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  • 1.
    Beháňová, Andrea
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3.
    Avenel, Christophe
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Andersson, Axel
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3.
    Chelebian, Eduard
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Klemm, Anna
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3.
    Wik, Lina
    Östman, Arne
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3.
    Visualization and quality control tools for large-scale multiplex tissue analysis in TissUUmaps32023In: Biological Imaging, E-ISSN 2633-903X, Vol. 3, article id e6Article in journal (Refereed)
  • 2.
    Beháňová, Andrea
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Klemm, Anna H
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Wählby, Carolina
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Spatial Statistics for Understanding Tissue Organization2022In: Frontiers in Physiology, E-ISSN 1664-042X, Vol. 13, article id 832417Article, review/survey (Refereed)
    Abstract [en]

    Interpreting tissue architecture plays an important role in gaining a better understanding of healthy tissue development and disease. Novel molecular detection and imaging techniques make it possible to locate many different types of objects, such as cells and/or mRNAs, and map their location across the tissue space. In this review, we present several methods that provide quantification and statistical verification of observed patterns in the tissue architecture. We categorize these methods into three main groups: Spatial statistics on a single type of object, two types of objects, and multiple types of objects. We discuss the methods in relation to four hypotheses regarding the methods' capability to distinguish random and non-random distributions of objects across a tissue sample, and present a number of openly available tools where these methods are provided. We also discuss other spatial statistics methods compatible with other types of input data.

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  • 3.
    Bekkhus, Tove
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Vascular Biology.
    Avenel, Christophe
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Hanna, Sabella
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Vascular Biology.
    Franzén Boger, Mathias
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
    Klemm, Anna H
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Vasiliu-Bacovia, Daniel
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
    Wärnberg, Fredrik
    Department of Surgery, Institute of Clinical Sciences, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Ulvmar, Maria H.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Vascular Biology.
    Automated detection of vascular remodeling in human tumor draining lymph nodes by the deep learning tool HEV-finder2022In: Journal of Pathology, ISSN 0022-3417, E-ISSN 1096-9896, Vol. 258, no 1, p. 4-11Article in journal (Refereed)
    Abstract [en]

    Vascular remodeling is common in human cancer and has potential as future biomarkers for prediction of disease progression and tumor immunity status. It can also affect metastatic sites, including the tumor-draining lymph nodes (TDLNs). Dilation of the high endothelial venules (HEVs) within TDLNs has been observed in several types of cancer. We recently demonstrated that it is a premetastatic effect that can be linked to tumor invasiveness in breast cancer. Manual visual assessment of changes in vascular morphology is a tedious and difficult task, limiting high-throughput analysis. Here we present a fully automated approach for detection and classification of HEV dilation. By using 12,524 manually classified HEVs, we trained a deep-learning model and created a graphical user interface for visualization of the results. The tool, named the HEV-finder, selectively analyses HEV dilation in specific regions of the lymph nodes. We evaluated the HEV-finder's ability to detect and classify HEV dilation in different types of breast cancer compared to manual annotations. Our results constitute a successful example of large-scale, fully automated, and user-independent, image-based quantitative assessment of vascular remodeling in human pathology and lay the ground for future exploration of HEV dilation in TDLNs as a biomarker.

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  • 4. Cojoc, Gheorghe
    et al.
    Florescu, Ana-Maria
    Krull, Alexander
    Klemm, Anna H
    Pavin, Nenad
    Jülicher, Frank
    Tolić, Iva M
    Paired arrangement of kinetochores together with microtubule pivoting and dynamics drive kinetochore capture in meiosis I.2016In: Scientific Reports, E-ISSN 2045-2322, Vol. 6, article id 25736Article in journal (Refereed)
    Abstract [en]

    Kinetochores are protein complexes on the chromosomes, whose function as linkers between spindle microtubules and chromosomes is crucial for proper cell division. The mechanisms that facilitate kinetochore capture by microtubules are still unclear. In the present study, we combine experiments and theory to explore the mechanisms of kinetochore capture at the onset of meiosis I in fission yeast. We show that kinetochores on homologous chromosomes move together, microtubules are dynamic and pivot around the spindle pole, and the average capture time is 3-4 minutes. Our theory describes paired kinetochores on homologous chromosomes as a single object, as well as angular movement of microtubules and their dynamics. For the experimentally measured parameters, the model reproduces the measured capture kinetics and shows that the paired configuration of kinetochores accelerates capture, whereas microtubule pivoting and dynamics have a smaller contribution. Kinetochore pairing may be a general feature that increases capture efficiency in meiotic cells.

  • 5.
    Dobson, Ellen T.A.
    et al.
    Laboratory for Optical and Computational Instrumentation (LOCI) Center for Quantitative Cell Imaging University of Wisconsin at Madison Madison Wisconsin.
    Cimini, Beth
    Imaging Platform Broad Institute of Harvard and MIT Cambridge Massachusetts.
    Klemm, Anna H
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
    Wählby, Carolina
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
    Carpenter, Anne E.
    Imaging Platform Broad Institute of Harvard and MIT Cambridge Massachusetts.
    Eliceiri, Kevin W
    Laboratory for Optical and Computational Instrumentation (LOCI) Center for Quantitative Cell Imaging University of Wisconsin at Madison Madison Wisconsin;Department of Medical Physics University of Wisconsin at Madison Madison Wisconsin;Morgridge Institute for Research Madison Wisconsin.
    ImageJ and CellProfiler: Complements in Open‐Source Bioimage Analysis2021In: Current Protocols in Microbiology, ISSN 1934-8525, E-ISSN 1088-7423, Vol. 1, no 5Article in journal (Refereed)
    Abstract [en]

