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Shi, M., Shi, M., Karlsson, M., Alvez, M. B., Jin, H., Yuan, M., . . . et al., . (2025). A resource for whole-body gene expression map of human tissues based on integration of single cell and bulk transcriptomics. Genome Biology, 26(1), Article ID 152.
Open this publication in new window or tab >>A resource for whole-body gene expression map of human tissues based on integration of single cell and bulk transcriptomics
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2025 (English)In: Genome Biology, ISSN 1465-6906, E-ISSN 1474-760X, Vol. 26, no 1, article id 152Article in journal (Refereed) Published
Abstract [en]

New technologies enable single-cell transcriptome analysis, mapping genome-wide expression across the human body. Here, we present an extended analysis of protein-coding genes in all major human tissues and organs, combining single-cell and bulk transcriptomics. To enhance transcriptome depth, 31 tissues were analyzed using a pooling method, identifying 557 unique cell clusters, manually annotated by marker gene expression. Genes were classified by body-wide expression and validated through antibody-based profiling. All results are available in the updated open-access Single Cell Type section of the Human Protein Atlas for genome-wide exploration of genes, proteins, and their spatial distribution in cells.

Place, publisher, year, edition, pages
Springer Nature, 2025
Keywords
Cell type classification, Gene expression mapping, Human Protein Atlas, Single-cell
National Category
Bioinformatics and Computational Biology Cell and Molecular Biology Medical Genetics and Genomics Medical Biotechnology (Focus on Cell Biology, (incl. Stem Cell Biology), Molecular Biology, Microbiology, Biochemistry or Biopharmacy)
Identifiers
urn:nbn:se:kth:diva-366187 (URN)10.1186/s13059-025-03616-4 (DOI)001502167900001 ()40462185 (PubMedID)2-s2.0-105007441526 (Scopus ID)
Note

Not duplicate with DiVA 1959447

QC 20250707

Available from: 2025-07-07 Created: 2025-07-07 Last updated: 2025-07-07Bibliographically approved
Karlsson, M., Sjostedt, E., Oksvold, P., Sivertsson, Å., Huang, J., Alvez, M. B., . . . Uhlén, M. (2022). Genome-wide annotation of protein-coding genes in pig. BMC Biology, 20(1), Article ID 25.
Open this publication in new window or tab >>Genome-wide annotation of protein-coding genes in pig
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2022 (English)In: BMC Biology, E-ISSN 1741-7007, Vol. 20, no 1, article id 25Article in journal (Refereed) Published
Abstract [en]

Background: There is a need for functional genome-wide annotation of the protein-coding genes to get a deeper understanding of mammalian biology. Here, a new annotation strategy is introduced based on dimensionality reduction and density-based clustering of whole-body co-expression patterns. This strategy has been used to explore the gene expression landscape in pig, and we present a whole-body map of all protein-coding genes in all major pig tissues and organs. Results: An open-access pig expression map (www.rnaatlas.org ) is presented based on the expression of 350 samples across 98 well-defined pig tissues divided into 44 tissue groups. A new UMAP-based classification scheme is introduced, in which all protein-coding genes are stratified into tissue expression clusters based on body-wide expression profiles. The distribution and tissue specificity of all 22,342 protein-coding pig genes are presented. Conclusions: Here, we present a new genome-wide annotation strategy based on dimensionality reduction and density-based clustering. A genome-wide resource of the transcriptome map across all major tissues and organs in pig is presented, and the data is available as an open-access resource (www.rnaatlas.org), including a comparison to the expression of human orthologs.

Place, publisher, year, edition, pages
Springer Nature, 2022
Keywords
Annotation, Protein coding genes, Genome wide, Transcriptome, Gene expression, Tissue expression profile
National Category
Biochemistry Molecular Biology Medical Biotechnology Bioinformatics and Computational Biology
Identifiers
urn:nbn:se:kth:diva-307759 (URN)10.1186/s12915-022-01229-y (DOI)000746863800002 ()35073880 (PubMedID)2-s2.0-85123754738 (Scopus ID)
Note

