocytes. We employed this high-quality model for the analysis of subcutaneous adipose tissue (SAT) gene expression data obtained from subjects included in the Swedish Obese Subjects Sib Pair study to reveal molecular differences between lean and obese individuals. We integrated SAT gene expression and plasma metabolomics data, investigated the contribution of the metabolic differences in the mitochondria of SAT to the occurrence of obesity, and eventually identified cytosolic branched-chain amino acid (BCAA) transaminase 1 as a potential target that can be used for drug development. We observed decreased glutaminolysis and alterations in the BCAAs metabolism in SAT of obese subjects compared to lean subjects. We also provided mechanistic explanations for the changes in the plasma level of BCAAs, glutamate, pyruvate, and alpha-ketoglutarate in obese subjects. Finally, we validated a subset of our model-based predictions in 20 SAT samples obtained from 10 lean and 10 obese male and female subjects.

  • 30. Nookaew, Intawat
    et al.
    Papini, Marta
    Pornputtapong, Natapol
    Scalcinati, Gionata
    Fagerberg, Linn
    KTH, School of Biotechnology (BIO), Proteomics.
    Uhlén, Matthias
    KTH, School of Biotechnology (BIO), Proteomics.
    Nielsen, Jens
    A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: a case study in Saccharomyces cerevisiae2012In: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 40, no 20, p. 10084-10097Article in journal (Refereed)
    Abstract [en]

    RNA-seq, has recently become an attractive method of choice in the studies of transcriptomes, promising several advantages compared with microarrays. In this study, we sought to assess the contribution of the different analytical steps involved in the analysis of RNA-seq data generated with the Illumina platform, and to perform a cross-platform comparison based on the results obtained through Affymetrix microarray. As a case study for our work we, used the Saccharomyces cerevisiae strain CEN.PK 113-7D, grown under two different conditions (batch and chemostat). Here, we asses the influence of genetic variation on the estimation of gene expression level using three different aligners for read-mapping (Gsnap, Stampy and TopHat) on S288c genome, the capabilities of five different statistical methods to detect differential gene expression (baySeq, Cuffdiff, DESeq, edgeR and NOISeq) and we explored the consistency between RNA-seq analysis using reference genome and de novo assembly approach. High reproducibility among biological replicates (correlation >= 0.99) and high consistency between the two platforms for analysis of gene expression levels (correlation >= 0.91) are reported. The results from differential gene expression identification derived from the different statistical methods, as well as their integrated analysis results based on gene ontology annotation are in good agreement. Overall, our study provides a useful and comprehensive comparison between the two platforms (RNA-seq and microrrays) for gene expression analysis and addresses the contribution of the different steps involved in the analysis of RNA-seq data.

  • 31. O'Hurley, Gillian
    et al.
    Busch, Christer
    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.
    Stadler, Charlotte
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Tolf, Anna
    Lundberg, Emma
    Schwenk, Jochen M.
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Jirstrom, Karin
    Bjartell, Anders
    Gallagher, William M.
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Ponten, Fredrik
    Analysis of the Human Prostate-Specific Proteome Defined by Transcriptomics and Antibody-Based Profiling Identifies TMEM79 and ACOXL as Two Putative, Diagnostic Markers in Prostate Cancer2015In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 10, no 8, article id e0133449Article in journal (Refereed)
    Abstract [en]

    To better understand prostate function and disease, it is important to define and explore the molecular constituents that signify the prostate gland. The aim of this study was to define the prostate specific transcriptome and proteome, in comparison to 26 other human tissues. Deep sequencing of mRNA (RNA-seq) and immunohistochemistry-based protein profiling were combined to identify prostate specific gene expression patterns and to explore tissue biomarkers for potential clinical use in prostate cancer diagnostics. We identified 203 genes with elevated expression in the prostate, 22 of which showed more than five-fold higher expression levels compared to all other tissue types. In addition to previously well-known proteins we identified two poorly characterized proteins, TMEM79 and ACOXL, with potential to differentiate between benign and cancerous prostatic glands in tissue biopsies. In conclusion, we have applied a genome-wide analysis to identify the prostate specific proteome using transcriptomics and antibody-based protein profiling to identify genes with elevated expression in the prostate. Our data provides a starting point for further functional studies to explore the molecular repertoire of normal and diseased prostate including potential prostate cancer markers such as TMEM79 and ACOXL.