    ImageJ and CellProfiler have long been leading open-source platforms in the field of bioimage analysis. ImageJ's traditional strength is in single-image processing and investigation, while CellProfiler is designed for building large-scale, modular analysis pipelines. Although many image analysis problems can be well solved with one or the other, using these two platforms together in a single workflow can be powerful. Here, we share two pipelines demonstrating mechanisms for productively and conveniently integrating ImageJ and CellProfiler for (1) studying cell morphology and migration via tracking, and (2) advanced stitching techniques for handling large, tiled image sets to improve segmentation. No single platform can provide all the key and most efficient functionality needed for all studies. While both programs can be and are often used separately, these pipelines demonstrate the benefits of using them together for image analysis workflows. ImageJ and CellProfiler are both committed to interoperability between their platforms, with ongoing development to improve how both are leveraged from the other

  • 6. Fabry, Ben
    et al.
    Klemm, Anna H
    Kienle, Sandra
    Schäffer, Tilman E
    Goldmann, Wolfgang H
    Focal adhesion kinase stabilizes the cytoskeleton.2011In: Biophysical Journal, ISSN 0006-3495, E-ISSN 1542-0086, Vol. 101, no 9, p. 2131-8Article in journal (Refereed)
    Abstract [en]

    Focal adhesion kinase (FAK) is a central focal adhesion protein that promotes focal adhesion turnover, but the role of FAK for cell mechanical stability is unknown. We measured the mechanical properties of wild-type (FAKwt), FAK-deficient (FAK-/-), FAK-silenced (siFAK), and siControl mouse embryonic fibroblasts by magnetic tweezer, atomic force microscopy, traction microscopy, and nanoscale particle tracking microrheology. FAK-deficient cells showed lower cell stiffness, reduced adhesion strength, and increased cytoskeletal dynamics compared to wild-type cells. These observations imply a reduced stability of the cytoskeleton in FAK-deficient cells. We attribute the reduced cytoskeletal stability to rho-kinase activation in FAK-deficient cells that suppresses the formation of ordered stress fiber bundles, enhances cortical actin distribution, and reduces cell spreading. In agreement with this interpretation is that cell stiffness and cytoskeletal stability in FAK-/- cells is partially restored to wild-type level after rho-kinase inhibition with Y27632.

  • 7. Follwaczny, Philipp
    et al.
    Schieweck, Rico
    Riedemann, Therese
    Demleitner, Antonia
    Straub, Tobias
    Klemm, Anna H
    Bilban, Martin
    Sutor, Bernd
    Popper, Bastian
    Kiebler, Michael A
    Pumilio2-deficient mice show a predisposition for epilepsy.2017In: Disease Models and Mechanisms, ISSN 1754-8403, E-ISSN 1754-8411, Vol. 10, no 11, p. 1333-1342Article in journal (Refereed)
    Abstract [en]

    Epilepsy is a neurological disease that is caused by abnormal hypersynchronous activities of neuronal ensembles leading to recurrent and spontaneous seizures in human patients. Enhanced neuronal excitability and a high level of synchrony between neurons seem to trigger these spontaneous seizures. The molecular mechanisms, however, regarding the development of neuronal hyperexcitability and maintenance of epilepsy are still poorly understood. Here, we show that pumilio RNA-binding family member 2 (Pumilio2; Pum2) plays a role in the regulation of excitability in hippocampal neurons of weaned and 5-month-old male mice. Almost complete deficiency of Pum2 in adult Pum2 gene-trap mice (Pum2 GT) causes misregulation of genes involved in neuronal excitability control. Interestingly, this finding is accompanied by the development of spontaneous epileptic seizures in Pum2 GT mice. Furthermore, we detect an age-dependent increase in Scn1a (Nav1.1) and Scn8a (Nav1.6) mRNA levels together with a decrease in Scn2a (Nav1.2) transcript levels in weaned Pum2 GT that is absent in older mice. Moreover, field recordings of CA1 pyramidal neurons show a tendency towards a reduced paired-pulse inhibition after stimulation of the Schaffer-collateral-commissural pathway in Pum2 GT mice, indicating a predisposition to the development of spontaneous seizures at later stages. With the onset of spontaneous seizures at the age of 5 months, we detect increased protein levels of Nav1.1 and Nav1.2 as well as decreased protein levels of Nav1.6 in those mice. In addition, GABA receptor subunit alpha-2 (Gabra2) mRNA levels are increased in weaned and adult mice. Furthermore, we observe an enhanced GABRA2 protein level in the dendritic field of the CA1 subregion in the Pum2 GT hippocampus. We conclude that altered expression levels of known epileptic risk factors such as Nav1.1, Nav1.2, Nav1.6 and GABRA2 result in enhanced seizure susceptibility and manifestation of epilepsy in the hippocampus. Thus, our results argue for a role of Pum2 in epileptogenesis and the maintenance of epilepsy.