QC 20220209

Available from: 2022-02-09 Created: 2022-02-09 Last updated: 2025-02-20Bibliographically approved
Zhong, W., Barde, S., Mitsios, N., Adori, C., Oksvold, P., von Feilitzen, K., . . . Hokfelt, T. (2022). The neuropeptide landscape of human prefrontal cortex. Proceedings of the National Academy of Sciences of the United States of America, 119(33), Article ID e2123146119.
Open this publication in new window or tab >>The neuropeptide landscape of human prefrontal cortex
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2022 (English)In: Proceedings of the National Academy of Sciences of the United States of America, ISSN 0027-8424, E-ISSN 1091-6490, Vol. 119, no 33, article id e2123146119Article in journal (Refereed) Published
Abstract [en]

Human prefrontal cortex (hPFC) is a complex brain region involved in cognitive and emotional processes and several psychiatric disorders. Here, we present an overview of the distribution of the peptidergic systems in 17 subregions of hPFC and three reference cortices obtained by microdissection and based on RNA sequencing and RNA-scope methods integrated with published single-cell transcriptomics data. We detected expression of 60 neuropeptides and 60 neuropeptide receptors in at least one of the hPFC subregions. The results reveal that the peptidergic landscape in PFC consists of closely located and functionally different subregions with unique peptide/transmitter- related profiles. Neuropeptide-rich PFC subregions were identified, encompassing regions from anterior cingulate cortex/orbitofrontal gyrus. Furthermore, marked differences in gene expression exist between different PFC regions (>5-fold; cocaine and amphetamine-regulated transcript peptide) as well as between PFC regions and reference regions, for example, for somatostatin and several receptors. We suggest that the present approach allows definition of, still hypothetical, microcircuits exemplified by glutamatergic neurons expressing a peptide cotransmitter either as an agonist (hypocretin/orexin) or antagonist (galanin). Specific neuropeptide receptors have been identified as possible targets for neuronal afferents and, interestingly, peripheral blood-borne peptide hormones (leptin, adiponectin, gastric inhibitory peptide, glucagon-like peptides, and peptide YY). Together with other recent publications, our results support the view that neuropeptide systems may play an important role in hPFC and underpin the concept that neuropeptide signaling helps stabilize circuit connectivity and fine-tune/modulate PFC functions executed during health and disease.

Place, publisher, year, edition, pages
Proceedings of the National Academy of Sciences, 2022
Keywords
anterior cingulate cortex, in situ hybridization, RNA-seq, classic neurotransmitter coexistence
National Category
Neurosciences
Identifiers
urn:nbn:se:kth:diva-322611 (URN)10.1073/pnas.2123146119 (DOI)000891285200003 ()35947618 (PubMedID)2-s2.0-85135939903 (Scopus ID)
Note

QC 20221223

Available from: 2022-12-23 Created: 2022-12-23 Last updated: 2023-12-07Bibliographically approved
Karlsson, M., Zhang, C., Mear, L., Zhong, W., Digre, A., Katona, B., . . . Lindskog, C. (2021). A single-cell type transcriptomics map of human tissues. Science Advances, 7(31), Article ID eabh2169.
Open this publication in new window or tab >>A single-cell type transcriptomics map of human tissues
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2021 (English)In: Science Advances, E-ISSN 2375-2548, Vol. 7, no 31, article id eabh2169Article in journal (Refereed) Published
Abstract [en]

Advances in molecular profiling have opened up the possibility to map the expression of genes in cells, tissues, and organs in the human body. Here, we combined single-cell transcriptomics analysis with spatial antibody-based protein profiling to create a high-resolution single-cell type map of human tissues. An open access atlas has been launched to allow researchers to explore the expression of human protein-coding genes in 192 individual cell type clusters. An expression specificity classification was performed to determine the number of genes elevated in each cell type, allowing comparisons with bulk transcriptomics data. The analysis highlights distinct expression clusters corresponding to cell types sharing similar functions, both within the same organs and between organs.

