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Publications (10 of 101) Show all publications
Lundberg, E. & Borner, G. H. H. (2019). Spatial proteomics: a powerful discovery tool for cell biology. Nature reviews. Molecular cell biology, 20(5), 285-302
Open this publication in new window or tab >>Spatial proteomics: a powerful discovery tool for cell biology
2019 (English)In: Nature reviews. Molecular cell biology, ISSN 1471-0072, E-ISSN 1471-0080, Vol. 20, no 5, p. 285-302Article, review/survey (Refereed) Published
Abstract [en]

Protein subcellular localization is tightly controlled and intimately linked to protein function in health and disease. Capturing the spatial proteome - that is, the localizations of proteins and their dynamics at the subcellular level - is therefore essential for a complete understanding of cell biology. Owing to substantial advances in microscopy, mass spectrometry and machine learning applications for data analysis, the field is now mature for proteome-wide investigations of spatial cellular regulation. Studies of the human proteome have begun to reveal a complex architecture, including single-cell variations, dynamic protein translocations, changing interaction networks and proteins localizing to multiple compartments. Furthermore, several studies have successfully harnessed the power of comparative spatial proteomics as a discovery tool to unravel disease mechanisms. We are at the beginning of an era in which spatial proteomics finally integrates with cell biology and medical research, thereby paving the way for unbiased systems-level insights into cellular processes. Here, we discuss current methods for spatial proteomics using imaging or mass spectrometry and specifically highlight global comparative applications. The aim of this Review is to survey the state of the field and also to encourage more cell biologists to apply spatial proteomics approaches.

Place, publisher, year, edition, pages
NATURE PUBLISHING GROUP, 2019
Keywords
DUVE C, 1964, JOURNAL OF THEORETICAL BIOLOGY, V6, P33 13, NAT METHODS, V10, P315 11, NAT CELL BIOL, V13, P331 18, MOL CELL, V69, P517 mon A. A, 2018, 17, CELL, V171, P133 17, NATURE, V546, P431 16, NAT METHODS, V13, P837 16, TRENDS CELL BIOL, V26, P804 nworth Marcellus J, 2018, Small GTPases, V9, P158 ll Sophia, 2017, NATURE COMMUNICATIONS, V8, n Shuo, 2018, CURRENT OPINION IN NEUROBIOLOGY, V50, P17 sp Fabian, 2017, NEURON, V96, P558 hl Steffen J., 2017, NATURE REVIEWS MOLECULAR CELL BIOLOGY, V18, P685 berts Brock, 2017, MOLECULAR BIOLOGY OF THE CELL, V28, P2854 zhak Daniel N., 2017, CELL REPORTS, V20, P2706 icedo Juan C., 2017, NATURE METHODS, V14, P849 ng Siyu, 2017, NATURE COMMUNICATIONS, V8, len Mathias, 2017, SCIENCE, V357, P660 18, NAT PROTOC, V13, P1445 zal F. M, 2018, lenberg J, 2018, 16, CELL SYST, V3, P361 eiroz R. M. L, 2018, 15, NAT NEUROSCI, V18, P1819
National Category
Biological Sciences
Identifiers
urn:nbn:se:kth:diva-252630 (URN)10.1038/s41580-018-0094-y (DOI)000465500200008 ()30659282 (PubMedID)2-s2.0-85060195232 (Scopus ID)
Note

QC 20190603

Available from: 2019-06-03 Created: 2019-06-03 Last updated: 2019-06-03Bibliographically approved
Sullivan, D. P., Winsnes, C. F., Åkesson, L., Hjelmare, M., Wiking, M., Schutten, R., . . . Lundberg, E. (2018). Deep learning is combined with massive-scale citizen science to improve large-scale image classification. Nature Biotechnology, 36(9), 820-+
Open this publication in new window or tab >>Deep learning is combined with massive-scale citizen science to improve large-scale image classification
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2018 (English)In: Nature Biotechnology, ISSN 1087-0156, E-ISSN 1546-1696, Vol. 36, no 9, p. 820-+Article in journal (Refereed) Published
Abstract [en]