  • 8.
    Gupta, Anindya
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Harrison, Philip J.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Wieslander, Håkan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Pielawski, Nicolas
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Kartasalo, Kimmo
    Partel, Gabriele
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Solorzano, Leslie
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Suveer, Amit
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Klemm, Anna H.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Deep Learning in Image Cytometry: A Review2019In: Cytometry Part A, ISSN 1552-4922, E-ISSN 1552-4930, Vol. 95, no 6, p. 366-380Article, review/survey (Refereed)
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  • 9.
    Haase, Robert
    et al.
    DFG Cluster of Excellence “Physics of Life” TU Dresden Germany;Center for Systems Biology Dresden Germany.
    Fazeli, Elnaz
    Biomedicum Imaging Unit, Faculty of Medicine and HiLIFE University of Helsinki Finland.
    Legland, David
    INRAE, UR BIA Nantes France;INRAE, PROBE Research Infrastructure, BIBS Facility Nantes France.
    Doube, Michael
    Department of Infectious Diseases and Public Health City University of Hong Kong Kowloon Hong Kong.
    Culley, Siân
    Randall Centre for Cell & Molecular Biophysics King's College London UK.
    Belevich, Ilya
    Institute of Biotechnology, Helsinki Institute of Life Science University of Helsinki Finland.
    Jokitalo, Eija
    Institute of Biotechnology, Helsinki Institute of Life Science University of Helsinki Finland.
    Schorb, Martin
    Electron Microscopy Core Facility European Molecular Biology Laboratory Heidelberg Germany;Centre for Bioimage Analysis European Molecular Biology Laboratory Heidelberg Germany.
    Klemm, Anna H
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Tischer, Christian
    Centre for Bioimage Analysis European Molecular Biology Laboratory Heidelberg Germany.
    A Hitchhiker's guide through the bio‐image analysis software universe2022In: FEBS Letters, ISSN 0014-5793, E-ISSN 1873-3468, Vol. 596, no 19, p. 2472-2485Article, review/survey (Refereed)
    Abstract [en]

    Modern research in the life sciences is unthinkable without computational methods for extracting, quantifying and visualising information derived from microscopy imaging data of biological samples. In the past decade, we observed a dramatic increase in available software packages for these purposes. As it is increasingly difficult to keep track of the number of available image analysis platforms, tool collections, components and emerging technologies, we provide a conservative overview of software that we use in daily routine and give insights into emerging new tools. We give guidance on which aspects to consider when choosing the platform that best suits the user's needs, including aspects such as image data type, skills of the team, infrastructure and community at the institute and availability of time and budget.

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  • 10.
    Hansel, Catherine S.
    et al.
    Karolinska Institutet/SciLifeLab.
    Yousefian, Schayan
    German Cancer Research Centre (DKFZ).
    Klemm, Anna H.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Carreras-Puigvert, Jordi
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    A workflow for high-throughput screening, data analysis, processing, and hit identification2020Report (Other (popular science, discussion, etc.))
  • 11. Kajtez, Janko
    et al.
    Solomatina, Anastasia
    Novak, Maja
    Polak, Bruno
    Vukušić, Kruno
    Rüdiger, Jonas
    Cojoc, Gheorghe
    Milas, Ana
    Šumanovac Šestak, Ivana
    Risteski, Patrik
    Tavano, Federica
    Klemm, Anna H
    Roscioli, Emanuele
    Welburn, Julie
    Cimini, Daniela
    Glunčić, Matko
    Pavin, Nenad
    Tolić, Iva M
    Overlap microtubules link sister k-fibres and balance the forces on bi-oriented kinetochores.2016In: Nature Communications, E-ISSN 2041-1723, Vol. 7, article id 10298Article in journal (Refereed)
    Abstract [en]

    During metaphase, forces on kinetochores are exerted by k-fibres, bundles of microtubules that end at the kinetochore. Interestingly, non-kinetochore microtubules have been observed between sister kinetochores, but their function is unknown. Here we show by laser-cutting of a k-fibre in HeLa and PtK1 cells that a bundle of non-kinetochore microtubules, which we term 'bridging fibre', bridges sister k-fibres and balances the interkinetochore tension. We found PRC1 and EB3 in the bridging fibre, suggesting that it consists of antiparallel dynamic microtubules. By using a theoretical model that includes a bridging fibre, we show that the forces at the pole and at the kinetochore depend on the bridging fibre thickness. Moreover, our theory and experiments show larger relaxation of the interkinetochore distance for cuts closer to kinetochores. We conclude that the bridging fibre, by linking sister k-fibres, withstands the tension between sister kinetochores and enables the spindle to obtain a curved shape.

  • 12. Kalinina, Iana
    et al.
    Nandi, Amitabha
    Delivani, Petrina
    Chacón, Mariola R
    Klemm, Anna H
    Ramunno-Johnson, Damien
    Krull, Alexander
    Lindner, Benjamin
    Pavin, Nenad
    Tolić-Nørrelykke, Iva M
    Pivoting of microtubules around the spindle pole accelerates kinetochore capture.2013In: Nature Cell Biology, ISSN 1465-7392, E-ISSN 1476-4679, Vol. 15, no 1, p. 82-7Article in journal (Refereed)
    Abstract [en]

    During cell division, spindle microtubules attach to chromosomes through kinetochores, protein complexes on the chromosome. The central question is how microtubules find kinetochores. According to the pioneering idea termed search-and-capture, numerous microtubules grow from a centrosome in all directions and by chance capture kinetochores. The efficiency of search-and-capture can be improved by a bias in microtubule growth towards the kinetochores, by nucleation of microtubules at the kinetochores and at spindle microtubules, by kinetochore movement, or by a combination of these processes. Here we show in fission yeast that kinetochores are captured by microtubules pivoting around the spindle pole, instead of growing towards the kinetochores. This pivoting motion of microtubules is random and independent of ATP-driven motor activity. By introducing a theoretical model, we show that the measured random movement of microtubules and kinetochores is sufficient to explain the process of kinetochore capture. Our theory predicts that the speed of capture depends mainly on how fast microtubules pivot, which was confirmed experimentally by speeding up and slowing down microtubule pivoting. Thus, pivoting motion allows microtubules to explore space laterally, as they search for targets such as kinetochores.

