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Käller Lundberg, EmmaORCID iD iconorcid.org/0000-0001-7034-0850
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Publications (10 of 156) Show all publications
Dou, D. R., Zhao, Y., Belk, J. A., Zhao, Y., Casey, K. M., Chen, D. C., . . . Chang, H. Y. (2024). Xist ribonucleoproteins promote female sex-biased autoimmunity. Cell, 187(3), 16-733
Open this publication in new window or tab >>Xist ribonucleoproteins promote female sex-biased autoimmunity
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2024 (English)In: Cell, ISSN 0092-8674, E-ISSN 1097-4172, Vol. 187, no 3, p. 16-733Article in journal (Refereed) Published
Abstract [en]

Autoimmune diseases disproportionately affect females more than males. The XX sex chromosome complement is strongly associated with susceptibility to autoimmunity. Xist long non-coding RNA (lncRNA) is expressed only in females to randomly inactivate one of the two X chromosomes to achieve gene dosage compensation. Here, we show that the Xist ribonucleoprotein (RNP) complex comprising numerous autoantigenic components is an important driver of sex-biased autoimmunity. Inducible transgenic expression of a non-silencing form of Xist in male mice introduced Xist RNP complexes and sufficed to produce autoantibodies. Male SJL/J mice expressing transgenic Xist developed more severe multi-organ pathology in a pristane-induced lupus model than wild-type males. Xist expression in males reprogrammed T and B cell populations and chromatin states to more resemble wild-type females. Human patients with autoimmune diseases displayed significant autoantibodies to multiple components of XIST RNP. Thus, a sex-specific lncRNA scaffolds ubiquitous RNP components to drive sex-biased immunity.

Place, publisher, year, edition, pages
Elsevier BV, 2024
Keywords
autoantibody, autoimmunity, long non-coding RNA, RNA binding protein, XIST
National Category
Medical Genetics
Identifiers
urn:nbn:se:kth:diva-343195 (URN)10.1016/j.cell.2023.12.037 (DOI)2-s2.0-85183518577 (Scopus ID)
Note

QC 20240209

Available from: 2024-02-08 Created: 2024-02-08 Last updated: 2024-02-09Bibliographically approved
Sountoulidis, A., Marco Salas, S., Braun, E., Avenel, C., Bergenstråhle, J., Theelke, J., . . . Samakovlis, C. (2023). A topographic atlas defines developmental origins of cell heterogeneity in the human embryonic lung. Nature Cell Biology
Open this publication in new window or tab >>A topographic atlas defines developmental origins of cell heterogeneity in the human embryonic lung
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2023 (English)In: Nature Cell Biology, ISSN 1465-7392, E-ISSN 1476-4679Article in journal (Refereed) Published
Abstract [en]

Sountoulidis et al. provide a spatial gene expression atlas of human embryonic lung during the first trimester of gestation and identify 83 cell identities corresponding to stable cell types or transitional states. The lung contains numerous specialized cell types with distinct roles in tissue function and integrity. To clarify the origins and mechanisms generating cell heterogeneity, we created a comprehensive topographic atlas of early human lung development. Here we report 83 cell states and several spatially resolved developmental trajectories and predict cell interactions within defined tissue niches. We integrated single-cell RNA sequencing and spatially resolved transcriptomics into a web-based, open platform for interactive exploration. We show distinct gene expression programmes, accompanying sequential events of cell differentiation and maturation of the secretory and neuroendocrine cell types in proximal epithelium. We define the origin of airway fibroblasts associated with airway smooth muscle in bronchovascular bundles and describe a trajectory of Schwann cell progenitors to intrinsic parasympathetic neurons controlling bronchoconstriction. Our atlas provides a rich resource for further research and a reference for defining deviations from homeostatic and repair mechanisms leading to pulmonary diseases.