Place, publisher, year, edition, pages
American Association for the Advancement of Science (AAAS), 2021
National Category
Biochemistry Molecular Biology
Identifiers
urn:nbn:se:kth:diva-299689 (URN)10.1126/sciadv.abh2169 (DOI)000678723800005 ()34321199 (PubMedID)2-s2.0-85111485342 (Scopus ID)
Note

QC 20210817

Available from: 2021-08-17 Created: 2021-08-17 Last updated: 2025-02-20Bibliographically approved
Li, X., Kim, W., Arif, M., Gao, C., Hober, A., Kotol, D., . . . Mardinoglu, A. (2021). Discovery of Functional Alternatively Spliced PKM Transcripts in Human Cancers. Cancers, 13(2), Article ID 348.
Open this publication in new window or tab >>Discovery of Functional Alternatively Spliced PKM Transcripts in Human Cancers
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2021 (English)In: Cancers, ISSN 2072-6694, Vol. 13, no 2, article id 348Article in journal (Refereed) Published
Abstract [en]

Simple Summary Pyruvate kinase muscle type (PKM) is a key enzyme in glycolysis and is a mediator of the Warburg effect in tumors. The association of PKM with survival of cancer patients is controversial. In this study, we investigated the associations of the alternatively spliced transcripts of PKM with cancer patients' survival outcomes and explained the conflicts in previous studies. We discovered three poorly studied alternatively spliced PKM transcripts that exhibited opposite prognostic indications in different human cancers based on integrative systems analysis. We also detected their protein products and explored their potential biological functions based on in-vitro experiments. Our analysis demonstrated that alternatively spliced transcripts of not only PKM but also other genes should be considered in cancer studies, since it may enable the discovery and targeting of the right protein product for development of the efficient treatment strategies. Pyruvate kinase muscle type (PKM) is a key enzyme in glycolysis and plays an important oncological role in cancer. However, the association of PKM expression and the survival outcome of patients with different cancers is controversial. We employed systems biology methods to reveal prognostic value and potential biological functions of PKM transcripts in different human cancers. Protein products of transcripts were shown and detected by western blot and mass spectrometry analysis. We focused on different transcripts of PKM and investigated the associations between their mRNA expression and the clinical survival of the patients in 25 different cancers. We find that the transcripts encoding PKM2 and three previously unstudied transcripts, namely ENST00000389093, ENST00000568883, and ENST00000561609, exhibited opposite prognostic indications in different cancers. Moreover, we validated the prognostic effect of these transcripts in an independent kidney cancer cohort. Finally, we revealed that ENST00000389093 and ENST00000568883 possess pyruvate kinase enzymatic activity and may have functional roles in metabolism, cell invasion, and hypoxia response in cancer cells. Our study provided a potential explanation to the controversial prognostic indication of PKM, and could invoke future studies focusing on revealing the biological and oncological roles of these alternative spliced variants of PKM.

Place, publisher, year, edition, pages
MDPI AG, 2021
Keywords
alternative splicing, cancer, PKM, transcriptomics
National Category
Cancer and Oncology
Identifiers
urn:nbn:se:kth:diva-289916 (URN)10.3390/cancers13020348 (DOI)000611155000001 ()33478099 (PubMedID)2-s2.0-85100137584 (Scopus ID)
Note

QC 20210211

Available from: 2021-02-11 Created: 2021-02-11 Last updated: 2023-12-07Bibliographically approved
Grapotte, M., Forsberg, M., Oksvold, P., Sivertsson, Å., Sjöstedt, E., Uhlén, M., . . . et al., . (2021). Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network. Nature Communications, 12(1), Article ID 3297.
Open this publication in new window or tab >>Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network
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2021 (English)In: Nature Communications, E-ISSN 2041-1723, Vol. 12, no 1, article id 3297Article in journal (Refereed) Published
Abstract [en]

Using the Cap Analysis of Gene Expression (CAGE) technology, the FANTOM5 consortium provided one of the most comprehensive maps of transcription start sites (TSSs) in several species. Strikingly, ~72% of them could not be assigned to a specific gene and initiate at unconventional regions, outside promoters or enhancers. Here, we probe these unassigned TSSs and show that, in all species studied, a significant fraction of CAGE peaks initiate at microsatellites, also called short tandem repeats (STRs). To confirm this transcription, we develop Cap Trap RNA-seq, a technology which combines cap trapping and long read MinION sequencing. We train sequence-based deep learning models able to predict CAGE signal at STRs with high accuracy. These models unveil the importance of STR surrounding sequences not only to distinguish STR classes, but also to predict the level of transcription initiation. Importantly, genetic variants linked to human diseases are preferentially found at STRs with high transcription initiation level, supporting the biological and clinical relevance of transcription initiation at STRs. Together, our results extend the repertoire of non-coding transcription associated with DNA tandem repeats and complexify STR polymorphism.