  • 13.
    Klemm, Anna
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University.
    TrackMate - My Favorite Image Analysis Tool, by Neubias Members2020In: Imaging & Microscopy, Vol. SeptemberArticle in journal (Other academic)
  • 14. Klemm, Anna H
    et al.
    Bosilj, Agneza
    Glunčic, Matko
    Pavin, Nenad
    Tolic, Iva M
    Metaphase kinetochore movements are regulated by kinesin-8 motors and microtubule dynamic instability.2018In: Molecular Biology of the Cell, ISSN 1059-1524, E-ISSN 1939-4586, Vol. 29, no 11, p. 1332-1345Article in journal (Refereed)
    Abstract [en]

    During metaphase, sister chromatids are connected to microtubules extending from the opposite spindle poles via kinetochores to protein complexes on the chromosome. Kinetochores congress to the equatorial plane of the spindle and oscillate around it, with kinesin-8 motors restricting these movements. Yet, the physical mechanism underlying kinetochore movements is unclear. We show that kinetochore movements in the fission yeast Schizosaccharomyces pombe are regulated by kinesin-8-promoted microtubule catastrophe, force-induced rescue, and microtubule dynamic instability. A candidate screen showed that among the selected motors only kinesin-8 motors Klp5/Klp6 are required for kinetochore centering. Kinesin-8 accumulates at the end of microtubules, where it promotes catastrophe. Laser ablation of the spindle resulted in kinetochore movement toward the intact spindle pole in wild-type and klp5Δ cells, suggesting that kinetochore movement is driven by pulling forces. Our theoretical model with Langevin description of microtubule dynamic instability shows that kinesin-8 motors are required for kinetochore centering, whereas sensitivity of rescue to force is necessary for the generation of oscillations. We found that irregular kinetochore movements occur for a broader range of parameters than regular oscillations. Thus, our work provides an explanation for how regulation of microtubule dynamic instability contributes to kinetochore congression and the accompanying movements around the spindle center.

  • 15. Klemm, Anna H
    et al.
    Diez, Gerold
    Alonso, Josè-Luis
    Goldmann, Wolfgang H
    Comparing the mechanical influence of vinculin, focal adhesion kinase and p53 in mouse embryonic fibroblasts.2009In: Biochemical and Biophysical Research Communications - BBRC, ISSN 0006-291X, E-ISSN 1090-2104, Vol. 379, no 3, p. 799-801Article in journal (Refereed)
    Abstract [en]

    Cytoskeletal reorganization is an ongoing process when cells adhere, move or invade extracellular substrates. The cellular force generation and transmission are determined by the intactness of the actomyosin-(focal adhesion complex)-integrin connection. We investigated the intracellular course of action in mouse embryonic fibroblasts deficient in the focal adhesion proteins vinculin and focal adhesion kinase (FAK) and the nuclear matrix protein p53 using magnetic tweezer and nanoparticle tracking techniques. Results show that the lack of these proteins decrease cellular stiffness and affect cell rheological behavior. The decrease in cellular binding strength was higher in FAK- to vinculin-deficient cells, whilst p53-deficient cells showed no effect compared to wildtype cells. The intracellular cytoskeletal activity was lowest in wildtype cells, but increased in the following order when cells lacked FAK+p53>p53>vinculin. In summary, cell mechanical processes are differently affected by the focal adhesion proteins vinculin and FAK than by the nuclear matrix protein, p53.

  • 16. Klemm, Anna H
    et al.
    Kienle, Sandra
    Rheinlaender, Johannes
    Schäffer, Tilman E
    Goldmann, Wolfgang H
    The influence of Pyk2 on the mechanical properties in fibroblasts.2010In: Biochemical and Biophysical Research Communications - BBRC, ISSN 0006-291X, E-ISSN 1090-2104, Vol. 393, no 4, p. 694-7Article in journal (Refereed)
    Abstract [en]

    The cell surface receptor integrin is involved in signaling mechanical stresses via the focal adhesion complex (FAC) into the cell. Within FAC, the focal adhesion kinase (FAK) and Pyk2 are believed to act as important scaffolding proteins. Based on the knowledge that many signal transducing molecules are transiently immobilized within FAC connecting the cytoskeleton with integrins, we applied magnetic tweezer and atomic force microscopic measurements to determine the influence of FAK and Pyk2 in cells mechanically. Using mouse embryonic fibroblasts (MEF; FAK(+/+), FAK(-/-), and siRNA-Pyk2 treated FAK(-/-) cells) provided a unique opportunity to describe the function of FAK and Pyk2 in more detail and to define their influence on FAC and actin distribution.

  • 17. Klemm, Anna H
    et al.
    Suchodolski, Klaudiusz
    Goldmann, Wolfgang H
    Mechano-chemical signaling in F9 cells.2006In: Cell Biology International, ISSN 1065-6995, E-ISSN 1095-8355, Vol. 30, no 9, p. 755-9Article in journal (Refereed)
    Abstract [en]

    We investigated the molecular mechanism by which cells recognize and respond to physical forces in their local environment. Using a model system, to study wild type mouse F9 embryonic carcinoma cells, we examined how these cells sense mechanical stresses and translate them into biochemical responses through their cell surface receptor integrin and via the focal adhesion complex (FAC). Based on studies that show that many signal transducing molecules are immobilized on the cytoskeleton at the site of integrin binding within the focal adhesion complex, we found a time-dependent increase of focal adhesion kinase (pp125(FAK)) phosphorylation possibly due to protein kinase C (PKC) activation as well as protein kinase A (PKA) activity increase upon cell adhesion/spreading. These studies provide some insight into intracellular mechano-chemical signaling.