  • 32. Ponten, Fredrik
    et al.
    Gry, Marcus
    KTH, School of Biotechnology (BIO), Proteomics.
    Fagerberg, Linn
    KTH, School of Biotechnology (BIO), Proteomics.
    Lundberg, Emma
    KTH, School of Biotechnology (BIO), Proteomics.
    Asplund, Anna
    Berglund, Lisa
    KTH, School of Biotechnology (BIO), Proteomics.
    Oksvold, Per
    KTH, School of Biotechnology (BIO), Proteomics.
    Björling, Erik
    KTH, School of Biotechnology (BIO), Proteomics.
    Hober, Sophia
    KTH, School of Biotechnology (BIO), Proteomics.
    Kampf, Caroline
    Navani, Sanjay
    Nilsson, Peter
    KTH, School of Biotechnology (BIO), Proteomics.
    Ottosson, Jenny
    KTH, School of Biotechnology (BIO), Proteomics.
    Persson, Anja
    KTH, School of Biotechnology (BIO), Proteomics.
    Wernérus, Henrik
    KTH, School of Biotechnology (BIO), Proteomics.
    Wester, Kenneth
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics.
    A global view of protein expression in human cells, tissues, and organs2009In: Molecular Systems Biology, ISSN 1744-4292, E-ISSN 1744-4292, Vol. 5Article in journal (Refereed)
    Abstract [en]

    Defining the protein profiles of tissues and organs is critical to understanding the unique characteristics of the various cell types in the human body. In this study, we report on an anatomically comprehensive analysis of 4842 protein profiles in 48 human tissues and 45 human cell lines. A detailed analysis of over 2 million manually annotated, high-resolution, immunohistochemistry- based images showed a high fraction (>65%) of expressed proteins in most cells and tissues, with very few proteins (<2%) detected in any single cell type. Similarly, confocal microscopy in three human cell lines detected expression of more than 70% of the analyzed proteins. Despite this ubiquitous expression, hierarchical clustering analysis, based on global protein expression patterns, shows that the analyzed cells can be still subdivided into groups according to the current concepts of histology and cellular differentiation. This study suggests that tissue specificity is achieved by precise regulation of protein levels in space and time, and that different tissues in the body acquire their unique characteristics by controlling not which proteins are expressed but how much of each is produced. Molecular Systems Biology 5: 337; published online 22 December 2009; doi:10.1038/msb.2009.93

  • 33. 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.

  • 34.
    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)
  • 35.
    Stadler, Charlotte
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Fagerberg, Linn
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Sivertsson, Åsa
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Oksvold, Per
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Zwahlen, Martin
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hallström, Björn M.
    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.
    RNA- and Antibody-Based Profiling of the Human Proteome with Focus on Chromosome 192014In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 13, no 4, p. 2019-2027Article in journal (Refereed)
    Abstract [en]

    An important part of the Human Proteome Project is to characterize the protein complement of the genome with antibody-based profiling. Within the framework of this effort, a new version 12 of the Human Protein Atlas (www.proteinatlas.org) has been launched, including transcriptomics data for 27 tissues and 44 cell lines to complement the protein expression data from antibody-based profiling. Besides the extensive addition of transcriptomics data, the Human Protein Atlas now contains antibody-based protein profiles for 82% of the 20 329 putative protein-coding genes. The comprehensive data resulting from RNA-seq analysis and antibody-based profiling performed within the Human Protein Atlas as well as information from UniProt were used to generate evidence summary scores for each of the 20 329 genes, of which 94% now have experimental evidence at least at transcript level. The evidence scores for all individual genes are displayed with regards to both RNA- and antibody-based protein profiles, including chromosome-centric visualizations. An analysis of the human chromosome 19 shows that similar to 43% of the genes are expressed at the transcript level in all 27 tissues analyzed, suggesting a "house-keeping" function, while 12% of the genes show a more tissue-specific pattern with enriched expression in one of the analyzed tissues only.