Place, publisher, year, edition, pages
Springer Nature, 2023
National Category
Cell and Molecular Biology
Identifiers
urn:nbn:se:kth:diva-328095 (URN)10.1038/s41556-022-01064-x (DOI)000916842700001 ()36646791 (PubMedID)2-s2.0-85146289982 (Scopus ID)
Note

QC 20231122

Available from: 2023-06-02 Created: 2023-06-02 Last updated: 2023-11-22Bibliographically approved
Johnson, G. T., Agmon, E., Akamatsu, M., Käller Lundberg, E., Lyons, B., Ouyang, W., . . . Horwitz, R. (2023). Building the next generation of virtual cells to understand cellular biology. Biophysical Journal, 122(18), 3560-3569
Open this publication in new window or tab >>Building the next generation of virtual cells to understand cellular biology
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2023 (English)In: Biophysical Journal, ISSN 0006-3495, E-ISSN 1542-0086, Vol. 122, no 18, p. 3560-3569Article in journal (Refereed) Published
Abstract [en]

Cell science has made significant progress by focusing on understanding individual cellular processes through reductionist approaches. However, the sheer volume of knowledge collected presents challenges in integrating this information across different scales of space and time to comprehend cellular behaviors, as well as making the data and methods more accessible for the community to tackle complex biological questions. This perspective proposes the creation of next-generation virtual cells, which are dynamic 3D models that integrate information from diverse sources, including simulations, biophysical models, image-based models, and evidence-based knowledge graphs. These virtual cells would provide statistically accurate and holistic views of real cells, bridging the gap between theoretical concepts and experimental data, and facilitating productive new collaborations among researchers across related fields.

Place, publisher, year, edition, pages
Elsevier BV, 2023
National Category
Bioinformatics and Systems Biology
Identifiers
urn:nbn:se:kth:diva-339361 (URN)10.1016/j.bpj.2023.04.006 (DOI)001082137000001 ()37050874 (PubMedID)2-s2.0-85153947534 (Scopus ID)
Note

QC 20231108

Available from: 2023-11-08 Created: 2023-11-08 Last updated: 2023-11-08Bibliographically approved
Zhang, Y., Ripley, B., Ouyang, W., Coloma, R., Sturtz, M., Upton, E., . . . Radoshevich, L. (2023). ISG15 modification of the Arp2/3 complex restricts pathogen spread. Paper presented at Annual Meeting of the American-Society-for-Cell-Biology/ European-Molecular-Biology-Organisation (ASCB/EMBO), DEC 03-07, 2022, Washington, DC. Molecular Biology of the Cell, 34(2), 818-818
Open this publication in new window or tab >>ISG15 modification of the Arp2/3 complex restricts pathogen spread
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2023 (English)In: Molecular Biology of the Cell, ISSN 1059-1524, E-ISSN 1939-4586, Vol. 34, no 2, p. 818-818Article in journal, Meeting abstract (Other academic) Published
Place, publisher, year, edition, pages
AMER SOC CELL BIOLOGY, 2023
National Category
Cell and Molecular Biology
Identifiers
urn:nbn:se:kth:diva-338751 (URN)001051001303366 ()
Conference
Annual Meeting of the American-Society-for-Cell-Biology/ European-Molecular-Biology-Organisation (ASCB/EMBO), DEC 03-07, 2022, Washington, DC
Note

QC 20231030

Available from: 2023-10-30 Created: 2023-10-30 Last updated: 2023-10-30Bibliographically approved
Quardokus, E. M., Martinez Casals, A., Björklund, F., Käller Lundberg, E., Radtke, A. J. & et al., . (2023). Organ Mapping Antibody Panels: a community resource for standardized multiplexed tissue imaging. Nature Methods, 20(8), 1174-1178
Open this publication in new window or tab >>Organ Mapping Antibody Panels: a community resource for standardized multiplexed tissue imaging
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2023 (English)In: Nature Methods, ISSN 1548-7091, E-ISSN 1548-7105, Vol. 20, no 8, p. 1174-1178Article in journal (Refereed) Published
Abstract [en]

Multiplexed antibody-based imaging enables the detailed characterization of molecular and cellular organization in tissues. Advances in the field now allow high-parameter data collection (>60 targets); however, considerable expertise and capital are needed to construct the antibody panels employed by these methods. Organ mapping antibody panels are community-validated resources that save time and money, increase reproducibility, accelerate discovery and support the construction of a Human Reference Atlas.