  • 18.
    Klemm, Anna H.
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Thomae, Andreas W.
    Wachal, Katarina
    Dietzel, Steffen
    Tracking Microscope Performance: A Workflow to Compare Point Spread Function Evaluations Over Time2019In: Microscopy and Microanalysis, ISSN 1431-9276, E-ISSN 1435-8115, Vol. 25, no 3, p. 699-704Article in journal (Refereed)
    Abstract [en]

    Routine system checks are essential for supervising the performance of an advanced light microscope. Recording and evaluating the point spread function (PSF) of a given system provides information about the resolution and imaging. We compared the performance of fluorescent and gold beads for PSF recordings. We then combined the open-source evaluation software PSFj with a newly developed KNIME pipeline named PSFtracker to create a standardized workflow to track a system's performance over several measurements and thus over long time periods. PSFtracker produces example images of recorded PSFs, plots full-width-half-maximum (FWHM) measurements over time and creates an html file which embeds the images and plots, together with a table of results. Changes of the PSF over time are thus easily spotted, either in FWHM plots or in the time series of bead images which allows recognition of aberrations in the shape of the PSF. The html file, viewed in a local browser or uploaded on the web, therefore provides intuitive visualization of the state of the PSF over time. In addition, uploading of the html file on the web allows other microscopists to compare such data with their own.

  • 19.
    Lasch, Manuel
    et al.
    Walter-Brendel-Centre of Experimental Medicine, University Hospital, LMU Munich, Marchioninistr.15, 81377, Munich, Germany.; Department of Otorhinolaryngology, Head & Neck Surgery, Klinikum der Universität München, Ludwig- Maximilians-Universität München, Munich, Germany..
    Nekolla, Katharina
    Walter-Brendel-Centre of Experimental Medicine, University Hospital, LMU Munich, Marchioninistr.15, 81377, Munich, Germany..
    Klemm, Anna H
    Walter-Brendel-Centre of Experimental Medicine, University Hospital, LMU Munich, Marchioninistr.15, 81377, Munich, Germany.; Core Facility Bioimaging at the Biomedical Center, LMU Munich, Planegg-Martinsried, Germany..
    Buchheim, Judith-Irina
    Present address: Department of Anesthesiology, Laboratory for Stress and Immunity, Hospital of the University of the LMU Munich, Munich, Germany.; Walter-Brendel-Centre of Experimental Medicine, University Hospital, LMU Munich, Marchioninistr.15, 81377, Munich, Germany..
    Pohl, Ulrich
    Walter-Brendel-Centre of Experimental Medicine, University Hospital, LMU Munich, Marchioninistr.15, 81377, Munich, Germany.; Core Facility Bioimaging at the Biomedical Center, LMU Munich, Planegg-Martinsried, Germany.; German Center for Cardiovascular Research, Partner Site Munich Heart Alliance, Munich, Germany.Walter-Brendel-Centre of Experimental Medicine, University Hospital, LMU Munich, Marchioninistr.15, 81377, Munich, Germany.
    Dietzel, Steffen
    Walter-Brendel-Centre of Experimental Medicine, University Hospital, LMU Munich, Marchioninistr.15, 81377, Munich, Germany.; Core Facility Bioimaging at the Biomedical Center, LMU Munich, Planegg-Martinsried, Germany..
    Deindl, Elisabeth
    Walter-Brendel-Centre of Experimental Medicine, University Hospital, LMU Munich, Marchioninistr.15, 81377, Munich, Germany..
    Estimating hemodynamic shear stress in murine peripheral collateral arteries by two-photon line scanning2019In: Molecular and Cellular Biochemistry, ISSN 0300-8177, E-ISSN 1573-4919, Vol. 453, p. 41-51Article in journal (Refereed)
    Abstract [en]

    Changes in wall shear stress of blood vessels are assumed to be an important component of many physiological and pathophysiological processes. However, due to technical limitations experimental in vivo data are rarely available. Here, we investigated two-photon excitation fluorescence microscopy as an option to measure vessel diameter as well as blood flow velocities in a murine hindlimb model of arteriogenesis (collateral artery growth). Using line scanning at high frequencies, we measured the movement of blood cells along the vessel axis. We found that peak systolic blood flow velocity averaged 9 mm/s and vessel diameter 42 µm in resting collaterals. Induction of arteriogenesis by femoral artery ligation resulted in a significant increase in centerline peak systolic velocity after 1 day with an average of 51 mm/s, whereas the averaged luminal diameter of collaterals (52 µm) changed much less. Thereof calculations revealed a significant fourfold increase in hemodynamic wall shear rate. Our results indicate that two-photon line scanning is a suitable tool to estimate wall shear stress e.g., in experimental animal models, such as of arteriogenesis, which may not only help to understand the relevance of mechanical forces in vivo, but also to adjust wall shear stress in ex vivo investigations on isolated vessels as well as cell culture experiments.

  • 20. Martins, Gabriel G.
    et al.
    Cordelières, Fabrice P.
    Colombelli, Julien
    D’Antuono, Rocco
    Golani, Ofra
    Guiet, Romain
    Haase, Robert
    Klemm, Anna H
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology. Bioimage Informatics Facility, SciLifeLab.
    Louveaux, Marion
    Paul-Gilloteaux, Perrine
    Tinevez, Jean-Yves
    Miura, Kota
    Highlights from the 2016-2020 NEUBIAS training schools for Bioimage Analysts: a success story and key asset for analysts and life scientists2021In: F1000 Research, E-ISSN 2046-1402, Vol. 10, p. 334-334Article in journal (Refereed)
    Abstract [en]

    NEUBIAS, the European Network of Bioimage Analysts, was created in 2016 with the goal of improving the communication and the knowledge transfer among the various stakeholders involved in the acquisition, processing and analysis of biological image data, and to promote the establishment and recognition of the profession of Bioimage Analyst. One of the most successful initiatives of the NEUBIAS programme was its series of 15 training schools, which trained over 400 new Bioimage Analysts, coming from over 40 countries. Here we outline the rationale behind the innovative three-level program of the schools, the curriculum, the trainer recruitment and turnover strategy, the outcomes for the community and the career path of analysts, including some success stories. We discuss the future of the materials created during this programme and some of the new initiatives emanating from the community of NEUBIAS-trained analysts, such as the NEUBIAS Academy. Overall, we elaborate on how this training programme played a key role in collectively leveraging Bioimaging and Life Science research by bringing the latest innovations into structured, frequent and intensive training activities, and on why we believe this should become a model to further develop in Life Sciences.  