Place, publisher, year, edition, pages
Springer Nature, 2021
National Category
Biochemistry Molecular Biology
Identifiers
urn:nbn:se:kth:diva-309717 (URN)10.1038/s41467-021-23143-7 (DOI)000660869500001 ()34078885 (PubMedID)2-s2.0-85107388625 (Scopus ID)
Note

Correction in: DOI 10.1038/s41467-022-28758-y, WOS:000771136200018

QC 20250402

Available from: 2022-03-09 Created: 2022-03-09 Last updated: 2025-04-02Bibliographically approved
Sjöstedt, E., Zhong, W., Fagerberg, L., Karlsson, M., Mitsios, N., Adori, C., . . . Mulder, J. (2020). An atlas of the protein-coding genes in the human, pig, and mouse brain. Science, 367(6482), 1090-+, Article ID eaay5947.
Open this publication in new window or tab >>An atlas of the protein-coding genes in the human, pig, and mouse brain
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2020 (English)In: Science, ISSN 0036-8075, E-ISSN 1095-9203, Vol. 367, no 6482, p. 1090-+, article id eaay5947Article in journal (Refereed) Published
Abstract [en]

The brain, with its diverse physiology and intricate cellular organization, is the most complex organ of the mammalian body. To expand our basic understanding of the neurobiology of the brain and its diseases, we performed a comprehensive molecular dissection of 10 major brain regions and multiple subregions using a variety of transcriptomics methods and antibody-based mapping. This analysis was carried out in the human, pig, and mouse brain to allow the identification of regional expression profiles, as well as to study similarities and differences in expression levels between the three species. The resulting data have been made available in an open-access Brain Atlas resource, part of the Human Protein Atlas, to allow exploration and comparison of the expression of individual protein-coding genes in various parts of the mammalian brain.

Place, publisher, year, edition, pages
American Association for the Advancement of Science (AAAS), 2020
National Category
Medical Genetics and Genomics
Identifiers
urn:nbn:se:kth:diva-271745 (URN)10.1126/science.aay5947 (DOI)000520018400034 ()32139519 (PubMedID)2-s2.0-85081532587 (Scopus ID)
Note

QC 20200408

Available from: 2020-04-08 Created: 2020-04-08 Last updated: 2025-02-10Bibliographically approved
Sivertsson, Å., Lindström, E., Oksvold, P., Katona, B., Hikmet, F., Vuu, J., . . . Lindskog, C. (2020). Enhanced Validation of Antibodies Enables the Discovery of Missing Proteins. Journal of Proteome Research, 19(12), 4766-4781
Open this publication in new window or tab >>Enhanced Validation of Antibodies Enables the Discovery of Missing Proteins
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2020 (English)In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 19, no 12, p. 4766-4781Article in journal (Refereed) Published
Abstract [en]

The localization of proteins at a tissue- or cell-type-specific level is tightly linked to the protein function. To better understand each protein's role in cellular systems, spatial information constitutes an important complement to quantitative data. The standard methods for determining the spatial distribution of proteins in single cells of complex tissue samples make use of antibodies. For a stringent analysis of the human proteome, we used orthogonal methods and independent antibodies to validate 5981 antibodies that show the expression of 3775 human proteins across all major human tissues. This enhanced validation uncovered 56 proteins corresponding to the group of "missing proteins" and 171 proteins of unknown function. The presented strategy will facilitate further discussions around criteria for evidence of protein existence based on immunohistochemistry and serves as a useful guide to identify candidate proteins for integrative studies with quantitative proteomics methods.