Pattern recognition and classification of images are key challenges throughout the life sciences. We combined two approaches for large-scale classification of fluorescence microscopy images. First, using the publicly available data set from the Cell Atlas of the Human Protein Atlas (HPA), we integrated an image-classification task into a mainstream video game (EVE Online) as a mini-game, named Project Discovery. Participation by 322,006 gamers over 1 year provided nearly 33 million classifications of subcellular localization patterns, including patterns that were not previously annotated by the HPA. Second, we used deep learning to build an automated Localization Cellular Annotation Tool (Loc-CAT). This tool classifies proteins into 29 subcellular localization patterns and can deal efficiently with multi-localization proteins, performing robustly across different cell types. Combining the annotations of gamers and deep learning, we applied transfer learning to create a boosted learner that can characterize subcellular protein distribution with F1 score of 0.72. We found that engaging players of commercial computer games provided data that augmented deep learning and enabled scalable and readily improved image classification.

Place, publisher, year, edition, pages
NATURE PUBLISHING GROUP, 2018
National Category
Biological Sciences
Identifiers
urn:nbn:se:kth:diva-235602 (URN)10.1038/nbt.4225 (DOI)000443986000023 ()30125267 (PubMedID)2-s2.0-85053076602 (Scopus ID)
Note

QC 20181001

Available from: 2018-10-01 Created: 2018-10-01 Last updated: 2018-10-01Bibliographically approved
Aebersold, R., Agar, J. N., Amster, I. J., Baker, M. S., Bertozzi, C. R., Boja, E. S., . . . Zhang, B. (2018). How many human proteoforms are there?. Nature Chemical Biology, 14(3), 206-214
Open this publication in new window or tab >>How many human proteoforms are there?
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2018 (English)In: Nature Chemical Biology, ISSN 1552-4450, E-ISSN 1552-4469, Vol. 14, no 3, p. 206-214Article in journal (Refereed) Published
Abstract [en]

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

Place, publisher, year, edition, pages
NATURE PUBLISHING GROUP, 2018
National Category
Biological Sciences
Identifiers
urn:nbn:se:kth:diva-223782 (URN)10.1038/NCHEMBIO.2576 (DOI)000425093100012 ()29443976 (PubMedID)2-s2.0-85042114532 (Scopus ID)
Note

QC 2018307

Available from: 2018-03-07 Created: 2018-03-07 Last updated: 2018-03-07Bibliographically approved
Sullivan, D. P. & Lundberg, E. (2018). Seeing More: A Future of Augmented Microscopy. Cell, 173(3), 546-548
Open this publication in new window or tab >>Seeing More: A Future of Augmented Microscopy
2018 (English)In: Cell, ISSN 0092-8674, E-ISSN 1097-4172, Vol. 173, no 3, p. 546-548Article in journal (Refereed) Published
Abstract [en]

Microscope images are information rich. In this issue of Cell, Christiansen et al. show that label-free images of cells can be used to predict fluorescent labels representing cell type, state, and organelle distribution using a deep-learning framework. This paves the way for computationally multiplexed assays derived from inexpensive label-free microscopy. Microscope images are information rich. In this issue of Cell, Christiansen et al. show that label-free images of cells can be used to predict fluorescent labels representing cell type, state, and organelle distribution using a deep-learning framework. This paves the way for computationally multiplexed assays derived from inexpensive label-free microscopy.

Place, publisher, year, edition, pages
Cell Press, 2018
National Category
Biochemistry and Molecular Biology
Identifiers
urn:nbn:se:kth:diva-227606 (URN)10.1016/j.cell.2018.04.003 (DOI)000430677400006 ()29677507 (PubMedID)2-s2.0-85045300908 (Scopus ID)
Funder
Science for Life Laboratory - a national resource center for high-throughput molecular bioscience
Note

QC 20180509

Available from: 2018-05-09 Created: 2018-05-09 Last updated: 2018-05-14Bibliographically approved
Danielsson, F., Fasterius, E., Sullivan, D., Hases, L., Sanli, K., Zhang, C., . . . Lundberg, E. (2018). Transcriptome profiling of the interconnection of pathways involved in malignant transformation and response to hypoxia. OncoTarget, 9(28), 19730-19744
Open this publication in new window or tab >>Transcriptome profiling of the interconnection of pathways involved in malignant transformation and response to hypoxia
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2018 (English)In: OncoTarget, ISSN 1949-2553, E-ISSN 1949-2553, Vol. 9, no 28, p. 19730-19744Article in journal (Refereed) Published
Abstract [en]