Place, publisher, year, edition, pages
Springer Nature, 2023
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-338534 (URN)10.1038/s41592-023-01846-7 (DOI)001031946900001 ()37468619 (PubMedID)2-s2.0-85165169913 (Scopus ID)
Note

QC 20231114

Available from: 2023-11-14 Created: 2023-11-14 Last updated: 2023-11-14Bibliographically approved
Jain, Y., Godwin, L. L., Joshi, S., Mandarapu, S., Le, T., Lindskog, C., . . . Börner, K. (2023). Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms. Nature Communications, 14(1), Article ID 4656.
Open this publication in new window or tab >>Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms
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2023 (English)In: Nature Communications, E-ISSN 2041-1723, Vol. 14, no 1, article id 4656Article in journal (Refereed) Published
Abstract [en]

The development of a reference atlas of the healthy human body requires automated image segmentation of major anatomical structures across multiple organs based on spatial bioimages generated from various sources with differences in sample preparation. We present the setup and results of the Hacking the Human Body machine learning algorithm development competition hosted by the Human Biomolecular Atlas (HuBMAP) and the Human Protein Atlas (HPA) teams on the Kaggle platform. We create a dataset containing 880 histology images with 12,901 segmented structures, engaging 1175 teams from 78 countries in community-driven, open-science development of machine learning models. Tissue variations in the dataset pose a major challenge to the teams which they overcome by using color normalization techniques and combining vision transformers with convolutional models. The best model will be productized in the HuBMAP portal to process tissue image datasets at scale in support of Human Reference Atlas construction.

Place, publisher, year, edition, pages
Springer Nature, 2023
National Category
Medical Image Processing
Identifiers
urn:nbn:se:kth:diva-335315 (URN)10.1038/s41467-023-40291-0 (DOI)001042819400020 ()37537179 (PubMedID)2-s2.0-85166591333 (Scopus ID)
Note

QC 20231123

Available from: 2023-09-05 Created: 2023-09-05 Last updated: 2024-03-18Bibliographically approved
Kuodyte, K., Galea, G., Käller Lundberg, E. & Pepperkok, R. (2023). The Golgi complex serves as a platform for the DNA damage response pathways. Paper presented at Annual Meeting of the American-Society-for-Cell-Biology/ European-Molecular-Biology-Organisation (ASCB/EMBO), DEC 03-07, 2022, Washington, DC. Molecular Biology of the Cell, 34(2), 63-63
Open this publication in new window or tab >>The Golgi complex serves as a platform for the DNA damage response pathways
2023 (English)In: Molecular Biology of the Cell, ISSN 1059-1524, E-ISSN 1939-4586, Vol. 34, no 2, p. 63-63Article in journal, Meeting abstract (Other academic) Published
Place, publisher, year, edition, pages
AMER SOC CELL BIOLOGY, 2023
National Category
Cell Biology
Identifiers
urn:nbn:se:kth:diva-338711 (URN)001051001300104 ()
Conference
Annual Meeting of the American-Society-for-Cell-Biology/ European-Molecular-Biology-Organisation (ASCB/EMBO), DEC 03-07, 2022, Washington, DC
Note