Place, publisher, year, edition, pages
American Chemical Society (ACS), 2020
Keywords
antibody-based proteomics, missing proteins, protein evidence, immunohistochemistry, transcriptomics, antibody validation, human proteome
National Category
Biochemistry Molecular Biology
Identifiers
urn:nbn:se:kth:diva-289061 (URN)10.1021/acs.jproteome.0c00486 (DOI)000598145200005 ()33170010 (PubMedID)2-s2.0-85096605899 (Scopus ID)
Note

QC 20210125

Available from: 2021-01-25 Created: 2021-01-25 Last updated: 2025-02-20Bibliographically approved
Aarestrup, F. M., Auffray, C., Benhabiles, N., Blomberg, N., Korbel, J. O., Oksvold, P. & Van Oyen, H. (2020). Towards a European health research and innovation cloud (HRIC). Genome Medicine, 12(1), Article ID 18.
Open this publication in new window or tab >>Towards a European health research and innovation cloud (HRIC)
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2020 (English)In: Genome Medicine, E-ISSN 1756-994X, Vol. 12, no 1, article id 18Article in journal (Refereed) Published
Abstract [en]

The European Union (EU) initiative on the Digital Transformation of Health and Care (Digicare) aims to provide the conditions necessary for building a secure, flexible, and decentralized digital health infrastructure. Creating a European Health Research and Innovation Cloud (HRIC) within this environment should enable data sharing and analysis for health research across the EU, in compliance with data protection legislation while preserving the full trust of the participants. Such a HRIC should learn from and build on existing data infrastructures, integrate best practices, and focus on the concrete needs of the community in terms of technologies, governance, management, regulation, and ethics requirements. Here, we describe the vision and expected benefits of digital data sharing in health research activities and present a roadmap that fosters the opportunities while answering the challenges of implementing a HRIC. For this, we put forward five specific recommendations and action points to ensure that a European HRIC: i) is built on established standards and guidelines, providing cloud technologies through an open and decentralized infrastructure; ii) is developed and certified to the highest standards of interoperability and data security that can be trusted by all stakeholders; iii) is supported by a robust ethical and legal framework that is compliant with the EU General Data Protection Regulation (GDPR); iv) establishes a proper environment for the training of new generations of data and medical scientists; and v) stimulates research and innovation in transnational collaborations through public and private initiatives and partnerships funded by the EU through Horizon 2020 and Horizon Europe.

Place, publisher, year, edition, pages
BMC, 2020
National Category
Information Systems, Social aspects
Identifiers
urn:nbn:se:kth:diva-271289 (URN)10.1186/s13073-020-0713-z (DOI)000517302600001 ()32075696 (PubMedID)2-s2.0-85079743802 (Scopus ID)
Note

QC 20200402

Available from: 2020-04-02 Created: 2020-04-02 Last updated: 2024-07-04Bibliographically approved
Uhlén, M., Karlsson, M., Zhong, W., Abdellah, T., Pou, C., Mikes, J., . . . Brodin, P. (2019). A genome-wide transcriptomic analysis of protein-coding genes in human blood cells. Science, 366(6472), 1471-+, Article ID eaax9198.
Open this publication in new window or tab >>A genome-wide transcriptomic analysis of protein-coding genes in human blood cells
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2019 (English)In: Science, ISSN 0036-8075, E-ISSN 1095-9203, Vol. 366, no 6472, p. 1471-+, article id eaax9198Article in journal (Refereed) Published
Abstract [en]

Blood is the predominant source for molecular analyses in humans, both in clinical and research settings. It is the target for many therapeutic strategies, emphasizing the need for comprehensive molecular maps of the cells constituting human blood. In this study, we performed a genome-wide transcriptomic analysis of protein-coding genes in sorted blood immune cell populations to characterize the expression levels of each individual gene across the blood cell types. All data are presented in an interactive, open-access Blood Atlas as part of the Human Protein Atlas and are integrated with expression profiles across all major tissues to provide spatial classification of all protein-coding genes. This allows for a genome-wide exploration of the expression profiles across human immune cell populations and all major human tissues and organs.

Place, publisher, year, edition, pages
American Association for the Advancement of Science, 2019
National Category
Genetics and Genomics
Identifiers
urn:nbn:se:kth:diva-266527 (URN)10.1126/science.aax9198 (DOI)000503861000045 ()31857451 (PubMedID)2-s2.0-85077091174 (Scopus ID)
Note

QC 20200205

Available from: 2020-02-05 Created: 2020-02-05 Last updated: 2025-02-07Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-3014-5502

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