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    fulltext
  • 21.
    Partel, Gabriele
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Hilscher, Markus M.
    Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University.
    Giorgia, Milli
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Solorzano, Leslie
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Klemm, Anna H
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Nilsson, Mats
    Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Identification of spatial compartments in tissue from in situ sequencing dataManuscript (preprint) (Other academic)
    Abstract [en]

    Spatial organization of tissue characterizes biological function, and spatially resolved gene expression has the power to reveal variations of features with high resolution. Here, we propose a novel graph-based in situ sequencing decoding approach that improves recall, enabling precise spatial gene expression analysis. We apply our method on in situ sequencing data from mouse brain sections, identify spatial compartments that correspond with known brain regions, and relate them with tissue morphology.

  • 22.
    Partel, Gabriele
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Hilscher, Markus M.
    Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden.
    Milli, Giorgia
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Solorzano, Leslie
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Klemm, Anna H
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Nilsson, Mats
    Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Automated identification of the mouse brain’s spatial compartments from in situ sequencing data2020In: BMC Biology, E-ISSN 1741-7007, Vol. 18, no 1, article id 144Article in journal (Refereed)
    Abstract [en]

    Background Neuroanatomical compartments of the mouse brain are identified and outlined mainly based on manual annotations of samples using features related to tissue and cellular morphology, taking advantage of publicly available reference atlases. However, this task is challenging since sliced tissue sections are rarely perfectly parallel or angled with respect to sections in the reference atlas and organs from different individuals may vary in size and shape. With the advent of in situ sequencing technologies, it is now possible to profile the gene expression of targeted genes inside preserved tissue samples and thus spatially map biological processes across anatomical compartments. This also opens up for new approaches to identifying tissue compartments.

    Results Here, we show how in situ sequencing data combined with dimensionality reduction and clustering can be used to identify spatial compartments that correspond to known anatomical compartments of the brain. We also visualize gradients in gene expression and sharp as well as smooth transitions between different compartments. We apply our method on mouse brain sections and show that computationally defined anatomical compartments are highly reproducible across individuals and have the potential to replace manual annotation based on cell and tissue morphology. 

    Conclusion Mapping the brain based on molecular information means that we can create detailed atlases independent of angle at sectioning or variations between individuals.

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  • 23. Paul-Gilloteaux, Perrine
    et al.
    Tosi, Sébastien
    Hériché, Jean-Karim
    Gaignard, Alban
    Ménager, Hervé
    Marée, Raphaël
    Baecker, Volker
    Klemm, Anna H
    Kalaš, Matúš
    Zhang, Chong
    Miura, Kota
    Colombelli, Julien
    Bioimage analysis workflows: community resources to navigate through a complex ecosystem2021In: F1000 Research, E-ISSN 2046-1402, Vol. 10, p. 320-320Article in journal (Other academic)
    Abstract [en]

    Workflows are the keystone of bioimage analysis, and the NEUBIAS (Network of European BioImage AnalystS) community is trying to gather the actors of this field and organize the information around them.  One of its most recent outputs is the opening of the F1000Research NEUBIAS gateway, whose main objective is to offer a channel of publication for bioimage analysis workflows and associated resources. In this paper we want to express some personal opinions and recommendations related to finding, handling and developing bioimage analysis workflows. The emergence of "big data” in bioimaging and resource-intensive analysis algorithms make local data storage and computing solutions a limiting factor. At the same time, the need for data sharing with collaborators and a general shift towards remote work, have created new challenges and avenues for the execution and sharing of bioimage analysis workflows.These challenges are to reproducibly run workflows in remote environments, in particular when their components come from different software packages, but also to document them and link their parameters and results by following the FAIR principles (Findable, Accessible, Interoperable, Reusable) to foster open and reproducible science.In this opinion paper, we focus on giving some directions to the reader to tackle these challenges and navigate through this complex ecosystem, in order to find and use workflows, and to compare workflows addressing the same problem. We also discuss tools to run workflows in the cloud and on High Performance Computing resources, and suggest ways to make these workflows FAIR. 

  • 24.
    Pielawski, Nicolas
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Andersson, Axel
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3.
    Avenel, Christophe
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Behanova, Andrea
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3.
    Chelebian, Eduard
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Klemm, Anna
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3.
    Nysjö, Fredrik
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Solorzano, Leslie
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3.
    TissUUmaps 3: Improvements in interactive visualization, exploration, and quality assessment of large-scale spatial omics dataManuscript (preprint) (Other academic)
    Abstract [en]

    Background and Objectives: Spatially resolved techniques for exploring the molecular landscape of tissue samples, such as spatial transcriptomics, often result in millions of data points and images too large to view on a regular desktop computer, limiting the possibilities in visual interactive data exploration. TissUUmaps is a free, open-source browser-based tool for GPU-accelerated visualization and interactive exploration of 107+ data points overlaying tissue samples.

    Methods: Herein we describe how TissUUmaps 3 provides instant multiresolution image viewing and can be customized, shared, and also integrated into Jupyter Notebooks. We introduce new modules where users can visualize markers and regions, explore spatial statistics, perform quantitative analyses of tissue morphology, and assess the quality of decoding in situ transcriptomics data.

    Results: We show that thanks to targeted optimizations the time and cost associated with interactive data exploration were reduced, enabling TissUUmaps 3 to handle the scale of today’s spatial transcriptomics methods.