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

Place, publisher, year, edition, pages
Impact Journals LLC, 2018
Keywords
Hypoxia, Malignant transformation, Transcriptomics
National Category
Cell and Molecular Biology
Identifiers
urn:nbn:se:kth:diva-227616 (URN)10.18632/oncotarget.24808 (DOI)2-s2.0-85045315705 (Scopus ID)
Funder
Science for Life Laboratory - a national resource center for high-throughput molecular bioscience
Note

QC 20180522

Available from: 2018-05-22 Created: 2018-05-22 Last updated: 2019-04-26Bibliographically approved
Carreras-Puigvert, J., Zitnik, M., Jemth, A.-S. -., Carter, M., Unterlass, J. E., Hallström, B. M., . . . Helleday, T. (2017). A comprehensive structural, biochemical and biological profiling of the human NUDIX hydrolase family. Nature Communications, 8(1), Article ID 1541.
Open this publication in new window or tab >>A comprehensive structural, biochemical and biological profiling of the human NUDIX hydrolase family
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2017 (English)In: Nature Communications, ISSN 2041-1723, E-ISSN 2041-1723, Vol. 8, no 1, article id 1541Article in journal (Refereed) Published
Abstract [en]

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

Place, publisher, year, edition, pages
Nature Publishing Group, 2017
Keywords
Nudix hydrolase, NUDT1 protein, NUDT10 protein, NUDT11 protein, NUDT12 protein, NUDT13 protein, NUDT14 protein, NUDT16 protein, NUDT17 protein, NUDT19 protein, NUDT20 protein, NUDT21 protein, NUDT22 protein, NUDT4 protein, NUDT5 protein, NUDT6 protein, NUDT7 protein, NUDT8 protein, unclassified drug, algorithm, biochemical composition, cancer, enzyme, enzyme activity, gene expression, homeostasis, metabolism, protein, A-549 cell line, Article, breast cancer, cancer cell line, cancer tissue, cell cycle, cell cycle regulation, cell survival, colorectal cancer, controlled study, DNA content, enzyme structure, functional genomics, gene interaction, gene silencing, human, human cell, human tissue, immunohistochemistry, melanoma, phylogenetic tree, protein expression, sequence alignment, tissue microarray, upregulation
National Category
Medical Biotechnology (with a focus on Cell Biology (including Stem Cell Biology), Molecular Biology, Microbiology, Biochemistry or Biopharmacy)
Identifiers
urn:nbn:se:kth:diva-227131 (URN)10.1038/s41467-017-01642-w (DOI)2-s2.0-85034433549 (Scopus ID)
Note

QC 20180503

Available from: 2018-05-03 Created: 2018-05-03 Last updated: 2018-05-03Bibliographically approved
Thul, P. J., Åkesson, L., Wiking, M., Mahdessian, D., Geladaki, A., Ait Blal, H., . . . Lundberg, E. (2017). A subcellular map of the human proteome. Science, 356(6340), Article ID 820.
Open this publication in new window or tab >>A subcellular map of the human proteome
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2017 (English)In: Science, ISSN 0036-8075, E-ISSN 1095-9203, Vol. 356, no 6340, article id 820Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
American Association for the Advancement of Science, 2017
Keywords
antibody, proteome, biology, cells and cell components, disease incidence, image analysis, physiological response, protein, proteomics, spatial distribution, Article, cell organelle, cellular distribution, human, human cell, immunofluorescence microscopy, mass spectrometry, priority journal, protein analysis, protein localization, protein protein interaction, single cell analysis, transcriptomics
National Category
Cell Biology
Identifiers
urn:nbn:se:kth:diva-216588 (URN)10.1126/science.aal3321 (DOI)000401957900032 ()2-s2.0-85019201137 (Scopus ID)
Note