  • 36.
    Thul, Peter J.
    et al.
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Åkesson, Lovisa
    KTH, School of Biotechnology (BIO). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Wiking, Mikaela
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Mahdessian, Diana
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Geladaki, A.
    Ait Blal, Hammou
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Alm, Tove L.
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Asplund, A.
    Björk, Lars
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Breckels, L. M.
    Bäckström, Anna
    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.
    Fall, Jenny
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Gatto, L.
    Gnann, Christian
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hober, Sophia
    KTH, School of Biotechnology (BIO), Protein Technology.
    Hjelmare, Martin
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Johansson, Fredric
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lee, Sunjae
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lindskog, C.
    Mulder, J.
    Mulvey, C. M.
    Nilsson, Peter
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Oksvold, Per
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Rockberg, Johan
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Schutten, Rutger
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Schwenk, Jochen M.
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Sivertsson, Åsa
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Sjöstedt, E.
    Skogs, Marie
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Stadler, Charlotte
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Sullivan, Devin P.
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Tegel, Hanna
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Winsnes, Casper F.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Zhang, Cheng
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Zwahlen, Martin
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Mardinoglu, Adil
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Pontén, F.
    von Feilitzen, Kalle
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lilley, K. S.
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Lundberg, Emma
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    A subcellular map of the human proteome2017In: Science, ISSN 0036-8075, E-ISSN 1095-9203, Vol. 356, no 6340, article id 820Article in journal (Refereed)
    Abstract [en]

    Resolving the spatial distribution of the human proteome at a subcellular level can greatly increase our understanding of human biology and disease. Here we present a comprehensive image-based map of subcellular protein distribution, the Cell Atlas, built by integrating transcriptomics and antibody-based immunofluorescence microscopy with validation by mass spectrometry. Mapping the in situ localization of 12,003 human proteins at a single-cell level to 30 subcellular structures enabled the definition of the proteomes of 13 major organelles. Exploration of the proteomes revealed single-cell variations in abundance or spatial distribution and localization of about half of the proteins to multiple compartments. This subcellular map can be used to refine existing protein-protein interaction networks and provides an important resource to deconvolute the highly complex architecture of the human cell.