    Conclusion: TissUUmaps 3 provides significantly improved performance for large multiplex datasets as compared to previous versions. We envision TissUUmaps to contribute to broader dissemination and flexible sharing of large-scale spatial omics data.

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    tissuumaps
  • 25.
    Pielawski, Nicolas
    et al.
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
    Andersson, Axel
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
    Avenel, Christophe
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
    Beháňová, Andrea
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
    Chelebian, Eduard
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Klemm, Anna
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
    Nysjö, Fredrik
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
    Solorzano, Leslie
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology. Karolinska Inst, Dept Med Epidemiol & Biostat, Stockholm, Sweden..
    Wählby, Carolina
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
    TissUUmaps 3: Improvements in interactive visualization, exploration, and quality assessment of large-scale spatial omics data2023In: Heliyon, E-ISSN 2405-8440, Vol. 9, no 5, article id e15306Article in journal (Refereed)
    Abstract [en]

    Background and objectives: Spatially resolved techniques for exploring the molecular landscape of tissue samples, such as spatial transcriptomics, often result in millions of data points and images too large to view on a regular desktop computer, limiting the possibilities in visual interactive data exploration. TissUUmaps is a free, open-source browser-based tool for GPU-accelerated visualization and interactive exploration of 107+ data points overlaying tissue samples.

    Methods: Herein we describe how TissUUmaps 3 provides instant multiresolution image viewing and can be customized, shared, and also integrated into Jupyter Notebooks. We introduce new modules where users can visualize markers and regions, explore spatial statistics, perform quantitative analyses of tissue morphology, and assess the quality of decoding in situ transcriptomics data.

    Results: We show that thanks to targeted optimizations the time and cost associated with interactive data exploration were reduced, enabling TissUUmaps 3 to handle the scale of today's spatial transcriptomics methods.

    Conclusion: TissUUmaps 3 provides significantly improved performance for large multiplex datasets as compared to previous versions. We envision TissUUmaps to contribute to broader dissemination and flexible sharing of largescale spatial omics data.

    Download full text (pdf)
    fulltext
  • 26.
    Raykova, Doroteya
    et al.
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Kermpatsou, Despoina
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Malmqvist, Tony
    Atlas Antibodies AB, Bromma, Sweden..
    Harrison, Philip J
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Rubin Sander, Marie
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Stiller, Christiane
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Heldin, Johan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Leino, Mattias
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Ricardo, Sara
    Univ Porto, Fac Med, Porto, Portugal.;Univ Porto, Differentiat & Canc Grp, Inst Mol Pathol & Immunol, Inst Res & Innovat Hlth I3S,Univ Porto Ipatimu, Porto, Portugal.;Univ Inst Hlth Sci IUCS, Dept Sci, CESPU, Gandra, Portugal..
    Klemm, Anna H
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    David, Leonor
    Univ Porto, Fac Med, Porto, Portugal.;Univ Porto, Differentiat & Canc Grp, Inst Mol Pathol & Immunol, Inst Res & Innovat Hlth I3S,Univ Porto Ipatimu, Porto, Portugal..
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Vemuri, Kalyani
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Vascular Biology.
    Dimberg, Anna
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Vascular Biology.
    Sundqvist, Anders
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Norlin, Maria
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Klaesson, Axel
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Kampf, Caroline
    Atlas Antibodies AB, Bromma, Sweden..
    Söderberg, Ola
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    A method for Boolean analysis of protein interactions at a molecular level2022In: Nature Communications, E-ISSN 2041-1723, Vol. 13, no 1, article id 4755Article in journal (Refereed)
    Abstract [en]

    Determination of interactions between native proteins in cells is important for understanding function. Here the authors report MolBoolean as a method to detect interactions between endogenous proteins in subcellular compartments, using antibody-DNA conjugates for identification and signal amplification. Determining the levels of protein-protein interactions is essential for the analysis of signaling within the cell, characterization of mutation effects, protein function and activation in health and disease, among others. Herein, we describe MolBoolean - a method to detect interactions between endogenous proteins in various subcellular compartments, utilizing antibody-DNA conjugates for identification and signal amplification. In contrast to proximity ligation assays, MolBoolean simultaneously indicates the relative abundances of protein A and B not interacting with each other, as well as the pool of A and B proteins that are proximal enough to be considered an AB complex. MolBoolean is applicable both in fixed cells and tissue sections. The specific and quantifiable data that the method generates provide opportunities for both diagnostic use and medical research.

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    FULLTEXT01
  • 27. Schewkunow, Vitali
    et al.
    Sharma, Karan P
    Diez, Gerold
    Klemm, Anna H
    Sharma, Pal C
    Goldmann, Wolfgang H
    Thermodynamic evidence of non-muscle myosin II-lipid-membrane interaction.2008In: Biochemical and Biophysical Research Communications - BBRC, ISSN 0006-291X, E-ISSN 1090-2104, Vol. 366, no 2, p. 500-5Article in journal (Refereed)
    Abstract [en]

    A unique feature of protein networks in living cells is that they can generate their own force. Proteins such as non-muscle myosin II are an integral part of the cytoskeleton and have the capacity to convert the energy of ATP hydrolysis into directional movement. Non-muscle myosin II can move actin filaments against each other, and depending on the orientation of the filaments and the way in which they are linked together, it can produce contraction, bending, extension, and stiffening. Our measurements with differential scanning calorimetry showed that non-muscle myosin II inserts into negatively charged phospholipid membranes. Using lipid vesicles made of DMPG/DMPC at a molar ratio of 1:1 at 10mg/ml in the presence of different non-muscle myosin II concentrations showed a variation of the main phase transition of the lipid vesicle at around 23 degrees C. With increasing concentrations of non-muscle myosin II the thermotropic properties of the lipid vesicle changed, which is indicative of protein-lipid interaction/insertion. We hypothesize that myosin tail binds to acidic phospholipids through an electrostatic interaction using the basic side groups of positive residues; the flexible, amphipathic helix then may partially penetrate into the bilayer to form an anchor. Using the stopped-flow method, we determined the binding affinity of non-muscle myosin II when anchored to lipid vesicles with actin, which was similar to a pure actin-non-muscle myosin II system. Insertion of myosin tail into the hydrophobic region of lipid membranes, a model known as the lever arm mechanism, might explain how its interaction with actin generates cellular movement.