QC 20171208

Available from: 2017-12-08 Created: 2017-12-08 Last updated: 2017-12-08Bibliographically approved
Skogs, M., Stadler, C., Schutten, R., Hjelmare, M., Gnann, C., Björk, L., . . . Lundberg, E. (2017). Antibody Validation in Bioimaging Applications Based on Endogenous Expression of Tagged Proteins. Journal of Proteome Research, 16(1), 147-155
Open this publication in new window or tab >>Antibody Validation in Bioimaging Applications Based on Endogenous Expression of Tagged Proteins
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2017 (English)In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 16, no 1, p. 147-155Article in journal (Refereed) Published
Abstract [en]

Antibodies are indispensible research tools, yet the scientific community has not adopted standardized procedures to validate their specificity. Here we present a strategy to systematically validate antibodies for immunofluorescence (IF) applications using gene tagging. We have assessed the on- and off-target binding capabilities of 197 antibodies using 108 cell lines expressing EGFP-tagged target proteins at endogenous levels. Furthermore, we assessed batch-to-batch effects for 35 target proteins, showing that both the on- and off-target binding patterns vary significantly between antibody batches and that the proposed strategy serves as a reliable procedure for ensuring reproducibility upon production of new antibody batches. In summary, we present a systematic scheme for antibody validation in IF applications using endogenous expression of tagged proteins. This is an important step toward a reproducible approach for context- and application-specific antibody validation and improved reliability of antibody-based experiments and research data.

Place, publisher, year, edition, pages
American Chemical Society (ACS), 2017
Keywords
antibody validation, spatial proteomics, GFP, Human Protein Atlas, Cell Atlas, subcellular localization, immunofluorescence, confocal microscopy
National Category
Medical Biotechnology
Identifiers
urn:nbn:se:kth:diva-201243 (URN)10.1021/acs.jproteome.6b00821 (DOI)000391782100014 ()2-s2.0-85017638855 (Scopus ID)
Note

QC 20170216

Available from: 2017-02-16 Created: 2017-02-16 Last updated: 2017-05-30Bibliographically approved
Boström, J., Sramkova, Z., Salasova, A., Johard, H., Mahdessian, D., Fedr, R., . . . Andang, M. (2017). Comparative cell cycle transcriptomics reveals synchronization of developmental transcription factor networks in cancer cells. PLoS ONE, 12(12), Article ID e0188772.
Open this publication in new window or tab >>Comparative cell cycle transcriptomics reveals synchronization of developmental transcription factor networks in cancer cells
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2017 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 12, no 12, article id e0188772Article in journal (Refereed) Published
Abstract [en]

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

Place, publisher, year, edition, pages
Public Library of Science, 2017
National Category
Biological Sciences
Identifiers
urn:nbn:se:kth:diva-220599 (URN)10.1371/journal.pone.0188772 (DOI)000417648600013 ()2-s2.0-85038411630 (Scopus ID)
Note

QC 20180115

Available from: 2018-01-15 Created: 2018-01-15 Last updated: 2018-01-15Bibliographically approved
Thul, P., Åkesson, L., Mahdessian, D., Bäckström, A., Danielsson, F., Gnann, C., . . . Lundberg, E. (2017). Exploring the Proteome of Multilocalizing Proteins. Paper presented at ASCB/EMBO Meeting, DEC 02-06, 2017, Philadelphia, PA. Molecular Biology of the Cell, 28
Open this publication in new window or tab >>Exploring the Proteome of Multilocalizing Proteins
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2017 (English)In: Molecular Biology of the Cell, ISSN 1059-1524, E-ISSN 1939-4586, Vol. 28Article in journal, Meeting abstract (Other academic) Published
Place, publisher, year, edition, pages
American Society for Cell Biology, 2017
National Category
Cell Biology
Identifiers
urn:nbn:se:kth:diva-224723 (URN)000426664302080 ()
Conference
ASCB/EMBO Meeting, DEC 02-06, 2017, Philadelphia, PA
Funder
Science for Life Laboratory - a national resource center for high-throughput molecular bioscience
Note

QC 20180323

Available from: 2018-03-23 Created: 2018-03-23 Last updated: 2018-03-23Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-7034-0850

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