QC 20231024

Available from: 2023-10-24 Created: 2023-10-24 Last updated: 2023-10-24Bibliographically approved
Kuodyte, K., Galea, G., Käller Lundberg, E. & Pepperkok, R. (2023). The Golgi complex serves as a platform for the DNA damage response pathways. Paper presented at Annual Meeting of the American-Society-for-Cell-Biology/ European-Molecular-Biology-Organisation (ASCB/EMBO), DEC 03-07, 2022, Washington, DC. Molecular Biology of the Cell, 34(2), 209-209
Open this publication in new window or tab >>The Golgi complex serves as a platform for the DNA damage response pathways
2023 (English)In: Molecular Biology of the Cell, ISSN 1059-1524, E-ISSN 1939-4586, Vol. 34, no 2, p. 209-209Article in journal, Meeting abstract (Other academic) Published
Place, publisher, year, edition, pages
AMER SOC CELL BIOLOGY, 2023
National Category
Cell Biology
Identifiers
urn:nbn:se:kth:diva-338713 (URN)001051001302019 ()
Conference
Annual Meeting of the American-Society-for-Cell-Biology/ European-Molecular-Biology-Organisation (ASCB/EMBO), DEC 03-07, 2022, Washington, DC
Note

QC 20231024

Available from: 2023-10-24 Created: 2023-10-24 Last updated: 2023-10-24Bibliographically approved
Kustatscher, G., Collins, T., Gingras, A.-C. -., Guo, T., Hermjakob, H., Ideker, T., . . . Rappsilber, J. (2022). An open invitation to the Understudied Proteins Initiative. Nature Biotechnology, 40(6), 815-817
Open this publication in new window or tab >>An open invitation to the Understudied Proteins Initiative
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2022 (English)In: Nature Biotechnology, ISSN 1087-0156, E-ISSN 1546-1696, Vol. 40, no 6, p. 815-817Article in journal (Refereed) Published
Place, publisher, year, edition, pages
Springer Nature, 2022
Keywords
early cancer diagnosis, mass screening, Early Detection of Cancer
National Category
Cancer and Oncology
Identifiers
urn:nbn:se:kth:diva-323802 (URN)10.1038/s41587-022-01316-z (DOI)000792550100001 ()35534555 (PubMedID)2-s2.0-85129733147 (Scopus ID)
Note

QC 20230213

Available from: 2023-02-13 Created: 2023-02-13 Last updated: 2023-02-13Bibliographically approved
Le, T., Winsnes, C. F., Axelsson, U., Xu, H., Mohanakrishnan Kaimal, J., Mahdessian, D., . . . Lundberg, E. (2022). Analysis of the Human Protein Atlas Weakly Supervised Single-Cell Classification competition. Nature Methods, 19(10), 1221-1229
Open this publication in new window or tab >>Analysis of the Human Protein Atlas Weakly Supervised Single-Cell Classification competition
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2022 (English)In: Nature Methods, ISSN 1548-7091, E-ISSN 1548-7105, Vol. 19, no 10, p. 1221-1229Article in journal (Refereed) Published
Abstract [en]

While spatial proteomics by fluorescence imaging has quickly become an essential discovery tool for researchers, fast and scalable methods to classify and embed single-cell protein distributions in such images are lacking. Here, we present the design and analysis of the results from the competition Human Protein Atlas – Single-Cell Classification hosted on the Kaggle platform. This represents a crowd-sourced competition to develop machine learning models trained on limited annotations to label single-cell protein patterns in fluorescent images. The particular challenges of this competition include class imbalance, weak labels and multi-label classification, prompting competitors to apply a wide range of approaches in their solutions. The winning models serve as the first subcellular omics tools that can annotate single-cell locations, extract single-cell features and capture cellular dynamics. 

Place, publisher, year, edition, pages
Springer Nature, 2022
Keywords
cell protein, protein, Article, cell nucleus inclusion body, classification, competition, computer model, fluorescence imaging, machine learning, multilabel classification, protein function, protein localization, proteomics, single cell analysis, human, Humans, Proteins
National Category
Cell Biology
Identifiers
urn:nbn:se:kth:diva-328119 (URN)10.1038/s41592-022-01606-z (DOI)000863153600001 ()36175767 (PubMedID)2-s2.0-85139247548 (Scopus ID)
Note

QC 20230602

Available from: 2023-06-02 Created: 2023-06-02 Last updated: 2023-06-02Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0001-7034-0850

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