  • 37.
    Uhlén, Mathias
    et al.
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101).
    Björling, Erik
    KTH, School of Biotechnology (BIO).
    Agaton, Charlotta
    KTH, School of Biotechnology (BIO).
    Al-Khalili Szigyarto, Cristina
    KTH, School of Biotechnology (BIO).
    Amini, Bahram
    KTH, School of Biotechnology (BIO).
    Andersen, Elisabet
    Uppsala Univ, Rudbeck Lab, Dept Genet & Pathol.
    Andersson, Ann-Catrin
    Uppsala Univ, Rudbeck Lab, Dept Genet & Pathol.
    Angelidou, Pia
    KTH, School of Biotechnology (BIO).
    Asplund, Anna
    Uppsala Univ, Rudbeck Lab, Dept Genet & Pathol.
    Asplund, Caroline
    KTH, School of Biotechnology (BIO).
    Berglund, Lisa
    KTH, School of Biotechnology (BIO).
    Bergström, Kristina
    KTH, School of Biotechnology (BIO).
    Brumer, Harry
    KTH, School of Biotechnology (BIO).
    Cerjan, Dijana
    Uppsala Univ, Rudbeck Lab, Dept Genet & Pathol.
    Ekström, Marica
    KTH, School of Biotechnology (BIO).
    Elobeid, Adila
    Uppsala Univ, Rudbeck Lab, Dept Genet & Pathol.
    Eriksson, Cecilia
    KTH, School of Biotechnology (BIO).
    Fagerberg, Linn
    KTH, School of Biotechnology (BIO).
    Falk, Ronny
    KTH, School of Biotechnology (BIO).
    Fall, Jenny
    KTH, School of Biotechnology (BIO).
    Forsberg, Mattias
    KTH, School of Biotechnology (BIO).
    Gry Björklund, Marcus
    KTH, School of Biotechnology (BIO).
    Gumbel, Kristoffer
    Uppsala Univ, Rudbeck Lab, Dept Genet & Pathol.
    Halimi, Asif
    KTH, School of Biotechnology (BIO).
    Hallin, Inga
    Uppsala Univ, Rudbeck Lab, Dept Genet & Pathol.
    Hamsten, Carl
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101).
    Hansson, Marianne
    KTH, School of Biotechnology (BIO).
    Hedhammar, My
    KTH, School of Biotechnology (BIO).
    Hercules, Görel
    Uppsala Univ, Rudbeck Lab, Dept Genet & Pathol.
    Kampf, Caroline
    Uppsala Univ, Rudbeck Lab, Dept Genet & Pathol.
    Larsson, Karin
    KTH, School of Biotechnology (BIO).
    Lindskog, Mats
    KTH, School of Biotechnology (BIO).
    Lodewyckx, Wald
    KTH, School of Biotechnology (BIO).
    Lund, Jan
    KTH, School of Biotechnology (BIO).
    Lundeberg, Joakim
    KTH, School of Biotechnology (BIO).
    Magnusson, Kristina
    Uppsala Univ, Rudbeck Lab, Dept Genet & Pathol.
    Malm, Erik
    KTH, School of Biotechnology (BIO).
    Nilsson, Peter
    KTH, School of Biotechnology (BIO).
    Ödling, Jenny
    KTH, School of Biotechnology (BIO).
    Oksvold, Per
    KTH, School of Biotechnology (BIO).
    Olsson, Ingmarie
    Uppsala Univ, Rudbeck Lab, Dept Genet & Pathol.
    Öster, Emma
    KTH, School of Biotechnology (BIO).
    Ottosson, Jenny
    KTH, School of Biotechnology (BIO).
    Paavilainen, Linda
    Uppsala Univ, Rudbeck Lab, Dept Genet & Pathol.
    Persson, Anja
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101).
    Rimini, Rebecca
    KTH, School of Biotechnology (BIO).
    Rockberg, Johan
    KTH, School of Biotechnology (BIO).
    Runeson, Marcus
    Uppsala Univ, Rudbeck Lab, Dept Genet & Pathol.
    Sivertsson, Åsa
    KTH, School of Biotechnology (BIO).
    Sköllermo, Anna
    KTH, School of Biotechnology (BIO).
    Steen, Johanna
    KTH, School of Biotechnology (BIO).
    Stenvall, Maria
    KTH, School of Biotechnology (BIO).
    Sterky, Fredrik
    KTH, School of Biotechnology (BIO).
    Strömberg, Sara
    Uppsala Univ, Rudbeck Lab, Dept Genet & Pathol.
    Sundberg, Mårten
    KTH, School of Biotechnology (BIO).
    Tegel, Hanna
    KTH, School of Biotechnology (BIO).
    Tourle, Samuel
    KTH, School of Biotechnology (BIO).
    Wahlund, Eva
    Uppsala Univ, Rudbeck Lab, Dept Genet & Pathol.
    Waldén, Annelie
    KTH, School of Biotechnology (BIO).
    Wan, Jinghong
    KTH, School of Biotechnology (BIO), Molecular Biotechnology (closed 20130101).
    Wernérus, Henrik
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101).
    Westberg, Joakim
    KTH, School of Biotechnology (BIO).
    Wester, Kenneth
    Uppsala Univ, Rudbeck Lab, Dept Genet & Pathol.
    Wrethagen, Ulla
    KTH, School of Biotechnology (BIO).
    Xu, Lan Lan
    KTH, School of Biotechnology (BIO).
    Hober, Sophia
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101).
    Pontén, Fredrik
    Uppsala Univ, Rudbeck Lab, Dept Genet & Pathol.
    A human protein atlas for normal and cancer tissues based on antibody proteomics2005In: Molecular & Cellular Proteomics, ISSN 1535-9476, E-ISSN 1535-9484, Vol. 4, no 12, p. 1920-1932Article in journal (Refereed)
    Abstract [en]