  • 28. Smith, James
    et al.
    Diez, Gerold
    Klemm, Anna H
    Schewkunow, Vitali
    Goldmann, Wolfgang H
    CapZ-lipid membrane interactions: a computer analysis.2006In: Theoretical Biology and Medical Modelling, E-ISSN 1742-4682, Vol. 3, article id 30Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: CapZ is a calcium-insensitive and lipid-dependent actin filament capping protein, the main function of which is to regulate the assembly of the actin cytoskeleton. CapZ is associated with membranes in cells and it is generally assumed that this interaction is mediated by polyphosphoinositides (PPI) particularly PIP2, which has been characterized in vitro.

    RESULTS: We propose that non-PPI lipids also bind CapZ. Data from computer-aided sequence and structure analyses further suggest that CapZ could become partially buried in the lipid bilayer probably under mildly acidic conditions, in a manner that is not only dependent on the presence of PPIs. We show that lipid binding could involve a number of sites that are spread throughout the CapZ molecule i.e., alpha- and beta-subunits. However, a beta-subunit segment between residues 134-151 is most likely to be involved in interacting with and inserting into lipid membrane due to a slighly higher ratio of positively to negatively charged residues and also due to the presence of a small hydrophobic helix.

    CONCLUSION: CapZ may therefore play an essential role in providing a stable membrane anchor for actin filaments.

  • 29.
    Solorzano, Leslie
    et al.
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Wik, Lina
    Department of Oncology-Pathology, Karolinska Institutet, Solna, Sweden.
    Bontell, Thomas Olsson
    Dept. of Physiology, Inst. of Neuroscience and Physiology, University of Gothenburg, Sahlgrenska Academy.
    Wang, Yuyu
    Department of Oncology-Pathology, Karolinska Institutet, Solna, Sweden.
    Klemm, Anna H
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Öfverstedt, Johan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Jakola, Asgeir S.
    Inst. of Neuroscience and Physiology, Dept. of Clinical Neuroscience, University of Gothenburg, Sahlgrenska Academy.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Machine learning for cell classification and neighborhood analysis in glioma tissue2021In: Cytometry Part A, ISSN 1552-4922, E-ISSN 1552-4930, ISSN 1552-4922, Vol. 99, no 12, p. 1176-1186Article in journal (Refereed)
    Abstract [en]

    Multiplexed and spatially resolved single-cell analyses that intend to study tissue heterogeneity and cell organization invariably face as a first step the challenge of cell classification. Accuracy and reproducibility are important for the downstream process of counting cells, quantifying cell–cell interactions, and extracting information on disease-specific localized cell niches. Novel staining techniques make it possible to visualize and quantify large numbers of cell-specific molecular markers in parallel. However, due to variations in sample handling and artifacts from staining and scanning, cells of the same type may present different marker profiles both within and across samples. We address multiplexed immunofluorescence data from tissue microarrays of low-grade gliomas and present a methodology using two different machine learning architectures and features insensitive to illumination to perform cell classification. The fully automated cell classification provides a measure of confidence for the decision and requires a comparably small annotated data set for training, which can be created using freely available tools. Using the proposed method, we reached an accuracy of 83.1% on cell classification without the need for standardization of samples. Using our confidence measure, cells with low-confidence classifications could be excluded, pushing the classification accuracy to 94.5%. Next, we used the cell classification results to search for cell niches with an unsupervised learning approach based on graph neural networks. We show that the approach can re-detect specialized tissue niches in previously published data, and that our proposed cell classification leads to niche definitions that may be relevant for sub-groups of glioma, if applied to larger data sets.

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  • 30.
    Tripathi, Rekha
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Aggarwal, Tanya
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Lindberg, Frida A.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Klemm, Anna H
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Fredriksson, Robert
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    SLC38A10 Regulate Glutamate Homeostasis and Modulate the AKT/TSC2/mTOR Pathway in Mouse Primary Cortex Cells2022In: Frontiers in cell and developmental biology, ISSN 2296-634X, Vol. 10, article id 854397Article in journal (Refereed)
    Abstract [en]

    Glutamate acts as a critical regulator of neurotransmitter balance, recycling, synaptic function and homeostasis in the brain and glutamate transporters control glutamate levels in the brain. SLC38A10 is a member of the SLC38 family and regulates protein synthesis and cellular stress responses. Here, we uncover the role of SLC38A10 as a transceptor involved in glutamate-sensing signaling pathways that control both the glutamate homeostasis and mTOR-signaling. The culture of primary cortex cells from SLC38A10 knockout mice had increased intracellular glutamate. In addition, under nutrient starvation, KO cells had an impaired response in amino acid-dependent mTORC1 signaling. Combined studies from transcriptomics, protein arrays and metabolomics established that SLC38A10 is involved in mTOR signaling and that SLC38A10 deficient primary cortex cells have increased protein synthesis. Metabolomic data showed decreased cholesterol levels, changed fatty acid synthesis, and altered levels of fumaric acid, citrate, 2-oxoglutarate and succinate in the TCA cycle. These data suggests that SLC38A10 may act as a modulator of glutamate homeostasis, and mTOR-sensing and loss of this transceptor result in lower cholesterol, which could have implications in neurodegenerative diseases.

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