    Antibody-based proteomics provides a powerful approach for the functional study of the human proteome involving the systematic generation of protein-specific affinity reagents. We used this strategy to construct a comprehensive, antibody-based protein atlas for expression and localization profiles in 48 normal human tissues and 20 different cancers. Here we report a new publicly available database containing, in the first version, similar to 400,000 high resolution images corresponding to more than 700 antibodies toward human proteins. Each image has been annotated by a certified pathologist to provide a knowledge base for functional studies and to allow queries about protein profiles in normal and disease tissues. Our results suggest it should be possible to extend this analysis to the majority of all human proteins thus providing a valuable tool for medical and biological research.

  • 38.
    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.

  • 39.
    Uhlén, Mathias
    et al.
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Oksvold, Per
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101).
    Fagerberg, Linn
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101).
    Lundberg, Emma
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Biotechnology (BIO), Proteomics (closed 20130101).
    Jonasson, Kalle
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101).
    Forsberg, Mattias
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101).
    Zwahlen, Martin
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101).
    Kampf, Caroline
    Uppsala Univ, Rudbeck Lab, Dept Genet & Pathol.
    Wester, Kenneth
    Uppsala Univ, Rudbeck Lab, Dept Genet & Pathol.
    Hober, Sophia
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101).
    Wernérus, Henrik
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101).
    Björling, Lisa
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101).
    Pontén, Fredrik
    Uppsala Univ, Rudbeck Lab, Dept Genet & Pathol.
    Towards a knowledge-based Human Protein Atlas2010In: Nature Biotechnology, ISSN 1087-0156, E-ISSN 1546-1696, Vol. 28, no 12, p. 1248-1250Article in journal (Refereed)
  • 40.
    Uhlén, Mathias
    et al.
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Oksvold, Per
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101).
    Älgenäs, Cajsa
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101).
    Hamsten, Carl
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101).
    Fagerberg, Linn
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101).
    Klevebring, Daniel
    Department of Medical Epidemiology, Karolinska Institute.
    Lundberg, Emma
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Odeberg, Jacob
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Pontén, Fredrik
    Kondo, Tadashi
    Sivertsson, Åsa
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101).
    Antibody-based Protein Profiling of the Human Chromosome 212012In: Molecular & Cellular Proteomics, ISSN 1535-9476, E-ISSN 1535-9484, Vol. 11, no 3Article in journal (Refereed)
    Abstract [en]

    A Human Proteome Project has been proposed to create a knowledgebased resource based on a systematical mapping of all human proteins, chromosome by chromosome, in a gene-centric manner. With this background, we here describe the systematic analysis of chromosome 21 using an antibody-based approach for protein profiling using both confocal microscopy and immunohistochemistry, complemented with transcript profiling using next generation sequencing data. We also describe a new approach for protein isoform analysis using a combination of antibody-based probing and isoelectric focusing. The analysis has identified several genes on chromosome 21 with no previous evidence on the protein level and the isoform analysis indicates that a large fraction of human proteins have multiple isoforms. A chromosome-wide matrix is presented with status for all chromosome 21 genes regarding subcellular localization, tissue distribution and molecular characterization of the corresponding proteins. The path to generate a chromosome-specific resource, including integrated data from complementary assay platforms, such as mass spectrometry and gene tagging analysis, is discussed.

  • 41.
    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.

  • 42. Yu, Nancy Yiu-Lin
    et al.
    Hallström, Björn M.
    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.
    Ponten, Fredrik
    Kawaji, Hideya
    Carninci, Piero
    Forrest, Alistair R. R.
    Hayashizaki, Yoshihide
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Daub, Carsten O.
    Complementing tissue characterization by integrating transcriptome profiling from the Human Protein Atlas and from the FANTOM5 consortium2015In: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 43, no 14, p. 6787-6798Article in journal (Refereed)
    Abstract [en]

    Understanding the normal state of human tissue transcriptome profiles is essential for recognizing tissue disease states and identifying disease markers. Recently, the Human Protein Atlas and the FANTOM5 consortium have each published extensive transcriptome data for human samples using Illumina-sequenced RNA-Seq and Heliscope-sequenced CAGE. Here, we report on the first large-scale complex tissue transcriptome comparison between full-length versus 5'-capped mRNA sequencing data. Overall gene expression correlation was high between the 22 corresponding tissues analyzed (R > 0.8). For genes ubiquitously expressed across all tissues, the two data sets showed high genome-wide correlation (91% agreement), with differences observed for a small number of individual genes indicating the need to update their gene models. Among the identified single-tissue enriched genes, up to 75% showed consensus of 7-fold enrichment in the same tissue in both methods, while another 17% exhibited multiple tissue enrichment and/or high expression variety in the other data set, likely dependent on the cell type proportions included in each tissue sample. Our results show that RNA-Seq and CAGE tissue transcriptome data sets are highly complementary for improving gene model annotations and highlight biological complexities within tissue transcriptomes. Furthermore, integration with image-based protein expression data is highly advantageous for understanding expression specificities for many genes.

  • 43.
    Älgenäs, Cajsa
    et al.
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Agaton, Charlotta
    Fagerberg, Linn
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Asplund, Anna
    Björling, Lisa
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Björling, Erik
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Kampf, Caroline
    Lundberg, Emma
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Nilsson, Peter
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Persson, Anja
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Wester, Kenneth
    Pontén, Fredrik
    Wernerus, Henrik
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Ottosson Takanen, Jenny
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Hober, Sophia
    KTH, School of Biotechnology (BIO), Protein Technology.
    Antibody performance in western blot applications is context- dependent2014In: Biotechnology Journal, ISSN 1860-6768, E-ISSN 1860-7314, Vol. 9, no 3, p. 435-445Article in journal (Refereed)
    Abstract [en]

    An important concern for the use of antibodies in various applications, such as western blot (WB) or immunohistochemistry (IHC), is specificity. This calls for systematic validations using well-designed conditions. Here, we have analyzed 13000 antibodies using western blot with lysates from human cell lines, tissues, and plasma. Standardized stratification showed that 45% of the antibodies yielded supportive staining, and the rest either no staining (12%) or protein bands of wrong size (43%). A comparative study of WB and IHC showed that the performance of antibodies is application-specific, although a correlation between no WB staining and weak IHC staining could be seen. To investigate the influence of protein abundance on the apparent specificity of the antibody, new WB analyses were performed for 1369 genes that gave unsupportive WBs in the initial screening using cell lysates with overexpressed full-length proteins. Then, more than 82% of the antibodies yielded a specific band corresponding to the full-length protein. Hence, the vast majority of the antibodies (90%) used in this study specifically recognize the target protein when present at sufficiently high levels. This demonstrates the context- and application-dependence of antibody validation and emphasizes that caution is needed when annotating binding reagents as specific or cross-reactive. WB is one of the most commonly used methods for validation of antibodies. Our data implicate that solely using one platform for antibody validation might give misleading information and therefore at least one additional method should be used to verify the achieved data.

1 - 43 of 43
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